From ea87cc6c33fe765bf9746c48526fd0ca06c8f198 Mon Sep 17 00:00:00 2001 From: afender Date: Wed, 5 Dec 2018 14:53:34 -0600 Subject: [PATCH 001/384] first commit --- docs/Makefile | 20 +++++ docs/README.md | 19 ++++ docs/make.bat | 36 ++++++++ docs/requirement.txt | 4 + docs/source/_static/EMPTY | 0 docs/source/api.rst | 15 ++++ docs/source/conf.py | 185 ++++++++++++++++++++++++++++++++++++++ docs/source/index.rst | 15 ++++ 8 files changed, 294 insertions(+) create mode 100644 docs/Makefile create mode 100644 docs/README.md create mode 100644 docs/make.bat create mode 100644 docs/requirement.txt create mode 100644 docs/source/_static/EMPTY create mode 100644 docs/source/api.rst create mode 100644 docs/source/conf.py create mode 100644 docs/source/index.rst diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 000000000..e88382797 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = cuGraph +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 000000000..e2654d443 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,19 @@ +# Building Documentation + +A basic python environment with packages listed in `./requirement.txt` is +enough to build the docs. + +## Get additional dependency + +```bash +pip install -r requirement.txt +``` + +## Run makefile: + +```bash +make html +``` + +Outputs to `build/html/index.html` + diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 000000000..807e0de85 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,36 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build +set SPHINXPROJ=cuGraph + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/docs/requirement.txt b/docs/requirement.txt new file mode 100644 index 000000000..f636fd879 --- /dev/null +++ b/docs/requirement.txt @@ -0,0 +1,4 @@ +sphinx +sphinx_rtd_theme +numpydoc +ipython \ No newline at end of file diff --git a/docs/source/_static/EMPTY b/docs/source/_static/EMPTY new file mode 100644 index 000000000..e69de29bb diff --git a/docs/source/api.rst b/docs/source/api.rst new file mode 100644 index 000000000..c5f1715d5 --- /dev/null +++ b/docs/source/api.rst @@ -0,0 +1,15 @@ +API Reference +============= + +.. currentmodule:: cuGraph + +Graph +--------- +.. autoclass:: Graph + :members: + +Pagerank +--------- +.. automodule:: cuGraph + :members: pagerank + :undoc-members: pagerank diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 000000000..24097e722 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,185 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright (c) 2018, NVIDIA CORPORATION. +# +# pygdf documentation build configuration file, created by +# sphinx-quickstart on Wed May 3 10:59:22 2017. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('../..')) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.intersphinx', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'numpydoc', + 'IPython.sphinxext.ipython_console_highlighting', + 'IPython.sphinxext.ipython_directive', +] +ipython_mplbackend = None + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = 'cuGraph' +copyright = '2018, NVIDIA' +author = 'NVIDIA' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = '0.3.0' +# The full version, including alpha/beta/rc tags. +release = '0.3.0' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = [] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# + +html_theme = 'sphinx_rtd_theme' + +# on_rtd is whether we are on readthedocs.org +on_rtd = os.environ.get('READTHEDOCS', None) == 'True' + +if not on_rtd: + # only import and set the theme if we're building docs locally + # otherwise, readthedocs.org uses their theme by default, + # so no need to specify it + import sphinx_rtd_theme + html_theme = 'sphinx_rtd_theme' + html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + + +# -- Options for HTMLHelp output ------------------------------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = 'cuGraphdoc' + + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'cuGraph.tex', 'cuGraph Documentation', + 'Continuum Analytics', 'manual'), +] + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'cuGraph', 'cuGraph Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'cuGraph', 'cuGraph Documentation', + author, 'cuGraph', 'One line description of project.', + 'Miscellaneous'), +] + + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = {'https://docs.python.org/': None} + + +# Config numpydoc +numpydoc_show_inherited_class_members = False +numpydoc_class_members_toctree = False diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 000000000..af73fbf1c --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,15 @@ +Welcome to cuDF's documentation! +================================= + +.. toctree:: + :maxdepth: 3 + :caption: Contents: + + api.rst + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` From 4c3fe8e68ba3dcdd1226af637f4fc37aa2f131d0 Mon Sep 17 00:00:00 2001 From: Dante Gama Dessavre Date: Thu, 6 Dec 2018 15:34:09 -0600 Subject: [PATCH 002/384] Added License file --- LICENSE | 201 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..3ba63d53f --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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From b1b7f82533b3b67d6f7f4b0a1111cc640e102ce4 Mon Sep 17 00:00:00 2001 From: Chuck Hastings Date: Thu, 24 Jan 2019 15:56:11 -0600 Subject: [PATCH 003/384] Add python bindings for SSSP, incorporates some of Ishika's PR 41 --- .gitignore | 1 + 1 file changed, 1 insertion(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..378eac25d --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +build From 2f8f930da765d648e9eabd9bbfd7fad1b947457f Mon Sep 17 00:00:00 2001 From: root Date: Fri, 25 Jan 2019 17:03:30 +0000 Subject: [PATCH 004/384] Quick and Dirty CMake fix and .gitignore --- .gitignore | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..1553230dd --- /dev/null +++ b/.gitignore @@ -0,0 +1,36 @@ +## Common +__pycache__ +*.pyc +*.a +*.o +*.so +*.dylib +.cache +.coverage +.vscode +*.swp +*.pytest_cache +DartConfiguration.tcl +.DS_Store + +## Python build directories & artifacts +htmlcov +dist/ +cudf.egg-info/ +python/build +python/cudf/bindings/*.cpp + +## Patching +*.diff +*.orig +*.rej + +## C++ build directories & artifacts +CMakeFiles/ +Debug +build/ + +## Eclipse IDE +.project +.cproject +.settings From 02f1acd343df9e67241802f82e66378c77a9a9e8 Mon Sep 17 00:00:00 2001 From: James Wyles Date: Mon, 4 Feb 2019 12:56:21 -0700 Subject: [PATCH 005/384] Reorganized directory structure to match cuDf, added in files to match cudf. Some files are left blank to be filled in later --- print_env.sh | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 print_env.sh diff --git a/print_env.sh b/print_env.sh new file mode 100644 index 000000000..ddb36bc65 --- /dev/null +++ b/print_env.sh @@ -0,0 +1,76 @@ +#!/usr/bin/env bash +# Reports relevant environment information useful for diagnosing and +# debugging cuGraph issues. +# Usage: +# "./print_env.sh" - prints to stdout +# "./print_env.sh > env.txt" - prints to file "env.txt" + +echo "**git***" +if [ "$(git rev-parse --is-inside-work-tree 2>/dev/null)" == "true" ]; then +git log --decorate -n 1 +else +echo "Not inside a git repository" +fi +echo + +echo "***OS Information***" +cat /etc/*-release +uname -a +echo + +echo "***GPU Information***" +nvidia-smi +echo + +echo "***CPU***" +lscpu +echo + +echo "***CMake***" +which cmake && cmake --version +echo + +echo "***g++***" +which g++ && g++ --version +echo + +echo "***nvcc***" +which nvcc && nvcc --version +echo + +echo "***Python***" +which python && python --version +echo + +echo "***Environment Variables***" + +printf '%-32s: %s\n' PATH $PATH + +printf '%-32s: %s\n' LD_LIBRARY_PATH $LD_LIBRARY_PATH + +printf '%-32s: %s\n' NUMBAPRO_NVVM $NUMBAPRO_NVVM + +printf '%-32s: %s\n' NUMBAPRO_LIBDEVICE $NUMBAPRO_LIBDEVICE + +printf '%-32s: %s\n' CONDA_PREFIX $CONDA_PREFIX + +printf '%-32s: %s\n' PYTHON_PATH $PYTHON_PATH + +echo + + +# Print conda packages if conda exists +if type "conda" &> /dev/null; then +echo '***conda packages***' +which conda && conda list +echo +# Print pip packages if pip exists +elif type "pip" &> /dev/null; then +echo "conda not found" +echo "***pip packages***" +which pip && pip list +echo +else +echo "conda not found" +echo "pip not found" +fi From 439f09e46ea2b476a885ba8a6f2a1312b2799d98 Mon Sep 17 00:00:00 2001 From: Chuck Hastings Date: Fri, 22 Feb 2019 15:22:07 -0500 Subject: [PATCH 006/384] Update document configuration so the python documentation will be constructed properly --- docs/source/api.rst | 53 +++++++++++++++++++++++++++++++++++++++++-- docs/source/conf.py | 18 +++++++-------- docs/source/index.rst | 2 +- 3 files changed, 61 insertions(+), 12 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index c5f1715d5..cbcedbe10 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -1,7 +1,7 @@ API Reference ============= -.. currentmodule:: cuGraph +.. currentmodule:: cugraph Graph --------- @@ -10,6 +10,55 @@ Graph Pagerank --------- -.. automodule:: cuGraph +.. automodule:: cugraph :members: pagerank :undoc-members: pagerank + +Bfs +--------- +.. automodule:: cugraph + :members: bfs + :undoc-members: bfs + +Jaccard +--------- +.. automodule:: cugraph + :members: nvJaccard + :undoc-members: nvJaccard + +Louvain +--------- +.. automodule:: cugraph + :members: nvLouvain + :undoc-members: nvLouvain + +Grmat +--------- +.. automodule:: cugraph + :members: grmat_gen + :undoc-members: grmat_gen + +Spectral Clustering +------------------- +.. automodule:: cugraph + :members: spectralBalancedCutClustering + :undoc-members: spectralBalancedCutClustering +.. automodule:: cugraph + :members: spectralModularityMaximizationClustering + :undoc-members: spectralModularityMaximizationClustering +.. automodule:: cugraph + :members: analyzeClustering_modularity + :undoc-members: analyzeClustering_modularity +.. automodule:: cugraph + :members: analyzeClustering_edge_cut + :undoc-members: analyzeClustering_edge_cut +.. automodule:: cugraph + :members: analyzeClustering_ratio_cut + :undoc-members: analyzeClustering_ratio_cut + +Sssp +--------- +.. automodule:: cugraph + :members: sssp + :undoc-members: sssp + diff --git a/docs/source/conf.py b/docs/source/conf.py index 24097e722..d0d2dfb9c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -55,8 +55,8 @@ master_doc = 'index' # General information about the project. -project = 'cuGraph' -copyright = '2018, NVIDIA' +project = 'cugraph' +copyright = '2019, NVIDIA' author = 'NVIDIA' # The version info for the project you're documenting, acts as replacement for @@ -64,9 +64,9 @@ # built documents. # # The short X.Y version. -version = '0.3.0' +version = '0.5.0' # The full version, including alpha/beta/rc tags. -release = '0.3.0' +release = '0.5.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -122,7 +122,7 @@ # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. -htmlhelp_basename = 'cuGraphdoc' +htmlhelp_basename = 'cugraphdoc' # -- Options for LaTeX output --------------------------------------------- @@ -149,7 +149,7 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'cuGraph.tex', 'cuGraph Documentation', + (master_doc, 'cugraph.tex', 'cugraph Documentation', 'Continuum Analytics', 'manual'), ] @@ -159,7 +159,7 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - (master_doc, 'cuGraph', 'cuGraph Documentation', + (master_doc, 'cugraph', 'cugraph Documentation', [author], 1) ] @@ -170,8 +170,8 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'cuGraph', 'cuGraph Documentation', - author, 'cuGraph', 'One line description of project.', + (master_doc, 'cugraph', 'cugraph Documentation', + author, 'cugraph', 'One line description of project.', 'Miscellaneous'), ] diff --git a/docs/source/index.rst b/docs/source/index.rst index af73fbf1c..eeb043551 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -2,7 +2,7 @@ Welcome to cuDF's documentation! ================================= .. toctree:: - :maxdepth: 3 + :maxdepth: 4 :caption: Contents: api.rst From bd475ae5d94593a693fb9750e8f2b6555c5e9027 Mon Sep 17 00:00:00 2001 From: Chuck Hastings Date: Fri, 22 Feb 2019 15:28:01 -0500 Subject: [PATCH 007/384] rename from cudf to cugraph --- docs/source/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index eeb043551..afac2bcee 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,4 +1,4 @@ -Welcome to cuDF's documentation! +Welcome to cugraph's documentation! ================================= .. toctree:: From b1baa9181c160cc381e822cc8151b1933a5039ee Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Fri, 22 Mar 2019 21:02:36 -0400 Subject: [PATCH 008/384] REL Update versions for release --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index d0d2dfb9c..f692003f4 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -64,9 +64,9 @@ # built documents. # # The short X.Y version. -version = '0.5.0' +version = '0.6' # The full version, including alpha/beta/rc tags. -release = '0.5.0' +release = '0.6.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 1b962d13b83410a204867a4a7646f8b1ca66d915 Mon Sep 17 00:00:00 2001 From: Ray Douglass <3107146+raydouglass@users.noreply.github.com> Date: Mon, 25 Mar 2019 16:49:47 -0400 Subject: [PATCH 009/384] Update docs version to 0.7 --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index f692003f4..affc1c0ec 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -64,9 +64,9 @@ # built documents. # # The short X.Y version. -version = '0.6' +version = '0.7' # The full version, including alpha/beta/rc tags. -release = '0.6.0' +release = '0.7.0.dev0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 7b4470895553c2ac6e032683bf10fc05d6747aef Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Mon, 29 Apr 2019 11:57:45 -0400 Subject: [PATCH 010/384] DOC Update version --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index affc1c0ec..1551147de 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -64,9 +64,9 @@ # built documents. # # The short X.Y version. -version = '0.7' +version = '0.8' # The full version, including alpha/beta/rc tags. -release = '0.7.0.dev0' +release = '0.8.0a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 826ff8e780096d3b79ce0be731aa107f6ff94cb1 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Fri, 10 May 2019 10:30:30 -0700 Subject: [PATCH 011/384] REL v0.7.0 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index affc1c0ec..d1e614da5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -66,7 +66,7 @@ # The short X.Y version. version = '0.7' # The full version, including alpha/beta/rc tags. -release = '0.7.0.dev0' +release = '0.7.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 1a554e70c13a3be3b20ae28185b45f8586424636 Mon Sep 17 00:00:00 2001 From: Kumar Aatish Date: Fri, 31 May 2019 21:24:55 -0400 Subject: [PATCH 012/384] Added datasets to gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 1553230dd..d9852fb9e 100644 --- a/.gitignore +++ b/.gitignore @@ -34,3 +34,5 @@ build/ .project .cproject .settings + +datasets/* From cbdebe07ce0f3a8dba076d6438a02a49f60972a2 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Mon, 17 Jun 2019 15:12:42 -0400 Subject: [PATCH 013/384] DOC Update to v0.9 --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1551147de..365be6320 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -64,9 +64,9 @@ # built documents. # # The short X.Y version. -version = '0.8' +version = '0.9' # The full version, including alpha/beta/rc tags. -release = '0.8.0a' +release = '0.9.0a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From b01fa274790317e61bb0d01ad14e32123acb8ecd Mon Sep 17 00:00:00 2001 From: Dillon Cullinan Date: Mon, 24 Jun 2019 12:46:38 -0700 Subject: [PATCH 014/384] Fix python docs --- docs/source/_static/params.css | 9 +++++++++ docs/source/conf.py | 5 +++++ 2 files changed, 14 insertions(+) create mode 100644 docs/source/_static/params.css diff --git a/docs/source/_static/params.css b/docs/source/_static/params.css new file mode 100644 index 000000000..dc5cb9640 --- /dev/null +++ b/docs/source/_static/params.css @@ -0,0 +1,9 @@ +/* Mirrors the change in: + * https://github.com/sphinx-doc/sphinx/pull/5976 + * which is not showing up in our theme. + */ +.classifier:before { + font-style: normal; + margin: 0.5em; + content: ":"; +} diff --git a/docs/source/conf.py b/docs/source/conf.py index 1551147de..4066f5344 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -40,6 +40,7 @@ 'IPython.sphinxext.ipython_console_highlighting', 'IPython.sphinxext.ipython_directive', ] + ipython_mplbackend = None # Add any paths that contain templates here, relative to this directory. @@ -183,3 +184,7 @@ # Config numpydoc numpydoc_show_inherited_class_members = False numpydoc_class_members_toctree = False + + +def setup(app): + app.add_stylesheet('params.css') From a4c567b837eab709422150a5df790407a353c6bd Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Thu, 27 Jun 2019 09:11:23 -0700 Subject: [PATCH 015/384] REL v0.8.0 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 4066f5344..03bf23266 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ # The short X.Y version. version = '0.8' # The full version, including alpha/beta/rc tags. -release = '0.8.0a' +release = '0.8.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 6cb6b5570f3a1c3270d490e2ed81c2e70daf39b5 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Fri, 28 Jun 2019 13:52:05 -0700 Subject: [PATCH 016/384] REL v0.8.1 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 03bf23266..5049c2575 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ # The short X.Y version. version = '0.8' # The full version, including alpha/beta/rc tags. -release = '0.8.0' +release = '0.8.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 9b4dfd4d52d7ab11358ecb52e749f7b8a7680060 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang Date: Tue, 23 Jul 2019 15:19:52 -0700 Subject: [PATCH 017/384] ipython_mplbackend = None => 'str' to silent WARNING: The config value `ipython_mplbackend' has type `NoneType', defaults to `str'. --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index f4539759a..6c34c3d5e 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -41,7 +41,7 @@ 'IPython.sphinxext.ipython_directive', ] -ipython_mplbackend = None +ipython_mplbackend = 'str' # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From f4ecb140b6734820ada33f32e4f03b5765ace4c4 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang Date: Tue, 23 Jul 2019 15:24:55 -0700 Subject: [PATCH 018/384] added underlines to silent WARNING: Title underline too short. --- docs/source/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index afac2bcee..fce08eaf2 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,5 @@ Welcome to cugraph's documentation! -================================= +=================================== .. toctree:: :maxdepth: 4 From 31300ddeba870531c67d2d4fa57cd42be95ca55e Mon Sep 17 00:00:00 2001 From: Seunghwa Kang Date: Tue, 23 Jul 2019 15:41:52 -0700 Subject: [PATCH 019/384] updated source/api.rst to reflect recent python directory restructuring. --- docs/source/api.rst | 178 ++++++++++++++++++++++++++++++-------------- 1 file changed, 124 insertions(+), 54 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index cbcedbe10..42396a5ce 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -1,64 +1,134 @@ -API Reference -============= +~~~~~~~~~~~~~~~~~~~~~ +cuGraph API Reference +~~~~~~~~~~~~~~~~~~~~~ + + -.. currentmodule:: cugraph +Structure +========= Graph ---------- -.. autoclass:: Graph +----- + +.. autoclass:: cugraph.structure.graph.Graph :members: + :undoc-members: -Pagerank ---------- -.. automodule:: cugraph - :members: pagerank - :undoc-members: pagerank +Renumbering +----------- + +.. automodule:: cugraph.structure.renumber + :members: + :undoc-members: + +Conversion from Other Formats +----------------------------- + +.. automodule:: cugraph.structure.convert_matrix + :members: + :undoc-members: + + +Community +========= -Bfs ---------- -.. automodule:: cugraph - :members: bfs - :undoc-members: bfs - -Jaccard ---------- -.. automodule:: cugraph - :members: nvJaccard - :undoc-members: nvJaccard - Louvain ---------- -.. automodule:: cugraph - :members: nvLouvain - :undoc-members: nvLouvain - -Grmat ---------- -.. automodule:: cugraph - :members: grmat_gen - :undoc-members: grmat_gen - +------- + +.. automodule:: cugraph.community.louvain + :members: + :undoc-members: + Spectral Clustering ------------------- -.. automodule:: cugraph - :members: spectralBalancedCutClustering - :undoc-members: spectralBalancedCutClustering -.. automodule:: cugraph - :members: spectralModularityMaximizationClustering - :undoc-members: spectralModularityMaximizationClustering -.. automodule:: cugraph - :members: analyzeClustering_modularity - :undoc-members: analyzeClustering_modularity -.. automodule:: cugraph - :members: analyzeClustering_edge_cut - :undoc-members: analyzeClustering_edge_cut -.. automodule:: cugraph - :members: analyzeClustering_ratio_cut - :undoc-members: analyzeClustering_ratio_cut - -Sssp + +.. automodule:: cugraph.community.spectral_clustering + :members: + :undoc-members: + +Subgraph Extraction +------------------- + +.. automodule:: cugraph.community.subgraph_extraction + :members: + :undoc-members: + +Tirangle Counting +----------------- + +.. automodule:: cugraph.community.triangle_count + :members: + :undoc-members: + +Components +========== + +Connected Components +-------------------- + +.. automodule:: cugraph.components.connectivity + :members: + :undoc-members: + +Link Analysis +============= + +Pagerank --------- -.. automodule:: cugraph - :members: sssp - :undoc-members: sssp - + +.. automodule:: cugraph.link_analysis.pagerank + :members: + :undoc-members: + +Link Prediction +=============== + +Jaccard Coefficient +------------------- + +.. automodule:: cugraph.link_prediction.jaccard + :members: + :undoc-members: + +.. automodule:: cugraph.link_prediction.wjaccard + :members: + :undoc-members: + +Overlap Coefficient +------------------- + +.. automodule:: cugraph.link_prediction.overlap + :members: + :undoc-members: + +.. automodule:: cugraph.link_prediction.woverlap + :members: + :undoc-members: + +Traversal +========= + +Breadth-first-search +-------------------- + +.. automodule:: cugraph.traversal.bfs + :members: + :undoc-members: + +Single-source-shortest-path +--------------------------- + +.. automodule:: cugraph.traversal.sssp + :members: + :undoc-members: + +Utilities +========= + +R-mat Graph Generation +---------------------- + +.. automodule:: cugraph.utilities.grmat + :members: + :undoc-members: + From cc84acbdd71b049f94e2eb8bf550522115c555fc Mon Sep 17 00:00:00 2001 From: afender Date: Thu, 25 Jul 2019 13:22:00 -0500 Subject: [PATCH 020/384] updated api.rst --- docs/source/api.rst | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index cbcedbe10..0f45c959d 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -62,3 +62,13 @@ Sssp :members: sssp :undoc-members: sssp + +Dask +========= + +Pagerank +------- + +.. automodule:: cugraph.dask.pagerank.pagerank + :members: + :undoc-members: \ No newline at end of file From d08fd863ded5e177aa0fd43ed74e50ebe3f3c120 Mon Sep 17 00:00:00 2001 From: Alex Fender <45606510+afender@users.noreply.github.com> Date: Thu, 25 Jul 2019 16:02:55 -0500 Subject: [PATCH 021/384] Update api.rst --- docs/source/api.rst | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 0f45c959d..cbcedbe10 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -62,13 +62,3 @@ Sssp :members: sssp :undoc-members: sssp - -Dask -========= - -Pagerank -------- - -.. automodule:: cugraph.dask.pagerank.pagerank - :members: - :undoc-members: \ No newline at end of file From 6d1c3800b2aab62651e60721d7d896a024d2226c Mon Sep 17 00:00:00 2001 From: Seunghwa Kang Date: Wed, 7 Aug 2019 11:08:36 -0700 Subject: [PATCH 022/384] Add multi-GPU section in the cugraph documentation --- docs/source/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/index.rst b/docs/source/index.rst index fce08eaf2..47360b88f 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -6,6 +6,7 @@ Welcome to cugraph's documentation! :caption: Contents: api.rst + dask-cugraph.rst Indices and tables ================== From 78fc678137629022ad00830ebf0a6ef14f4791d6 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang Date: Wed, 7 Aug 2019 12:39:07 -0700 Subject: [PATCH 023/384] add a documentation page explaining supported multi-GPU graph analytics --- docs/source/dask-cugraph.rst | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 docs/source/dask-cugraph.rst diff --git a/docs/source/dask-cugraph.rst b/docs/source/dask-cugraph.rst new file mode 100644 index 000000000..b1246d416 --- /dev/null +++ b/docs/source/dask-cugraph.rst @@ -0,0 +1,15 @@ +~~~~~~~~~~~~~~~~~~~ +Multi-GPU with Dask +~~~~~~~~~~~~~~~~~~~ + +cuGraph is a single-GPU library. For Multi-GPU cuGraph solutions we use Dask (https://dask.org), which is able to scale cuGraph across multiple GPUs on a single machine, or in future releases, multiple GPUs across many machines in a cluster. + +Supported Graph Analytics +========================= + +Pagerank +-------- + +.. automodule:: cugraph.dask.pagerank.pagerank + :members: pagerank + :undoc-members: pagerank From 34c9cc1547a73a0c2786d3a6891caa1878dc193a Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 8 Aug 2019 17:04:05 -0400 Subject: [PATCH 024/384] DOC Update to v0.10 --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6c34c3d5e..1c6ce5d3d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.9' +version = '0.10' # The full version, including alpha/beta/rc tags. -release = '0.9.0a' +release = '0.10.0a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 7c9c5e87e1d7166ad04af9acbee375699a452b41 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang Date: Fri, 9 Aug 2019 09:56:43 -0700 Subject: [PATCH 025/384] update the multi-gpu section reflecting reviewer comments --- docs/source/dask-cugraph.rst | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/docs/source/dask-cugraph.rst b/docs/source/dask-cugraph.rst index b1246d416..f61805227 100644 --- a/docs/source/dask-cugraph.rst +++ b/docs/source/dask-cugraph.rst @@ -1,8 +1,17 @@ -~~~~~~~~~~~~~~~~~~~ -Multi-GPU with Dask -~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~ +Multi-GPU with cuGraph +~~~~~~~~~~~~~~~~~~~~~~ -cuGraph is a single-GPU library. For Multi-GPU cuGraph solutions we use Dask (https://dask.org), which is able to scale cuGraph across multiple GPUs on a single machine, or in future releases, multiple GPUs across many machines in a cluster. +cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_. + +The current solution is able to scale across multiple GPUs on a single machine. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory. + +With cuGraph and Dask, whether you’re using a single NVIDIA GPU or using all 16 NVIDIA V100 GPUs on a DGX-2, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources. + +When to Use Multiple GPUs in cuGraph +==================================== + +If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph's multi-GPU features. Supported Graph Analytics ========================= From 940b42eff8bf2bf0499df2b3bbd93dcebe6b306a Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 21 Aug 2019 13:03:08 -0700 Subject: [PATCH 026/384] REL v0.9.0 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6c34c3d5e..faedbfc0e 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ # The short X.Y version. version = '0.9' # The full version, including alpha/beta/rc tags. -release = '0.9.0a' +release = '0.9.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 3cc2219f2b5ff08ca3f5baa3a25f588e94a8c56b Mon Sep 17 00:00:00 2001 From: Charles Hastings Date: Thu, 5 Sep 2019 07:22:06 -0700 Subject: [PATCH 027/384] Address issues from PR review - add testing for values - add configuration information so documentation will be generated - can't use drop_duplicates with weights and get a consistent answer, so change implementation to use groupby --- docs/source/api.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index 42396a5ce..1a2ea5225 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -21,6 +21,13 @@ Renumbering :members: :undoc-members: +Symmetrize +---------- + +.. automodule:: cugraph.structure.symmetrize + :members: + :undoc-members: + Conversion from Other Formats ----------------------------- From bcad1b17c95d3cd49fca8f7ea6d36e4c2b763e9b Mon Sep 17 00:00:00 2001 From: Kumar Aatish Date: Wed, 11 Sep 2019 16:30:02 -0400 Subject: [PATCH 028/384] PR Review fixes --- docs/source/api.rst | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 42396a5ce..4680daaaf 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -29,6 +29,16 @@ Conversion from Other Formats :undoc-members: +Centrality +========== + +Katz Centrality +--------------- + +.. automodule:: cugraph.centrality.katz_centrality + :members: + :undoc-members: + Community ========= @@ -53,7 +63,7 @@ Subgraph Extraction :members: :undoc-members: -Tirangle Counting +Triangle Counting ----------------- .. automodule:: cugraph.community.triangle_count From 0503fc00b266d03408b1691cab0eb4ccd4120081 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 26 Sep 2019 15:53:54 -0400 Subject: [PATCH 029/384] DOC Update to v0.11 --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1c6ce5d3d..80754217f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.10' +version = '0.11' # The full version, including alpha/beta/rc tags. -release = '0.10.0a' +release = '0.11.0a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From f7758ba602192a00e605f9f023f590dd5ef0c622 Mon Sep 17 00:00:00 2001 From: Kumar Aatish Date: Tue, 8 Oct 2019 11:26:27 -0400 Subject: [PATCH 030/384] Added missing cores documentation --- docs/source/api.rst | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index 5f72ac63f..ffbf5b71f 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -87,6 +87,24 @@ Connected Components :members: :undoc-members: +Cores +===== + +K-Core +------ + +.. automodule:: cugraph.cores.k_core + :members: + :undoc-members: + +Core Number +----------- + +.. automodule:: cugraph.cores.core_number + :members: + :undoc-members: + + Link Analysis ============= From 6161e2416b03a2c250a3684d6820288795e0350b Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 11 Oct 2019 17:08:38 -0500 Subject: [PATCH 031/384] Revert "DOC Update to v0.11" This reverts commit 0503fc00b266d03408b1691cab0eb4ccd4120081. --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 80754217f..1c6ce5d3d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.11' +version = '0.10' # The full version, including alpha/beta/rc tags. -release = '0.11.0a' +release = '0.10.0a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From b41617cb5c4cba3833e702081382b93656c1b9e7 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Tue, 15 Oct 2019 16:42:48 -0400 Subject: [PATCH 032/384] Revert "Merge pull request #558 from afender/revert_0.11_changes" This reverts commit 7df3a1afacfc3bec2bfa16b791eb46a9b8625174, reversing changes made to e3182de266b8162bcc3296deb1bef61c7f0cb01f. This reintroduces v0.11 changes that were reverted from v0.10 and the revert was forward merged to v0.11 --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1c6ce5d3d..80754217f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.10' +version = '0.11' # The full version, including alpha/beta/rc tags. -release = '0.10.0a' +release = '0.11.0a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 217fca7b3afa7ff86ad91fc15799a0fb64f37b4c Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Thu, 17 Oct 2019 10:56:36 -0700 Subject: [PATCH 033/384] REL v0.10.0 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1c6ce5d3d..9ca739c52 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ # The short X.Y version. version = '0.10' # The full version, including alpha/beta/rc tags. -release = '0.10.0a' +release = '0.10.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From feca9d1d181b7d755d5bd6bb6852817a5c64dac0 Mon Sep 17 00:00:00 2001 From: Oded Green Date: Tue, 12 Nov 2019 13:19:48 -0500 Subject: [PATCH 034/384] Updating api.rst --- docs/source/api.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index ffbf5b71f..d115ad17d 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -105,6 +105,14 @@ Core Number :undoc-members: +K-Truss +------- + +.. automodule:: cugraph.ktruss.ktruss_max + :members: + :undoc-members: + + Link Analysis ============= From a3c7627ffeb6a63f23453fbce0cb0aa35ee9f58e Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Fri, 22 Nov 2019 16:21:54 -0500 Subject: [PATCH 035/384] DOC Update to v0.12 --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 80754217f..ffede64e5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.11' +version = '0.12' # The full version, including alpha/beta/rc tags. -release = '0.11.0a' +release = '0.12.0a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From f7fc8671e3ae997d61d1b19ab7fa844ca6e41249 Mon Sep 17 00:00:00 2001 From: brees Date: Mon, 2 Dec 2019 13:36:03 -0500 Subject: [PATCH 036/384] updated API argument --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index d9852fb9e..b30745da9 100644 --- a/.gitignore +++ b/.gitignore @@ -36,3 +36,4 @@ build/ .settings datasets/* +.pydevproject From 55f4b17b22b23bead1567edaaa7b894ed2d7e921 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 11 Dec 2019 12:13:14 -0800 Subject: [PATCH 037/384] REL v0.11.0 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 80754217f..e8acbb7ae 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ # The short X.Y version. version = '0.11' # The full version, including alpha/beta/rc tags. -release = '0.11.0a' +release = '0.11.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 333a0faf0bea4a6a2af98232aba31463cc1a10e5 Mon Sep 17 00:00:00 2001 From: James Wyles Date: Tue, 24 Dec 2019 13:14:12 -0700 Subject: [PATCH 038/384] Udated api.rst to add ECG --- docs/source/api.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index ffbf5b71f..df9119fd7 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -56,6 +56,13 @@ Louvain :members: :undoc-members: +ECG +--- + +.. automodule:: cugraph.community.ecg + :members: + :undoc-members: + Spectral Clustering ------------------- From ee212c55edc53a32c65785ebafec5d964eca1167 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 16 Jan 2020 19:05:56 -0500 Subject: [PATCH 039/384] DOC Updates for v0.13 --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index ffede64e5..66efcc45d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.12' +version = '0.13' # The full version, including alpha/beta/rc tags. -release = '0.12.0a' +release = '0.13.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From ec47da79b53e98e88cd04fbc303eb1871f67d0a7 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Tue, 4 Feb 2020 10:35:14 -0800 Subject: [PATCH 040/384] REL v0.12.0 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index ffede64e5..36bbb18bd 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ # The short X.Y version. version = '0.12' # The full version, including alpha/beta/rc tags. -release = '0.12.0a' +release = '0.12.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 1f5b927abd720ae99feb01d2dcbf90ffa92d41b0 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Mon, 9 Mar 2020 15:09:32 -0400 Subject: [PATCH 041/384] DOC v0.14 Updates --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 66efcc45d..ea49ca0e6 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.13' +version = '0.14' # The full version, including alpha/beta/rc tags. -release = '0.13.0' +release = '0.14.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 076cb936619830b96c7d358c67b26f89dcb1ddfc Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Tue, 31 Mar 2020 10:18:22 -0700 Subject: [PATCH 042/384] REL v0.13.0 release From 15132b4dcf859c2d69d5c04e4a17727a598a35cf Mon Sep 17 00:00:00 2001 From: Chuck Hastings Date: Tue, 31 Mar 2020 17:23:06 -0400 Subject: [PATCH 043/384] update some documentation --- docs/source/api.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index b84d8344d..7e7c6ce7d 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -46,6 +46,13 @@ Katz Centrality :members: :undoc-members: +Betweenness Centrality +---------------------- + +.. automodule:: cugraph.centrality.betweenness_centrality + :members: + :undoc-members: + Community ========= From 7ac9dc1961bf71828c446ae803c38dfd5858bd1d Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Wed, 8 Apr 2020 01:27:40 -0500 Subject: [PATCH 044/384] Initial WIP version of updated benchmarks based on pytest-benchmark. --- benchmarks/pytest.ini | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 benchmarks/pytest.ini diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini new file mode 100644 index 000000000..920163d67 --- /dev/null +++ b/benchmarks/pytest.ini @@ -0,0 +1,17 @@ +[pytest] +addopts = + --benchmark-min-rounds=25 + --benchmark-warmup=on + --benchmark-warmup-iterations=10 + +python_classes = + Bench* + Test* + +python_files = + bench_* + test_* + +python_functions = + bench_* + test_* From f59e5de3561fb4c5b418f8e810b2583a6c669cd7 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Mon, 20 Apr 2020 12:06:04 -0400 Subject: [PATCH 045/384] Updated and replpaced cudf with cugraph --- .gitignore | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index b30745da9..7733803bd 100644 --- a/.gitignore +++ b/.gitignore @@ -16,9 +16,9 @@ DartConfiguration.tcl ## Python build directories & artifacts htmlcov dist/ -cudf.egg-info/ +cugraph.egg-info/ python/build -python/cudf/bindings/*.cpp +python/cugraph/bindings/*.cpp ## Patching *.diff @@ -37,3 +37,6 @@ build/ datasets/* .pydevproject + +# Jupyter Notebooks +.ipynb_checkpoints \ No newline at end of file From dcfb75dfb39f5edc7e2edb12446a6db8b091460c Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Tue, 21 Apr 2020 16:23:20 -0400 Subject: [PATCH 046/384] updating list of ignored files --- .gitignore | 30 ++++++++++++++++++++++++++++-- 1 file changed, 28 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 7733803bd..c9df7c2eb 100644 --- a/.gitignore +++ b/.gitignore @@ -12,7 +12,20 @@ __pycache__ *.pytest_cache DartConfiguration.tcl .DS_Store - + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +junit-cugraph.xml +*.cover +.hypothesis/ +test-results + ## Python build directories & artifacts htmlcov dist/ @@ -29,14 +42,27 @@ python/cugraph/bindings/*.cpp CMakeFiles/ Debug build/ +cpp/build/ +cpp/include/cugraph/ipc_generated/*.h +cpp/thirdparty/googletest/ ## Eclipse IDE .project .cproject .settings +## IntelliJ IDE +.idea/ +.idea_modules/ +*.iml +*.ipr +*.iws + datasets/* .pydevproject # Jupyter Notebooks -.ipynb_checkpoints \ No newline at end of file +.ipynb_checkpoints + +## Doxygen +cpp/doxygen/html From 5d7f00a109d653352a468f67110bd1b52df404f4 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 24 Apr 2020 14:50:18 -0400 Subject: [PATCH 047/384] Updated text and validated execution --- docs/source/api.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 7e7c6ce7d..c537a74d3 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -122,7 +122,7 @@ Core Number K-Truss ------- -.. automodule:: cugraph.ktruss.ktruss_max +.. automodule:: cugraph.ktruss.ktruss_subgraph :members: :undoc-members: From 1e2148254cb89538072772117dde517ff893f227 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 24 Apr 2020 15:28:36 -0400 Subject: [PATCH 048/384] updated data location --- .gitignore | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.gitignore b/.gitignore index c9df7c2eb..1c90a7edc 100644 --- a/.gitignore +++ b/.gitignore @@ -58,7 +58,13 @@ cpp/thirdparty/googletest/ *.ipr *.iws +## Datasets datasets/* +!datasets/cyber.csv +!datasets/karate-data.csv +!datasets/netscience.csv + + .pydevproject # Jupyter Notebooks From b97b8ce306bec339bde8e2853369f67c1a425ae2 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Mon, 27 Apr 2020 11:49:41 -0400 Subject: [PATCH 049/384] missed a dataset --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 1c90a7edc..c6e81ce89 100644 --- a/.gitignore +++ b/.gitignore @@ -62,6 +62,7 @@ cpp/thirdparty/googletest/ datasets/* !datasets/cyber.csv !datasets/karate-data.csv +!datasets/karate_undirected.csv !datasets/netscience.csv From 91ec55c8cc0bf09cdd0ddff67925a0c4f5525549 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Tue, 12 May 2020 12:36:46 -0400 Subject: [PATCH 050/384] added how to view text --- docs/README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/docs/README.md b/docs/README.md index e2654d443..10b2c265e 100644 --- a/docs/README.md +++ b/docs/README.md @@ -17,3 +17,16 @@ make html Outputs to `build/html/index.html` +## View docs web page by opening HTML in browser: + +First navigate to `/build/html/` folder, i.e., `cd build/html` and then run the following command: + +```bash +python -m http.server +``` +Then, navigate a web browser to the IP address or hostname of the host machine at port 8000: + +``` +https://:8000 +``` +Now you can check if your docs edits formatted correctly, and read well. From e8fe19422fa2238e92b98f28711f61f29e546345 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Tue, 12 May 2020 13:08:04 -0400 Subject: [PATCH 051/384] updated list of features and dropped SNMG --- docs/source/api.rst | 16 +++++++++------- docs/source/conf.py | 2 +- .../{dask-cugraph.rst => dask-cugraph.rst.tmp} | 0 3 files changed, 10 insertions(+), 8 deletions(-) rename docs/source/{dask-cugraph.rst => dask-cugraph.rst.tmp} (100%) diff --git a/docs/source/api.rst b/docs/source/api.rst index c537a74d3..848318171 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -39,20 +39,21 @@ Conversion from Other Formats Centrality ========== -Katz Centrality ---------------- +Betweenness Centrality +---------------------- -.. automodule:: cugraph.centrality.katz_centrality +.. automodule:: cugraph.centrality.betweenness_centrality :members: :undoc-members: -Betweenness Centrality ----------------------- +Katz Centrality +--------------- -.. automodule:: cugraph.centrality.betweenness_centrality +.. automodule:: cugraph.centrality.katz_centrality :members: :undoc-members: + Community ========= @@ -91,6 +92,7 @@ Triangle Counting :members: :undoc-members: + Components ========== @@ -122,7 +124,7 @@ Core Number K-Truss ------- -.. automodule:: cugraph.ktruss.ktruss_subgraph +.. automodule:: cugraph.core.ktruss_subgraph :members: :undoc-members: diff --git a/docs/source/conf.py b/docs/source/conf.py index ea49ca0e6..a461e4d69 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -57,7 +57,7 @@ # General information about the project. project = 'cugraph' -copyright = '2019, NVIDIA' +copyright = '2019-2020, NVIDIA' author = 'NVIDIA' # The version info for the project you're documenting, acts as replacement for diff --git a/docs/source/dask-cugraph.rst b/docs/source/dask-cugraph.rst.tmp similarity index 100% rename from docs/source/dask-cugraph.rst rename to docs/source/dask-cugraph.rst.tmp From ab6416c10964d8a84e9534d71737357ec78f05e5 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Tue, 12 May 2020 13:10:04 -0400 Subject: [PATCH 052/384] ki_truss --- docs/source/api.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 848318171..4467d8645 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -124,7 +124,7 @@ Core Number K-Truss ------- -.. automodule:: cugraph.core.ktruss_subgraph +.. automodule:: cugraph.cores.ktruss_subgraph :members: :undoc-members: From 627974e6110147b26bee4e1b8ef94c6f9a25a35d Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Tue, 12 May 2020 13:44:12 -0500 Subject: [PATCH 053/384] Updated to use new marks, enabled more benchmarks, doing additional ETL steps (pre-compute adj lists, renumber), flake8, CHANGELOG, cleanup. --- benchmarks/pytest.ini | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index 920163d67..3ac22a6a7 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -1,9 +1,14 @@ [pytest] addopts = - --benchmark-min-rounds=25 --benchmark-warmup=on --benchmark-warmup-iterations=10 +markers = + ETL: benchmarks for ETL steps + small: small datasets + directed: directed datasets + undirected: undirected datasets + python_classes = Bench* Test* From 84100b2863bfc50d0757cd1218624aa32b83ca48 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Tue, 12 May 2020 16:09:28 -0400 Subject: [PATCH 054/384] extra text on manual running --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index 10b2c265e..199e743cd 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,7 +1,7 @@ # Building Documentation A basic python environment with packages listed in `./requirement.txt` is -enough to build the docs. +enough to build the docs. Doc generation does not get run by default ## Get additional dependency From 2f71cec3312b6ebcb0e030cf4824cc7bab7fa1ed Mon Sep 17 00:00:00 2001 From: Kumar Aatish Date: Wed, 13 May 2020 16:52:55 -0400 Subject: [PATCH 055/384] Moved ktruss to community in python --- docs/source/api.rst | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 4467d8645..8768b3b29 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -64,6 +64,13 @@ Louvain :members: :undoc-members: +K-Truss +------- + +.. automodule:: cugraph.community.ktruss_subgraph + :members: + :undoc-members: + ECG --- @@ -121,14 +128,6 @@ Core Number :undoc-members: -K-Truss -------- - -.. automodule:: cugraph.cores.ktruss_subgraph - :members: - :undoc-members: - - Link Analysis ============= From 77db35e557004c4d8fafeadaebc2236575ba78ed Mon Sep 17 00:00:00 2001 From: Hugo Linsenmaier Date: Fri, 8 May 2020 21:25:18 -0500 Subject: [PATCH 056/384] Add fa2 to README and doc --- docs/source/api.rst | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index 8768b3b29..99f5f577f 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -127,6 +127,15 @@ Core Number :members: :undoc-members: +Layout +====== + +Force Atlas 2 +------------- + +.. automodule:: cugraph.layout.force_atlas_2 + :members: + :undoc-members: Link Analysis ============= From 82b11f11b4cd4ea22d3d87742afa8729ff5af1cd Mon Sep 17 00:00:00 2001 From: Hugo Linsenmaier Date: Wed, 13 May 2020 02:13:47 -0500 Subject: [PATCH 057/384] Fix typo in docs --- docs/source/api.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 99f5f577f..47a4e29b2 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -133,7 +133,7 @@ Layout Force Atlas 2 ------------- -.. automodule:: cugraph.layout.force_atlas_2 +.. automodule:: cugraph.layout.force_atlas2 :members: 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we will show how to scale to 4x larger graphs than before without performance drop using managed memory features in cuGraph. We will compute the PageRank of each user in Twitter's dataset on a single GPU as an example. This technique applies to all features.\n", + "\n", + "Unified Memory is a single memory address space accessible from any processor in a system. If a kernel tries to access any absent pages,the Page Migration Engine migrates the pages. When the GPU memory is full, least recently used pages are evicted. In other words, Unified Memory transparently enables oversubscribing GPU memory, enabling out-of-core computations.\n", + "\n", + "\n", + "This notebook was tested on an NVIDIA 48GB RTX8000 GPU using RAPIDS 0.14 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "We will be analyzing 1.47 billion social relations on 41.7 million user profiles from the Twitter dataset. The CSV file is 26GB and was collected in :
\n", + "*What is Twitter, a social network or a news media? Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010.*
\n", + "\n", + "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PageRank with cuGraph\n", + "### Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries. We recommend using cugraph_dev env through conda\n", + "import time\n", + "import rmm \n", + "import cudf \n", + "import cugraph " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Twitter dataset is in our S3 bucket and zipped. \n", + "1. We'll need to create a folder for our data in the `/data` folder\n", + "1. Download the zipped data into that folder from S3 (it will take some time as it it 6GB)\n", + "1. Decompress the zipped data for use (it will take some time as it it 26GB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import urllib.request\n", + "import os\n", + "\n", + "data_dir = '../data/twitter/'\n", + "if not os.path.exists(data_dir):\n", + " print('creating twitter data directory')\n", + " os.system('mkdir ../data/twitter')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# download the Twitter dataset\n", + "base_url = 'https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/'\n", + "fn = 'twitter-2010.csv'\n", + "comp = '.gz'\n", + "if not os.path.isfile(data_dir+fn):\n", + " if not os.path.isfile(data_dir+fn+comp):\n", + " print(f'Downloading {base_url+fn+comp} to {data_dir+fn+comp}')\n", + " urllib.request.urlretrieve(base_url+fn+comp, data_dir+fn+comp)\n", + " print(f'Decompressing {data_dir+fn+comp}...')\n", + " os.system('gunzip '+data_dir+fn+comp)\n", + " print(f'{data_dir+fn+comp} decompressed!')\n", + "else:\n", + " print(f'Your data file, {data_dir+fn}, already exists')\n", + "\n", + "# File path, assuming Notebook directory\n", + "input_data_path = data_dir+fn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize RMM\n", + "RAPIDS Memory Manager (RMM) is a central place for all device memory allocations in RAPIDS libraries. Using RMM in python code is straightforward. Import `rmm` and configure RMM options to be used before making any call to a RAPIDS library." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Set RMM to allocate all memory as managed memory (cudaMallocManaged underlying allocator)\n", + "rmm.reinitialize(managed_memory=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data from disk\n", + "cuGraph depends on cudf for data loading and the initial DataFrame creation. The CSV data file contains an edge list, which represents the connection of a vertex to another. The source to destination pairs is what is known as Coordinate Format (COO). In this test case, the data is just two columns. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Start timer\n", + "t_start = time.time()\n", + "\n", + "# CSV reader\n", + "e_list = cudf.read_csv(input_data_path, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n", + "\n", + "# Print time\n", + "print(\"Reader: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a graph\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t_start = time.time()\n", + "\n", + "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", + "G = cugraph.DiGraph()\n", + "G.from_cudf_edgelist(e_list, source='src', destination='dst', renumber=False)\n", + "\n", + "# (optional) request the transposed here so that we can analyse pagerank solver time alone\n", + "G.view_transposed_adj_list()\n", + "\n", + "# Print time\n", + "print(\"Load and transpose: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Call PageRank algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Start timer\n", + "t_start = time.time()\n", + "\n", + "# Get the pagerank scores\n", + "pr_df = cugraph.pagerank(G, tol=1e-4)\n", + "\n", + "# Print time\n", + "print(\"Pagerank: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It was that easy! PageRank should only take a few seconds to run on this 26GB input with one GPU.
\n", + "Check out how it compares to published Spark results in the [Annex](#annex_cell)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Further analysis on the PageRank result\n", + "\n", + "We can now identify the most influent users in the network.
\n", + "Notice that the PageRank result is already in a regular `cudf.DataFrame`. We can then sort by PageRank value and print the *Top 3*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Start timer\n", + "t_start = time.time()\n", + "\n", + "# Sort, descending order\n", + "pr_sorted_df = pr_df.sort_values('pagerank',ascending=False)\n", + "\n", + "# Print time\n", + "print(time.time()-t_start, \"s\")\n", + "\n", + "# Print the Top 3\n", + "print(pr_sorted_df.head(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the [map](https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/twitter-2010-ids.csv.gz) to convert Vertex ID into to Twitter's numeric ID. The user name can also be retrieved using the [TwitterID](https://tweeterid.com/) web app.
\n", + "The table below shows more information on our *Top 3*. Notice that this ranking is much better at capturing network influence compared the number of followers for instance. Further analysis of this dataset was published [here](https://doi.org/10.1145/1772690.1772751).\n", + "\n", + "| Vertex ID\t| Twitter ID\t| User name\t| Description |\n", + "| --------- | --------- | -------- | ---------- |\n", + "| 21513299\t| 813286\t| barackobama\t| US President (2009-2017) |\n", + "| 23933989\t| 14224719\t| 10DowningStreet | UK Prime Minister office |\n", + "| 23933986\t| 15131310\t| WholeFoods\t| Food store from Austin |\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annex\n", + "\n", + "An experiment comparing various porducts for this workflow was published in *GraphX: Graph Processing in a Distributed Dataflow Framework,OSDI, 2014*. They used 16 m2.4xlarge worker nodes on Amazon EC2. There was a total of 128 CPU cores and 1TB of memory in this 2014 setup.\n", + "\n", + "![twitter-2010-spark.png](twitter-2010-spark.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2019, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From acb7e80d311ab2f70949d5fb7c3ed90b6a6b0ca5 Mon Sep 17 00:00:00 2001 From: Alex Fender Date: Fri, 15 May 2020 14:01:29 -0500 Subject: [PATCH 059/384] Update uvm.ipynb --- notebooks/demo/uvm.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index d80e8e523..cf32f6450 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -23,7 +23,7 @@ "We will be analyzing 1.47 billion social relations on 41.7 million user profiles from the Twitter dataset. The CSV file is 26GB and was collected in :
\n", "*What is Twitter, a social network or a news media? Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010.*
\n", "\n", - "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. " + "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. While we are not limited by the device memory size in this case, the whole system (host+device memory) should still have at least 80GB of memory available" ] }, { From 17e895591615dd90f3e3e96e63e1af98afac445c Mon Sep 17 00:00:00 2001 From: Alex Fender Date: Fri, 15 May 2020 14:05:57 -0500 Subject: [PATCH 060/384] Update uvm.ipynb --- notebooks/demo/uvm.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index cf32f6450..44aaaa091 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -109,7 +109,7 @@ "metadata": {}, "source": [ "### Initialize RMM\n", - "RAPIDS Memory Manager (RMM) is a central place for all device memory allocations in RAPIDS libraries. Using RMM in python code is straightforward. Import `rmm` and configure RMM options to be used before making any call to a RAPIDS library." + "RAPIDS Memory Manager (RMM) is a central place for all device memory allocations in RAPIDS libraries. Using RMM in python code is straightforward. Simply `import rmm` and configure RMM options to be used before making any call to a RAPIDS library." ] }, { From e6ed059827650c632e2f30910cd00769e84e1e76 Mon Sep 17 00:00:00 2001 From: afender Date: Mon, 18 May 2020 11:18:17 -0500 Subject: [PATCH 061/384] addressed reviews --- notebooks/demo/uvm.ipynb | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index 44aaaa091..7932b3d3f 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -74,10 +74,10 @@ "import urllib.request\n", "import os\n", "\n", - "data_dir = '../data/twitter/'\n", + "data_dir = '../data/'\n", "if not os.path.exists(data_dir):\n", - " print('creating twitter data directory')\n", - " os.system('mkdir ../data/twitter')" + " print('creating data directory')\n", + " os.system('mkdir ../data')" ] }, { @@ -119,7 +119,8 @@ "outputs": [], "source": [ "#Set RMM to allocate all memory as managed memory (cudaMallocManaged underlying allocator)\n", - "rmm.reinitialize(managed_memory=True)" + "rmm.reinitialize(managed_memory=True)\n", + "assert(rmm.is_initialized())" ] }, { @@ -267,7 +268,7 @@ "metadata": {}, "source": [ "___\n", - "Copyright (c) 2019, NVIDIA CORPORATION.\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", From 7699363ebd35aa048224041e82eefa2c77778b31 Mon Sep 17 00:00:00 2001 From: afender Date: Mon, 18 May 2020 11:29:02 -0500 Subject: [PATCH 062/384] added a couple of emphasis --- notebooks/demo/uvm.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index 7932b3d3f..2021b4c38 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -7,7 +7,7 @@ "# Oversubscribing GPU memory in cuGraph\n", "#### Author : Alex Fender\n", "\n", - "In this notebook, we will show how to scale to 4x larger graphs than before without performance drop using managed memory features in cuGraph. We will compute the PageRank of each user in Twitter's dataset on a single GPU as an example. This technique applies to all features.\n", + "In this notebook, we will show how to **scale to 4x larger graphs than before** without performance drop using managed memory features in cuGraph. We will compute the PageRank of each user in Twitter's dataset on a single GPU as an example. This technique applies to all features.\n", "\n", "Unified Memory is a single memory address space accessible from any processor in a system. If a kernel tries to access any absent pages,the Page Migration Engine migrates the pages. When the GPU memory is full, least recently used pages are evicted. In other words, Unified Memory transparently enables oversubscribing GPU memory, enabling out-of-core computations.\n", "\n", @@ -20,7 +20,7 @@ "metadata": {}, "source": [ "## Data\n", - "We will be analyzing 1.47 billion social relations on 41.7 million user profiles from the Twitter dataset. The CSV file is 26GB and was collected in :
\n", + "We will be analyzing **1.47 billion social relations** on 41.7 million user profiles from the Twitter dataset. The CSV file is 26GB and was collected in :
\n", "*What is Twitter, a social network or a news media? Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010.*
\n", "\n", "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. While we are not limited by the device memory size in this case, the whole system (host+device memory) should still have at least 80GB of memory available" From f837ecf9846ee72809c32575fc1b30cc153294fd Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Mon, 18 May 2020 12:29:31 -0500 Subject: [PATCH 063/384] Updates to address review feedback: added RMM reinit code, markers, and custom option, precompute adj lists as part of setup fixtures, updated README with examples, reenabled some additional benchmarks. --- benchmarks/pytest.ini | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index 3ac22a6a7..fb4e43965 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -1,9 +1,15 @@ [pytest] addopts = + -x --benchmark-warmup=on - --benchmark-warmup-iterations=10 + --benchmark-warmup-iterations=1 + --benchmark-min-rounds=3 markers = + managedmem_on: RMM managed memory enabled + managedmem_off: RMM managed memory disabled + poolallocator_on: RMM pool allocator enabled + poolallocator_off: RMM pool allocator disabled ETL: benchmarks for ETL steps small: small datasets directed: directed datasets From 2eb4e2ea023af5724c97278de984686d4b289dd5 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Wed, 20 May 2020 11:05:14 -0400 Subject: [PATCH 064/384] DOC v0.15 Updates --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index a461e4d69..41674b9b3 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.14' +version = '0.15' # The full version, including alpha/beta/rc tags. -release = '0.14.0' +release = '0.15.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 77f4bb7517f90fe2d331dddb9cffa1fcf45ac31d Mon Sep 17 00:00:00 2001 From: ptaylor Date: Thu, 21 May 2020 10:04:48 -0700 Subject: [PATCH 065/384] add external repositories and raft symlinks to .gitignore --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index c6e81ce89..517ceab56 100644 --- a/.gitignore +++ b/.gitignore @@ -73,3 +73,7 @@ datasets/* ## Doxygen cpp/doxygen/html + +# Raft symlink +python/cugraph/raft +python/_external_repositories/ \ No newline at end of file From e7852204379feefe3f8d9dc6f7e9e3a05940aa5b Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 3 Jun 2020 10:02:48 -0700 Subject: [PATCH 066/384] REL v0.14.0 release From cee3014d1ddcac25b7376a2476d13a02156c2561 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 10 Jun 2020 12:30:46 -0400 Subject: [PATCH 067/384] updated script to avoid extra downloads --- notebooks/cugraph_benchmarks/dataPrep.sh | 41 ++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100755 notebooks/cugraph_benchmarks/dataPrep.sh diff --git a/notebooks/cugraph_benchmarks/dataPrep.sh b/notebooks/cugraph_benchmarks/dataPrep.sh new file mode 100755 index 000000000..1dbc8d563 --- /dev/null +++ b/notebooks/cugraph_benchmarks/dataPrep.sh @@ -0,0 +1,41 @@ +#!/bin/bash + +if [ ! -d "./data" ] +then + mkdir ./data +fi + +cd data + +if [ ! -f "./preferentialAttachment.mtx" ] +then + if [ ! -d "./tmp" ] + then + mkdir tmp + cd tmp + + wget -N https://sparse.tamu.edu/MM/DIMACS10/preferentialAttachment.tar.gz + wget -N https://sparse.tamu.edu/MM/DIMACS10/caidaRouterLevel.tar.gz + wget -N https://sparse.tamu.edu/MM/DIMACS10/coAuthorsDBLP.tar.gz + wget -N https://sparse.tamu.edu/MM/LAW/dblp-2010.tar.gz + wget -N https://sparse.tamu.edu/MM/DIMACS10/citationCiteseer.tar.gz + wget -N https://sparse.tamu.edu/MM/DIMACS10/coPapersDBLP.tar.gz + wget -N https://sparse.tamu.edu/MM/DIMACS10/coPapersCiteseer.tar.gz + wget -N https://sparse.tamu.edu/MM/SNAP/as-Skitter.tar.gz + + tar xvzf preferentialAttachment.tar.gz + tar xvzf caidaRouterLevel.tar.gz + tar xvzf coAuthorsDBLP.tar.gz + tar xvzf dblp-2010.tar.gz + tar xvzf citationCiteseer.tar.gz + tar xvzf coPapersDBLP.tar.gz + tar xvzf coPapersCiteseer.tar.gz + tar xvzf as-Skitter.tar.gz + + cd .. + + find ./tmp -name *.mtx -exec mv {} . \; + + rm -rf tmp + fi +fi From 9950385efe6aeff771799ccee44291110f2d7257 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 10 Jun 2020 12:31:38 -0400 Subject: [PATCH 068/384] updated text --- notebooks/cugraph_benchmarks/README.md | 108 +++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 notebooks/cugraph_benchmarks/README.md diff --git a/notebooks/cugraph_benchmarks/README.md b/notebooks/cugraph_benchmarks/README.md new file mode 100644 index 000000000..32b8a8a44 --- /dev/null +++ b/notebooks/cugraph_benchmarks/README.md @@ -0,0 +1,108 @@ +# cuGraph Benchmarking + +This folder contains a collection of graph algorithm benchmarking notebooks. Each notebook will compare one cuGraph algorithm against NetworkX and potentially other frameworks. + +_NOTE: Before any benchmarking can be done, it is important to first download the test data sets_ + + +## Getting the Data Sets + +Run the data prep script. + +```bash +sh ./dataPrep.sh +``` + +## Benchmarks + +1. Louvain +2. PageRank +3. BSF +4. SSSP + + + +The benchmark does not include data reading time, but does include: + +- Creating the Graph object +- Running the analytic + + + + + + +#### The data prep script +By default, each files would be created in its own directory. The goal here is to have all the MTX files in a single directory. + + +```bash +#!/bin/bash + +mkdir data +cd data +mkdir tmp +cd tmp + +wget https://sparse.tamu.edu/MM/DIMACS10/preferentialAttachment.tar.gz +wget https://sparse.tamu.edu/MM/DIMACS10/caidaRouterLevel.tar.gz +wget https://sparse.tamu.edu/MM/DIMACS10/coAuthorsDBLP.tar.gz +wget https://sparse.tamu.edu/MM/LAW/dblp-2010.tar.gz +wget https://sparse.tamu.edu/MM/DIMACS10/citationCiteseer.tar.gz +wget https://sparse.tamu.edu/MM/DIMACS10/coPapersDBLP.tar.gz +wget https://sparse.tamu.edu/MM/DIMACS10/coPapersCiteseer.tar.gz +wget https://sparse.tamu.edu/MM/SNAP/as-Skitter.tar.gz + +tar xvzf preferentialAttachment.tar.gz +tar xvzf caidaRouterLevel.tar.gz +tar xvzf coAuthorsDBLP.tar.gz +tar xvzf dblp-2010.tar.gz +tar xvzf citationCiteseer.tar.gz +tar xvzf coPapersDBLP.tar.gz +tar xvzf coPapersCiteseer.tar.gz +tar xvzf as-Skitter.tar.gz + +cd .. + +find ./tmp -name *.mtx -exec mv {} . \; + +rm -rf tmp +``` + + + +**About the Test files** + +| File Name | Num of Vertices | Num of Edges | Format | Graph Type | Symmetric | +| ---------------------- | --------------: | -----------: |--------|---------------------------|-------------| +| preferentialAttachment | 100,000 | 999,970 | MTX | Random Undirected Graph | Yes | +| caidaRouterLevel | 192,244 | 1,218,132 | MTX | Undirected Graph | Yes | +| coAuthorsDBLP | 299,067 | 1,955,352 |MTX | Undirected Graph | Yes | +| dblp-2010 | 326,186 | 1,615,400 | MTX | Undirected Graph | Yes | +| citationCiteseer | 268,495 | 2,313,294 | MTX | Undirected Graph | Yes | +| coPapersDBLP | 540,486 | 30,491,458 | MTX | Undirected Graph | Yes | +| coPapersCiteseer | 434,102 | 32,073,440 | MTX | Undirected Graph | Yes | +| as-Skitter | 1,696,415 | 22,190,596 | MTX | Undirected Graph | Yes | + + + +### Dataset Acknowlegments + +The dataset are downloaded from the Texas A&M SuiteSparse Matrix Collection + +``` +The SuiteSparse Matrix Collection (formerly known as the University of Florida Sparse Matrix Collection), is a large and actively growing set of sparse matrices that arise in real applications. +... +The Collection is hosted here, and also mirrored at the University of Florida at www.cise.ufl.edu/research/sparse/matrices. The Collection is maintained by Tim Davis, Texas A&M University (email: davis@tamu.edu), Yifan Hu, Yahoo! Labs, and Scott Kolodziej, Texas A&M University. +``` + +| File Name | Author | +| ---------------------- |----------------| +| preferentialAttachment | H. Meyerhenke | +| caidaRouterLevel | Unknown | +| coAuthorsDBLP | R. Geisberger, P. Sanders, and D. Schultes | +| dblp-2010 | Laboratory for Web Algorithmics (LAW), | +| citationCiteseer | R. Geisberger, P. Sanders, and D. Schultes | +| coPapersDBLP | R. Geisberger, P. Sanders, and D. Schultes | +| coPapersCiteseer | R. Geisberger, P. Sanders, and D. Schultes | +| as-Skitter | J. Leskovec, J. Kleinberg and C. Faloutsos | From a6be407997769bd194b072d7a5fd7ec04eadb3ec Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 10 Jun 2020 16:49:50 -0400 Subject: [PATCH 069/384] bfs benchmark --- .../cugraph_benchmarks/bfs_benchmark.ipynb | 440 ++++++++++++++++++ 1 file changed, 440 insertions(+) create mode 100644 notebooks/cugraph_benchmarks/bfs_benchmark.ipynb diff --git a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb new file mode 100644 index 000000000..41262ee5d --- /dev/null +++ b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb @@ -0,0 +1,440 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BFS Performance Benchmarking\n", + "\n", + "This notebook benchmarks performance of running BFS within cuGraph against NetworkX. \n", + "\n", + "Notebook Credits\n", + "\n", + " Original Authors: Bradley Rees\n", + " Last Edit: 06/10/2020\n", + " \n", + "RAPIDS Versions: 0.15\n", + "\n", + "Test Hardware\n", + "\n", + " GV100 32G, CUDA 10.2\n", + " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", + " 32GB system memory\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test Data\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "|:---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| caidaRouterLevel | 192,244 | 1,218,132 |\n", + "| coAuthorsDBLP | 299,067 | 1,955,352 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| citationCiteseer | 268,495 | 2,313,294 |\n", + "| coPapersDBLP | 540,486 | 30,491,458 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Timing \n", + "What is not timed: Reading the data

\n", + "What is timmed: (1) creating a Graph, (2) running BSF\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NOTICE:\n", + "You must have run the dataPrep script prior to running this notebook so that the data is downloaded\n", + "\n", + "See the README file in this folder for a discription of how to get the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If you have more than one GPU, set the GPU to use\n", + "This is not needed on a Single GPU system or if the default GPU is to be used" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the GPU to use\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now load the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries\n", + "import gc\n", + "import time\n", + "import rmm\n", + "import cugraph\n", + "import cudf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX libraries\n", + "import networkx as nx\n", + "from scipy.io import mmread" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import matplotlib\n", + "except ModuleNotFoundError:\n", + " os.system('pip install matplotlib')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt; plt.rcdefaults()\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Print the name of the used GPU" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cudf._cuda.gpu.deviceGetName(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test File\n", + "data = {\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'caidaRouterLevel' : './data/caidaRouterLevel.mtx',\n", + " 'coAuthorsDBLP' : './data/coAuthorsDBLP.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'citationCiteseer' : './data/citationCiteseer.mtx',\n", + " 'coPapersDBLP' : './data/coPapersDBLP.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the testing functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data reader - the file format is MTX, so we will use the reader from SciPy\n", + "def read_mtx_file(mm_file):\n", + " print('Reading ' + str(mm_file) + '...')\n", + " M = mmread(mm_file).asfptype()\n", + " \n", + " return M" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CuGraph BFS\n", + "\n", + "def cugraph_call(M):\n", + "\n", + " gdf = cudf.DataFrame()\n", + " gdf['src'] = M.row\n", + " gdf['dst'] = M.col\n", + " \n", + " print('\\tcuGraph Solving... ')\n", + " \n", + " t1 = time.time()\n", + " \n", + " # cugraph Pagerank Call\n", + " G = cugraph.Graph()\n", + " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", + " \n", + " df = cugraph.bfs(G, 1)\n", + " t2 = time.time() - t1\n", + " \n", + " return t2\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Basic NetworkX BFS\n", + "\n", + "def networkx_call(M):\n", + " nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n", + " for nnz in range(M.getnnz()):\n", + " nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n", + " for nnz in range(M.getnnz()):\n", + " M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n", + "\n", + " M = M.tocsr()\n", + " if M is None:\n", + " raise TypeError('Could not read the input graph')\n", + " if M.shape[0] != M.shape[1]:\n", + " raise TypeError('Shape is not square')\n", + "\n", + " # should be autosorted, but check just to make sure\n", + " if not M.has_sorted_indices:\n", + " print('sort_indices ... ')\n", + " M.sort_indices()\n", + "\n", + " z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}\n", + " \n", + " print('\\tNetworkX Solving... ')\n", + " \n", + " # start timer\n", + " t1 = time.time()\n", + " \n", + " Gnx = nx.DiGraph(M)\n", + "\n", + " pr = nx.bfs_edges(Gnx, 1)\n", + " \n", + " t2 = time.time() - t1\n", + "\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# arrays to capture performance gains\n", + "perf_nx = []\n", + "names = []\n", + "time_cu = []\n", + "time_nx = []\n", + "\n", + "# do a simple pass just to get all the libraries initiallized\n", + "v = './data/preferentialAttachment.mtx'\n", + "M = read_mtx_file(v)\n", + "trapids = cugraph_call(M)\n", + "del M\n", + "\n", + "for k,v in data.items():\n", + " gc.collect()\n", + "\n", + " # Saved the file Name\n", + " names.append(k)\n", + " \n", + " # read the data\n", + " M = read_mtx_file(v)\n", + " \n", + " \n", + " # call cuGraph - this will be the baseline\n", + " trapids = cugraph_call(M)\n", + " \n", + " # Now call NetworkX\n", + " tn = networkx_call(M)\n", + " speedUp = (tn / trapids)\n", + " perf_nx.append(speedUp)\n", + " time_cu.append(trapids)\n", + " time_nx.append(tn)\n", + " del M\n", + " \n", + " print(\"\\tcuGraph (\" + str(trapids) + \") Nx (\" + str(tn) + \")\" )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "plt.figure(figsize=(11,9))\n", + "\n", + "bar_width = 0.5\n", + "index = np.arange(len(names))\n", + "\n", + "_ = plt.bar(index, perf_nx, bar_width, color='g', label='vs NetworkX')\n", + "\n", + "\n", + "\n", + "plt.xlabel('Datasets')\n", + "plt.ylabel('Speedup')\n", + "plt.title('BFS Performance Speedup')\n", + "plt.xticks(index + (bar_width/4), names)\n", + "plt.xticks(rotation=90) \n", + "\n", + "# Text on the top of each barplot\n", + "for i in range(len(perf_nx)):\n", + " plt.text(x = (i - .5) + bar_width, y = perf_nx[i] + 25, s = round(perf_nx[i], 1), size = 12)\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dump the raw data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the speed-up numbers\n", + "perf_nx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the cuGraph runtimes\n", + "time_cu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the NetworkX runtimes\n", + "time_nx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From b42a8b6956bc2e6f2e4ade14610357c08c86cea5 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 10 Jun 2020 18:02:55 -0400 Subject: [PATCH 070/384] benchmark notebooks --- .../louvain_benchmark.ipynb | 466 ++++++++++++++++ .../pagerank_benchmark.ipynb | 510 ++++++++++++++++++ .../cugraph_benchmarks/sssp_benchmark.ipynb | 415 ++++++++++++++ 3 files changed, 1391 insertions(+) create mode 100644 notebooks/cugraph_benchmarks/louvain_benchmark.ipynb create mode 100644 notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb create mode 100644 notebooks/cugraph_benchmarks/sssp_benchmark.ipynb diff --git a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb new file mode 100644 index 000000000..c427996a2 --- /dev/null +++ b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb @@ -0,0 +1,466 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Louvain Performance Benchmarking\n", + "\n", + "This notebook benchmarks performance improvement of running the Louvain clustering algorithm within cuGraph against NetworkX. The test is run over eight test networks (graphs) and then results plotted. \n", + "

\n", + "\n", + "\n", + "#### Notebook Credits\n", + "\n", + " Original Authors: Bradley Rees\n", + " Last Edit: 06/10/2020\n", + "\n", + "\n", + "#### Test Environment\n", + "\n", + " RAPIDS Versions: 0.15\n", + "\n", + " Test Hardware:\n", + " GV100 32G, CUDA 10,0\n", + " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", + " 32GB system memory\n", + "\n", + "\n", + "\n", + "#### Updates\n", + "- moved loading ploting libraries to front so that dependencies can be checked before running algorithms\n", + "- added edge values \n", + "- changed timing to including Graph creation for both cuGraph and NetworkX. This will better represent end-to-end times\n", + "\n", + "\n", + "\n", + "#### Dependencies\n", + "- RAPIDS cuDF and cuGraph version 0.6.0 \n", + "- NetworkX \n", + "- Matplotlib \n", + "- Scipy \n", + "- data prep script run\n", + "\n", + "\n", + "\n", + "#### Note: Comparison against published results\n", + "\n", + "\n", + "The cuGraph blog post included performance numbers that were collected over a year ago. For the test graphs, int32 values are now used. That improves GPUs performance. Additionally, the initial benchamrks were measured on a P100 GPU. \n", + "\n", + "This test only comparse the modularity scores and a success is if the scores are within 15% of each other. That comparison is done by adjusting the NetworkX modularity score and then verifying that the cuGraph score is higher.\n", + "\n", + "cuGraph did a full validation of NetworkX results against cuGraph results. That included cross-validation of every cluster. That test is very slow and not included here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If you have more than one GPU, set the GPU to use\n", + "This is not needed on a Single GPU system or if the default GPU is to be used" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wed Jun 10 17:38:29 2020 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "|===============================+======================+======================|\n", + "| 0 GeForce GT 710 On | 00000000:03:00.0 N/A | N/A |\n", + "| 50% 44C P8 N/A / N/A | 374MiB / 1998MiB | N/A Default |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Quadro GV100 On | 00000000:04:00.0 Off | Off |\n", + "| 33% 45C P2 28W / 250W | 1MiB / 32508MiB | 0% Default |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Quadro GP100 On | 00000000:84:00.0 Off | Off |\n", + "| 28% 44C P0 27W / 235W | 2MiB / 16278MiB | 0% Default |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: GPU Memory |\n", + "| GPU PID Type Process name Usage |\n", + "|=============================================================================|\n", + "| 0 Not Supported |\n", + "+-----------------------------------------------------------------------------+\n", + "WARNING: infoROM is corrupted at gpu 0000:04:00.0\n", + "WARNING: infoROM is corrupted at gpu 0000:84:00.0\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# since this is a shared machine - let's pick a GPU that no one else is using\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now load the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries\n", + "import time\n", + "import cugraph\n", + "import cudf\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX libraries\n", + "import networkx as nx\n", + "from scipy.io import mmread" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX libraries\n", + "try: \n", + " import community\n", + "except ModuleNotFoundError:\n", + " os.system('pip install python-louvain')\n", + " import community" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import matplotlib\n", + "except ModuleNotFoundError:\n", + " os.system('pip install matplotlib')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading plotting libraries\n", + "import matplotlib.pyplot as plt; plt.rcdefaults()\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Quadro GV100'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print out GPU Name\n", + "cudf._cuda.gpu.deviceGetName(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the test data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Test File\n", + "data = {\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'caidaRouterLevel' : './data/caidaRouterLevel.mtx',\n", + " 'coAuthorsDBLP' : './data/coAuthorsDBLP.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'citationCiteseer' : './data/citationCiteseer.mtx',\n", + " 'coPapersDBLP' : './data/coPapersDBLP.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the testing functions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in a dataset in MTX format \n", + "def read_mtx_file(mm_file):\n", + " print('Reading ' + str(mm_file) + '...')\n", + " M = mmread(mm_file).asfptype()\n", + " \n", + " return M" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the cuGraph Louvain analytic (using nvGRAPH function)\n", + "def cugraph_call(M):\n", + "\n", + " t1 = time.time()\n", + "\n", + " # data\n", + " gdf = cudf.DataFrame()\n", + " gdf['src'] = M.row\n", + " gdf['dst'] = M.col\n", + " \n", + " # create graph \n", + " G = cugraph.Graph()\n", + " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", + " \n", + " # cugraph Louvain Call\n", + " print(' cuGraph Solving... ')\n", + " df, mod = cugraph.louvain(G) \n", + " \n", + " t2 = time.time() - t1\n", + " return t2, mod\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the NetworkX Louvain analytic. THis is done in two parts since the modularity score is not returned \n", + "def networkx_call(M):\n", + " nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n", + " for nnz in range(M.getnnz()):\n", + " nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n", + " for nnz in range(M.getnnz()):\n", + " M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n", + "\n", + " M = M.tocsr()\n", + " if M is None:\n", + " raise TypeError('Could not read the input graph')\n", + " if M.shape[0] != M.shape[1]:\n", + " raise TypeError('Shape is not square')\n", + " \n", + " t1 = time.time()\n", + "\n", + " # Directed NetworkX graph\n", + " Gnx = nx.Graph(M)\n", + "\n", + " # Networkx \n", + " print(' NetworkX Solving... ')\n", + " parts = community.best_partition(Gnx)\n", + " \n", + " # Calculating modularity scores for comparison \n", + " mod = community.modularity(parts, Gnx) \n", + " \n", + " t2 = time.time() - t1\n", + " \n", + " return t2, mod" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + " cuGraph Solving... \n", + "Reading ./data/preferentialAttachment.mtx...\n", + " cuGraph Solving... \n", + " NetworkX Solving... \n" + ] + } + ], + "source": [ + "# Loop through each test file and compute the speedup\n", + "perf = []\n", + "names = []\n", + "time_cu = []\n", + "time_nx = []\n", + "\n", + "#init libraries by doing quick pass\n", + "v = './data/preferentialAttachment.mtx'\n", + "M = read_mtx_file(v)\n", + "trapids = cugraph_call(M)\n", + "del M\n", + "\n", + "\n", + "for k,v in data.items():\n", + " M = read_mtx_file(v)\n", + " tr, modc = cugraph_call(M)\n", + " tn, modx = networkx_call(M)\n", + " \n", + " speedUp = (tn / tr)\n", + " names.append(k)\n", + " perf.append(speedUp)\n", + " time_cu.append(tr)\n", + " time_nx.append(tn)\n", + " # mod_delta = (0.85 * modx)\n", + " \n", + " print(str(speedUp) + \"x faster => cugraph \" + str(tr) + \" vs \" + str(tn))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plot the output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "y_pos = np.arange(len(names))\n", + " \n", + "plt.bar(y_pos, perf, align='center', alpha=0.5)\n", + "plt.xticks(y_pos, names)\n", + "plt.ylabel('Speed Up')\n", + "plt.title('Performance Speedup: cuGraph vs NetworkX')\n", + "plt.xticks(rotation=90) \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dump the raw stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "perf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_cu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_nx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb new file mode 100644 index 000000000..d8d149aaa --- /dev/null +++ b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb @@ -0,0 +1,510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PageRank Performance Benchmarking\n", + "\n", + "This notebook benchmarks performance of running PageRank within cuGraph against NetworkX. NetworkX contains several implementations of PageRank. This benchmark will compare cuGraph versus the defaukt Nx implementation as well as the SciPy version\n", + "\n", + "Notebook Credits\n", + "\n", + " Original Authors: Bradley Rees\n", + " Last Edit: 06/10/2019\n", + " \n", + "RAPIDS Versions: 0.15\n", + "\n", + "Test Hardware\n", + "\n", + " GV100 32G, CUDA 10,0\n", + " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", + " 32GB system memory\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test Data\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "|:---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| caidaRouterLevel | 192,244 | 1,218,132 |\n", + "| coAuthorsDBLP | 299,067 | 1,955,352 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| citationCiteseer | 268,495 | 2,313,294 |\n", + "| coPapersDBLP | 540,486 | 30,491,458 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Timing \n", + "What is not timed: Reading the data\n", + "\n", + "What is timmed: (1) creating a Graph, (2) running PageRank\n", + "\n", + "The data file is read in once for all flavors of PageRank. Each timed block will craete a Graph and then execute the algorithm. The results of the algorithm are not compared. If you are interested in seeing the comparison of results, then please see PageRank in the __notebooks__ repo. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NOTICE\n", + "_You must have run the __dataPrep__ script prior to running this notebook so that the data is downloaded_\n", + "\n", + "See the README file in this folder for a discription of how to get the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If you have more than one GPU, set the GPU to use\n", + "This is not needed on a Single GPU system or if the default GPU is to be used" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# since this is a shared machine - let's pick a GPU that no one else is using\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now load the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries\n", + "import gc\n", + "import time\n", + "import rmm\n", + "import cugraph\n", + "import cudf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX libraries\n", + "import networkx as nx\n", + "from scipy.io import mmread" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import matplotlib\n", + "except ModuleNotFoundError:\n", + " os.system('pip install matplotlib')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt; plt.rcdefaults()\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print out GPU Name\n", + "cudf._cuda.gpu.deviceGetName(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test File\n", + "data = {\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'caidaRouterLevel' : './data/caidaRouterLevel.mtx',\n", + " 'coAuthorsDBLP' : './data/coAuthorsDBLP.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'citationCiteseer' : './data/citationCiteseer.mtx',\n", + " 'coPapersDBLP' : './data/coPapersDBLP.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the testing functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data reader - the file format is MTX, so we will use the reader from SciPy\n", + "def read_mtx_file(mm_file):\n", + " print('Reading ' + str(mm_file) + '...')\n", + " M = mmread(mm_file).asfptype()\n", + " \n", + " return M" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CuGraph PageRank\n", + "\n", + "def cugraph_call(M, max_iter, tol, alpha):\n", + "\n", + " gdf = cudf.DataFrame()\n", + " gdf['src'] = M.row\n", + " gdf['dst'] = M.col\n", + " \n", + " print('\\tcuGraph Solving... ')\n", + " \n", + " t1 = time.time()\n", + " \n", + " # cugraph Pagerank Call\n", + " G = cugraph.Graph()\n", + " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", + " \n", + " df = cugraph.pagerank(G, alpha=alpha, max_iter=max_iter, tol=tol)\n", + " t2 = time.time() - t1\n", + " \n", + " return t2\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Basic NetworkX PageRank\n", + "\n", + "def networkx_call(M, max_iter, tol, alpha):\n", + " nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n", + " for nnz in range(M.getnnz()):\n", + " nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n", + " for nnz in range(M.getnnz()):\n", + " M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n", + "\n", + " M = M.tocsr()\n", + " if M is None:\n", + " raise TypeError('Could not read the input graph')\n", + " if M.shape[0] != M.shape[1]:\n", + " raise TypeError('Shape is not square')\n", + "\n", + " # should be autosorted, but check just to make sure\n", + " if not M.has_sorted_indices:\n", + " print('sort_indices ... ')\n", + " M.sort_indices()\n", + "\n", + " z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}\n", + " \n", + " print('\\tNetworkX Solving... ')\n", + " \n", + " # start timer\n", + " t1 = time.time()\n", + " \n", + " Gnx = nx.DiGraph(M)\n", + "\n", + " pr = nx.pagerank(Gnx, alpha, z, max_iter, tol)\n", + " \n", + " t2 = time.time() - t1\n", + "\n", + " return t2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# SciPy PageRank\n", + "\n", + "def networkx_scipy_call(M, max_iter, tol, alpha):\n", + " nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n", + " for nnz in range(M.getnnz()):\n", + " nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n", + " for nnz in range(M.getnnz()):\n", + " M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n", + "\n", + " M = M.tocsr()\n", + " if M is None:\n", + " raise TypeError('Could not read the input graph')\n", + " if M.shape[0] != M.shape[1]:\n", + " raise TypeError('Shape is not square')\n", + "\n", + " # should be autosorted, but check just to make sure\n", + " if not M.has_sorted_indices:\n", + " print('sort_indices ... ')\n", + " M.sort_indices()\n", + "\n", + " z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}\n", + "\n", + " # SciPy Pagerank Call\n", + " print('\\tSciPy Solving... ')\n", + " t1 = time.time()\n", + " \n", + " Gnx = nx.DiGraph(M) \n", + " \n", + " pr = nx.pagerank_scipy(Gnx, alpha, z, max_iter, tol)\n", + " t2 = time.time() - t1\n", + "\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# arrays to capture performance gains\n", + "time_cu = []\n", + "time_nx = []\n", + "time_sp = []\n", + "perf_nx = []\n", + "perf_sp = []\n", + "names = []\n", + "\n", + "# init libraries by doing a simple task \n", + "v = './data/preferentialAttachment.mtx'\n", + "M = read_mtx_file(v)\n", + "trapids = cugraph_call(M, 100, 0.00001, 0.85)\n", + "del M\n", + "\n", + "\n", + "for k,v in data.items():\n", + " gc.collect()\n", + "\n", + " # Saved the file Name\n", + " names.append(k)\n", + " \n", + " # read the data\n", + " M = read_mtx_file(v)\n", + " \n", + " # call cuGraph - this will be the baseline\n", + " trapids = cugraph_call(M, 100, 0.00001, 0.85)\n", + " time_cu.append(trapids)\n", + " \n", + " # Now call NetworkX\n", + " tn = networkx_call(M, 100, 0.00001, 0.85)\n", + " speedUp = (tn / trapids)\n", + " perf_nx.append(speedUp)\n", + " time_nx.append(tn)\n", + " \n", + " # Now call SciPy\n", + " tsp = networkx_scipy_call(M, 100, 0.00001, 0.85)\n", + " speedUp = (tsp / trapids)\n", + " perf_sp.append(speedUp) \n", + " time_sp.append(tsp)\n", + " \n", + " print(\"cuGraph (\" + str(trapids) + \") Nx (\" + str(tn) + \") SciPy (\" + str(tsp) + \")\" )\n", + " del M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plot the output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "plt.figure(figsize=(10,8))\n", + "\n", + "bar_width = 0.35\n", + "index = np.arange(len(names))\n", + "\n", + "_ = plt.bar(index, perf_nx, bar_width, color='g', label='vs Nx')\n", + "_ = plt.bar(index + bar_width, perf_sp, bar_width, color='b', label='vs SciPy')\n", + "\n", + "plt.xlabel('Datasets')\n", + "plt.ylabel('Speedup')\n", + "plt.title('PageRank Performance Speedup')\n", + "plt.xticks(index + (bar_width / 2), names)\n", + "plt.xticks(rotation=90) \n", + "\n", + "# Text on the top of each barplot\n", + "for i in range(len(perf_nx)):\n", + " plt.text(x = (i - 0.55) + bar_width, y = perf_nx[i] + 25, s = round(perf_nx[i], 1), size = 12)\n", + "\n", + "for i in range(len(perf_sp)):\n", + " plt.text(x = (i - 0.1) + bar_width, y = perf_sp[i] + 25, s = round(perf_sp[i], 1), size = 12)\n", + "\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dump the raw stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "perf_nx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "perf_sp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_cu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_nx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_sp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb new file mode 100644 index 000000000..91cb0509f --- /dev/null +++ b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SSSP Performance Benchmarking\n", + "\n", + "This notebook benchmarks performance of running SSSP within cuGraph against NetworkX. \n", + "\n", + "Notebook Credits\n", + "\n", + " Original Authors: Bradley Rees\n", + " Last Edit: 06/10/2020\n", + " \n", + "RAPIDS Versions: 0.15\n", + "\n", + "Test Hardware\n", + "\n", + " GV100 32G, CUDA 10,0\n", + " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", + " 32GB system memory\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test Data\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "|:---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| caidaRouterLevel | 192,244 | 1,218,132 |\n", + "| coAuthorsDBLP | 299,067 | 1,955,352 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| citationCiteseer | 268,495 | 2,313,294 |\n", + "| coPapersDBLP | 540,486 | 30,491,458 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Timing \n", + "What is not timed: Reading the data\n", + "\n", + "What is timmed: (1) creating a Graph, (2) running SSSP\n", + "\n", + "The data file is read and used for both cuGraph and NetworkX. Each timed block will craete a Graph and then execute the algorithm. The results of the algorithm are not compared. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NOTICE\n", + "You must have run the dataPrep script prior to running this notebook so that the data is downloaded\n", + "\n", + "See the README file in this folder for a discription of how to get the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If you have more than one GPU, set the GPU to use\n", + "This is not needed on a Single GPU system or if the default GPU is to be used" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# since this is a shared machine - let's pick a GPU that no one else is using\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now load the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries\n", + "import gc\n", + "import time\n", + "import rmm\n", + "import cugraph\n", + "import cudf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX libraries\n", + "import networkx as nx\n", + "from scipy.io import mmread" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import matplotlib\n", + "except ModuleNotFoundError:\n", + " os.system('pip install matplotlib')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt; plt.rcdefaults()\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test File\n", + "data = {\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'caidaRouterLevel' : './data/caidaRouterLevel.mtx',\n", + " 'coAuthorsDBLP' : './data/coAuthorsDBLP.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'citationCiteseer' : './data/citationCiteseer.mtx',\n", + " 'coPapersDBLP' : './data/coPapersDBLP.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the testing functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data reader - the file format is MTX, so we will use the reader from SciPy\n", + "def read_mtx_file(mm_file):\n", + " print('Reading ' + str(mm_file) + '...')\n", + " M = mmread(mm_file).asfptype()\n", + " \n", + " return M" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CuGraph SSSP\n", + "\n", + "def cugraph_call(M):\n", + "\n", + " gdf = cudf.DataFrame()\n", + " gdf['src'] = M.row\n", + " gdf['dst'] = M.col\n", + " \n", + " print('\\tcuGraph Solving... ')\n", + " \n", + " t1 = time.time()\n", + " \n", + " # cugraph SSSP Call\n", + " G = cugraph.Graph()\n", + " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", + " \n", + " df = cugraph.sssp(G, 1)\n", + " t2 = time.time() - t1\n", + " \n", + " return t2\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Basic NetworkX SSSP\n", + "\n", + "def networkx_call(M):\n", + " nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n", + " for nnz in range(M.getnnz()):\n", + " nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n", + " for nnz in range(M.getnnz()):\n", + " M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n", + "\n", + " M = M.tocsr()\n", + " if M is None:\n", + " raise TypeError('Could not read the input graph')\n", + " if M.shape[0] != M.shape[1]:\n", + " raise TypeError('Shape is not square')\n", + "\n", + " # should be autosorted, but check just to make sure\n", + " if not M.has_sorted_indices:\n", + " print('sort_indices ... ')\n", + " M.sort_indices()\n", + "\n", + " z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}\n", + " \n", + " print('\\tNetworkX Solving... ')\n", + " \n", + " # start timer\n", + " t1 = time.time()\n", + " \n", + " Gnx = nx.DiGraph(M)\n", + "\n", + " pr = nx.shortest_path(Gnx, 1)\n", + " \n", + " t2 = time.time() - t1\n", + "\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# arrays to capture performance gains\n", + "perf_nx = []\n", + "names = []\n", + "\n", + "time_cu = []\n", + "time_nx = []\n", + "\n", + "#init libraries by doing quick pass\n", + "v = './data/preferentialAttachment.mtx'\n", + "M = read_mtx_file(v)\n", + "trapids = cugraph_call(M)\n", + "del M\n", + "\n", + "for k,v in data.items():\n", + " gc.collect()\n", + " \n", + " # Saved the file Name\n", + " names.append(k)\n", + " \n", + " # read the data\n", + " M = read_mtx_file(v)\n", + " \n", + " # call cuGraph - this will be the baseline\n", + " trapids = cugraph_call(M)\n", + " \n", + " # Now call NetworkX\n", + " tn = networkx_call(M)\n", + " speedUp = (tn / trapids)\n", + " perf_nx.append(speedUp)\n", + " time_cu.append(trapids)\n", + " time_nx.append(tn)\n", + " \n", + " print(\"\\tcuGraph (\" + str(trapids) + \") Nx (\" + str(tn) + \")\" )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "plt.figure(figsize=(10,8))\n", + "\n", + "bar_width = 0.4\n", + "index = np.arange(len(names))\n", + "\n", + "_ = plt.bar(index, perf_nx, bar_width, color='g', label='vs Nx')\n", + "\n", + "plt.xlabel('Datasets')\n", + "plt.ylabel('Speedup')\n", + "plt.title('SSSP Performance Speedup of cuGraph vs NetworkX')\n", + "plt.xticks(index, names)\n", + "plt.xticks(rotation=90) \n", + "\n", + "# Text on the top of each barplot\n", + "for i in range(len(perf_nx)):\n", + " #plt.text(x = (i - 0.6) + bar_width, y = perf_nx[i] + 25, s = round(perf_nx[i], 1), size = 12)\n", + " plt.text(x = i - (bar_width/2), y = perf_nx[i] + 25, s = round(perf_nx[i], 1), size = 12)\n", + "\n", + "#plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dump the raw data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "perf_nx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_cu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_nx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 8c95b19e3f313ccbed2ced10390fdbaf56c1dab5 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Thu, 11 Jun 2020 17:57:54 -0400 Subject: [PATCH 071/384] updated to use DiGraph --- notebooks/cugraph_benchmarks/bfs_benchmark.ipynb | 2 +- notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb | 2 +- notebooks/cugraph_benchmarks/sssp_benchmark.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb index 41262ee5d..f5580691d 100644 --- a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb @@ -226,7 +226,7 @@ " t1 = time.time()\n", " \n", " # cugraph Pagerank Call\n", - " G = cugraph.Graph()\n", + " G = cugraph.DiGraph()\n", " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", " \n", " df = cugraph.bfs(G, 1)\n", diff --git a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb index d8d149aaa..cb2f5ed0d 100644 --- a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb @@ -223,7 +223,7 @@ " t1 = time.time()\n", " \n", " # cugraph Pagerank Call\n", - " G = cugraph.Graph()\n", + " G = cugraph.DiGraph()\n", " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", " \n", " df = cugraph.pagerank(G, alpha=alpha, max_iter=max_iter, tol=tol)\n", diff --git a/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb index 91cb0509f..2d040e0ac 100644 --- a/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb @@ -213,7 +213,7 @@ " t1 = time.time()\n", " \n", " # cugraph SSSP Call\n", - " G = cugraph.Graph()\n", + " G = cugraph.DiGraph()\n", " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", " \n", " df = cugraph.sssp(G, 1)\n", From 4d6a96dc1de4e379e55d48c98b01f5183dd5c664 Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Thu, 11 Jun 2020 23:05:46 -0500 Subject: [PATCH 072/384] Simplified --no-rmm-reinit to use existing markers instead of modifying fixture params directly (this also means all runs always have the same number of params, which helps reporting), updated ini file to only show common columns in report by default, remove -x from default options to work better with nightly runs. --- benchmarks/pytest.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index fb4e43965..7096c677b 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -1,9 +1,9 @@ [pytest] addopts = - -x --benchmark-warmup=on --benchmark-warmup-iterations=1 --benchmark-min-rounds=3 + --benchmark-columns="min, max, mean, stddev, outliers, gpu_mem, rounds" markers = managedmem_on: RMM managed memory enabled From 5e0ddfc1e2c2cf7e5e4eed02138dae2db8b4730b Mon Sep 17 00:00:00 2001 From: ptaylor Date: Thu, 25 Jun 2020 14:35:57 -0700 Subject: [PATCH 073/384] add dask-worker-space to gitignore --- .gitignore | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 517ceab56..30bcd5a84 100644 --- a/.gitignore +++ b/.gitignore @@ -76,4 +76,7 @@ cpp/doxygen/html # Raft symlink python/cugraph/raft -python/_external_repositories/ \ No newline at end of file +python/_external_repositories/ + +# created by Dask tests +python/dask-worker-space From c0bbaa41f3ed67d7d57b8ebca7df1e67535915ea Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 26 Jun 2020 13:03:32 -0500 Subject: [PATCH 074/384] print_env.sh update --- print_env.sh | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/print_env.sh b/print_env.sh index ddb36bc65..ede280611 100644 --- a/print_env.sh +++ b/print_env.sh @@ -1,13 +1,16 @@ #!/usr/bin/env bash # Reports relevant environment information useful for diagnosing and -# debugging cuGraph issues. +# debugging cuDF issues. # Usage: # "./print_env.sh" - prints to stdout # "./print_env.sh > env.txt" - prints to file "env.txt" +print_env() { echo "**git***" if [ "$(git rev-parse --is-inside-work-tree 2>/dev/null)" == "true" ]; then git log --decorate -n 1 +echo "**git submodules***" +git submodule status --recursive else echo "Not inside a git repository" fi @@ -27,19 +30,19 @@ lscpu echo echo "***CMake***" -which cmake && cmake --version +which cmake && cmake --version echo echo "***g++***" -which g++ && g++ --version +which g++ && g++ --version echo echo "***nvcc***" -which nvcc && nvcc --version +which nvcc && nvcc --version echo echo "***Python***" -which python && python --version +which python && python -c "import sys; print('Python {0}.{1}.{2}'.format(sys.version_info[0], sys.version_info[1], sys.version_info[2]))" echo echo "***Environment Variables***" @@ -62,7 +65,7 @@ echo # Print conda packages if conda exists if type "conda" &> /dev/null; then echo '***conda packages***' -which conda && conda list +which conda && conda list echo # Print pip packages if pip exists elif type "pip" &> /dev/null; then @@ -74,3 +77,11 @@ else echo "conda not found" echo "pip not found" fi +} + +echo "

Click here to see environment details
"
+echo "     "
+print_env | while read -r line; do
+    echo "     $line"
+done
+echo "
" \ No newline at end of file From 72e14de3f3d56c152bc6881bbd78da6cea4b4761 Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 26 Jun 2020 13:10:18 -0500 Subject: [PATCH 075/384] typo --- print_env.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/print_env.sh b/print_env.sh index ede280611..27b94563b 100644 --- a/print_env.sh +++ b/print_env.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash # Reports relevant environment information useful for diagnosing and -# debugging cuDF issues. +# debugging cuGraph issues. # Usage: # "./print_env.sh" - prints to stdout # "./print_env.sh > env.txt" - prints to file "env.txt" From 114ec535e589a90a8e7fbb5082b26ffc443f8106 Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 26 Jun 2020 13:11:34 -0500 Subject: [PATCH 076/384] EOF --- print_env.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/print_env.sh b/print_env.sh index 27b94563b..6f2d33b0e 100644 --- a/print_env.sh +++ b/print_env.sh @@ -84,4 +84,4 @@ echo " " print_env | while read -r line; do echo " $line" done -echo "" \ No newline at end of file +echo "" From 88b1b9252e7c2cbb276dc5e295a77c4785a45de0 Mon Sep 17 00:00:00 2001 From: Ishika Roy Date: Sun, 28 Jun 2020 22:28:57 -0500 Subject: [PATCH 077/384] remove prints, update doc --- docs/source/dask-cugraph.rst.tmp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/dask-cugraph.rst.tmp b/docs/source/dask-cugraph.rst.tmp index f61805227..313d34127 100644 --- a/docs/source/dask-cugraph.rst.tmp +++ b/docs/source/dask-cugraph.rst.tmp @@ -4,7 +4,7 @@ Multi-GPU with cuGraph cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_. -The current solution is able to scale across multiple GPUs on a single machine. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory. +The current solution is able to scale across multiple GPUs on a multiple machines. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory. With cuGraph and Dask, whether you’re using a single NVIDIA GPU or using all 16 NVIDIA V100 GPUs on a DGX-2, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources. From d4d52d9c4ad2d19d1df54fb3231189b9cb41298e Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Thu, 2 Jul 2020 12:01:44 -0500 Subject: [PATCH 078/384] Using yml file to disable CI check blocking instead. --- codecov.yml | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 codecov.yml diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 000000000..c0a3a2fba --- /dev/null +++ b/codecov.yml @@ -0,0 +1,5 @@ +#Configuration File for CodeCov +coverage: + status: + project: off + patch: off From 1f7a9b5b1abe6d8e3274f989813cb27f04e10c93 Mon Sep 17 00:00:00 2001 From: afender Date: Thu, 9 Jul 2020 17:50:56 -0500 Subject: [PATCH 079/384] doc --- ...{dask-cugraph.rst.tmp => dask-cugraph.rst} | 20 ++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) rename docs/source/{dask-cugraph.rst.tmp => dask-cugraph.rst} (53%) diff --git a/docs/source/dask-cugraph.rst.tmp b/docs/source/dask-cugraph.rst similarity index 53% rename from docs/source/dask-cugraph.rst.tmp rename to docs/source/dask-cugraph.rst index 313d34127..4d22af4df 100644 --- a/docs/source/dask-cugraph.rst.tmp +++ b/docs/source/dask-cugraph.rst @@ -4,20 +4,22 @@ Multi-GPU with cuGraph cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_. -The current solution is able to scale across multiple GPUs on a multiple machines. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory. +Consolidation +============= -With cuGraph and Dask, whether you’re using a single NVIDIA GPU or using all 16 NVIDIA V100 GPUs on a DGX-2, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources. +cuGraph can transparently interpret the Dask cuDF Dataframe as a regular Dataframe when loading the edge list. This is particularly helpful for workflows extracting a single GPU sized edge list from a distributed dataset. From there any existing single GPU feature will just work on this input. -When to Use Multiple GPUs in cuGraph -==================================== +For instance, consolidation allows leveraging Dask cuDF CSV reader to load file(s) on multiple GPUs and consolidate this input to a single GPU graph. Reading is often the time and memory bottleneck, with this feature users can call the Multi-GPU version of the reader without changing anything else. -If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph's multi-GPU features. +Distributed graph analytics +=========================== + +The current solution is able to scale across multiple GPUs on multiple machines. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory. -Supported Graph Analytics -========================= +With cuGraph and Dask, whether you’re using a single NVIDIA GPU or using all 16 NVIDIA V100 GPUs on a DGX-2, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources. + +If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph's multi-GPU features. -Pagerank --------- .. automodule:: cugraph.dask.pagerank.pagerank :members: pagerank From 58d86252cb1601b5a882413b24292df9a0c623a2 Mon Sep 17 00:00:00 2001 From: afender Date: Thu, 9 Jul 2020 17:54:47 -0500 Subject: [PATCH 080/384] doc2 --- docs/source/dask-cugraph.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/source/dask-cugraph.rst b/docs/source/dask-cugraph.rst index 4d22af4df..0bcd0a233 100644 --- a/docs/source/dask-cugraph.rst +++ b/docs/source/dask-cugraph.rst @@ -20,6 +20,10 @@ With cuGraph and Dask, whether you’re using a single NVIDIA GPU or using all 1 If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph's multi-GPU features. +Distributed Graph Analytics Support +----------------------------------- + +Pagerank .. automodule:: cugraph.dask.pagerank.pagerank :members: pagerank From 43b838125a5482c8949f8f9fb8fcb77839f729b8 Mon Sep 17 00:00:00 2001 From: afender Date: Thu, 30 Jul 2020 17:51:57 -0500 Subject: [PATCH 081/384] doc --- docs/source/dask-cugraph.rst | 47 +++++++++++++++++++++++++----------- 1 file changed, 33 insertions(+), 14 deletions(-) diff --git a/docs/source/dask-cugraph.rst b/docs/source/dask-cugraph.rst index 0bcd0a233..93241538b 100644 --- a/docs/source/dask-cugraph.rst +++ b/docs/source/dask-cugraph.rst @@ -2,29 +2,48 @@ Multi-GPU with cuGraph ~~~~~~~~~~~~~~~~~~~~~~ -cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_. - -Consolidation -============= - -cuGraph can transparently interpret the Dask cuDF Dataframe as a regular Dataframe when loading the edge list. This is particularly helpful for workflows extracting a single GPU sized edge list from a distributed dataset. From there any existing single GPU feature will just work on this input. - -For instance, consolidation allows leveraging Dask cuDF CSV reader to load file(s) on multiple GPUs and consolidate this input to a single GPU graph. Reading is often the time and memory bottleneck, with this feature users can call the Multi-GPU version of the reader without changing anything else. +cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_. The maximum graph size is currently limited to 2 Billion vertices (to be waived in the next versions). Distributed graph analytics =========================== The current solution is able to scale across multiple GPUs on multiple machines. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory. -With cuGraph and Dask, whether you’re using a single NVIDIA GPU or using all 16 NVIDIA V100 GPUs on a DGX-2, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources. +With cuGraph and Dask, whether you’re using a single NVIDIA GPU or multiple nodes, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources. If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph's multi-GPU features. -Distributed Graph Analytics Support ------------------------------------ -Pagerank +Distributed Graph Algorithms +---------------------------- -.. automodule:: cugraph.dask.pagerank.pagerank +.. automodule:: cugraph.dask.link_analysis.pagerank :members: pagerank - :undoc-members: pagerank + :undoc-members: + +.. automodule:: cugraph.dask.traversal.bfs + :members: bfs + :undoc-members: + + +Helper functions +---------------- + +.. automodule:: cugraph.comms.comms + :members: initialize + :undoc-members: + +.. automodule:: cugraph.comms.comms + :members: destroy + :undoc-members: + +.. automodule:: cugraph.dask.common.read_utils + :members: get_chunksize + :undoc-members: + +Consolidation +============= + +cuGraph can transparently interpret the Dask cuDF Dataframe as a regular Dataframe when loading the edge list. This is particularly helpful for workflows extracting a single GPU sized edge list from a distributed dataset. From there any existing single GPU feature will just work on this input. + +For instance, consolidation allows leveraging Dask cuDF CSV reader to load file(s) on multiple GPUs and consolidate this input to a single GPU graph. Reading is often the time and memory bottleneck, with this feature users can call the Multi-GPU version of the reader without changing anything else. From 5e0a32c3d7a4a141baa3e88cc842fa22e8b05b7a Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 31 Jul 2020 14:44:56 -0500 Subject: [PATCH 082/384] notebook WIP --- notebooks/demo/mg_pagerank.ipynb | 326 +++++++++++++++++++++++++++++++ 1 file changed, 326 insertions(+) create mode 100644 notebooks/demo/mg_pagerank.ipynb diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb new file mode 100644 index 000000000..ada6a9a4c --- /dev/null +++ b/notebooks/demo/mg_pagerank.ipynb @@ -0,0 +1,326 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple GPU in cuGraph\n", + "#### Author : Alex Fender\n", + "\n", + "In this notebook, we will show how to use multiple GPUs in cuGraph to compute the PageRank of each user in Twitter's dataset.\n", + "\n", + "This notebook was tested on 2 NVIDIA 48GB RTX8000 GPU using RAPIDS 0.15 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", + "\n", + "\n", + "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "We will be analyzing **1.47 billion social relations** on 41.7 million user profiles from the Twitter dataset. The CSV file is 26GB and was collected in :
\n", + "*What is Twitter, a social network or a news media? Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010.*
\n", + "\n", + "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. While we are not limited by the device memory size in this case, the whole system (host+device memory) should still have at least 80GB of memory available" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PageRank with cuGraph\n", + "### Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries. We recommend using cugraph_dev env through conda\n", + "from dask.distributed import Client, wait\n", + "from dask_cuda import LocalCUDACluster\n", + "import cugraph.comms as Comms\n", + "import cugraph.dask as dask_cugraph\n", + "import dask_cudf\n", + "import time\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Twitter dataset is in our S3 bucket and zipped. \n", + "1. We'll need to create a folder for our data in the `/data` folder\n", + "1. Download the zipped data into that folder from S3 (it will take some time as it it 6GB)\n", + "1. Decompress the zipped data for use (it will take some time as it it 26GB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "import os\n", + "\n", + "data_dir = '../data/'\n", + "if not os.path.exists(data_dir):\n", + " print('creating data directory')\n", + " os.system('mkdir ../data')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# download the Twitter dataset\n", + "base_url = 'https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/'\n", + "fn = 'twitter-2010.csv'\n", + "comp = '.gz'\n", + "if not os.path.isfile(data_dir+fn):\n", + " if not os.path.isfile(data_dir+fn+comp):\n", + " print(f'Downloading {base_url+fn+comp} to {data_dir+fn+comp}')\n", + " urllib.request.urlretrieve(base_url+fn+comp, data_dir+fn+comp)\n", + " print(f'Decompressing {data_dir+fn+comp}...')\n", + " os.system('gunzip '+data_dir+fn+comp)\n", + " print(f'{data_dir+fn+comp} decompressed!')\n", + "else:\n", + " print(f'Your data file, {data_dir+fn}, already exists')\n", + "\n", + "# File path, assuming Notebook directory\n", + "input_data_path = data_dir+fn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize multi-GPU environment\n", + "Before we get started, we need to setup a Dask local cluster of workers to execute our work and a client to coordinate and schedule work for that cluster. As we see below, we can initiate a cluster and client using only 3 lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster = LocalCUDACluster()\n", + "client = Client(cluster)\n", + "Comms.initialize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data from disk\n", + "cuGraph depends on cudf for data loading and the initial DataFrame creation. The CSV data file contains an edge list, which represents the connection of a vertex to another. The source to destination pairs is what is known as Coordinate Format (COO). In this test case, the data is just two columns. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Start timer\n", + "t_start = time.time()\n", + "\n", + "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", + "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", + "\n", + "# Multi-GPU CSV reader\n", + "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n", + "\n", + "# (optional) wait for the reader to complete\n", + "tmp = wait(client.compute(e_list.to_delayed()))\n", + "\n", + "# Print time\n", + "print(\"Reader: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a graph\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t_start = time.time()\n", + "\n", + "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", + "G = cugraph.DiGraph()\n", + "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst', renumber=False)\n", + "\n", + "# (optional) request the transposed here so that we can analyse pagerank solver time alone\n", + "G.compute_local_data(by='dst')\n", + "\n", + "# Print time\n", + "print(\"Load and transpose: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Call PageRank algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Start timer\n", + "t_start = time.time()\n", + "\n", + "# Get the pagerank scores\n", + "pr_ddf = dask_cugraph.pagerank(G, tol=1e-4)\n", + "\n", + "# Print time\n", + "print(\"Pagerank: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It was that easy! PageRank should only take a few seconds to run on this 26GB input with one GPU.
\n", + "Check out how it compares to published Spark results in the [Annex](#annex_cell)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Further analysis on the PageRank result\n", + "\n", + "We can now identify the most influent users in the network.
\n", + "Notice that the PageRank result is already in a regular `cudf.DataFrame`. We can then sort by PageRank value and print the *Top 3*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Start timer\n", + "t_start = time.time()\n", + "\n", + "# Sort, descending order\n", + "pr_sorted_df = pr_df.sort_values('pagerank',ascending=False)\n", + "\n", + "# Print time\n", + "print(time.time()-t_start, \"s\")\n", + "\n", + "# Print the Top 3\n", + "print(pr_sorted_df.head(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the [map](https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/twitter-2010-ids.csv.gz) to convert Vertex ID into to Twitter's numeric ID. The user name can also be retrieved using the [TwitterID](https://tweeterid.com/) web app.
\n", + "The table below shows more information on our *Top 3*. Notice that this ranking is much better at capturing network influence compared the number of followers for instance. Further analysis of this dataset was published [here](https://doi.org/10.1145/1772690.1772751).\n", + "\n", + "| Vertex ID\t| Twitter ID\t| User name\t| Description |\n", + "| --------- | --------- | -------- | ---------- |\n", + "| 21513299\t| 813286\t| barackobama\t| US President (2009-2017) |\n", + "| 23933989\t| 14224719\t| 10DowningStreet | UK Prime Minister office |\n", + "| 23933986\t| 15131310\t| WholeFoods\t| Food store from Austin |\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Close multi-GPU environment\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " Comms.destroy()\n", + " client.close()\n", + " cluster.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annex\n", + "\n", + "An experiment comparing various porducts for this workflow was published in *GraphX: Graph Processing in a Distributed Dataflow Framework,OSDI, 2014*. They used 16 m2.4xlarge worker nodes on Amazon EC2. There was a total of 128 CPU cores and 1TB of memory in this 2014 setup.\n", + "\n", + "![twitter-2010-spark.png](twitter-2010-spark.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file From 7c445e4d183d4afe8235264ec29923b7a1dc5627 Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 31 Jul 2020 15:22:17 -0500 Subject: [PATCH 083/384] notebook tested --- notebooks/demo/mg_pagerank.ipynb | 53 +++++++++++++++----------------- 1 file changed, 24 insertions(+), 29 deletions(-) diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index ada6a9a4c..e7f1acab8 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -9,7 +9,7 @@ "\n", "In this notebook, we will show how to use multiple GPUs in cuGraph to compute the PageRank of each user in Twitter's dataset.\n", "\n", - "This notebook was tested on 2 NVIDIA 48GB RTX8000 GPU using RAPIDS 0.15 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", + "This notebook was tested using RAPIDS 0.15 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", "\n", "\n", "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." @@ -23,7 +23,7 @@ "We will be analyzing **1.47 billion social relations** on 41.7 million user profiles from the Twitter dataset. The CSV file is 26GB and was collected in :
\n", "*What is Twitter, a social network or a news media? Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010.*
\n", "\n", - "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. While we are not limited by the device memory size in this case, the whole system (host+device memory) should still have at least 80GB of memory available" + "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. While we are not limited by the device memory size in this case, the whole system should still have at least 60GB of memory available" ] }, { @@ -45,6 +45,7 @@ "from dask_cuda import LocalCUDACluster\n", "import cugraph.comms as Comms\n", "import cugraph.dask as dask_cugraph\n", + "import cugraph\n", "import dask_cudf\n", "import time\n" ] @@ -69,7 +70,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import urllib.request\n", @@ -78,15 +81,8 @@ "data_dir = '../data/'\n", "if not os.path.exists(data_dir):\n", " print('creating data directory')\n", - " os.system('mkdir ../data')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + " os.system('mkdir ../data')\n", + "\n", "# download the Twitter dataset\n", "base_url = 'https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/'\n", "fn = 'twitter-2010.csv'\n", @@ -135,23 +131,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "# Start timer\n", + "# Start ETL timer\n", "t_start = time.time()\n", + "input_data_path = '/datasets/GAP/GAP-twitter.csv'\n", "\n", "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", "\n", "# Multi-GPU CSV reader\n", - "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n", - "\n", - "# (optional) wait for the reader to complete\n", - "tmp = wait(client.compute(e_list.to_delayed()))\n", - "\n", - "# Print time\n", - "print(\"Reader: \", time.time()-t_start, \"s\")" + "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n" ] }, { @@ -164,11 +157,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "t_start = time.time()\n", - "\n", "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", "G = cugraph.DiGraph()\n", "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst', renumber=False)\n", @@ -177,7 +170,7 @@ "G.compute_local_data(by='dst')\n", "\n", "# Print time\n", - "print(\"Load and transpose: \", time.time()-t_start, \"s\")" + "print(\"Read, load and transpose: \", time.time()-t_start, \"s\")" ] }, { @@ -191,15 +184,16 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": true, + "tags": [] }, "outputs": [], "source": [ - "# Start timer\n", + "# Start Pagerank timer\n", "t_start = time.time()\n", "\n", "# Get the pagerank scores\n", - "pr_ddf = dask_cugraph.pagerank(G, tol=1e-4)\n", + "pr_df = dask_cugraph.pagerank(G, tol=1e-4)\n", "\n", "# Print time\n", "print(\"Pagerank: \", time.time()-t_start, \"s\")" @@ -227,7 +221,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": true + "scrolled": true, + "tags": [] }, "outputs": [], "source": [ From 2247f19e383efa04cb13f8463ecb64945b12f2bf Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 31 Jul 2020 15:29:19 -0500 Subject: [PATCH 084/384] forgot to remove my local path --- notebooks/demo/mg_pagerank.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index e7f1acab8..9116410b5 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -138,7 +138,6 @@ "source": [ "# Start ETL timer\n", "t_start = time.time()\n", - "input_data_path = '/datasets/GAP/GAP-twitter.csv'\n", "\n", "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", From f0f4abc0e4ff969ad1c2c7e597523f5b7a0e3efb Mon Sep 17 00:00:00 2001 From: afender Date: Fri, 31 Jul 2020 15:30:26 -0500 Subject: [PATCH 085/384] fix indent --- notebooks/demo/mg_pagerank.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index 9116410b5..d333580ba 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -266,9 +266,9 @@ "metadata": {}, "outputs": [], "source": [ - " Comms.destroy()\n", - " client.close()\n", - " cluster.close()" + "Comms.destroy()\n", + "client.close()\n", + "cluster.close()" ] }, { From daf34b4d675b32ba27a49919c7b195aae6aadb56 Mon Sep 17 00:00:00 2001 From: Xavier Cadet Date: Wed, 5 Aug 2020 16:50:01 -0500 Subject: [PATCH 086/384] batch: rename, use comms, move notebook --- notebooks/demo/batch_betweenness.ipynb | 510 +++++++++++++++++++++++++ 1 file changed, 510 insertions(+) create mode 100644 notebooks/demo/batch_betweenness.ipynb diff --git a/notebooks/demo/batch_betweenness.ipynb b/notebooks/demo/batch_betweenness.ipynb new file mode 100644 index 000000000..2135c7333 --- /dev/null +++ b/notebooks/demo/batch_betweenness.ipynb @@ -0,0 +1,510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-GPU Batch Betweenness Centrality\n", + "#### Author : Xavier Cadet\n", + "In this notebook, we will compute Betweenness Centrality for vertices using cuGraph and will see how to **use Multiple GPUs to compute Betweenness Centrality scores**.\n", + "\n", + "This notebook was tested using 4 NVIDIA Tesla V100-DGX 32G GPUs, using RAPIDS 0.15, and CUDA 10.1. Please be aware that your system may be different and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "Betweennes Centrality can be slow to compute on large graphs, in order to speed up the process we can leverage multiple GPUs.\n", + "In this notebook we will showcase how it would have been done with a Single GPU approach, then we will show how it can be done using multiple GPUs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "The soc-LiveJournal1 dataset which can be obtained on [SNAP](https://snap.stanford.edu/data/soc-LiveJournal1.html). This graph contains roughly 5 million nodes, and 70 million edges and was extracted from the LiveJournal online social network, further information can be found in:\n", + "\n", + "*Group Formation in Large Social Networks: Membership, Growth, and Evolution., L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan., KDD, 2006.*\n", + "\n", + "and:\n", + "\n", + "*Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters., J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney., Internet Mathematics 6(1) 29--123, 2009.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Betweenness Centrality with cuGraph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cugraph\n", + "import cudf\n", + "\n", + "import dask\n", + "import dask_cuda\n", + "import cugraph.comms as Comms" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import cupy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get the data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "import os\n", + "\n", + "data_dir = '../data/'\n", + "if not os.path.exists(data_dir):\n", + " print('creating data directory')\n", + " os.system('mkdir ../data')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your data file, ../data/soc-LiveJournal1.txt, already exists\n" + ] + } + ], + "source": [ + "# download the soc-LiveJournal1 dataset\n", + "base_url = 'https://snap.stanford.edu/data/'\n", + "fn = 'soc-LiveJournal1.txt'\n", + "comp = '.gz'\n", + "if not os.path.isfile(data_dir + fn):\n", + " if not os.path.isfile(data_dir + fn + comp):\n", + " print(f'Downloading {base_url + fn + comp} to {data_dir + fn + comp}')\n", + " urllib.request.urlretrieve(base_url + fn + comp, data_dir + fn + comp)\n", + " print(f'Decompressing {data_dir + fn + comp}...')\n", + " os.system('gunzip ' + data_dir + fn + comp)\n", + " print(f'{data_dir + fn + comp} decompressed!')\n", + "else:\n", + " print(f'Your data file, {data_dir + fn}, already exists')\n", + "input_data_path = data_dir + fn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single GPU" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading the Data - Single GPU\n", + "The following shows how we would read the csv file using a single GPU as it is commonly done when using a single GPU with CuGraph." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "t_start_read_sg = time.perf_counter()\n", + "e_list = cudf.read_csv(input_data_path, delimiter='\\t', names=['src', 'dst'], dtype=['int32', 'int32'])\n", + "t_stop_read_sg = time.perf_counter()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SG Read time: 1.4840830960310996s\n" + ] + } + ], + "source": [ + "print(\"SG Read time: {}s\".format(t_stop_read_sg - t_start_read_sg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Building the Graph - Single GPU\n", + "Once we read the file, we need to build the Graph, we will use a DiGraph, and use the content extracted from the .csv file as an edge list." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "t_start_build_sg = time.perf_counter()\n", + "G = cugraph.DiGraph()\n", + "G.from_cudf_edgelist(e_list, source='src', destination='dst')\n", + "t_stop_build_sg = time.perf_counter()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SG Build time: 0.43075192789547145s\n" + ] + } + ], + "source": [ + "print(\"SG Build time: {}s\".format(t_stop_build_sg - t_start_build_sg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calling the Algorithm - Single GPU\n", + "Now that our graph is built, we can get its betweenness centrality score. Here we will use a sub-sample of 1024 sources in order to have a better approximation of the overall betweenness centrality. We set the seed for comparability with the multi GPU version that comes next." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "t_start_sg = time.perf_counter()\n", + "sg_df = cugraph.betweenness_centrality(G, k=1024, seed=123)\n", + "t_stop_sg = time.perf_counter()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SG Time elapsed: 45.48291095509194s\n" + ] + } + ], + "source": [ + "print(\"SG Time elapsed: {}s\".format(t_stop_sg - t_start_sg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now let's use multiple GPUs!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a Dask Cluster\n", + "In order to use multiple GPU, we need to ensure that we have Dask Cluster and Client running, further more we need to initialize the CuGraph Communicator." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "cluster = dask_cuda.LocalCUDACluster()\n", + "client = dask.distributed.Client(cluster)\n", + "Comms.initialize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enabling Multi GPU Batch Processing\n", + "The good thing is that with a simple `enable_mg_batch` call you can harness the power of Multiple GPUs to operate Batch Processing.\n", + "This step might take a few seconds, indeed we need to get the graph available to all GPUS, do not worry, this is only required once or when adding new representations to the graph (adjacency list for example)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/xcadet/miniconda3/envs/cugraph_dev/lib/python3.7/site-packages/distributed/client.py:3493: RuntimeWarning: coroutine 'Client._update_scheduler_info' was never awaited\n", + " self.sync(self._update_scheduler_info)\n", + "/home/xcadet/miniconda3/envs/cugraph_dev/lib/python3.7/site-packages/distributed/client.py:3493: RuntimeWarning: coroutine 'Client._update_scheduler_info' was never awaited\n", + " self.sync(self._update_scheduler_info)\n", + "/home/xcadet/miniconda3/envs/cugraph_dev/lib/python3.7/site-packages/distributed/client.py:3493: RuntimeWarning: coroutine 'Client._update_scheduler_info' was never awaited\n", + " self.sync(self._update_scheduler_info)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MG Batch Enabling Time elapsed: 5.887182566104457s\n" + ] + } + ], + "source": [ + "t_start_mg = time.perf_counter()\n", + "G.enable_batch()\n", + "print(\"MG Batch Enabling Time elapsed: {}s\".format(time.perf_counter() - t_start_mg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calling the algorithm\n", + "We call the algorithm the same way as we used to, but this time it is much faster as we leverage multiple GPUs to compute the Betweenness Centrality scores." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "t_start_mg = time.perf_counter()\n", + "batch_df = cugraph.betweenness_centrality(G, k=1024, seed=123)\n", + "t_stop_mg = time.perf_counter()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MG Time elapsed: 11.294120778795332s\n" + ] + } + ], + "source": [ + "print(\"MG Time elapsed: {}s\".format(t_stop_mg - t_start_mg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Verification\n", + "Order in the DataFrame might vary, but scores for each vertices match, in order to display them side by side we will first sort the resluts based on the `vertex` key, and renew the DataFramee index." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_sg_df = sg_df.sort_values(\"vertex\").reset_index(drop=True)\n", + "sorted_batch_df = batch_df.sort_values(\"vertex\").reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compare score for each of the vertices:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cupy.allclose(sorted_sg_df[\"betweenness_centrality\"], sorted_batch_df[\"betweenness_centrality\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And just to visually compare the results we can display the DataFrames:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " betweenness_centrality vertex\n", + "0 4.586460e-06 0\n", + "1 1.715658e-04 1\n", + "2 1.361555e-05 2\n", + "3 7.880936e-06 3\n", + "4 8.486780e-06 4\n", + "... ... ...\n", + "4847566 0.000000e+00 4847566\n", + "4847567 0.000000e+00 4847567\n", + "4847568 9.186307e-07 4847568\n", + "4847569 0.000000e+00 4847569\n", + "4847570 4.984750e-08 4847570\n", + "\n", + "[4847571 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(sorted_sg_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " betweenness_centrality vertex\n", + "0 4.586460e-06 0\n", + "1 1.715658e-04 1\n", + "2 1.361555e-05 2\n", + "3 7.880936e-06 3\n", + "4 8.486780e-06 4\n", + "... ... ...\n", + "4847566 0.000000e+00 4847566\n", + "4847567 0.000000e+00 4847567\n", + "4847568 9.186307e-07 4847568\n", + "4847569 0.000000e+00 4847569\n", + "4847570 4.984750e-08 4847570\n", + "\n", + "[4847571 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(sorted_batch_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do not forget to clear the Communicator / client /cluster if required." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "Comms.destroy()\n", + "client.close()\n", + "cluster.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 25a96a40bee69b1a80d7522da98a2c31fa704ef9 Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Wed, 5 Aug 2020 21:03:55 -0500 Subject: [PATCH 087/384] Updates to benchmarks for new renumbering API, fixes for to_directed() and to_undirected() on Graph class to properly reference renumber map when making a new instance, added quick benchmark run to test script so future benchmark breakages can be seen in CI, minor updates to comments for clarity. --- benchmarks/pytest.ini | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index 7096c677b..06a67a060 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -12,6 +12,7 @@ markers = poolallocator_off: RMM pool allocator disabled ETL: benchmarks for ETL steps small: small datasets + tiny: tiny datasets directed: directed datasets undirected: undirected datasets From 90753bc673314f336f9e79bbf08587fc4093f04e Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 6 Aug 2020 15:37:58 -0400 Subject: [PATCH 088/384] DOC v0.16 Updates --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 41674b9b3..6b144e93c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.15' +version = '0.16' # The full version, including alpha/beta/rc tags. -release = '0.15.0' +release = '0.16.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 5a58a09e6d8747e06dbe9c54be8e2dd158a3466c Mon Sep 17 00:00:00 2001 From: Xavier Cadet Date: Fri, 7 Aug 2020 15:30:26 -0500 Subject: [PATCH 089/384] batch: add Batch Processing to docs --- docs/source/dask-cugraph.rst | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/docs/source/dask-cugraph.rst b/docs/source/dask-cugraph.rst index 93241538b..b27ad3828 100644 --- a/docs/source/dask-cugraph.rst +++ b/docs/source/dask-cugraph.rst @@ -47,3 +47,24 @@ Consolidation cuGraph can transparently interpret the Dask cuDF Dataframe as a regular Dataframe when loading the edge list. This is particularly helpful for workflows extracting a single GPU sized edge list from a distributed dataset. From there any existing single GPU feature will just work on this input. For instance, consolidation allows leveraging Dask cuDF CSV reader to load file(s) on multiple GPUs and consolidate this input to a single GPU graph. Reading is often the time and memory bottleneck, with this feature users can call the Multi-GPU version of the reader without changing anything else. + +Batch Processing +================ + +cuGraph can leverage multi GPUs to increase processing speed for graphs that fit on a single GPU, providing faster analytics on such graphs. +You will be able to use the Graph the same way as you used to in a Single GPU environment, but analytics that support batch processing will automatically use the GPUs available to the dask client. +For example, Betweenness Centrality scores can be slow to obtain depending on the number of vertices used in the approximation. Thank to Multi GPUs Batch Processing, +you can create Single GPU graph as you would regularly do it using cuDF CSV reader, enable Batch analytics on it, and obtain scores much faster as each GPU will handle a sub-set of the sources. +In order to use Batch Analytics you need to set up a Dask Cluster and Client in addition to the cuGraph communicator, then you can simply call `enable_batch()` on you graph, and algorithms supporting batch processing will use multiple GPUs. + +Algorithms supporting Batch Processing +-------------------------------------- +.. automodule:: cugraph.centrality + :members: betweenness_centrality + :undoc-members: + :noindex: + +.. automodule:: cugraph.centrality + :members: edge_betweenness_centrality + :undoc-members: + :noindex: From 39b61df196b9aad80141db656f5c6ea408c1289e Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Sun, 16 Aug 2020 21:57:52 -0500 Subject: [PATCH 090/384] Fixes for cugraph_benchmarks notebooks to allow them to find data when run from their containing dir, updated note about renumbering in almost every notebook. --- notebooks/cugraph_benchmarks/README.md | 10 +-- notebooks/cugraph_benchmarks/data | 1 + notebooks/cugraph_benchmarks/dataPrep.sh | 2 +- .../louvain_benchmark.ipynb | 85 ++++--------------- 4 files changed, 22 insertions(+), 76 deletions(-) create mode 120000 notebooks/cugraph_benchmarks/data diff --git a/notebooks/cugraph_benchmarks/README.md b/notebooks/cugraph_benchmarks/README.md index 32b8a8a44..dc8bef4b2 100644 --- a/notebooks/cugraph_benchmarks/README.md +++ b/notebooks/cugraph_benchmarks/README.md @@ -1,6 +1,6 @@ # cuGraph Benchmarking -This folder contains a collection of graph algorithm benchmarking notebooks. Each notebook will compare one cuGraph algorithm against NetworkX and potentially other frameworks. +This folder contains a collection of graph algorithm benchmarking notebooks. Each notebook will compare one cuGraph algorithm against NetworkX and potentially other frameworks. _NOTE: Before any benchmarking can be done, it is important to first download the test data sets_ @@ -32,7 +32,7 @@ The benchmark does not include data reading time, but does include: -#### The data prep script +#### The data prep script By default, each files would be created in its own directory. The goal here is to have all the MTX files in a single directory. @@ -64,7 +64,7 @@ tar xvzf as-Skitter.tar.gz cd .. -find ./tmp -name *.mtx -exec mv {} . \; +find ./tmp -name "*.mtx" -exec mv {} . \; rm -rf tmp ``` @@ -91,9 +91,9 @@ rm -rf tmp The dataset are downloaded from the Texas A&M SuiteSparse Matrix Collection ``` -The SuiteSparse Matrix Collection (formerly known as the University of Florida Sparse Matrix Collection), is a large and actively growing set of sparse matrices that arise in real applications. +The SuiteSparse Matrix Collection (formerly known as the University of Florida Sparse Matrix Collection), is a large and actively growing set of sparse matrices that arise in real applications. ... -The Collection is hosted here, and also mirrored at the University of Florida at www.cise.ufl.edu/research/sparse/matrices. The Collection is maintained by Tim Davis, Texas A&M University (email: davis@tamu.edu), Yifan Hu, Yahoo! Labs, and Scott Kolodziej, Texas A&M University. +The Collection is hosted here, and also mirrored at the University of Florida at www.cise.ufl.edu/research/sparse/matrices. The Collection is maintained by Tim Davis, Texas A&M University (email: davis@tamu.edu), Yifan Hu, Yahoo! Labs, and Scott Kolodziej, Texas A&M University. ``` | File Name | Author | diff --git a/notebooks/cugraph_benchmarks/data b/notebooks/cugraph_benchmarks/data new file mode 120000 index 000000000..4909e06ef --- /dev/null +++ b/notebooks/cugraph_benchmarks/data @@ -0,0 +1 @@ +../data \ No newline at end of file diff --git a/notebooks/cugraph_benchmarks/dataPrep.sh b/notebooks/cugraph_benchmarks/dataPrep.sh index 1dbc8d563..b59130a98 100755 --- a/notebooks/cugraph_benchmarks/dataPrep.sh +++ b/notebooks/cugraph_benchmarks/dataPrep.sh @@ -34,7 +34,7 @@ then cd .. - find ./tmp -name *.mtx -exec mv {} . \; + find ./tmp -name "*.mtx" -exec mv {} . \; rm -rf tmp fi diff --git a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb index c427996a2..7a234c9c1 100644 --- a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb @@ -63,48 +63,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Jun 10 17:38:29 2020 \n", - "+-----------------------------------------------------------------------------+\n", - "| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 |\n", - "|-------------------------------+----------------------+----------------------+\n", - "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", - "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", - "|===============================+======================+======================|\n", - "| 0 GeForce GT 710 On | 00000000:03:00.0 N/A | N/A |\n", - "| 50% 44C P8 N/A / N/A | 374MiB / 1998MiB | N/A Default |\n", - "+-------------------------------+----------------------+----------------------+\n", - "| 1 Quadro GV100 On | 00000000:04:00.0 Off | Off |\n", - "| 33% 45C P2 28W / 250W | 1MiB / 32508MiB | 0% Default |\n", - "+-------------------------------+----------------------+----------------------+\n", - "| 2 Quadro GP100 On | 00000000:84:00.0 Off | Off |\n", - "| 28% 44C P0 27W / 235W | 2MiB / 16278MiB | 0% Default |\n", - "+-------------------------------+----------------------+----------------------+\n", - " \n", - "+-----------------------------------------------------------------------------+\n", - "| Processes: GPU Memory |\n", - "| GPU PID Type Process name Usage |\n", - "|=============================================================================|\n", - "| 0 Not Supported |\n", - "+-----------------------------------------------------------------------------+\n", - "WARNING: infoROM is corrupted at gpu 0000:04:00.0\n", - "WARNING: infoROM is corrupted at gpu 0000:84:00.0\n" - ] - } - ], + "outputs": [], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -183,20 +151,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Quadro GV100'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print out GPU Name\n", "cudf._cuda.gpu.deviceGetName(0)" @@ -211,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -251,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -327,19 +284,7 @@ "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading ./data/preferentialAttachment.mtx...\n", - " cuGraph Solving... \n", - "Reading ./data/preferentialAttachment.mtx...\n", - " cuGraph Solving... \n", - " NetworkX Solving... \n" - ] - } - ], + "outputs": [], "source": [ "# Loop through each test file and compute the speedup\n", "perf = []\n", From 68491e256a061d0e581af1cfdd8f0045dea49081 Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Mon, 17 Aug 2020 04:38:56 -0500 Subject: [PATCH 091/384] Updated last modified dates, cleared outputs on some notebooks, fixed typos. --- .../cugraph_benchmarks/bfs_benchmark.ipynb | 9 +- .../pagerank_benchmark.ipynb | 9 +- notebooks/demo/batch_betweenness.ipynb | 191 ++++-------------- notebooks/demo/uvm.ipynb | 4 +- 4 files changed, 43 insertions(+), 170 deletions(-) diff --git a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb index f5580691d..1c1362d04 100644 --- a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb @@ -11,7 +11,7 @@ "Notebook Credits\n", "\n", " Original Authors: Bradley Rees\n", - " Last Edit: 06/10/2020\n", + " Last Edit: 08/16/2020\n", " \n", "RAPIDS Versions: 0.15\n", "\n", @@ -407,13 +407,6 @@ "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", "___" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb index cb2f5ed0d..52388fc1a 100644 --- a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb @@ -11,7 +11,7 @@ "Notebook Credits\n", "\n", " Original Authors: Bradley Rees\n", - " Last Edit: 06/10/2019\n", + " Last Edit: 08/16/2020\n", " \n", "RAPIDS Versions: 0.15\n", "\n", @@ -477,13 +477,6 @@ "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", "___" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/demo/batch_betweenness.ipynb b/notebooks/demo/batch_betweenness.ipynb index 2135c7333..c2fb52279 100644 --- a/notebooks/demo/batch_betweenness.ipynb +++ b/notebooks/demo/batch_betweenness.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -96,17 +96,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your data file, ../data/soc-LiveJournal1.txt, already exists\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# download the soc-LiveJournal1 dataset\n", "base_url = 'https://snap.stanford.edu/data/'\n", @@ -141,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -152,17 +144,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SG Read time: 1.4840830960310996s\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"SG Read time: {}s\".format(t_stop_read_sg - t_start_read_sg))" ] @@ -177,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -189,17 +173,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SG Build time: 0.43075192789547145s\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"SG Build time: {}s\".format(t_stop_build_sg - t_start_build_sg))" ] @@ -214,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -225,17 +201,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SG Time elapsed: 45.48291095509194s\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"SG Time elapsed: {}s\".format(t_stop_sg - t_start_sg))" ] @@ -257,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -277,29 +245,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/xcadet/miniconda3/envs/cugraph_dev/lib/python3.7/site-packages/distributed/client.py:3493: RuntimeWarning: coroutine 'Client._update_scheduler_info' was never awaited\n", - " self.sync(self._update_scheduler_info)\n", - "/home/xcadet/miniconda3/envs/cugraph_dev/lib/python3.7/site-packages/distributed/client.py:3493: RuntimeWarning: coroutine 'Client._update_scheduler_info' was never awaited\n", - " self.sync(self._update_scheduler_info)\n", - "/home/xcadet/miniconda3/envs/cugraph_dev/lib/python3.7/site-packages/distributed/client.py:3493: RuntimeWarning: coroutine 'Client._update_scheduler_info' was never awaited\n", - " self.sync(self._update_scheduler_info)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MG Batch Enabling Time elapsed: 5.887182566104457s\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t_start_mg = time.perf_counter()\n", "G.enable_batch()\n", @@ -316,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -327,17 +275,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MG Time elapsed: 11.294120778795332s\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"MG Time elapsed: {}s\".format(t_stop_mg - t_start_mg))" ] @@ -352,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -369,20 +309,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cupy.allclose(sorted_sg_df[\"betweenness_centrality\"], sorted_batch_df[\"betweenness_centrality\"])" ] @@ -396,60 +325,18 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " betweenness_centrality vertex\n", - "0 4.586460e-06 0\n", - "1 1.715658e-04 1\n", - "2 1.361555e-05 2\n", - "3 7.880936e-06 3\n", - "4 8.486780e-06 4\n", - "... ... ...\n", - "4847566 0.000000e+00 4847566\n", - "4847567 0.000000e+00 4847567\n", - "4847568 9.186307e-07 4847568\n", - "4847569 0.000000e+00 4847569\n", - "4847570 4.984750e-08 4847570\n", - "\n", - "[4847571 rows x 2 columns]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(sorted_sg_df)" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " betweenness_centrality vertex\n", - "0 4.586460e-06 0\n", - "1 1.715658e-04 1\n", - "2 1.361555e-05 2\n", - "3 7.880936e-06 3\n", - "4 8.486780e-06 4\n", - "... ... ...\n", - "4847566 0.000000e+00 4847566\n", - "4847567 0.000000e+00 4847567\n", - "4847568 9.186307e-07 4847568\n", - "4847569 0.000000e+00 4847569\n", - "4847570 4.984750e-08 4847570\n", - "\n", - "[4847571 rows x 2 columns]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(sorted_batch_df)" ] @@ -463,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -488,9 +375,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "cugraph_dev", "language": "python", - "name": "python3" + "name": "cugraph_dev" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index 2021b4c38..d279be8ed 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -7,9 +7,9 @@ "# Oversubscribing GPU memory in cuGraph\n", "#### Author : Alex Fender\n", "\n", - "In this notebook, we will show how to **scale to 4x larger graphs than before** without performance drop using managed memory features in cuGraph. We will compute the PageRank of each user in Twitter's dataset on a single GPU as an example. This technique applies to all features.\n", + "In this notebook, we will show how to **scale to 4x larger graphs than before** without incurring a performance drop using managed memory features in cuGraph. We will compute the PageRank of each user in Twitter's dataset on a single GPU as an example. This technique applies to all features.\n", "\n", - "Unified Memory is a single memory address space accessible from any processor in a system. If a kernel tries to access any absent pages,the Page Migration Engine migrates the pages. When the GPU memory is full, least recently used pages are evicted. In other words, Unified Memory transparently enables oversubscribing GPU memory, enabling out-of-core computations.\n", + "Unified Memory is a single memory address space accessible from any processor in a system. If a kernel tries to access any absent pages,the Page Migration Engine migrates the pages. When the GPU memory is full, the least recently used pages are evicted. In other words, Unified Memory transparently enables oversubscribing GPU memory, enabling out-of-core computations.\n", "\n", "\n", "This notebook was tested on an NVIDIA 48GB RTX8000 GPU using RAPIDS 0.14 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)" From cfa1ff29f59a22c8fbc1af01bebcee971fca02a0 Mon Sep 17 00:00:00 2001 From: Chuck Hastings Date: Tue, 18 Aug 2020 10:06:30 -0400 Subject: [PATCH 092/384] Update documentation per comments on PR 1063 --- docs/source/api.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index 47a4e29b2..84a01604d 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -57,6 +57,13 @@ Katz Centrality Community ========= +Leiden +------- + +.. automodule:: cugraph.community.leiden + :members: + :undoc-members: + Louvain ------- From cf127b746595f5abfcbc7b29191de94820ac7daf Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Tue, 18 Aug 2020 15:34:37 -0400 Subject: [PATCH 093/384] new notebook for benchmarking --- notebooks/cugraph_benchmarks/release.ipynb | 597 +++++++++++++++++++++ 1 file changed, 597 insertions(+) create mode 100644 notebooks/cugraph_benchmarks/release.ipynb diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb new file mode 100644 index 000000000..b4cbf572d --- /dev/null +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -0,0 +1,597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Release Benchmarking\n", + "\n", + "With every release, RAPIDS publishes a release slide deck that includes the current performance state of cuGraph. \n", + "This notebook run all the various algorithms to compuite the performance gain. \n", + "\n", + "### Algorithms\n", + "| Algorithm |\n", + "| ------------------------|\n", + "| BFS |\n", + "| SSSP |\n", + "| PageRank |\n", + "| WCC |\n", + "| Betweenness Centrality (vertex) |\n", + "| Louvain |\n", + "| Triangle Counting |\n", + "\n", + "### Test Data\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "| ---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |\n", + "\n", + "\n", + "Notebook Credits\n", + "\n", + " Original Authors: Bradley Rees\n", + " Last Edit: 08/17/2020\n", + " \n", + "RAPIDS Versions: 0.15\n", + "\n", + "Test Hardware\n", + " GV100 32G, CUDA 10.2\n", + " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", + " 32GB system memory\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Timing \n", + "What is not timed: Reading the data

\n", + "What is timmed: (1) creating a Graph, (2) running the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# system and other\n", + "import gc\n", + "import os\n", + "import time\n", + "import numpy as np\n", + "\n", + "# rapids\n", + "import cugraph\n", + "import cudf\n", + "\n", + "# NetworkX libraries\n", + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import community\n", + "except ModuleNotFoundError:\n", + " os.system('pip install python-louvain')\n", + " import community" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import matplotlib\n", + "except ModuleNotFoundError:\n", + " os.system('pip install matplotlib')\n", + "\n", + "import matplotlib.pyplot as plt; plt.rcdefaults()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the test data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Test File\n", + "data = {\n", + " 'preferentialAttachment' : '../data/preferentialAttachment.csv',\n", + " 'dblp' : '../data/dblp.csv',\n", + " 'coPapersCiteseer' : '../data/coPapersCiteseer.csv',\n", + " 'as-Skitter' : '../data/as-Skitter.csv'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read data\n", + "The data is read in once once and used for both cuGraph and NetworkX." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def read_data(datafile):\n", + " print (f\"reading {v}\")\n", + " _gdf = cudf.read_csv(datafile, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'] )\n", + " return _gdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Graph functions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX\n", + "def create_nx_graph(_df):\n", + " t1 = time.time()\n", + "\n", + " _gnx = nx.from_pandas_edgelist(_df, source='src', target='dst', edge_attr=None, create_using=nx.DiGraph)\n", + "\n", + " t2 = time.time() - t1\n", + "\n", + " return _gnx, t2\n", + "\n", + "# cuGraph - force CSR creation\n", + "def create_cu_graph(_df):\n", + " t1 = time.time()\n", + "\n", + " _g = cugraph.DiGraph()\n", + " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", + " _ = _g.view_adj_list()\n", + " t2 = time.time() - t1\n", + "\n", + " return _g, t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BFS" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_bfs(_G):\n", + " t1 = time.time()\n", + " _ = nx.bfs_edges(_G, 1)\n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_bfs(_G):\n", + " t1 = time.time()\n", + " _ = cugraph.bfs(_G, 1)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SSSP" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_sssp(_G):\n", + " t1 = time.time()\n", + " _ = nx.shortest_path(_G, 1)\n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_sssp(_G):\n", + " t1 = time.time()\n", + " _ = cugraph.sssp(_G, 1)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PageRank" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_pagerank(_G):\n", + " t1 = time.time()\n", + " _ = nx.pagerank(_G)\n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_pagerank(_G):\n", + " t1 = time.time() \n", + " _ = cugraph.pagerank(_G)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### WCC" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_wcc(_G):\n", + " t1 = time.time()\n", + " _ = nx.weakly_connected_components(_G)\n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_wcc(_G):\n", + " t1 = time.time()\n", + " _ = cugraph.weakly_connected_components(_G)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Betweenness Centrality (vertex)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_bc(_G):\n", + " t1 = time.time()\n", + " _ = nx.betweenness_centrality(_G, k=100)\n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_bc(_G):\n", + " t1 = time.time()\n", + " _ = cugraph.betweenness_centrality(_G, k=100)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Louvain" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_louvain(_G):\n", + " t1 = time.time()\n", + " ug = _G.to_undirected()\n", + " \n", + " parts = community.best_partition(ug)\n", + " \n", + " # Calculating modularity scores for comparison \n", + " _ = community.modularity(parts, ug) \n", + " \n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_louvain(_G):\n", + " t1 = time.time()\n", + " _,_ = cugraph.louvain(_G)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Triangle Counting" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_tc(_G):\n", + " t1 = time.time()\n", + " nx_count = nx.triangles(_G)\n", + " \n", + " # To get the number of triangles, we would need to loop through the array and add up each count\n", + " count = 0\n", + " for key, value in nx_count.items():\n", + " count = count + value \n", + " \n", + " \n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_tc(_G):\n", + " t1 = time.time()\n", + " _ = cugraph.triangles(_G)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# number of datasets\n", + "num_datasets = len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "reading ../data/preferentialAttachment.csv\n", + "reading ../data/preferentialAttachment.csv\n", + "\tdata in gdf 999970 and data in pandas 999970\n", + "\tcugraph Size 999970\n", + "\tcugraph Order 100000\n", + "\tcreated Gx in 2.2733235359191895 seconds vs Cu in 0.0037124156951904297\n", + "\tBFS\n", + "\tSSSP\n", + "\tPageRank\n", + "\tWCC\n", + "\tBC\n", + "\tLouvain\n" + ] + } + ], + "source": [ + "# arrays to capture performance gains\n", + "names = []\n", + "time_create_cu = []\n", + "time_create_nx = []\n", + "\n", + "# Two dimension data\n", + "time_algo_cu = [] # will be two dimensional\n", + "time_algo_nx = [] # will be two dimensional\n", + "perf = []\n", + "\n", + "# do a simple pass just to get all the libraries initiallized\n", + "v = '../data/preferentialAttachment.csv'\n", + "gdf = read_data(v)\n", + "#trapids = cugraph_call(M)\n", + "del gdf\n", + "gc.collect()\n", + "\n", + "\n", + "i = 0\n", + "for k,v in data.items():\n", + " time_algo_cu.append([])\n", + " time_algo_nx.append([])\n", + " perf.append([])\n", + " \n", + " # Saved the file Name\n", + " names.append(k)\n", + "\n", + " # read data\n", + " gdf = read_data(v)\n", + " pdf = gdf.to_pandas()\n", + " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", + "\n", + " # Create the DiGraphs\n", + "\n", + " Gx, tx = create_nx_graph(pdf)\n", + " Gc, tc = create_cu_graph(gdf)\n", + " \n", + " time_create_nx.append(tx)\n", + " time_create_cu.append(tc)\n", + " \n", + " print(f\"\\tcugraph Size {Gc.number_of_edges()}\")\n", + " print(f\"\\tcugraph Order {Gc.number_of_vertices()}\")\n", + " print(f\"\\tcreated Gx in {tx} seconds vs Cu in {tc}\")\n", + " \n", + " # BFS\n", + " print(\"\\tBFS\")\n", + " tx = nx_bfs(Gx)\n", + " tc = cu_bfs(Gc)\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + " \n", + " # SSSP\n", + " print(\"\\tSSSP\")\n", + " tx = nx_sssp(Gx)\n", + " tc = cu_sssp(Gc)\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + "\n", + " # PageRank\n", + " print(\"\\tPageRank\") \n", + " tx = nx_pagerank(Gx)\n", + " tc = cu_pagerank(Gc)\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + "\n", + " # WCC\n", + " print(\"\\tWCC\")\n", + " tx = nx_wcc(Gx)\n", + " tc = cu_wcc(Gc)\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + "\n", + " # BC\n", + " print(\"\\tBC\")\n", + " tx = nx_bc(Gx)\n", + " tc = cu_bc(Gc)\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + "\n", + " # Louvain\n", + " print(\"\\tLouvain\")\n", + " tx = nx_louvain(Gx)\n", + " tc = cu_lovain(Gc)\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + "\n", + " # TC\n", + " print(\"\\tTC\")\n", + " tx = nx_tc(Gx)\n", + " tc = cu_tc(Gc)\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + "\n", + " i = i + 1\n", + " gc.collect()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Print results\n", + "for i in range(num_datasets):\n", + " perf[i]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 2bfbdc1b65ec589abb93210db392687f1b180b4d Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 19 Aug 2020 10:51:12 -0400 Subject: [PATCH 094/384] updated processing and what is timed --- notebooks/cugraph_benchmarks/release.ipynb | 221 +++++++++++++-------- 1 file changed, 137 insertions(+), 84 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index b4cbf572d..a4f8c9887 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -10,15 +10,15 @@ "This notebook run all the various algorithms to compuite the performance gain. \n", "\n", "### Algorithms\n", - "| Algorithm |\n", - "| ------------------------|\n", - "| BFS |\n", - "| SSSP |\n", - "| PageRank |\n", - "| WCC |\n", - "| Betweenness Centrality (vertex) |\n", - "| Louvain |\n", - "| Triangle Counting |\n", + "| Algorithm | Graph | DiGraph |\n", + "| ------------------------| -------- | ----------- |\n", + "| BFS | X | |\n", + "| SSSP | X | |\n", + "| PageRank | | X |\n", + "| WCC | | X |\n", + "| Betweenness Centrality | X | |\n", + "| Louvain | X | |\n", + "| Triangle Counting | X | |\n", "\n", "### Test Data\n", "\n", @@ -77,7 +77,10 @@ "import cudf\n", "\n", "# NetworkX libraries\n", - "import networkx as nx" + "import networkx as nx\n", + "\n", + "# MTX file reader\n", + "from scipy.io import mmread" ] }, { @@ -122,10 +125,10 @@ "source": [ "# Test File\n", "data = {\n", - " 'preferentialAttachment' : '../data/preferentialAttachment.csv',\n", - " 'dblp' : '../data/dblp.csv',\n", - " 'coPapersCiteseer' : '../data/coPapersCiteseer.csv',\n", - " 'as-Skitter' : '../data/as-Skitter.csv'\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx'\n", + "# 'dblp' : './data/dblp.mtx',\n", + "# 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + "# 'as-Skitter' : './data/as-Skitter.mtx'\n", "}" ] }, @@ -143,9 +146,15 @@ "metadata": {}, "outputs": [], "source": [ + "# Data reader - the file format is MTX, so we will use the reader from SciPy\n", "def read_data(datafile):\n", - " print (f\"reading {v}\")\n", - " _gdf = cudf.read_csv(datafile, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'] )\n", + " print('Reading ' + str(datafile) + '...')\n", + " M = mmread(datafile).asfptype()\n", + "\n", + " _gdf = cudf.DataFrame()\n", + " _gdf['src'] = M.row\n", + " _gdf['dst'] = M.col\n", + " \n", " return _gdf" ] }, @@ -164,24 +173,26 @@ "source": [ "# NetworkX\n", "def create_nx_graph(_df):\n", - " t1 = time.time()\n", - "\n", " _gnx = nx.from_pandas_edgelist(_df, source='src', target='dst', edge_attr=None, create_using=nx.DiGraph)\n", + " return _gnx\n", "\n", - " t2 = time.time() - t1\n", + "def create_nx_ugraph(_df):\n", + " _gnx = nx.from_pandas_edgelist(_df, source='src', target='dst', edge_attr=None, create_using=nx.Graph)\n", + " return _gnx\n", "\n", - " return _gnx, t2\n", "\n", - "# cuGraph - force CSR creation\n", + "# cuGraph\n", "def create_cu_graph(_df):\n", - " t1 = time.time()\n", - "\n", " _g = cugraph.DiGraph()\n", " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", " _ = _g.view_adj_list()\n", - " t2 = time.time() - t1\n", + " return _g\n", "\n", - " return _g, t2" + "def create_cu_ugraph(_df):\n", + " _g = cugraph.Graph()\n", + " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", + " _ = _g.view_adj_list()\n", + " return _g" ] }, { @@ -197,14 +208,16 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_bfs(_G):\n", + "def nx_bfs(_df):\n", " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", " _ = nx.bfs_edges(_G, 1)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_bfs(_G):\n", + "def cu_bfs(_df):\n", " t1 = time.time()\n", + " _G = create_cu_ugraph(_df)\n", " _ = cugraph.bfs(_G, 1)\n", " t2 = time.time() - t1\n", " return t2" @@ -223,14 +236,16 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_sssp(_G):\n", + "def nx_sssp(_df):\n", " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", " _ = nx.shortest_path(_G, 1)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_sssp(_G):\n", + "def cu_sssp(_df):\n", " t1 = time.time()\n", + " _G = create_cu_ugraph(_df) \n", " _ = cugraph.sssp(_G, 1)\n", " t2 = time.time() - t1\n", " return t2" @@ -249,14 +264,16 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_pagerank(_G):\n", + "def nx_pagerank(_df):\n", " t1 = time.time()\n", + " _G = create_nx_graph(_df)\n", " _ = nx.pagerank(_G)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_pagerank(_G):\n", - " t1 = time.time() \n", + "def cu_pagerank(_df):\n", + " t1 = time.time()\n", + " _G = create_cu_graph(_df)\n", " _ = cugraph.pagerank(_G)\n", " t2 = time.time() - t1\n", " return t2" @@ -275,14 +292,16 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_wcc(_G):\n", + "def nx_wcc(_df):\n", " t1 = time.time()\n", + " _G = create_nx_graph(_df)\n", " _ = nx.weakly_connected_components(_G)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_wcc(_G):\n", + "def cu_wcc(_df):\n", " t1 = time.time()\n", + " _G = create_cu_graph(_df) \n", " _ = cugraph.weakly_connected_components(_G)\n", " t2 = time.time() - t1\n", " return t2" @@ -301,14 +320,16 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_bc(_G):\n", + "def nx_bc(_df):\n", " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", " _ = nx.betweenness_centrality(_G, k=100)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_bc(_G):\n", + "def cu_bc(_df):\n", " t1 = time.time()\n", + " _G = create_cu_ugraph(_df)\n", " _ = cugraph.betweenness_centrality(_G, k=100)\n", " t2 = time.time() - t1\n", " return t2" @@ -327,11 +348,10 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_louvain(_G):\n", + "def nx_louvain(_df):\n", " t1 = time.time()\n", - " ug = _G.to_undirected()\n", - " \n", - " parts = community.best_partition(ug)\n", + " _G = create_nx_ugraph(_df)\n", + " parts = community.best_partition(_G)\n", " \n", " # Calculating modularity scores for comparison \n", " _ = community.modularity(parts, ug) \n", @@ -339,8 +359,9 @@ " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_louvain(_G):\n", + "def cu_louvain(_df):\n", " t1 = time.time()\n", + " _G = create_cu_ugraph(_df)\n", " _,_ = cugraph.louvain(_G)\n", " t2 = time.time() - t1\n", " return t2" @@ -359,8 +380,9 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_tc(_G):\n", + "def nx_tc(_df):\n", " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", " nx_count = nx.triangles(_G)\n", " \n", " # To get the number of triangles, we would need to loop through the array and add up each count\n", @@ -368,12 +390,12 @@ " for key, value in nx_count.items():\n", " count = count + value \n", " \n", - " \n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_tc(_G):\n", + "def cu_tc(_df):\n", " t1 = time.time()\n", + " _G = create_cu_ugraph(_df)\n", " _ = cugraph.triangles(_G)\n", " t2 = time.time() - t1\n", " return t2" @@ -403,6 +425,55 @@ "num_datasets = len(data)" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tGDF Size 999970\n", + "\tcugraph Size 499985\n", + "\tcugraph Order 100000\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# do a simple pass just to get all the libraries initiallized\n", + "v = './data/preferentialAttachment.mtx'\n", + "gdf = read_data(v)\n", + "print(f\"\\tGDF Size {len(gdf)}\")\n", + "\n", + "g = create_cu_ugraph(gdf)\n", + "\n", + "print(f\"\\tcugraph Size {g.number_of_edges()}\")\n", + "print(f\"\\tcugraph Order {g.number_of_vertices()}\")\n", + "\n", + "del gdf\n", + "del g\n", + "gc.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -412,12 +483,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "reading ../data/preferentialAttachment.csv\n", - "reading ../data/preferentialAttachment.csv\n", + "Reading ./data/preferentialAttachment.mtx...\n", "\tdata in gdf 999970 and data in pandas 999970\n", - "\tcugraph Size 999970\n", - "\tcugraph Order 100000\n", - "\tcreated Gx in 2.2733235359191895 seconds vs Cu in 0.0037124156951904297\n", "\tBFS\n", "\tSSSP\n", "\tPageRank\n", @@ -430,20 +497,12 @@ "source": [ "# arrays to capture performance gains\n", "names = []\n", - "time_create_cu = []\n", - "time_create_nx = []\n", "\n", "# Two dimension data\n", "time_algo_cu = [] # will be two dimensional\n", "time_algo_nx = [] # will be two dimensional\n", "perf = []\n", "\n", - "# do a simple pass just to get all the libraries initiallized\n", - "v = '../data/preferentialAttachment.csv'\n", - "gdf = read_data(v)\n", - "#trapids = cugraph_call(M)\n", - "del gdf\n", - "gc.collect()\n", "\n", "\n", "i = 0\n", @@ -460,83 +519,77 @@ " pdf = gdf.to_pandas()\n", " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", "\n", - " # Create the DiGraphs\n", - "\n", - " Gx, tx = create_nx_graph(pdf)\n", - " Gc, tc = create_cu_graph(gdf)\n", - " \n", - " time_create_nx.append(tx)\n", - " time_create_cu.append(tc)\n", - " \n", - " print(f\"\\tcugraph Size {Gc.number_of_edges()}\")\n", - " print(f\"\\tcugraph Order {Gc.number_of_vertices()}\")\n", - " print(f\"\\tcreated Gx in {tx} seconds vs Cu in {tc}\")\n", - " \n", " # BFS\n", " print(\"\\tBFS\")\n", - " tx = nx_bfs(Gx)\n", - " tc = cu_bfs(Gc)\n", + " tx = nx_bfs(pdf)\n", + " tc = cu_bfs(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", + " gc.collect()\n", " \n", " # SSSP\n", " print(\"\\tSSSP\")\n", - " tx = nx_sssp(Gx)\n", - " tc = cu_sssp(Gc)\n", + " tx = nx_sssp(pdf)\n", + " tc = cu_sssp(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", + " gc.collect()\n", "\n", " # PageRank\n", " print(\"\\tPageRank\") \n", - " tx = nx_pagerank(Gx)\n", - " tc = cu_pagerank(Gc)\n", + " tx = nx_pagerank(pdf)\n", + " tc = cu_pagerank(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", + " gc.collect()\n", "\n", " # WCC\n", " print(\"\\tWCC\")\n", - " tx = nx_wcc(Gx)\n", - " tc = cu_wcc(Gc)\n", + " tx = nx_wcc(pdf)\n", + " tc = cu_wcc(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", + " gc.collect()\n", "\n", " # BC\n", " print(\"\\tBC\")\n", - " tx = nx_bc(Gx)\n", - " tc = cu_bc(Gc)\n", + " tx = nx_bc(pdf)\n", + " tc = cu_bc(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", + " gc.collect()\n", "\n", " # Louvain\n", " print(\"\\tLouvain\")\n", - " tx = nx_louvain(Gx)\n", - " tc = cu_lovain(Gc)\n", + " tx = nx_louvain(pdf)\n", + " tc = cu_lovain(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", + " gc.collect()\n", "\n", " # TC\n", " print(\"\\tTC\")\n", - " tx = nx_tc(Gx)\n", - " tc = cu_tc(Gc)\n", + " tx = nx_tc(pdf)\n", + " tc = cu_tc(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", + " gc.collect()\n", "\n", " i = i + 1\n", - " gc.collect()\n", "\n" ] }, From 894c3f2c5adcb2a67814635a8d26db605ea32089 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 19 Aug 2020 11:37:49 -0400 Subject: [PATCH 095/384] typos --- notebooks/cugraph_benchmarks/release.ipynb | 74 ++++++---------------- 1 file changed, 19 insertions(+), 55 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index a4f8c9887..1fd742d62 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -260,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -354,7 +354,7 @@ " parts = community.best_partition(_G)\n", " \n", " # Calculating modularity scores for comparison \n", - " _ = community.modularity(parts, ug) \n", + " _ = community.modularity(parts, _G) \n", " \n", " t2 = time.time() - t1\n", " return t2\n", @@ -376,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -417,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -427,30 +427,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading ./data/preferentialAttachment.mtx...\n", - "\tGDF Size 999970\n", - "\tcugraph Size 499985\n", - "\tcugraph Order 100000\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# do a simple pass just to get all the libraries initiallized\n", "v = './data/preferentialAttachment.mtx'\n", @@ -478,22 +457,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading ./data/preferentialAttachment.mtx...\n", - "\tdata in gdf 999970 and data in pandas 999970\n", - "\tBFS\n", - "\tSSSP\n", - "\tPageRank\n", - "\tWCC\n", - "\tBC\n", - "\tLouvain\n" - ] - } - ], + "outputs": [], "source": [ "# arrays to capture performance gains\n", "names = []\n", From 42325b03ac0a93ffdcf63dbfe6d636469025d799 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Thu, 20 Aug 2020 09:14:33 -0400 Subject: [PATCH 096/384] tested and updated --- notebooks/cugraph_benchmarks/release.ipynb | 28 ++++++++++++++++------ 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index 1fd742d62..bb1a373b6 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -7,7 +7,7 @@ "# Release Benchmarking\n", "\n", "With every release, RAPIDS publishes a release slide deck that includes the current performance state of cuGraph. \n", - "This notebook run all the various algorithms to compuite the performance gain. \n", + "This notebook, starting with release 0.15, run all the various algorithms to compuite the performance gain. \n", "\n", "### Algorithms\n", "| Algorithm | Graph | DiGraph |\n", @@ -125,10 +125,10 @@ "source": [ "# Test File\n", "data = {\n", - " 'preferentialAttachment' : './data/preferentialAttachment.mtx'\n", - "# 'dblp' : './data/dblp.mtx',\n", - "# 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", - "# 'as-Skitter' : './data/as-Skitter.mtx'\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", "}" ] }, @@ -536,7 +536,7 @@ " # Louvain\n", " print(\"\\tLouvain\")\n", " tx = nx_louvain(pdf)\n", - " tc = cu_lovain(gdf)\n", + " tc = cu_louvain(gdf)\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", @@ -565,7 +565,8 @@ "source": [ "#Print results\n", "for i in range(num_datasets):\n", - " perf[i]" + " print(f\"{names[i]}\")\n", + " print(f\"{perf[i]}\")" ] }, { @@ -588,6 +589,19 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] } ], "metadata": { From c508dd48338b8c31549ed35e8c08085c9043b44a Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Thu, 20 Aug 2020 10:37:11 -0400 Subject: [PATCH 097/384] fix issues found in PR --- notebooks/cugraph_benchmarks/release.ipynb | 54 +++++----------------- 1 file changed, 11 insertions(+), 43 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index bb1a373b6..c5360ff26 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -137,7 +137,7 @@ "metadata": {}, "source": [ "### Read data\n", - "The data is read in once once and used for both cuGraph and NetworkX." + "The data is read in once and used for both cuGraph and NetworkX." ] }, { @@ -162,7 +162,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create Graph functions" + "## Create Graph functions\n", + "There are two types of graphs created:\n", + "Directed Graphs - calls to create_xx_digraph\n", + "Undirected Graphs - calls to create_xx_ugraph <- fully syemmeterized " ] }, { @@ -172,7 +175,7 @@ "outputs": [], "source": [ "# NetworkX\n", - "def create_nx_graph(_df):\n", + "def create_nx_digraph(_df):\n", " _gnx = nx.from_pandas_edgelist(_df, source='src', target='dst', edge_attr=None, create_using=nx.DiGraph)\n", " return _gnx\n", "\n", @@ -182,16 +185,14 @@ "\n", "\n", "# cuGraph\n", - "def create_cu_graph(_df):\n", + "def create_cu_digraph(_df):\n", " _g = cugraph.DiGraph()\n", " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", - " _ = _g.view_adj_list()\n", " return _g\n", "\n", "def create_cu_ugraph(_df):\n", " _g = cugraph.Graph()\n", " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", - " _ = _g.view_adj_list()\n", " return _g" ] }, @@ -266,7 +267,7 @@ "source": [ "def nx_pagerank(_df):\n", " t1 = time.time()\n", - " _G = create_nx_graph(_df)\n", + " _G = create_nx_digraph(_df)\n", " _ = nx.pagerank(_G)\n", " t2 = time.time() - t1\n", " return t2\n", @@ -294,7 +295,7 @@ "source": [ "def nx_wcc(_df):\n", " t1 = time.time()\n", - " _G = create_nx_graph(_df)\n", + " _G = create_nx_digraph(_df)\n", " _ = nx.weakly_connected_components(_G)\n", " t2 = time.time() - t1\n", " return t2\n", @@ -401,13 +402,6 @@ " return t2" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -432,6 +426,7 @@ "outputs": [], "source": [ "# do a simple pass just to get all the libraries initiallized\n", + "# This cell might not be needed\n", "v = './data/preferentialAttachment.mtx'\n", "gdf = read_data(v)\n", "print(f\"\\tGDF Size {len(gdf)}\")\n", @@ -441,18 +436,12 @@ "print(f\"\\tcugraph Size {g.number_of_edges()}\")\n", "print(f\"\\tcugraph Order {g.number_of_vertices()}\")\n", "\n", + "# clean up what we just created\n", "del gdf\n", "del g\n", "gc.collect()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -569,27 +558,6 @@ " print(f\"{perf[i]}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, From a875c2409b5ff1680acdc9be0c3b272ba60552d7 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Thu, 20 Aug 2020 10:52:17 -0400 Subject: [PATCH 098/384] more PR fixes --- notebooks/cugraph_benchmarks/release.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index c5360ff26..ff5ed5abf 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -7,7 +7,7 @@ "# Release Benchmarking\n", "\n", "With every release, RAPIDS publishes a release slide deck that includes the current performance state of cuGraph. \n", - "This notebook, starting with release 0.15, run all the various algorithms to compuite the performance gain. \n", + "This notebook, starting with release 0.15, runs all the various algorithms to computes the performance gain. \n", "\n", "### Algorithms\n", "| Algorithm | Graph | DiGraph |\n", From 3955985d0f7e0047655956e7e81f5bccb097bba3 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 26 Aug 2020 12:13:28 -0700 Subject: [PATCH 099/384] REL v0.15.0 release From 9ac6334c5a1902281c2c52d6a98e739094ebe2af Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 28 Aug 2020 09:40:34 -0400 Subject: [PATCH 100/384] reference doc --- docs/source/cugraph_blogs.rst | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 docs/source/cugraph_blogs.rst diff --git a/docs/source/cugraph_blogs.rst b/docs/source/cugraph_blogs.rst new file mode 100644 index 000000000..3cc37a302 --- /dev/null +++ b/docs/source/cugraph_blogs.rst @@ -0,0 +1,32 @@ +cuGraph blogs and other references +=============================== + +The RAPIDS team blogs at https://medium.com/rapids-ai, and many of +these blog posts provide deeper dives into models or key features from +cuGraph. Here, we've selected just a few that are of particular interest +to cuML users: + +BLOGS +------------------------------------------------ + +2019 +* `RAPIDS cuGraph `_ March +* `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ August + +2020 + + + + + +Academic Papers +--------------- + +* `Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (Sebastian Raschka, Joshua Patterson, Corey Nolet) `_ + + + +References +--------------- + + From 4b33d6be7359992f63e7ae68a87f77803a7f37c0 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 28 Aug 2020 09:43:49 -0400 Subject: [PATCH 101/384] update --- docs/source/cugraph_blogs.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/cugraph_blogs.rst b/docs/source/cugraph_blogs.rst index 3cc37a302..b4150c459 100644 --- a/docs/source/cugraph_blogs.rst +++ b/docs/source/cugraph_blogs.rst @@ -10,8 +10,8 @@ BLOGS ------------------------------------------------ 2019 -* `RAPIDS cuGraph `_ March -* `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ August +* March `RAPIDS cuGraph `_ +* August `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ 2020 From 05c443bf215efb87dc7450872eae0ebfb454e883 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 28 Aug 2020 14:01:17 -0400 Subject: [PATCH 102/384] docs --- docs/source/conf.py | 8 +++++++ docs/source/cugraph_blogs.rst | 39 ++++++++++++++++++++--------------- docs/source/cugraph_intro.md | 6 ++++++ docs/source/cugraph_ref.md | 26 +++++++++++++++++++++++ docs/source/index.rst | 4 ++++ 5 files changed, 66 insertions(+), 17 deletions(-) create mode 100644 docs/source/cugraph_intro.md create mode 100644 docs/source/cugraph_ref.md diff --git a/docs/source/conf.py b/docs/source/conf.py index 6b144e93c..0c8a03162 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -188,3 +188,11 @@ def setup(app): app.add_stylesheet('params.css') + +from recommonmark.parser import CommonMarkParser + +source_parsers = { + '.md': CommonMarkParser, +} + +source_suffix = ['.rst', '.md'] \ No newline at end of file diff --git a/docs/source/cugraph_blogs.rst b/docs/source/cugraph_blogs.rst index b4150c459..e42151049 100644 --- a/docs/source/cugraph_blogs.rst +++ b/docs/source/cugraph_blogs.rst @@ -1,32 +1,37 @@ -cuGraph blogs and other references -=============================== + +cuGraph BLOGS and Presentations +************************************************ The RAPIDS team blogs at https://medium.com/rapids-ai, and many of -these blog posts provide deeper dives into models or key features from -cuGraph. Here, we've selected just a few that are of particular interest -to cuML users: +these blog posts provide deeper dives into features from cuGraph. +Here, we've selected just a few that are of particular interest to cuGraph users: + BLOGS ------------------------------------------------- +============== 2019 -* March `RAPIDS cuGraph `_ -* August `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ +------- + * `RAPIDS cuGraph `_ + * `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ + * `RAPIDS cuGraph : multi-GPU PageRank `_ + * `Similarity in graphs: Jaccard versus the Overlap Coefficient `_ + 2020 +------ + * `Status of RAPIDS cuGraph — Refactoring Code And Rethinking Graphs `_ + * `Tackling Large Graphs with RAPIDS cuGraph and CUDA Unified Memory on GPUs `_ - +Media +=============== + * `Nvidia Rapids cuGraph: Making graph analysis ubiquitous `_ Academic Papers ---------------- - -* `Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (Sebastian Raschka, Joshua Patterson, Corey Nolet) `_ - - - -References ---------------- +=============== + * HPEC +Copyright (c) 2020, NVIDIA CORPORATION. diff --git a/docs/source/cugraph_intro.md b/docs/source/cugraph_intro.md new file mode 100644 index 000000000..ca3e62a84 --- /dev/null +++ b/docs/source/cugraph_intro.md @@ -0,0 +1,6 @@ + +cuGraph Intro +------------------------------ + + + diff --git a/docs/source/cugraph_ref.md b/docs/source/cugraph_ref.md new file mode 100644 index 000000000..7251cac0b --- /dev/null +++ b/docs/source/cugraph_ref.md @@ -0,0 +1,26 @@ +# References +------------------------------ + +Betweenness Centrality +* +Katz +* Cohen, J., “Trusses: Cohesive subgraphs for social network analysis” National security agency technical report, 2008 +* O. Green, J. Fox, E. Kim, F. Busato, et al. “Quickly Finding a Truss in a Haystack” IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038 +* O. Green, P. Yalamanchili, L.M. Munguia, “Fast Triangle Counting on GPU” Irregular Applications: Architectures and Algorithms (IA3), 2014 + + + +------------------------ +## Data Sets + +| Data File | Reference | +|------------------|-------------------------------------------------------------------------| +| karate | W. W. Zachary, Aninformation flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452-473 (1977). | +| dolphins | D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations, Behavioral Ecology and Sociobiology 54, 396-405 (2003). | +| netscience | M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices, Preprint physics/0605087 (2006). | +| email-Eu-core | Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. "Local Higher-order Graph Clustering." In Proceedings of the 23rd ACM SIGKDD nternational Conference on Knowledge Discovery and Data Mining. 2017.

J. Leskovec, J. Kleinberg and C. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf | +| polbooks | V. Krebs, unpublished, http://www.orgnet.com/. | + + + + diff --git a/docs/source/index.rst b/docs/source/index.rst index 47360b88f..cae4d9ed5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -7,6 +7,10 @@ Welcome to cugraph's documentation! api.rst dask-cugraph.rst + cugraph_intro.md + cugraph_blogs.rst + cugraph_ref.rst + Indices and tables ================== From 6ae8788c956b4d5ed3ba843d059bdc812dab7b13 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 28 Aug 2020 14:11:32 -0400 Subject: [PATCH 103/384] changed formating --- docs/source/cugraph_intro.md | 9 ++++++- docs/source/cugraph_ref.md | 26 ------------------ docs/source/cugraph_ref.rst | 52 ++++++++++++++++++++++++++++++++++++ 3 files changed, 60 insertions(+), 27 deletions(-) delete mode 100644 docs/source/cugraph_ref.md create mode 100644 docs/source/cugraph_ref.rst diff --git a/docs/source/cugraph_intro.md b/docs/source/cugraph_intro.md index ca3e62a84..3a875e58e 100644 --- a/docs/source/cugraph_intro.md +++ b/docs/source/cugraph_intro.md @@ -1,6 +1,13 @@ -cuGraph Intro +# cuGraph Intro ------------------------------ +## Graph Type + + +## Algorithms + + +## Using diff --git a/docs/source/cugraph_ref.md b/docs/source/cugraph_ref.md deleted file mode 100644 index 7251cac0b..000000000 --- a/docs/source/cugraph_ref.md +++ /dev/null @@ -1,26 +0,0 @@ -# References ------------------------------- - -Betweenness Centrality -* -Katz -* Cohen, J., “Trusses: Cohesive subgraphs for social network analysis” National security agency technical report, 2008 -* O. Green, J. Fox, E. Kim, F. Busato, et al. “Quickly Finding a Truss in a Haystack” IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038 -* O. Green, P. Yalamanchili, L.M. Munguia, “Fast Triangle Counting on GPU” Irregular Applications: Architectures and Algorithms (IA3), 2014 - - - ------------------------- -## Data Sets - -| Data File | Reference | -|------------------|-------------------------------------------------------------------------| -| karate | W. W. Zachary, Aninformation flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452-473 (1977). | -| dolphins | D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations, Behavioral Ecology and Sociobiology 54, 396-405 (2003). | -| netscience | M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices, Preprint physics/0605087 (2006). | -| email-Eu-core | Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. "Local Higher-order Graph Clustering." In Proceedings of the 23rd ACM SIGKDD nternational Conference on Knowledge Discovery and Data Mining. 2017.

J. Leskovec, J. Kleinberg and C. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf | -| polbooks | V. Krebs, unpublished, http://www.orgnet.com/. | - - - - diff --git a/docs/source/cugraph_ref.rst b/docs/source/cugraph_ref.rst new file mode 100644 index 000000000..d5953bc9e --- /dev/null +++ b/docs/source/cugraph_ref.rst @@ -0,0 +1,52 @@ +# References +=============== + +Betweenness Centrality +------------------------- + + +Katz +------------------------- + +- J. Cohen, *Trusses: Cohesive subgraphs for social network analysis* National security agency technical report, 2008 +- O. Green, J. Fox, E. Kim, F. Busato, et al. *Quickly Finding a Truss in a Haystack* IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038 +- O. Green, P. Yalamanchili, L.M. Munguia, “*ast Triangle Counting on GPU* Irregular Applications: Architectures and Algorithms (IA3), 2014 + + + + + + +Data Sets +=============== + ++------------------+-------------------------------------------------------------------------+ +| Data File | Reference | ++==================+=========================================================================+ +| karate | W. W. Zachary, | +| | Aninformation flow model for conflict and fission in small groups, | +| | Journal of Anthropological Research 33, 452-473 (1977). | ++------------------+-------------------------------------------------------------------------+ +| dolphins | D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and | +| | S. M. Dawson, The bottlenose dolphin community of Doubtful Sound | +| | features a large proportion of long-lasting associations, | +| | Behavioral Ecology and Sociobiology 54, 396-405 (2003). | ++------------------+-------------------------------------------------------------------------+ +| netscience | M. E. J. Newman, Finding community structure in networks using the | +| | eigenvectors of matrices, Preprint physics/0605087 (2006). | ++------------------+-------------------------------------------------------------------------+ +| email-Eu-core | Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. "Local | +| | Higher-order Graph Clustering." In Proceedings of the 23rd ACM SIGKDD | +| | International Conference on Knowledge Discovery and Data Mining. 2017. | +| | | +| | J. Leskovec, J. Kleinberg and C. Faloutsos. Graph Evolution: | +| | Densification and Shrinking Diameters. ACM Transactions on Knowledge | +| | Discovery from Data (ACM TKDD), 1(1), 2007. | +| | http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf | ++------------------+-------------------------------------------------------------------------+ +| polbooks | V. Krebs, unpublished, http://www.orgnet.com/. | ++------------------+-------------------------------------------------------------------------+ + + + + From a0d51ff16bd625b7bbf8102a2445f1c69208e355 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 28 Aug 2020 14:34:46 -0400 Subject: [PATCH 104/384] better formating --- docs/source/cugraph_ref.rst | 49 +++++++++++++++++-------------------- 1 file changed, 22 insertions(+), 27 deletions(-) diff --git a/docs/source/cugraph_ref.rst b/docs/source/cugraph_ref.rst index d5953bc9e..b5d0709a1 100644 --- a/docs/source/cugraph_ref.rst +++ b/docs/source/cugraph_ref.rst @@ -3,6 +3,9 @@ Betweenness Centrality ------------------------- +- Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177. +- Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136-145. +- McLaughlin, A., & Bader, D. A. (2018). Accelerating GPU betweenness centrality. Communications of the ACM, 61(8), 85-92. Katz @@ -19,33 +22,25 @@ Katz Data Sets =============== - -+------------------+-------------------------------------------------------------------------+ -| Data File | Reference | -+==================+=========================================================================+ -| karate | W. W. Zachary, | -| | Aninformation flow model for conflict and fission in small groups, | -| | Journal of Anthropological Research 33, 452-473 (1977). | -+------------------+-------------------------------------------------------------------------+ -| dolphins | D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and | -| | S. M. Dawson, The bottlenose dolphin community of Doubtful Sound | -| | features a large proportion of long-lasting associations, | -| | Behavioral Ecology and Sociobiology 54, 396-405 (2003). | -+------------------+-------------------------------------------------------------------------+ -| netscience | M. E. J. Newman, Finding community structure in networks using the | -| | eigenvectors of matrices, Preprint physics/0605087 (2006). | -+------------------+-------------------------------------------------------------------------+ -| email-Eu-core | Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. "Local | -| | Higher-order Graph Clustering." In Proceedings of the 23rd ACM SIGKDD | -| | International Conference on Knowledge Discovery and Data Mining. 2017. | -| | | -| | J. Leskovec, J. Kleinberg and C. Faloutsos. Graph Evolution: | -| | Densification and Shrinking Diameters. ACM Transactions on Knowledge | -| | Discovery from Data (ACM TKDD), 1(1), 2007. | -| | http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf | -+------------------+-------------------------------------------------------------------------+ -| polbooks | V. Krebs, unpublished, http://www.orgnet.com/. | -+------------------+-------------------------------------------------------------------------+ +karate + - W. W. Zachary, *Aninformation flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). +dolphins + - D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, + *The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations*, + Behavioral Ecology and Sociobiology 54, 396-405 (2003). +netscience + - M. E. J. Newman, + *Finding community structure in networks using the eigenvectors of matrices*, + Preprint physics/0605087 (2006). +email-Eu-core + - Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. + *Local Higher-order Graph Clustering.* + In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. + - J. Leskovec, J. Kleinberg and C. Faloutsos. + *Graph Evolution: Densification and Shrinking Diameters*. + ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf +polbooks + - V. Krebs, unpublished, http://www.orgnet.com/. From 1bd0adcfb43992d5f32f5d4e0fbb3720aae4d4ad Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 28 Aug 2020 14:41:10 -0400 Subject: [PATCH 105/384] formating --- docs/source/cugraph_intro.md | 13 ------------- docs/source/cugraph_intro.rst | 13 +++++++++++++ docs/source/cugraph_ref.rst | 8 ++++++-- docs/source/index.rst | 2 +- 4 files changed, 20 insertions(+), 16 deletions(-) delete mode 100644 docs/source/cugraph_intro.md create mode 100644 docs/source/cugraph_intro.rst diff --git a/docs/source/cugraph_intro.md b/docs/source/cugraph_intro.md deleted file mode 100644 index 3a875e58e..000000000 --- a/docs/source/cugraph_intro.md +++ /dev/null @@ -1,13 +0,0 @@ - -# cuGraph Intro ------------------------------- - - - -## Graph Type - - -## Algorithms - - -## Using diff --git a/docs/source/cugraph_intro.rst b/docs/source/cugraph_intro.rst new file mode 100644 index 000000000..cd2d750e3 --- /dev/null +++ b/docs/source/cugraph_intro.rst @@ -0,0 +1,13 @@ + +cuGraph Intro +------------------------------ + + + +Graph Type + + +Algorithms + + +Using diff --git a/docs/source/cugraph_ref.rst b/docs/source/cugraph_ref.rst index b5d0709a1..4c627c4c2 100644 --- a/docs/source/cugraph_ref.rst +++ b/docs/source/cugraph_ref.rst @@ -1,6 +1,9 @@ -# References +References =============== +Algorithms +************** + Betweenness Centrality ------------------------- - Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177. @@ -21,7 +24,8 @@ Katz Data Sets -=============== +************** + karate - W. W. Zachary, *Aninformation flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). dolphins diff --git a/docs/source/index.rst b/docs/source/index.rst index cae4d9ed5..2cd95e7f1 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -7,7 +7,7 @@ Welcome to cugraph's documentation! api.rst dask-cugraph.rst - cugraph_intro.md + cugraph_intro.rst cugraph_blogs.rst cugraph_ref.rst From fc1dea6461ce49fce3a04643cf98d8593215b182 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Fri, 28 Aug 2020 15:11:08 -0400 Subject: [PATCH 106/384] more ref --- docs/source/cugraph_blogs.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/cugraph_blogs.rst b/docs/source/cugraph_blogs.rst index e42151049..ec8c0f0e2 100644 --- a/docs/source/cugraph_blogs.rst +++ b/docs/source/cugraph_blogs.rst @@ -27,7 +27,7 @@ BLOGS Media =============== * `Nvidia Rapids cuGraph: Making graph analysis ubiquitous `_ - + * `RAPIDS cuGraph – Accelerating all your Graph needs `_ Academic Papers =============== From a47275093155a16ff1afa03e2296743b5d2f6296 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Mon, 31 Aug 2020 14:12:19 -0400 Subject: [PATCH 107/384] addressing code review --- docs/source/cugraph_ref.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/cugraph_ref.rst b/docs/source/cugraph_ref.rst index 4c627c4c2..0742d5f5a 100644 --- a/docs/source/cugraph_ref.rst +++ b/docs/source/cugraph_ref.rst @@ -27,7 +27,7 @@ Data Sets ************** karate - - W. W. Zachary, *Aninformation flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). + - W. W. Zachary, *An information flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). dolphins - D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, *The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations*, From 50655deb6674c81351faede47924a4dd4c8b6c7c Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 2 Sep 2020 10:07:27 -0400 Subject: [PATCH 108/384] Added HITS and sorted algorithms alphabetically --- docs/source/api.rst | 51 ++++++++++++++++++++++++++++----------------- 1 file changed, 32 insertions(+), 19 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 84a01604d..538cd8527 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -57,34 +57,34 @@ Katz Centrality Community ========= -Leiden +Ensemble clustering for graphs (ECG) +------------------------------------ + +.. automodule:: cugraph.community.ecg + :members: + :undoc-members: + +K-Truss ------- -.. automodule:: cugraph.community.leiden +.. automodule:: cugraph.community.ktruss_subgraph :members: :undoc-members: -Louvain +Leiden ------- -.. automodule:: cugraph.community.louvain +.. automodule:: cugraph.community.leiden :members: :undoc-members: -K-Truss +Louvain ------- -.. automodule:: cugraph.community.ktruss_subgraph +.. automodule:: cugraph.community.louvain :members: :undoc-members: -ECG ---- - -.. automodule:: cugraph.community.ecg - :members: - :undoc-members: - Spectral Clustering ------------------- @@ -117,9 +117,17 @@ Connected Components :members: :undoc-members: + Cores ===== +Core Number +----------- + +.. automodule:: cugraph.cores.core_number + :members: + :undoc-members: + K-Core ------ @@ -127,12 +135,6 @@ K-Core :members: :undoc-members: -Core Number ------------ - -.. automodule:: cugraph.cores.core_number - :members: - :undoc-members: Layout ====== @@ -144,9 +146,17 @@ Force Atlas 2 :members: :undoc-members: + Link Analysis ============= +HITS +--------- + +.. automodule:: cugraph.link_analysis.hits + :members: + :undoc-members: + Pagerank --------- @@ -154,6 +164,7 @@ Pagerank :members: :undoc-members: + Link Prediction =============== @@ -179,6 +190,7 @@ Overlap Coefficient :members: :undoc-members: + Traversal ========= @@ -196,6 +208,7 @@ Single-source-shortest-path :members: :undoc-members: + Utilities ========= From 0e900b19612fe32405ab69f051b5a0bb90303c2d Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 2 Sep 2020 10:54:36 -0400 Subject: [PATCH 109/384] removed Renumbering since it is now in Graph. Drop RMAT since it is not implemented --- docs/source/api.rst | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 538cd8527..b194aa0e0 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -14,12 +14,6 @@ Graph :members: :undoc-members: -Renumbering ------------ - -.. automodule:: cugraph.structure.renumber - :members: - :undoc-members: Symmetrize ---------- @@ -212,10 +206,5 @@ Single-source-shortest-path Utilities ========= -R-mat Graph Generation ----------------------- -.. automodule:: cugraph.utilities.grmat - :members: - :undoc-members: From df71b675fd734119b357333864edb1bda45bd08e Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Thu, 3 Sep 2020 11:04:25 -0400 Subject: [PATCH 110/384] added a version of Karate that contains a second disjoint version --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 30bcd5a84..c2498b35c 100644 --- a/.gitignore +++ b/.gitignore @@ -63,6 +63,7 @@ datasets/* !datasets/cyber.csv !datasets/karate-data.csv !datasets/karate_undirected.csv +!datasets/karate-disjoint.csv !datasets/netscience.csv From 7f7bdd6176e0a51eb26d7ea07c4e0b61092a64ed Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Thu, 3 Sep 2020 13:34:45 -0400 Subject: [PATCH 111/384] added additional blogs --- docs/source/cugraph_blogs.rst | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/docs/source/cugraph_blogs.rst b/docs/source/cugraph_blogs.rst index ec8c0f0e2..a9954aee5 100644 --- a/docs/source/cugraph_blogs.rst +++ b/docs/source/cugraph_blogs.rst @@ -31,7 +31,18 @@ Media Academic Papers =============== - * HPEC +* S. Kang, A. Fender, J. Eaton, B. Rees: Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters. In IEEE HPEC, Sep. 2020 + + +Other BLOGS +======================== +* `4 graph algorithms on steroids for data scientists with cugraph + `_ +* `Where should I walk `_ +* `Where really are the parking spots? `_ +* `Accelerating Single Cell Genomic Analysis using RAPIDS `_ + + Copyright (c) 2020, NVIDIA CORPORATION. From 5c29c901abd1488f62bb503fcc468b2d22e4195f Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 29 Sep 2020 19:22:45 -0400 Subject: [PATCH 112/384] [REVIEW] ENH added more Nx compatibility (#1165) * updated Katz API * added entry for edge betweennees centrality * Added SSSP, BFS, and subgraph as Nx compatible Co-authored-by: BradReesWork --- docs/source/api.rst | 7 + .../nx_cugraph_bc_benchmarking.ipynb | 202 ++++++++++++++++++ 2 files changed, 209 insertions(+) create mode 100644 notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb diff --git a/docs/source/api.rst b/docs/source/api.rst index b194aa0e0..d334b488d 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -40,6 +40,13 @@ Betweenness Centrality :members: :undoc-members: +Edge Betweenness Centrality +--------------------------- + +.. automodule:: cugraph.centrality.edge_betweenness_centrality + :members: + :undoc-members: + Katz Centrality --------------- diff --git a/notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb b/notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb new file mode 100644 index 000000000..6f76868f9 --- /dev/null +++ b/notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Benchmarking NetworkX compatibility\n", + "This notebook benchmark the use of a NetworkX Graph object as input into algorithms.

\n", + "The intention of the feature is to be able to drop cuGraph into existing NetworkX code in spot where performance is not optimal.\n", + "\n", + "\n", + "### Betweenness Centrality\n", + "Both NetworkX and cuGraph allow for estimating the betweenness centrality score by using a subset of vertices rather than all the vertices. WHile that does produce a less accurate answer, it dramatically improves performance when the sample is small. For this test, the algorithms will use only 10% of the vertices to compute the estimate \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Notebook Credits__\n", + "\n", + "* Original Authors: Bradley Rees\n", + "* Last Edit: 09/27/2020\n", + "\n", + "RAPIDS Versions: 0.16\n", + "\n", + "Test Hardware\n", + "```\n", + " GV100 32G, CUDA 10,0\n", + " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", + " 32GB system memory\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import cugraph as cnx\n", + "import time\n", + "import operator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# starting number of Nodes\n", + "N = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# average degree\n", + "M = 16" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def run_nx(G, k=None):\n", + " t1 = time.time()\n", + " bc = nx.betweenness_centrality(G, k)\n", + " t2 = time.time() - t1\n", + " return t2, bc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def run_cu(G, k=None):\n", + " t1 = time.time()\n", + " bc = cnx.betweenness_centrality(G, k)\n", + " t2 = time.time() - t1\n", + " return t2, bc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Betweenness Cenytrality - use all nodes - limit scale to 3,200 nodes so that executing time is not days\")\n", + "print(f\"Node \\tEdges \\tSpeedup \\t\\tcreate time \\t\\tnx time \\t\\tcu time \")\n", + "\n", + "for x in range(6):\n", + " if x == 0:\n", + " n = N\n", + " else:\n", + " n = n * 2\n", + "\n", + " \n", + " t1 = time.time() \n", + " # create a random graph\n", + " G = nx.barabasi_albert_graph(n, M)\n", + " g_time = time.time() - t1\n", + " \n", + " num_edges = G.number_of_edges()\n", + " num_nodes = G.number_of_nodes()\n", + " \n", + " time_nx, bc = run_nx(G)\n", + " time_cu, bcc = run_cu(G)\n", + "\n", + " speedup = time_nx / time_cu\n", + " print(f\"{num_nodes}\\t{num_edges}\\t{speedup}\\t{g_time}\\t{time_nx}\\t{time_cu}\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"PageRank - run larger graph since algorithm is fast\")\n", + "print(f\"Node \\tEdges \\tSpeedup \\t\\tnx time \\t\\tcu time \")\n", + "\n", + "pr_speedup = []\n", + "\n", + "for x in range(15):\n", + " if x == 0:\n", + " n = N\n", + " else:\n", + " n = n * 2\n", + "\n", + " # create a random graph\n", + " G = nx.barabasi_albert_graph(n, M)\n", + " num_edges = G.number_of_edges()\n", + " num_nodes = G.number_of_nodes()\n", + " \n", + " t1 = time.time() \n", + " nx_pr = nx.pagerank(G)\n", + " time_nx = time.time() - t1\n", + " \n", + " t1 = time.time() \n", + " cp_pr = cnx.pagerank(G)\n", + " time_cu = time.time() - t1\n", + "\n", + " speedup = time_nx / time_cu\n", + " print(f\"{num_nodes}\\t{num_edges} \\t{speedup}\\t{time_nx}\\t{time_cu}\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 549b0ab63ea95a724e79ffedab02d0f81f2b1bb7 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Fri, 2 Oct 2020 10:41:58 -0400 Subject: [PATCH 113/384] DOC v0.17 Updates --- docs/source/conf.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0c8a03162..fad2f3e21 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '0.16' +version = '0.17' # The full version, including alpha/beta/rc tags. -release = '0.16.0' +release = '0.17.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -195,4 +195,4 @@ def setup(app): '.md': CommonMarkParser, } -source_suffix = ['.rst', '.md'] \ No newline at end of file +source_suffix = ['.rst', '.md'] From fd1786a786e2f338dfe6eda6ec65b87205dae776 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 7 Oct 2020 09:34:21 -0400 Subject: [PATCH 114/384] [REVIEW] BUG fix benchmark notebooks for recent cudf changes (#1192) * fix notebooks for recent cudf changes * updated docs * changelog * fix notebooks for recent cudf changes * updated docs * changelog * reset * flake8 * fixed typo in function name * fixed typo * clean. this notebook can take 12+ hours to run. making data set small for nightly testing Co-authored-by: BradReesWork --- .../cugraph_benchmarks/bfs_benchmark.ipynb | 46 +------------------ .../louvain_benchmark.ipynb | 42 +---------------- .../pagerank_benchmark.ipynb | 40 +--------------- notebooks/cugraph_benchmarks/release.ipynb | 25 ++++++---- 4 files changed, 19 insertions(+), 134 deletions(-) diff --git a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb index 1c1362d04..58eb94bf0 100644 --- a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb @@ -62,34 +62,6 @@ "See the README file in this folder for a discription of how to get the data" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## If you have more than one GPU, set the GPU to use\n", - "This is not needed on a Single GPU system or if the default GPU is to be used" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!nvidia-smi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the GPU to use\n", - "import os\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -144,22 +116,6 @@ "import numpy as np" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Print the name of the used GPU" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cudf._cuda.gpu.deviceGetName(0)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -425,7 +381,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb index 7a234c9c1..a12b7c4bc 100644 --- a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb @@ -57,35 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## If you have more than one GPU, set the GPU to use\n", - "This is not needed on a Single GPU system or if the default GPU is to be used" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!nvidia-smi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# since this is a shared machine - let's pick a GPU that no one else is using\n", - "import os\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Now load the required libraries" + "## Load the required libraries" ] }, { @@ -149,16 +121,6 @@ "import numpy as np" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print out GPU Name\n", - "cudf._cuda.gpu.deviceGetName(0)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -403,7 +365,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb index 52388fc1a..c2933a10c 100644 --- a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb @@ -65,34 +65,6 @@ "See the README file in this folder for a discription of how to get the data" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## If you have more than one GPU, set the GPU to use\n", - "This is not needed on a Single GPU system or if the default GPU is to be used" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!nvidia-smi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# since this is a shared machine - let's pick a GPU that no one else is using\n", - "import os\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -147,16 +119,6 @@ "import numpy as np" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print out GPU Name\n", - "cudf._cuda.gpu.deviceGetName(0)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -495,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index ff5ed5abf..d3110da36 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -33,9 +33,9 @@ "Notebook Credits\n", "\n", " Original Authors: Bradley Rees\n", - " Last Edit: 08/17/2020\n", + " Last Edit: 10/06/2020\n", " \n", - "RAPIDS Versions: 0.15\n", + "RAPIDS Versions: 0.16\n", "\n", "Test Hardware\n", " GV100 32G, CUDA 10.2\n", @@ -124,12 +124,17 @@ "outputs": [], "source": [ "# Test File\n", + "# data = {\n", + "# 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + "# 'dblp' : './data/dblp-2010.mtx',\n", + "# 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + "# 'as-Skitter' : './data/as-Skitter.mtx'\n", + "#}\n", + "\n", + "# for quick testing\n", "data = {\n", - " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", - " 'dblp' : './data/dblp-2010.mtx',\n", - " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", - " 'as-Skitter' : './data/as-Skitter.mtx'\n", - "}" + " 'polbooks' : './data/polbooks.mtx', \n", + "}\n" ] }, { @@ -274,7 +279,7 @@ "\n", "def cu_pagerank(_df):\n", " t1 = time.time()\n", - " _G = create_cu_graph(_df)\n", + " _G = create_cu_digraph(_df)\n", " _ = cugraph.pagerank(_G)\n", " t2 = time.time() - t1\n", " return t2" @@ -302,7 +307,7 @@ "\n", "def cu_wcc(_df):\n", " t1 = time.time()\n", - " _G = create_cu_graph(_df) \n", + " _G = create_cu_digraph(_df) \n", " _ = cugraph.weakly_connected_components(_G)\n", " t2 = time.time() - t1\n", " return t2" @@ -588,7 +593,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.8.5" } }, "nbformat": 4, From e2ab85a81d17634e402daad16d9319222260f23d Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 14 Oct 2020 12:02:37 -0400 Subject: [PATCH 115/384] [REVIEW] DOC Adding Nx transition doc and preping for more (#1217) * Added new Medium entry * updated to match cuML * copied to match cuML * updated list of MG algorithms * converted to Markdown * updated reference to new markdown file * removed rst file * should really be a HTML file (next release) * copy pdf files over * updates * removed ref to cuml * changelog * addressing review issues * migrated to RST Co-authored-by: BradReesWork --- docs/Makefile | 2 + docs/source/_static/copybutton.css | 42 ++++++ docs/source/_static/example_mod.js | 61 +++++++++ docs/source/_static/references.css | 23 ++++ docs/source/conf.py | 44 ++++-- docs/source/cugraph_blogs.rst | 1 + docs/source/cugraph_intro.md | 22 +++ docs/source/cugraph_intro.rst | 13 -- docs/source/images/Nx_Cg_1.png | Bin 0 -> 69661 bytes docs/source/images/Nx_Cg_2.png | Bin 0 -> 49831 bytes docs/source/index.rst | 4 +- docs/source/nx_transition.rst | 198 +++++++++++++++++++++++++++ docs/source/sphinxext/github_link.py | 146 ++++++++++++++++++++ 13 files changed, 527 insertions(+), 29 deletions(-) create mode 100644 docs/source/_static/copybutton.css create mode 100644 docs/source/_static/example_mod.js create mode 100644 docs/source/_static/references.css create mode 100644 docs/source/cugraph_intro.md delete mode 100644 docs/source/cugraph_intro.rst create mode 100644 docs/source/images/Nx_Cg_1.png create mode 100644 docs/source/images/Nx_Cg_2.png create mode 100644 docs/source/nx_transition.rst create mode 100644 docs/source/sphinxext/github_link.py diff --git a/docs/Makefile b/docs/Makefile index e88382797..9c35aa6fc 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -7,6 +7,7 @@ SPHINXBUILD = sphinx-build SPHINXPROJ = cuGraph SOURCEDIR = source BUILDDIR = build +IMGDIR = images # Put it first so that "make" without argument is like "make help". help: @@ -18,3 +19,4 @@ help: # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + cp -r $(SOURCEDIR)/$(IMGDIR) $(BUILDDIR)/html diff --git a/docs/source/_static/copybutton.css b/docs/source/_static/copybutton.css new file mode 100644 index 000000000..5eef6e366 --- /dev/null +++ b/docs/source/_static/copybutton.css @@ -0,0 +1,42 @@ +/* This contains code with copyright by the scikit-learn project, subject to +the license in /thirdparty/LICENSES/LICENSE.scikit_learn */ + +/* copybutton */ +/* Adds "Show/Hide Output" button to Examples */ + +.copybutton { + cursor: pointer; + position: absolute; + top: 0px; + right: 0px; + border: 1px solid rgb(221, 221, 221); + color: rgb(221, 221, 221); + font-family: monospace; + padding-left: 0.2rem; + padding-right: 0.2rem; +} + +div.highlight:hover span.copybutton::after { + background: #3F556B; + border-radius: 0.25rem; + color: white; + content: attr(title); + padding: 0.25rem; + position: absolute; + z-index: 98; + width: 100px; + font-size: 0.7rem; + top: 0; + right: 0; +} + +/* copy buttonn */ +div.highlight:hover span.copybutton { + background-color: #3F556B; + color: white; +} + +div.highlight:hover span.copybutton:hover { + background-color: #20252B; +} + diff --git a/docs/source/_static/example_mod.js b/docs/source/_static/example_mod.js new file mode 100644 index 000000000..77dc618a8 --- /dev/null +++ b/docs/source/_static/example_mod.js @@ -0,0 +1,61 @@ +// This contains code with copyright by the scikit-learn project, subject to +// the license in /thirdparty/LICENSES/LICENSE.scikit_learn + +$(document).ready(function () { + /* Add a [>>>] button on the top-right corner of code samples to hide + * the >>> and ... prompts and the output and thus make the code + * copyable. */ + var div = $('.highlight-python .highlight,' + + '.highlight-python3 .highlight,' + + '.highlight-pycon .highlight,' + + '.highlight-default .highlight') + var pre = div.find('pre'); + + // get the styles from the current theme + pre.parent().parent().css('position', 'relative'); + var hide_text = 'Hide prompts and outputs'; + var show_text = 'Show prompts and outputs'; + + // create and add the button to all the code blocks that contain >>> + div.each(function (index) { + var jthis = $(this); + if (jthis.find('.gp').length > 0) { + var button = $('>>>'); + button.attr('title', hide_text); + button.data('hidden', 'false'); + jthis.prepend(button); + } + // tracebacks (.gt) contain bare text elements that need to be + // wrapped in a span to work with .nextUntil() (see later) + jthis.find('pre:has(.gt)').contents().filter(function () { + return ((this.nodeType == 3) && (this.data.trim().length > 0)); + }).wrap(''); + }); + + // define the behavior of the button when it's clicked + $('.copybutton').click(function (e) { + e.preventDefault(); + var button = $(this); + if (button.data('hidden') === 'false') { + // hide the code output + button.parent().find('.go, .gp, .gt').hide(); + button.next('pre') + .find('.gt') + .nextUntil('.gp, .go') + .css('visibility', 'hidden'); + button.css('text-decoration', 'line-through'); + button.attr('title', show_text); + button.data('hidden', 'true'); + } else { + // show the code output + button.parent().find('.go, .gp, .gt').show(); + button.next('pre') + .find('.gt') + .nextUntil('.gp, .go') + .css('visibility', 'visible'); + button.css('text-decoration', 'none'); + button.attr('title', hide_text); + button.data('hidden', 'false'); + } + }); +}); \ No newline at end of file diff --git a/docs/source/_static/references.css b/docs/source/_static/references.css new file mode 100644 index 000000000..225cf13ba --- /dev/null +++ b/docs/source/_static/references.css @@ -0,0 +1,23 @@ + +/* Fix references to not look like parameters */ +dl.citation > dt.label { + display: unset !important; + float: left !important; + border: unset !important; + background: unset !important; + padding: unset !important; + margin: unset !important; + font-size: unset !important; + line-height: unset !important; + padding-right: 0.5rem !important; +} + +/* Add opening bracket */ +dl.citation > dt.label > span::before { + content: "["; +} + +/* Add closing bracket */ +dl.citation > dt.label > span::after { + content: "]"; +} \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 0c8a03162..a1b7d3483 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # -# Copyright (c) 2018, NVIDIA CORPORATION. +# Copyright (c) 2018-2020 NVIDIA CORPORATION. # # pygdf documentation build configuration file, created by # sphinx-quickstart on Wed May 3 10:59:22 2017. @@ -21,8 +21,17 @@ # import os import sys + +# If extensions (or modules to document with autodoc) are in another +# directory, add these directories to sys.path here. If the directory +# is relative to the documentation root, use os.path.abspath to make it +# absolute, like shown here. +sys.path.insert(0, os.path.abspath('sphinxext')) +sys.path.insert(0, os.path.abspath('../../python')) sys.path.insert(0, os.path.abspath('../..')) +from github_link import make_linkcode_resolve # noqa + # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. @@ -33,14 +42,20 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'sphinx.ext.intersphinx', + 'numpydoc', 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', - 'numpydoc', - 'IPython.sphinxext.ipython_console_highlighting', - 'IPython.sphinxext.ipython_directive', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.linkcode', + "IPython.sphinxext.ipython_console_highlighting", + "IPython.sphinxext.ipython_directive", + "nbsphinx", + "recommonmark", + "sphinx_markdown_tables", ] + ipython_mplbackend = 'str' # Add any paths that contain templates here, relative to this directory. @@ -50,7 +65,7 @@ # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] -source_suffix = '.rst' +source_suffix = {".rst": "restructuredtext", ".md": "markdown"} # The master toctree document. master_doc = 'index' @@ -151,7 +166,7 @@ # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'cugraph.tex', 'cugraph Documentation', - 'Continuum Analytics', 'manual'), + 'nvidia', 'manual'), ] @@ -187,12 +202,13 @@ def setup(app): - app.add_stylesheet('params.css') + app.add_css_file('copybutton.css') + app.add_css_file('params.css') + app.add_css_file('references.css') -from recommonmark.parser import CommonMarkParser - -source_parsers = { - '.md': CommonMarkParser, -} -source_suffix = ['.rst', '.md'] \ No newline at end of file +# The following is used by sphinx.ext.linkcode to provide links to github +linkcode_resolve = make_linkcode_resolve( + 'cugraph', 'https://github.com/rapidsai/' + 'cugraph/blob/{revision}/python/' + '{package}/{path}#L{lineno}') \ No newline at end of file diff --git a/docs/source/cugraph_blogs.rst b/docs/source/cugraph_blogs.rst index a9954aee5..84e31d40a 100644 --- a/docs/source/cugraph_blogs.rst +++ b/docs/source/cugraph_blogs.rst @@ -22,6 +22,7 @@ BLOGS ------ * `Status of RAPIDS cuGraph — Refactoring Code And Rethinking Graphs `_ * `Tackling Large Graphs with RAPIDS cuGraph and CUDA Unified Memory on GPUs `_ + * `RAPIDS cuGraph adds NetworkX and DiGraph Compatibility `_ Media diff --git a/docs/source/cugraph_intro.md b/docs/source/cugraph_intro.md new file mode 100644 index 000000000..5bf2b7154 --- /dev/null +++ b/docs/source/cugraph_intro.md @@ -0,0 +1,22 @@ + +# cuGraph Introduction + + +## Terminology + +cuGraph is a collection of GPU accelerated graph algorithms and graph utility +functions. The application of graph analysis covers a lot of areas. +For Example: +* [Network Science](https://en.wikipedia.org/wiki/Network_science) +* [Complex Network](https://en.wikipedia.org/wiki/Complex_network) +* [Graph Theory](https://en.wikipedia.org/wiki/Graph_theory) +* [Social Network Analysis](https://en.wikipedia.org/wiki/Social_network_analysis) + +cuGraph does not favor one field over another. Our developers span the +breadth of fields with the focus being to produce the best graph library +possible. However, each field has its own argot (jargon) for describing the +graph (or network). In our documentation, we try to be consistent. In Python +documentation we will mostly use the terms __Node__ and __Edge__ to better +match NetworkX preferred term use, as well as other Python-based tools. At +the CUDA/C layer, we favor the mathematical terms of __Vertex__ and __Edge__. + diff --git a/docs/source/cugraph_intro.rst b/docs/source/cugraph_intro.rst deleted file mode 100644 index cd2d750e3..000000000 --- a/docs/source/cugraph_intro.rst +++ /dev/null @@ -1,13 +0,0 @@ - -cuGraph Intro ------------------------------- - - - -Graph Type - - -Algorithms - - -Using diff --git a/docs/source/images/Nx_Cg_1.png b/docs/source/images/Nx_Cg_1.png new file mode 100644 index 0000000000000000000000000000000000000000..6d29f76ad4ab2ec93c784fe5afd9db2379ae7629 GIT binary patch literal 69661 zcmce;1ymeS*CmR(lK{bkh2ZW^aDux#!5xC9AwYr#cMI;py>WM!#)3ORJNPst`TlwT zy!F<4Yi3PWb-GerRekH;)93EJ&%XJpq#%WkLWlwd1%)mn{Z0i63bqgm3c3La0dhnd zX)p!y2JNCEB?eVKO1ua8$47H58H@Mtq39s{NKkOlgi!ELr$Amp&_w^)mxQK;f_**? z0|gag1qJu_90kbx)1PmU*V8qB-eI$#|DFvwmId?gY}mpq*nj)b4NsS?^9!&+-jE%o zwOyc~@Ti|&&@w7izo4K*pk&^Os(pk$Oh^2vu6}ie|5gM?I%wScB#VPyidDWORKL8yzQ`2kiS9wZ)_sy6iQ?IiGG&CSM^@XtT{_Np7JnMB2SxJeEY+Cr{cEX 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diff --git a/docs/source/nx_transition.rst b/docs/source/nx_transition.rst new file mode 100644 index 000000000..6c57ab89c --- /dev/null +++ b/docs/source/nx_transition.rst @@ -0,0 +1,198 @@ +************************************** +NetworkX Compatibility and Transition +************************************** + +*Note: this is a work in progress and will be updatred and changed as we better flesh out +compatibility issues* + +One of the goals of RAPIDS cuGraph is to mimic the NetworkX API to simplify +the transition to accelerated GPU data science. However, graph analysis, +also called network science, like most other data science workflow, is more +than just running an algorithm. Graph data requires cleaning and prep (ETL) +and then the construction of a graph object; that is all before the execution +of a graph algorithm. RAPIDS and cuGraph allow a portion or the complete +analytic workflow to be accelerated. To achieve the maximum amount of +acceleration, we encourage fully replacing existing code with cuGraph. +But sometimes it is easier to replace just a portion. + +Last Update +########### + +Last Update: Oct 14th, 2020 +Release: 0.16 + +Information on `NetworkX `_ + +This transition guide in an expansion of the Medium Blog on `NetworkX Compatibility +`_ + + +Easy Path – Use NetworkX Graph Objects, Accelerated Algorithms +############################################################## + +Rather than updating all of your existing code, simply update the calls to +graph algorithms by replacing the module name. This allows all the complicated +ETL code to be unchanged while still seeing significate performance +improvements. + +In the following example, the cuGraph module is being imported as “cnx”. +While module can be assigned any name can be used, we picked cnx to reduce +the amount of text to be changed. The text highlighted in yellow indicates +changes. + +.. image:: ./images/Nx_Cg_1.png + :width: 600 + +It is that easy. All algorithms in cuGraph support a NetworkX graph object as +input and match the NetworkX API list of arguments. + +Currently, cuGraph accepts both NetworkX Graph and DiGraph objects. We will be +adding support for Bipartite graph and Multigraph over the next few releases. + +| + + +Differences in Algorithms +########################## + +Since cuGraph currently does not support attribute rich graphs, those +algorithms that return simple scores (centrality, clustering, etc.) best match +the NetworkX process. Algorithms that return a subgraph will do so without +any additional attributes on the nodes or edges. + +Algorithms that exactly match +***************************** + ++-------------------------------+------------------------+ +| Algorithm | Differences | ++===============================+========================+ +| Core Number | None | ++-------------------------------+------------------------+ +| HITS | None | ++-------------------------------+------------------------+ +| PageRank | None | ++-------------------------------+------------------------+ +| Personal PageRank | None | ++-------------------------------+------------------------+ +| Strongly Connected Components | None | ++-------------------------------+------------------------+ +| Weakly Connected Components | None | ++-------------------------------+------------------------+ + +| + + + +Algorithms that do not copy over additional attributes +************************************************************************ + ++-------------------------------+-------------------------------------+ +| Algorithm | Differences | ++===============================+=====================================+ +| K-Truss | Does not copy over attributes | ++-------------------------------+-------------------------------------+ +| K-Core | Does not copy over attributes | ++-------------------------------+-------------------------------------+ +| Subgraph Extraction | Does not copy over attributes | ++-------------------------------+-------------------------------------+ + +| + + +Algorithms not in NetworkX +************************** + ++--------------------------------------+----------------------------+ +| Algorithm | Differences | ++======================================+============================+ +| Ensemble Clustering for Graphs (ECG) | Currently not in NetworkX | ++--------------------------------------+----------------------------+ +| Force Atlas 2 | Currently not in NetworkX | ++--------------------------------------+----------------------------+ +| Leiden | Currently not in NetworkX | ++--------------------------------------+----------------------------+ +| Louvain | Currently not in NetworkX | ++--------------------------------------+----------------------------+ +| Overlap coefficient | Currently not in NetworkX | ++--------------------------------------+----------------------------+ +| Spectral Clustering | Currently not in NetworkX | ++--------------------------------------+----------------------------+ + +| + + +Algorithm where not all arguments are supported +*********************************************** + ++----------------------------+-------------------------------------------------+ +| Algorithm | Differences | ++============================+=================================================+ +|Betweenness Centrality | weight is currently not supported – ignored | +| | endpoints is currently not supported – ignored | ++----------------------------+-------------------------------------------------+ +|Edge Betweenness Centrality | weight is currently not supported – ignored | ++----------------------------+-------------------------------------------------+ +| Katz Centrality | beta is currently not supported – ignored | +| | max_iter defaults to 100 versus 1000 | ++----------------------------+-------------------------------------------------+ + +| + +Algorithms where the results are different +****************************************** + + +For example, the NetworkX traversal algorithms typically return a generator +rather than a dictionary. + + ++----------------------------+-------------------------------------------------+ +| Algorithm | Differences | ++============================+=================================================+ +| Triangle Counting | this algorithm simply returns the total number | +| | of triangle and not the number per vertex | +| | (on roadmap to update) | ++----------------------------+-------------------------------------------------+ +| Jaccard coefficient | Currently we only do a 1-hop computation rather | +| | than an all-pairs. Fix is on roadmap | ++----------------------------+-------------------------------------------------+ +| Breadth First Search (BFS) | Returns a Pandas DataFrame with: | +| | [vertex][distance][predecessor] | ++----------------------------+-------------------------------------------------+ +| Single Source | Returns a Pandas DataFrame with: | +| Shortest Path (SSSP) | [vertex][distance][predecessor] | ++----------------------------+-------------------------------------------------+ + +| + +Graph Building +############## + +The biggest difference between NetworkX and cuGraph is with how Graph objects +are built. NetworkX, for the most part, stores graph data in a dictionary. +That structure allows easy insertion of new records. Consider the following +code for building a NetworkX Graph:: + + # Read the node data + df = pd.read_csv( data_file) + + # Construct graph from edge list. + G = nx.DiGraph() + + for row in df.iterrows(): + G.add_edge( + row[1]["1"], row[1]["2"], count=row[1]["3"] + ) + + +The code block is perfectly fine for NetworkX. However, the process of iterating over the dataframe and adding one node at a time is problematic for GPUs and something that we try and avoid. cuGraph stores data in columns (i.e. arrays). Resizing an array requires allocating a new array one element larger, copying the data, and adding the new value. That is not very efficient. + +If your code follows the above model of inserting one element at a time, the we suggest either rewriting that code or using it as is within NetworkX and just accelerating the algorithms with cuGraph. + +Now, if your code bulk loads the data from Pandas, then RAPIDS can accelerate that process by orders of magnitude. + +.. image:: ./images/Nx_Cg_2.png + :width: 600 + +The above cuGraph code will create cuGraph.Graph object and not a NetworkX.Graph object. + diff --git a/docs/source/sphinxext/github_link.py b/docs/source/sphinxext/github_link.py new file mode 100644 index 000000000..a7a46fdd9 --- /dev/null +++ b/docs/source/sphinxext/github_link.py @@ -0,0 +1,146 @@ +# This contains code with copyright by the scikit-learn project, subject to the +# license in /thirdparty/LICENSES/LICENSE.scikit_learn + +import inspect +import os +import re +import subprocess +import sys +from functools import partial +from operator import attrgetter + +orig = inspect.isfunction + + +# See https://opendreamkit.org/2017/06/09/CythonSphinx/ +def isfunction(obj): + + orig_val = orig(obj) + + new_val = hasattr(type(obj), "__code__") + + if (orig_val != new_val): + return new_val + + return orig_val + + +inspect.isfunction = isfunction + +REVISION_CMD = 'git rev-parse --short HEAD' + +source_regex = re.compile(r"^File: (.*?) \(starting at line ([0-9]*?)\)$", + re.MULTILINE) + + +def _get_git_revision(): + try: + revision = subprocess.check_output(REVISION_CMD.split()).strip() + except (subprocess.CalledProcessError, OSError): + print('Failed to execute git to get revision') + return None + return revision.decode('utf-8') + + +def _linkcode_resolve(domain, info, package, url_fmt, revision): + """Determine a link to online source for a class/method/function + + This is called by sphinx.ext.linkcode + + An example with a long-untouched module that everyone has + >>> _linkcode_resolve('py', {'module': 'tty', + ... 'fullname': 'setraw'}, + ... package='tty', + ... url_fmt='http://hg.python.org/cpython/file/' + ... '{revision}/Lib/{package}/{path}#L{lineno}', + ... revision='xxxx') + 'http://hg.python.org/cpython/file/xxxx/Lib/tty/tty.py#L18' + """ + + if revision is None: + return + if domain not in ('py', 'pyx'): + return + if not info.get('module') or not info.get('fullname'): + return + + class_name = info['fullname'].split('.')[0] + module = __import__(info['module'], fromlist=[class_name]) + obj = attrgetter(info['fullname'])(module) + + # Unwrap the object to get the correct source + # file in case that is wrapped by a decorator + obj = inspect.unwrap(obj) + + fn: str = None + lineno: str = None + + try: + fn = inspect.getsourcefile(obj) + except Exception: + fn = None + if not fn: + try: + fn = inspect.getsourcefile(sys.modules[obj.__module__]) + except Exception: + fn = None + + if not fn: + # Possibly Cython code. Search docstring for source + m = source_regex.search(obj.__doc__) + + if (m is not None): + source_file = m.group(1) + lineno = m.group(2) + + # fn is expected to be the absolute path. + fn = os.path.relpath(source_file, start=package) + print("{}:{}".format( + os.path.abspath(os.path.join("..", "python", "cuml", fn)), + lineno)) + else: + return + else: + # Test if we are absolute or not (pyx are relative) + if (not os.path.isabs(fn)): + # Should be relative to docs right now + fn = os.path.abspath(os.path.join("..", "python", fn)) + + # Convert to relative from module root + fn = os.path.relpath(fn, + start=os.path.dirname( + __import__(package).__file__)) + + # Get the line number if we need it. (Can work without it) + if (lineno is None): + try: + lineno = inspect.getsourcelines(obj)[1] + except Exception: + + # Can happen if its a cyfunction. See if it has `__code__` + if (hasattr(obj, "__code__")): + lineno = obj.__code__.co_firstlineno + else: + lineno = '' + return url_fmt.format(revision=revision, + package=package, + path=fn, + lineno=lineno) + + +def make_linkcode_resolve(package, url_fmt): + """Returns a linkcode_resolve function for the given URL format + + revision is a git commit reference (hash or name) + + package is the name of the root module of the package + + url_fmt is along the lines of ('https://github.com/USER/PROJECT/' + 'blob/{revision}/{package}/' + '{path}#L{lineno}') + """ + revision = _get_git_revision() + return partial(_linkcode_resolve, + revision=revision, + package=package, + url_fmt=url_fmt) From 9f73e5fe8780e43a8d2703901a1d8690ab9b332e Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 21 Oct 2020 12:41:58 -0700 Subject: [PATCH 116/384] REL v0.16.0 release From a8efc534dc7a1c943642abaabca009e098fee357 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Fri, 6 Nov 2020 11:13:16 -0900 Subject: [PATCH 117/384] REL v0.16.1 release --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index a1b7d3483..5ea710fa8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -82,7 +82,7 @@ # The short X.Y version. version = '0.16' # The full version, including alpha/beta/rc tags. -release = '0.16.0' +release = '0.16.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 78897f8f9cd69399d26b8c26722494d27f78bbbf Mon Sep 17 00:00:00 2001 From: Ishika Roy Date: Fri, 20 Nov 2020 09:28:59 -0600 Subject: [PATCH 118/384] update notebooks --- notebooks/demo/batch_betweenness.ipynb | 2 +- notebooks/demo/mg_pagerank.ipynb | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/notebooks/demo/batch_betweenness.ipynb b/notebooks/demo/batch_betweenness.ipynb index c2fb52279..e2ad83ff1 100644 --- a/notebooks/demo/batch_betweenness.ipynb +++ b/notebooks/demo/batch_betweenness.ipynb @@ -231,7 +231,7 @@ "source": [ "cluster = dask_cuda.LocalCUDACluster()\n", "client = dask.distributed.Client(cluster)\n", - "Comms.initialize()" + "Comms.initialize(p2p=True)" ] }, { diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index d333580ba..a0db55eda 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -117,7 +117,7 @@ "source": [ "cluster = LocalCUDACluster()\n", "client = Client(cluster)\n", - "Comms.initialize()" + "Comms.initialize(p2p=True)" ] }, { @@ -317,4 +317,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From ce4794090cecf5f2c0ab68be12a60e61e9f47f81 Mon Sep 17 00:00:00 2001 From: AJ Schmidt Date: Tue, 24 Nov 2020 15:45:03 -0500 Subject: [PATCH 119/384] DOC v0.18 Updates --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index adec59a2f..6b484a5f5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -80,9 +80,9 @@ # built documents. # # The short X.Y version. -version = '0.17' +version = '0.18' # The full version, including alpha/beta/rc tags. -release = '0.17.0' +release = '0.18.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 373ffef0da2a04b7f83356802dcada10f7ab9d30 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 1 Dec 2020 16:52:26 -0500 Subject: [PATCH 120/384] Update README and some docs(#1281) Update the README with new algorithms and multi-gpu status Also cleaned up a few other stops Authors: - BradReesWork Approvers: - Alex Fender - Alex Fender URL: https://github.com/rapidsai/cugraph/pull/1281 --- docs/source/cugraph_ref.rst | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/docs/source/cugraph_ref.rst b/docs/source/cugraph_ref.rst index 0742d5f5a..591619fb3 100644 --- a/docs/source/cugraph_ref.rst +++ b/docs/source/cugraph_ref.rst @@ -1,30 +1,35 @@ +########## References -=============== +########## +********** Algorithms -************** +********** Betweenness Centrality -------------------------- + - Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177. - Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136-145. - McLaughlin, A., & Bader, D. A. (2018). Accelerating GPU betweenness centrality. Communications of the ACM, 61(8), 85-92. Katz -------------------------- - J. Cohen, *Trusses: Cohesive subgraphs for social network analysis* National security agency technical report, 2008 - O. Green, J. Fox, E. Kim, F. Busato, et al. *Quickly Finding a Truss in a Haystack* IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038 - O. Green, P. Yalamanchili, L.M. Munguia, “*ast Triangle Counting on GPU* Irregular Applications: Architectures and Algorithms (IA3), 2014 +Hungarian Algorithm +- Date, K., & Nagi, R. (2016). GPU-accelerated Hungarian algorithms for the Linear Assignment Problem. Parallel Computing, 57, 52-72. +| +| - +********** Data Sets -************** +********** karate - W. W. Zachary, *An information flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). From b1a9b167cedb531c3f904ac190644745ff9979d3 Mon Sep 17 00:00:00 2001 From: Alex Fender Date: Tue, 1 Dec 2020 20:37:26 -0600 Subject: [PATCH 121/384] Update api.rst for MST(#1289) Authors: - Alex Fender Approvers: - Brad Rees URL: https://github.com/rapidsai/cugraph/pull/1289 --- docs/source/api.rst | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index d334b488d..459e5fbf4 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -210,8 +210,20 @@ Single-source-shortest-path :undoc-members: -Utilities +Tree ========= +Minimum Spanning Tree +--------------------- +.. automodule:: cugraph.tree.minimum_spanning_tree + :members: + :undoc-members: + +Maximum Spanning Tree +--------------------- + +.. automodule:: cugraph.tree.maximum_spanning_tree + :members: + :undoc-members: From 62e32371daac6ec84584d3a66ae557b3cc5988ea Mon Sep 17 00:00:00 2001 From: Ishika Roy Date: Mon, 7 Dec 2020 23:27:57 -0600 Subject: [PATCH 122/384] update doc and notebook --- notebooks/demo/mg_pagerank.ipynb | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index a0db55eda..db1f69ccb 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -163,13 +163,10 @@ "source": [ "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", "G = cugraph.DiGraph()\n", - "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst', renumber=False)\n", - "\n", - "# (optional) request the transposed here so that we can analyse pagerank solver time alone\n", - "G.compute_local_data(by='dst')\n", + "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')\n", "\n", "# Print time\n", - "print(\"Read, load and transpose: \", time.time()-t_start, \"s\")" + "print(\"Read, load and renumber: \", time.time()-t_start, \"s\")" ] }, { From 8b7f4167d551e7b642739c68e600cd028f4492b0 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Thu, 10 Dec 2020 12:54:24 -0900 Subject: [PATCH 123/384] REL v0.17.0 release From 5359350e92fe0248c5224f345c96a4bcf97e38d7 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Mon, 11 Jan 2021 14:13:58 -0500 Subject: [PATCH 124/384] drop duplicate edge centrality entry --- docs/source/api.rst | 7 ------- 1 file changed, 7 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index 459e5fbf4..b36fc6449 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -40,13 +40,6 @@ Betweenness Centrality :members: :undoc-members: -Edge Betweenness Centrality ---------------------------- - -.. automodule:: cugraph.centrality.edge_betweenness_centrality - :members: - :undoc-members: - Katz Centrality --------------- From 61f2ea8c5dd49dce2b84efd08ab8ff6e5b0bf2af Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Mon, 11 Jan 2021 14:14:09 -0500 Subject: [PATCH 125/384] added more introp doc --- docs/source/cugraph_intro.md | 66 +++++++++++++++++++++++++++++++++++- 1 file changed, 65 insertions(+), 1 deletion(-) diff --git a/docs/source/cugraph_intro.md b/docs/source/cugraph_intro.md index 5bf2b7154..073e13d37 100644 --- a/docs/source/cugraph_intro.md +++ b/docs/source/cugraph_intro.md @@ -1,8 +1,72 @@ # cuGraph Introduction +The Data Scientist has a collection of techniques within their +proverbial toolbox. Data engineering, statistical analysis, and +machine learning are among the most commonly known. However, there +are numerous cases where the focus of the analysis is on the +relationship between data elements. In those cases, the data is best +represented as a graph. Graph analysis, also called network analysis, +is a collection of algorithms for answering questions posed against +graph data. Graph analysis is not new. +The first graph problem was posed by Euler in 1736, the [Seven Bridges of +Konigsberg](https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg), +and laid the foundation for the mathematical field of graph theory. +The application of graph analysis covers a wide variety of fields, including +marketing, biology, physics, computer science, sociology, and cyber to name a few. -## Terminology +RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates +into the RAPIDS data science ecosystem and allows the data scientist to easily +call graph algorithms using data stored in a GPU DataFrame, NetworkX Graphs, or even +CuPy or SciPy sparse Matrix. + + +# Vision +The vision of RAPIDS cuGraph is to ___make graph analysis ubiquitous to the +point that users just think in terms of analysis and not technologies or +frameworks___. This is a goal that many of us on the cuGraph team have been +working on for almost twenty years. Many of the early attempts focused on +solving one problem or using one technique. Those early attempts worked for +the initial goal but tended to break as the scope changed (e.g., shifting +to solving a dynamic graph problem with a static graph solution). The limiting +factors usually came down to compute power, ease-of-use, or choosing a data +structure that was not suited for all problems. NVIDIA GPUs, CUDA, and RAPIDS +have totally changed the paradigm and the goal of an accelerated unified graph +analytic library is now possible. + +The compute power of the latest NVIDIA GPUs (RAPIDS supports Pascal and later +GPU architectures) make graph analytics 20x faster on average over NetworkX. +Moreover, the internal memory speed within a GPU allows cuGraph to rapidly +switch the data structure to best suit the needs of the analytic rather than +being restricted to a single data structure. cuGraph is working with several +frameworks for both static and dynamic graph data structures so that we always +have a solution to any graph problem. Since Python has emerged as the de facto +language for data science, allowing interactivity and the ability to run graph +analytics in Python makes cuGraph familiar and approachable. RAPIDS wraps all +the graph analytic goodness mentioned above with the ability to perform +high-speed ETL, statistics, and machine learning. To make things even better, +RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support +multi-billion edge graphs. + +Every release of RAPIDS is accompanied with one or more wonderful BLOG(s) +about the features of that release (for example, see the release 0.8 blog [1] +or Life after Hadoop). This article is slightly different. While current +features will be discussed, the main focus is on presenting the vision of +cuGraph and how we envision users will interact with the libraries. The cuGraph +team has been working hard to provide a rich set of initial features. Over the +past three releases (RAPIDS release 0.6 was the first to include cuGraph), the +team has provided over a dozen algorithms. The initial goal was to simply get +graph analytics released and available with a familiar NetworkX-like API. That +is great in the short term since it allows an easy path to switch to RAPIDS. +However, cuGraph development will slowly shift towards improving ease-of-use, +interoperability, and integration with the rest of RAPIDS. That is not an easy +task since there is still so much to be added to the cuGraph ecosystem. Don’t +worry, the goal of getting new analytics out will continue since there is a very +long list of algorithms to release. + + + +# Terminology cuGraph is a collection of GPU accelerated graph algorithms and graph utility functions. The application of graph analysis covers a lot of areas. From 84334c66ac1ae1c35cd5f824d0f672001885a6cd Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 13 Jan 2021 15:32:29 -0500 Subject: [PATCH 126/384] updated intro --- docs/source/cugraph_intro.md | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/docs/source/cugraph_intro.md b/docs/source/cugraph_intro.md index 073e13d37..cbbb9ef4e 100644 --- a/docs/source/cugraph_intro.md +++ b/docs/source/cugraph_intro.md @@ -48,23 +48,6 @@ high-speed ETL, statistics, and machine learning. To make things even better, RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support multi-billion edge graphs. -Every release of RAPIDS is accompanied with one or more wonderful BLOG(s) -about the features of that release (for example, see the release 0.8 blog [1] -or Life after Hadoop). This article is slightly different. While current -features will be discussed, the main focus is on presenting the vision of -cuGraph and how we envision users will interact with the libraries. The cuGraph -team has been working hard to provide a rich set of initial features. Over the -past three releases (RAPIDS release 0.6 was the first to include cuGraph), the -team has provided over a dozen algorithms. The initial goal was to simply get -graph analytics released and available with a familiar NetworkX-like API. That -is great in the short term since it allows an easy path to switch to RAPIDS. -However, cuGraph development will slowly shift towards improving ease-of-use, -interoperability, and integration with the rest of RAPIDS. That is not an easy -task since there is still so much to be added to the cuGraph ecosystem. Don’t -worry, the goal of getting new analytics out will continue since there is a very -long list of algorithms to release. - - # Terminology From d8604692d2148c3f76f1f02d76593e3bfad9d577 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Wed, 13 Jan 2021 16:03:01 -0500 Subject: [PATCH 127/384] updated perf gain --- docs/source/cugraph_intro.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/cugraph_intro.md b/docs/source/cugraph_intro.md index cbbb9ef4e..142395fb7 100644 --- a/docs/source/cugraph_intro.md +++ b/docs/source/cugraph_intro.md @@ -35,7 +35,7 @@ have totally changed the paradigm and the goal of an accelerated unified graph analytic library is now possible. The compute power of the latest NVIDIA GPUs (RAPIDS supports Pascal and later -GPU architectures) make graph analytics 20x faster on average over NetworkX. +GPU architectures) make graph analytics 1000x faster on average over NetworkX. Moreover, the internal memory speed within a GPU allows cuGraph to rapidly switch the data structure to best suit the needs of the analytic rather than being restricted to a single data structure. cuGraph is working with several From 8224036e080eacf25967d6d047c14868c1981119 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Wed, 27 Jan 2021 13:52:59 -0500 Subject: [PATCH 128/384] DOC v0.19 Updates --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6b484a5f5..eb4745a61 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -80,9 +80,9 @@ # built documents. # # The short X.Y version. -version = '0.18' +version = '0.19' # The full version, including alpha/beta/rc tags. -release = '0.18.0' +release = '0.19.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From a23f51ff56bd7fc96773655fcbd63c95c05af475 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 3 Feb 2021 15:42:49 -0500 Subject: [PATCH 129/384] Update the conda environments and README file (#1369) updated the conda environments to have cmake 3.18 and update the README file Authors: - Brad Rees (@BradReesWork) Approvers: - AJ Schmidt (@ajschmidt8) - Rick Ratzel (@rlratzel) - Alex Fender (@afender) URL: https://github.com/rapidsai/cugraph/pull/1369 --- docs/source/cugraph_blogs.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/source/cugraph_blogs.rst b/docs/source/cugraph_blogs.rst index 84e31d40a..cbbc93a1b 100644 --- a/docs/source/cugraph_blogs.rst +++ b/docs/source/cugraph_blogs.rst @@ -23,6 +23,12 @@ BLOGS * `Status of RAPIDS cuGraph — Refactoring Code And Rethinking Graphs `_ * `Tackling Large Graphs with RAPIDS cuGraph and CUDA Unified Memory on GPUs `_ * `RAPIDS cuGraph adds NetworkX and DiGraph Compatibility `_ + * `Large Graph Visualization with RAPIDS cuGraph `_ + +2021 +------ + * + Media From a63193fe723891711e0753ef88ab979ff01940a2 Mon Sep 17 00:00:00 2001 From: Alex Fender Date: Thu, 4 Feb 2021 15:14:43 -0600 Subject: [PATCH 130/384] EgoNet (#1365) ### Description Let the egonet graph of a node x be the subgraph that includes the neighborhood of x and all edges between them. Here is a basic description (1-hop, single seed) : - Add center node x to the graph. - Go through all the neighbors y of this center node x, add edge (x, y) to the graph. - For each neighbor y of center node x, go through all the neighbors z of center node x, if there is an edge between y and z in the original graph, add edge (y, z) to our new graph. ### Proposed solution Rather than doing custom one/two hops features, we propose a generic k-hops solution leveraging BFS with cutoff to identify neighbors within a given radius. In addition to the single source version (matching what's available in Nx), we propose to handle multiple sources (seeds) at once which allows better performances. This PR also enables a path in the experimental stack for returning multiple graphs (edge list format) from CUDA prims to python without using the legacy classes. As future work, we want to enable concurrency for the cutoff BFS for each seed. This is dependent of #957 Close #475 Authors: - Alex Fender (@afender) - @Iroy30 Approvers: - @Iroy30 - Brad Rees (@BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/1365 --- docs/source/api.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index b36fc6449..dcdf3e6ff 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -51,6 +51,13 @@ Katz Centrality Community ========= +EgoNet +------------------------------------ + +.. automodule:: cugraph.community.egonet + :members: + :undoc-members: + Ensemble clustering for graphs (ECG) ------------------------------------ From 164c793aed01d09567e5697edef9ff4704af1610 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Fri, 5 Feb 2021 14:43:30 -0500 Subject: [PATCH 131/384] Build a Dendrogram class, adapt Louvain/Leiden/ECG to use it (#1359) Preparing for MNMG Leiden and ECG identified an area for code cleanup. The original cuGraph implementation of Louvain would flatten the hierarchical clustering as it was computed, filling (and returning) the final clustering. This adds an awkward step in the middle of the Louvain computation. Additionally, since Louvain (and Leiden and ECG which derive from it) is actually a hierarchical clustering algorithm it would be nice to generate the actual Dendrogram. This PR implements a Dendrogram class, a function for flattening the Dendrogram, and modifies Louvain, Leiden and ECG to use the Dendrogram class. It was suggested that the Dendrogram class could be moved to raft, decided to defer that until later, it's easy enough to move. Authors: - Chuck Hastings (@ChuckHastings) Approvers: - Alex Fender (@afender) - Brad Rees (@BradReesWork) - Andrei Schaffer (@aschaffer) - Rick Ratzel (@rlratzel) - AJ Schmidt (@ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/1359 --- notebooks/cugraph_benchmarks/bfs_benchmark.ipynb | 1 + notebooks/cugraph_benchmarks/louvain_benchmark.ipynb | 1 + notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb | 1 + notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb | 1 + notebooks/cugraph_benchmarks/release.ipynb | 1 + notebooks/cugraph_benchmarks/sssp_benchmark.ipynb | 1 + notebooks/demo/uvm.ipynb | 1 + 7 files changed, 7 insertions(+) diff --git a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb index 58eb94bf0..6ae695e20 100644 --- a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# BFS Performance Benchmarking\n", + "# Skip notebook test\n", "\n", "This notebook benchmarks performance of running BFS within cuGraph against NetworkX. \n", "\n", diff --git a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb index a12b7c4bc..00e99a286 100644 --- a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# Louvain Performance Benchmarking\n", + "# Skip notebook test\n", "\n", "This notebook benchmarks performance improvement of running the Louvain clustering algorithm within cuGraph against NetworkX. The test is run over eight test networks (graphs) and then results plotted. \n", "

\n", diff --git a/notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb b/notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb index 6f76868f9..403c317ac 100644 --- a/notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb +++ b/notebooks/cugraph_benchmarks/nx_cugraph_bc_benchmarking.ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# Benchmarking NetworkX compatibility\n", + "# Skip notebook test\n", "This notebook benchmark the use of a NetworkX Graph object as input into algorithms.

\n", "The intention of the feature is to be able to drop cuGraph into existing NetworkX code in spot where performance is not optimal.\n", "\n", diff --git a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb index c2933a10c..d0416efdd 100644 --- a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# PageRank Performance Benchmarking\n", + "# Skip notebook test\n", "\n", "This notebook benchmarks performance of running PageRank within cuGraph against NetworkX. NetworkX contains several implementations of PageRank. This benchmark will compare cuGraph versus the defaukt Nx implementation as well as the SciPy version\n", "\n", diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index d3110da36..3c6da55ab 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# Release Benchmarking\n", + "# Skip notebook test\n", "\n", "With every release, RAPIDS publishes a release slide deck that includes the current performance state of cuGraph. \n", "This notebook, starting with release 0.15, runs all the various algorithms to computes the performance gain. \n", diff --git a/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb index 2d040e0ac..32b562e7a 100644 --- a/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb @@ -5,6 +5,7 @@ "metadata": {}, "source": [ "# SSSP Performance Benchmarking\n", + "# Skip notebook test\n", "\n", "This notebook benchmarks performance of running SSSP within cuGraph against NetworkX. \n", "\n", diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index d279be8ed..8fa2b08b6 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -6,6 +6,7 @@ "source": [ "# Oversubscribing GPU memory in cuGraph\n", "#### Author : Alex Fender\n", + "# Skip notebook test\n", "\n", "In this notebook, we will show how to **scale to 4x larger graphs than before** without incurring a performance drop using managed memory features in cuGraph. We will compute the PageRank of each user in Twitter's dataset on a single GPU as an example. This technique applies to all features.\n", "\n", From c4e56d481521e1d8af1d8d7bc2274aa14ba2e8e5 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 24 Feb 2021 13:41:52 -0800 Subject: [PATCH 132/384] REL v0.18.0 release From 7f0a43b420cccca13ca93b215d5508bbad3ace14 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 17 Mar 2021 12:17:46 -0400 Subject: [PATCH 133/384] Updating docs (#1448) * added Hungarian to api.rst * fixed missing examples * fixed syntax Authors: - Brad Rees (@BradReesWork) Approvers: - Rick Ratzel (@rlratzel) - Alex Fender (@afender) URL: https://github.com/rapidsai/cugraph/pull/1448 --- docs/source/api.rst | 62 +++++++++++++++++++++++++++++++ docs/source/dask-cugraph.rst | 71 ++++++++++++++---------------------- 2 files changed, 89 insertions(+), 44 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index dcdf3e6ff..b02f8f488 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -48,6 +48,13 @@ Katz Centrality :undoc-members: +Katz Centrality (MG) +-------------------- + +.. automodule:: cugraph.dask.centrality.katz_centrality + :members: + :undoc-members: + Community ========= @@ -86,6 +93,14 @@ Louvain :members: :undoc-members: +Louvain (MG) +------------ + +.. automodule:: cugraph.dask.community.louvain + :members: + :undoc-members: + + Spectral Clustering ------------------- @@ -148,6 +163,17 @@ Force Atlas 2 :undoc-members: +Linear Assignment +================= + +Hungarian +------------- + +.. automodule:: cugraph.linear_assignment.hungarian + :members: + :undoc-members: + + Link Analysis ============= @@ -165,6 +191,13 @@ Pagerank :members: :undoc-members: +Pagerank (MG) +--------- + +.. automodule:: cugraph.dask.link_analysis.pagerank + :members: pagerank + :undoc-members: + Link Prediction =============== @@ -202,6 +235,13 @@ Breadth-first-search :members: :undoc-members: +Breadth-first-search (MG) +-------------------- + +.. automodule:: cugraph.dask.traversal.bfs + :members: + :undoc-members: + Single-source-shortest-path --------------------------- @@ -209,6 +249,13 @@ Single-source-shortest-path :members: :undoc-members: +Single-source-shortest-path (MG) +--------------------------- + +.. automodule:: cugraph.dask.traversal.sssp + :members: + :undoc-members: + Tree ========= @@ -227,3 +274,18 @@ Maximum Spanning Tree :members: :undoc-members: + +DASK MG Helper functions +=========================== + +.. automodule:: cugraph.comms.comms + :members: initialize + :undoc-members: + +.. automodule:: cugraph.comms.comms + :members: destroy + :undoc-members: + +.. automodule:: cugraph.dask.common.read_utils + :members: get_chunksize + :undoc-members: diff --git a/docs/source/dask-cugraph.rst b/docs/source/dask-cugraph.rst index b27ad3828..51487bfbf 100644 --- a/docs/source/dask-cugraph.rst +++ b/docs/source/dask-cugraph.rst @@ -13,58 +13,41 @@ With cuGraph and Dask, whether you’re using a single NVIDIA GPU or multiple no If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph's multi-GPU features. +Example +======== -Distributed Graph Algorithms ----------------------------- +.. code-block:: python -.. automodule:: cugraph.dask.link_analysis.pagerank - :members: pagerank - :undoc-members: + from dask.distributed import Client, wait + from dask_cuda import LocalCUDACluster + import cugraph.comms as Comms + import cugraph.dask as dask_cugraph -.. automodule:: cugraph.dask.traversal.bfs - :members: bfs - :undoc-members: + cluster = LocalCUDACluster() + client = Client(cluster) + Comms.initialize(p2p=True) + # Helper function to set the reader chunk size to automatically get one partition per GPU + chunksize = dask_cugraph.get_chunksize(input_data_path) -Helper functions ----------------- + # Multi-GPU CSV reader + e_list = dask_cudf.read_csv(input_data_path, + chunksize = chunksize, + delimiter=' ', + names=['src', 'dst'], + dtype=['int32', 'int32']) -.. automodule:: cugraph.comms.comms - :members: initialize - :undoc-members: + G = cugraph.DiGraph() + G.from_dask_cudf_edgelist(e_list, source='src', destination='dst') -.. automodule:: cugraph.comms.comms - :members: destroy - :undoc-members: + # now run PageRank + pr_df = dask_cugraph.pagerank(G, tol=1e-4) -.. automodule:: cugraph.dask.common.read_utils - :members: get_chunksize - :undoc-members: + # All done, clean up + Comms.destroy() + client.close() + cluster.close() -Consolidation -============= -cuGraph can transparently interpret the Dask cuDF Dataframe as a regular Dataframe when loading the edge list. This is particularly helpful for workflows extracting a single GPU sized edge list from a distributed dataset. From there any existing single GPU feature will just work on this input. +| -For instance, consolidation allows leveraging Dask cuDF CSV reader to load file(s) on multiple GPUs and consolidate this input to a single GPU graph. Reading is often the time and memory bottleneck, with this feature users can call the Multi-GPU version of the reader without changing anything else. - -Batch Processing -================ - -cuGraph can leverage multi GPUs to increase processing speed for graphs that fit on a single GPU, providing faster analytics on such graphs. -You will be able to use the Graph the same way as you used to in a Single GPU environment, but analytics that support batch processing will automatically use the GPUs available to the dask client. -For example, Betweenness Centrality scores can be slow to obtain depending on the number of vertices used in the approximation. Thank to Multi GPUs Batch Processing, -you can create Single GPU graph as you would regularly do it using cuDF CSV reader, enable Batch analytics on it, and obtain scores much faster as each GPU will handle a sub-set of the sources. -In order to use Batch Analytics you need to set up a Dask Cluster and Client in addition to the cuGraph communicator, then you can simply call `enable_batch()` on you graph, and algorithms supporting batch processing will use multiple GPUs. - -Algorithms supporting Batch Processing --------------------------------------- -.. automodule:: cugraph.centrality - :members: betweenness_centrality - :undoc-members: - :noindex: - -.. automodule:: cugraph.centrality - :members: edge_betweenness_centrality - :undoc-members: - :noindex: From a5fa9bb45417aee742f1b08042d95679a49a5325 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 25 Mar 2021 14:19:09 -0400 Subject: [PATCH 134/384] DOC v0.20 Updates --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index eb4745a61..77053a346 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -80,9 +80,9 @@ # built documents. # # The short X.Y version. -version = '0.19' +version = '0.20' # The full version, including alpha/beta/rc tags. -release = '0.19.0' +release = '0.20.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From ca960553afb18bc12dddb9c6fd6ae684ad82f2ce Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Wed, 7 Apr 2021 18:51:32 -0500 Subject: [PATCH 135/384] Random Walks - Python Bindings (#1516) Python bindings for random walks closes #1488 check the rendering after the PR is merged to make sure everything render as expected Authors: - Joseph Nke (https://github.com/jnke2016) Approvers: - Brad Rees (https://github.com/BradReesWork) - Andrei Schaffer (https://github.com/aschaffer) - Alex Fender (https://github.com/afender) URL: https://github.com/rapidsai/cugraph/pull/1516 --- docs/source/api.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/source/api.rst b/docs/source/api.rst index b02f8f488..b9b8ea485 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -225,6 +225,17 @@ Overlap Coefficient :undoc-members: +Sampling +======== + +Random Walks +------------ + +.. automodule:: cugraph.sampling.random_walks + :members: + :undoc-members: + + Traversal ========= From 8a8a3ec88e5aef4e0a47f900ec40af2305266b86 Mon Sep 17 00:00:00 2001 From: Ayush Dattagupta Date: Thu, 8 Apr 2021 09:53:50 -0700 Subject: [PATCH 136/384] Update docs and remove all warnings (#1521) This pr fixes the following - Add traveling salesperson problem to the docs - Update docs to address all build warnings To remove some warnings. updated the use of `NOTE:` in cases like the one shown below. | Old | New | | ------------- | ------------- | | ![image](https://user-images.githubusercontent.com/19949207/113936070-283a2380-97ac-11eb-9705-9f261c965fa9.png) | ![image](https://user-images.githubusercontent.com/19949207/113935703-b06bf900-97ab-11eb-93a4-7df2f711c1aa.png) | Authors: - Ayush Dattagupta (https://github.com/ayushdg) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/1521 --- docs/source/api.rst | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/docs/source/api.rst b/docs/source/api.rst index b9b8ea485..e2c2c19cf 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -192,7 +192,7 @@ Pagerank :undoc-members: Pagerank (MG) ---------- +------------- .. automodule:: cugraph.dask.link_analysis.pagerank :members: pagerank @@ -247,7 +247,7 @@ Breadth-first-search :undoc-members: Breadth-first-search (MG) --------------------- +------------------------- .. automodule:: cugraph.dask.traversal.bfs :members: @@ -261,12 +261,19 @@ Single-source-shortest-path :undoc-members: Single-source-shortest-path (MG) ---------------------------- +-------------------------------- .. automodule:: cugraph.dask.traversal.sssp :members: :undoc-members: +Traveling-salesperson-problem +----------------------------- + +.. automodule:: cugraph.traversal.traveling_salesperson + :members: + :undoc-members: + Tree ========= @@ -275,27 +282,25 @@ Minimum Spanning Tree --------------------- .. automodule:: cugraph.tree.minimum_spanning_tree - :members: + :members: minimum_spanning_tree :undoc-members: Maximum Spanning Tree --------------------- -.. automodule:: cugraph.tree.maximum_spanning_tree - :members: +.. automodule:: cugraph.tree.minimum_spanning_tree + :members: maximum_spanning_tree :undoc-members: + :noindex: -DASK MG Helper functions +DASK MG Helper functions =========================== .. automodule:: cugraph.comms.comms - :members: initialize - :undoc-members: - -.. automodule:: cugraph.comms.comms - :members: destroy + :members: initialize, destroy :undoc-members: + :member-order: bysource .. automodule:: cugraph.dask.common.read_utils :members: get_chunksize From 15a0e40e79ae972622ea0147fba3a5369519faa5 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Mon, 12 Apr 2021 08:39:03 -0500 Subject: [PATCH 137/384] Fixed copyright date and format. (#1526) Update copyright data and format Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/1526 --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index eb4745a61..3422428c9 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # -# Copyright (c) 2018-2020 NVIDIA CORPORATION. +# Copyright (c) 2018-2021, NVIDIA CORPORATION. # # pygdf documentation build configuration file, created by # sphinx-quickstart on Wed May 3 10:59:22 2017. From ec3f007dbe11dfde5fd35e1acf76d043b593cfdf Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Fri, 16 Apr 2021 09:15:49 -0400 Subject: [PATCH 138/384] Reorganized docs and environment files (#1537) * reorganized docs so that information is under ./docs/cugraph that better matches the rest of rapids * Updated yml files * removed unneeded packages in the yml files * drop old notebook * marked FA2 notebook as skip Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) - https://github.com/Iroy30 URL: https://github.com/rapidsai/cugraph/pull/1537 --- docs/{ => cugraph}/Makefile | 0 docs/{ => cugraph}/README.md | 0 docs/{ => cugraph}/make.bat | 0 docs/{ => cugraph}/requirement.txt | 0 docs/{ => cugraph}/source/_static/EMPTY | 0 .../source/_static/copybutton.css | 0 .../source/_static/copybutton_pydocs.js | 65 ++++++++++++++++++ .../source/_static/example_mod.js | 0 docs/{ => cugraph}/source/_static/params.css | 0 .../source/_static/references.css | 0 docs/{ => cugraph}/source/api.rst | 0 docs/{ => cugraph}/source/conf.py | 10 +-- docs/{ => cugraph}/source/cugraph_blogs.rst | 0 docs/{ => cugraph}/source/cugraph_intro.md | 0 docs/{ => cugraph}/source/cugraph_ref.rst | 30 ++++++-- docs/{ => cugraph}/source/dask-cugraph.rst | 0 docs/{ => cugraph}/source/images/Nx_Cg_1.png | Bin docs/{ => cugraph}/source/images/Nx_Cg_2.png | Bin docs/{ => cugraph}/source/index.rst | 0 docs/{ => cugraph}/source/nx_transition.rst | 0 .../source/sphinxext/github_link.py | 14 ++++ 21 files changed, 110 insertions(+), 9 deletions(-) rename docs/{ => cugraph}/Makefile (100%) rename docs/{ => cugraph}/README.md (100%) rename docs/{ => cugraph}/make.bat (100%) rename docs/{ => cugraph}/requirement.txt (100%) rename docs/{ => cugraph}/source/_static/EMPTY (100%) rename docs/{ => cugraph}/source/_static/copybutton.css (100%) create mode 100644 docs/cugraph/source/_static/copybutton_pydocs.js rename docs/{ => cugraph}/source/_static/example_mod.js (100%) rename docs/{ => cugraph}/source/_static/params.css (100%) rename docs/{ => cugraph}/source/_static/references.css (100%) rename docs/{ => cugraph}/source/api.rst (100%) rename docs/{ => cugraph}/source/conf.py (98%) rename docs/{ => cugraph}/source/cugraph_blogs.rst (100%) rename docs/{ => cugraph}/source/cugraph_intro.md (100%) rename docs/{ => cugraph}/source/cugraph_ref.rst (61%) rename docs/{ => cugraph}/source/dask-cugraph.rst (100%) rename docs/{ => cugraph}/source/images/Nx_Cg_1.png (100%) rename docs/{ => cugraph}/source/images/Nx_Cg_2.png (100%) rename docs/{ => cugraph}/source/index.rst (100%) rename docs/{ => cugraph}/source/nx_transition.rst (100%) rename docs/{ => cugraph}/source/sphinxext/github_link.py (88%) diff --git a/docs/Makefile b/docs/cugraph/Makefile similarity index 100% rename from docs/Makefile rename to docs/cugraph/Makefile diff --git a/docs/README.md b/docs/cugraph/README.md similarity index 100% rename from docs/README.md rename to docs/cugraph/README.md diff --git a/docs/make.bat b/docs/cugraph/make.bat similarity index 100% rename from docs/make.bat rename to docs/cugraph/make.bat diff --git a/docs/requirement.txt b/docs/cugraph/requirement.txt similarity index 100% rename from docs/requirement.txt rename to docs/cugraph/requirement.txt diff --git a/docs/source/_static/EMPTY b/docs/cugraph/source/_static/EMPTY similarity index 100% rename from docs/source/_static/EMPTY rename to docs/cugraph/source/_static/EMPTY diff --git a/docs/source/_static/copybutton.css b/docs/cugraph/source/_static/copybutton.css similarity index 100% rename from docs/source/_static/copybutton.css rename to docs/cugraph/source/_static/copybutton.css diff --git a/docs/cugraph/source/_static/copybutton_pydocs.js b/docs/cugraph/source/_static/copybutton_pydocs.js new file mode 100644 index 000000000..cec05777e --- /dev/null +++ b/docs/cugraph/source/_static/copybutton_pydocs.js @@ -0,0 +1,65 @@ +$(document).ready(function() { + /* Add a [>>>] button on the top-right corner of code samples to hide + * the >>> and ... prompts and the output and thus make the code + * copyable. */ + var div = $('.highlight-python .highlight,' + + '.highlight-python3 .highlight,' + + '.highlight-pycon .highlight,' + + '.highlight-default .highlight'); + var pre = div.find('pre'); + + // get the styles from the current theme + pre.parent().parent().css('position', 'relative'); + var hide_text = 'Hide the prompts and output'; + var show_text = 'Show the prompts and output'; + var border_width = pre.css('border-top-width'); + var border_style = pre.css('border-top-style'); + var border_color = pre.css('border-top-color'); + var button_styles = { + 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', + 'border-color': border_color, 'border-style': border_style, + 'border-width': border_width, 'text-size': '75%', + 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '1.5em', + 'border-radius': '0 3px 0 0', + 'transition': "0.5s" + } + + // create and add the button to all the code blocks that contain >>> + div.each(function(index) { + var jthis = $(this); + if (jthis.find('.gp').length > 0) { + var button = $('>>>'); + button.css(button_styles) + button.attr('title', hide_text); + button.data('hidden', 'false'); + jthis.prepend(button); + } + // tracebacks (.gt) contain bare text elements that need to be + // wrapped in a span to work with .nextUntil() (see later) + jthis.find('pre:has(.gt)').contents().filter(function() { + return ((this.nodeType == 3) && (this.data.trim().length > 0)); + }).wrap(''); + }); + + // define the behavior of the button when it's clicked + $('.copybutton').click(function(e){ + e.preventDefault(); + var button = $(this); + if (button.data('hidden') === 'false') { + // hide the code output + button.parent().find('.go, .gp, .gt').hide(); + button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); + button.css('text-decoration', 'line-through'); + button.attr('title', show_text); + button.data('hidden', 'true'); + } else { + // show the code output + button.parent().find('.go, .gp, .gt').show(); + button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); + button.css('text-decoration', 'none'); + button.attr('title', hide_text); + button.data('hidden', 'false'); + } + }); +}); + diff --git a/docs/source/_static/example_mod.js b/docs/cugraph/source/_static/example_mod.js similarity index 100% rename from docs/source/_static/example_mod.js rename to docs/cugraph/source/_static/example_mod.js diff --git a/docs/source/_static/params.css b/docs/cugraph/source/_static/params.css similarity index 100% rename from docs/source/_static/params.css rename to docs/cugraph/source/_static/params.css diff --git a/docs/source/_static/references.css b/docs/cugraph/source/_static/references.css similarity index 100% rename from docs/source/_static/references.css rename to docs/cugraph/source/_static/references.css diff --git a/docs/source/api.rst b/docs/cugraph/source/api.rst similarity index 100% rename from docs/source/api.rst rename to docs/cugraph/source/api.rst diff --git a/docs/source/conf.py b/docs/cugraph/source/conf.py similarity index 98% rename from docs/source/conf.py rename to docs/cugraph/source/conf.py index 5e87622bd..a4633d04f 100644 --- a/docs/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -42,17 +42,17 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'numpydoc', - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', + "sphinx.ext.intersphinx", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "numpydoc", + "sphinx_markdown_tables", 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', 'sphinx.ext.linkcode', "IPython.sphinxext.ipython_console_highlighting", "IPython.sphinxext.ipython_directive", "nbsphinx", "recommonmark", - "sphinx_markdown_tables", ] diff --git a/docs/source/cugraph_blogs.rst b/docs/cugraph/source/cugraph_blogs.rst similarity index 100% rename from docs/source/cugraph_blogs.rst rename to docs/cugraph/source/cugraph_blogs.rst diff --git a/docs/source/cugraph_intro.md b/docs/cugraph/source/cugraph_intro.md similarity index 100% rename from docs/source/cugraph_intro.md rename to docs/cugraph/source/cugraph_intro.md diff --git a/docs/source/cugraph_ref.rst b/docs/cugraph/source/cugraph_ref.rst similarity index 61% rename from docs/source/cugraph_ref.rst rename to docs/cugraph/source/cugraph_ref.rst index 591619fb3..e0f113eab 100644 --- a/docs/source/cugraph_ref.rst +++ b/docs/cugraph/source/cugraph_ref.rst @@ -2,22 +2,35 @@ References ########## +************ +Architecture +************ + +2-D Data Partitioning + +- Kang, S., Fender, A., Eaton, J., & Rees, B. (2020, September) *Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters*. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE. + + +| + +| + ********** Algorithms ********** Betweenness Centrality -- Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177. -- Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136-145. -- McLaughlin, A., & Bader, D. A. (2018). Accelerating GPU betweenness centrality. Communications of the ACM, 61(8), 85-92. +- Brandes, U. (2001). *A faster algorithm for betweenness centrality*. Journal of mathematical sociology, 25(2), 163-177. +- Brandes, U. (2008). *On variants of shortest-path betweenness centrality and their generic computation*. Social Networks, 30(2), 136-145. +- McLaughlin, A., & Bader, D. A. (2018). *Accelerating GPU betweenness centrality*. Communications of the ACM, 61(8), 85-92. Katz - J. Cohen, *Trusses: Cohesive subgraphs for social network analysis* National security agency technical report, 2008 - O. Green, J. Fox, E. Kim, F. Busato, et al. *Quickly Finding a Truss in a Haystack* IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038 -- O. Green, P. Yalamanchili, L.M. Munguia, “*ast Triangle Counting on GPU* Irregular Applications: Architectures and Algorithms (IA3), 2014 +- O. Green, P. Yalamanchili, L.M. Munguia, *Fast Triangle Counting on GPU* Irregular Applications: Architectures and Algorithms (IA3), 2014 Hungarian Algorithm @@ -27,6 +40,15 @@ Hungarian Algorithm | +************* +Other Papers +************* +- Hricik, T., Bader, D., & Green, O. (2020, September). *Using RAPIDS AI to Accelerate Graph Data Science Workflows*. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE. + +| + +| + ********** Data Sets ********** diff --git a/docs/source/dask-cugraph.rst b/docs/cugraph/source/dask-cugraph.rst similarity index 100% rename from docs/source/dask-cugraph.rst rename to docs/cugraph/source/dask-cugraph.rst diff --git a/docs/source/images/Nx_Cg_1.png b/docs/cugraph/source/images/Nx_Cg_1.png similarity index 100% rename from docs/source/images/Nx_Cg_1.png rename to docs/cugraph/source/images/Nx_Cg_1.png diff --git a/docs/source/images/Nx_Cg_2.png b/docs/cugraph/source/images/Nx_Cg_2.png similarity index 100% rename from docs/source/images/Nx_Cg_2.png rename to docs/cugraph/source/images/Nx_Cg_2.png diff --git a/docs/source/index.rst b/docs/cugraph/source/index.rst similarity index 100% rename from docs/source/index.rst rename to docs/cugraph/source/index.rst diff --git a/docs/source/nx_transition.rst b/docs/cugraph/source/nx_transition.rst similarity index 100% rename from docs/source/nx_transition.rst rename to docs/cugraph/source/nx_transition.rst diff --git a/docs/source/sphinxext/github_link.py b/docs/cugraph/source/sphinxext/github_link.py similarity index 88% rename from docs/source/sphinxext/github_link.py rename to docs/cugraph/source/sphinxext/github_link.py index a7a46fdd9..fa8fe3f5f 100644 --- a/docs/source/sphinxext/github_link.py +++ b/docs/cugraph/source/sphinxext/github_link.py @@ -1,3 +1,17 @@ +# Copyright (c) 2019-2021, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# NOTE: # This contains code with copyright by the scikit-learn project, subject to the # license in /thirdparty/LICENSES/LICENSE.scikit_learn From 4beef09ec481abd8dbccf747b7252ab6a80fd9d8 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 21 Apr 2021 12:49:06 -0700 Subject: [PATCH 139/384] REL v0.19.0 release From 06a44eec5928c079ade4bed33a811569f010977b Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 28 Apr 2021 11:25:23 -0400 Subject: [PATCH 140/384] Add centrality and random walk notebook (#1546) This PR adds three new notebooks: - A general Centrality notebook that summarizes all the centrality metrics - A example notebook for Random Walk - A performance benchmarking notebook on Random Walk Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/1546 --- .../random_walk_benchmark.ipynb | 544 +++++++++++++++ .../cugraph_benchmarks/random_walk_perf.ipynb | 621 ++++++++++++++++++ notebooks/cugraph_benchmarks/release.ipynb | 3 +- 3 files changed, 1167 insertions(+), 1 deletion(-) create mode 100644 notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb create mode 100644 notebooks/cugraph_benchmarks/random_walk_perf.ipynb diff --git a/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb b/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb new file mode 100644 index 000000000..be50c0754 --- /dev/null +++ b/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb @@ -0,0 +1,544 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Walk Performance\n", + "# Skip notebook test¶ \n", + "\n", + "Random walk performance is governed by the length of the paths to find, the number of seeds, and the size or structure of the graph.\n", + "This benchmark will use several test graphs of increasingly larger sizes. While not even multiples in scale, the four test graphs should give an indication of how well Random Walk performs as data size increases. \n", + "\n", + "### Test Data\n", + "Users must run the _dataPrep.sh_ script before running this notebook so that the test files are downloaded\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "| ---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the modules\n", + "import cugraph\n", + "import cudf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# system and other\n", + "import gc\n", + "import os\n", + "import time\n", + "import random\n", + "\n", + "# MTX file reader\n", + "from scipy.io import mmread" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import matplotlib\n", + "except ModuleNotFoundError:\n", + " os.system('pip install matplotlib')\n", + "\n", + "import matplotlib.pyplot as plt; plt.rcdefaults()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Test File\n", + "data = {\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the data and create a graph" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Data reader - the file format is MTX, so we will use the reader from SciPy\n", + "def read_and_create(datafile):\n", + " print('Reading ' + str(datafile) + '...')\n", + " M = mmread(datafile).asfptype()\n", + "\n", + " _gdf = cudf.DataFrame()\n", + " _gdf['src'] = M.row\n", + " _gdf['dst'] = M.col\n", + " _gdf['wt'] = 1.0\n", + " \n", + " _g = cugraph.Graph()\n", + " _g.from_cudf_edgelist(_gdf, source='src', destination='dst', edge_attr='wt', renumber=False)\n", + " \n", + " print(\"\\t{:,} nodes, {:,} edges\".format(_g.number_of_nodes(), _g.number_of_edges() ))\n", + " \n", + " return _g" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the call to RandomWalk\n", + "We are only interested in the runtime, so throw away the results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def run_rw(_G, _seeds, _depth):\n", + " t1 = time.time()\n", + " _, _ = cugraph.random_walks(_G, _seeds, _depth)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 1: Runtime versus path depth" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\t100,000 nodes, 499,985 edges\n", + "update i\n", + "Reading ./data/dblp-2010.mtx...\n", + "\t326,183 nodes, 807,700 edges\n", + "update i\n", + "Reading ./data/coPapersCiteseer.mtx...\n", + "\t434,102 nodes, 16,036,720 edges\n", + "update i\n", + "Reading ./data/as-Skitter.mtx...\n", + "\t1,696,415 nodes, 11,095,298 edges\n", + "update i\n" + ] + } + ], + "source": [ + "# some parameters\n", + "max_depth = 6\n", + "num_seeds = 500\n", + "\n", + "# arrays to capture performance gains\n", + "names = []\n", + "\n", + "# Two dimension data\n", + "time_algo_cu = [] # will be two dimensional\n", + "\n", + "i = 0\n", + "for k,v in data.items():\n", + " time_algo_cu.append([])\n", + " \n", + " # Saved the file Name\n", + " names.append(k)\n", + "\n", + " # read data\n", + " G = read_and_create(v)\n", + " \n", + " num_nodes = G.number_of_nodes()\n", + " nodes = G.nodes().to_array().tolist()\n", + "\n", + " seeds = random.sample(nodes, num_seeds)\n", + "\n", + " for j in range (2, max_depth+1) :\n", + " t = run_rw(G, seeds, j)\n", + " time_algo_cu[i].append(t)\n", + "\n", + " # update i\n", + " i = i + 1\n", + " print(\"update i\")\n", + " \n", + " del G\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "seed_idx = list(range(2,max_depth +1))\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "\n", + "for i in range(len(data)):\n", + " plt.plot(seed_idx, time_algo_cu[i], label = names[i])\n", + "\n", + "\n", + "plt.title(f'Runtime vs. Path Length ({num_seeds} Seeds)')\n", + "plt.xlabel('Path length')\n", + "plt.ylabel('Runtime')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12979" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del time_algo_cu\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 2: Runtime versus number of seeds\n", + "The number of seeds will be increased over a range in increments of 10. \n", + "The runtime will be the sum of runtime per increment. Increaing number of seeds by 1 would make for very long execution times " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\t100,000 nodes, 499,985 edges\n", + "\t.................................................................................................... \n", + "Reading ./data/dblp-2010.mtx...\n", + "\t326,183 nodes, 807,700 edges\n", + "\t.................................................................................................... \n", + "Reading ./data/coPapersCiteseer.mtx...\n", + "\t434,102 nodes, 16,036,720 edges\n", + "\t.................................................................................................... \n", + "Reading ./data/as-Skitter.mtx...\n", + "\t1,696,415 nodes, 11,095,298 edges\n", + "\t.................................................................................................... \n" + ] + } + ], + "source": [ + "# some parameters\n", + "rw_depth = 4\n", + "max_seeds = 1000\n", + "\n", + "# arrays to capture performance gains\n", + "names = []\n", + "\n", + "# Two dimension data\n", + "time_algo_cu = [] # will be two dimensional\n", + "\n", + "i = 0\n", + "for k,v in data.items():\n", + " time_algo_cu.append([])\n", + " \n", + " # Saved the file Name\n", + " names.append(k)\n", + "\n", + " # read data\n", + " G = read_and_create(v)\n", + " \n", + " num_nodes = G.number_of_nodes()\n", + " nodes = G.nodes().to_array().tolist()\n", + " \n", + " print('\\t', end='')\n", + " for j in range (10, max_seeds +1, 10) :\n", + " print('.', end='')\n", + " seeds = random.sample(nodes, j+1)\n", + " t = run_rw(G, seeds, rw_depth)\n", + " time_algo_cu[i].append(t)\n", + "\n", + " # update i\n", + " i = i + 1\n", + " print(\" \")\n", + " \n", + " del G\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1Wi9bbrFYcHNzy/ycLRYLVqsVESE0NJQ1a9bc1HaVUkrlLyLC8pjlfBn5Jfatu3h0kYVSR+0UqFObYq++ikfVqk5tX25OpTEJaAEEG2NigNcBNwARGeMo1gOYLyIXslQtDEx3/MPqCkwUkbnkgOud4cothw8fZs2aNTRq1IiJEyfStGlTNm/enLm+YcOGPP744+zbt48KFSpw4cIFYmNjqVSpUrbttGvXLnP8mY+PD7GxsVecnf5Gy2Xl6+t7xTB1/vx5vL298fPz48SJE8yZM+eyALVo0SJq1qzJvHnzMpcNHjyY6dOnExoamm27l+4nPj6e4sWLAxk3JVzUpk0bvv32W1q2bImrqytxcXHZzp79G5UrV+bUqVOZxyI9PZ29e/cSGnr1n4urfS5KKaVuPyLC2mNr+XLzl5zas4X713pQe6sNl8JBFP54JAU7dcwT0yTl5t2a94pIURFxE5ESIvK9iIzJEswQkQki0u+SegdEpJbjT6iIvJNbbbxVKleuzFdffUXVqlU5e/Ysw4YNy7Y+JCSECRMmcO+991KzZk0aNWqUOdA9q7Zt29K/f38aNWpEjRo16N279xWDw42Wy+r+++9n6NChl03ZUatWLWrXrk2VKlXo379/5qXXrCZNmkSPHj2yLevVqxeTJk2iZs2auLi4UKtWLT799FNatmzJzp07MwfYjxw5klGjRlG7du1sZ6sefvhhSpUqRc2aNalVq1a2Oz7/LXd3d6ZOncoLL7xArVq1CAsLY/Xq1desc2l7lVJK3Z42ntjIg/Me5NXfH6Htz7v5bJydOlE2goYOocLs2fh17pQnghmAyRiPlj+Eh4dL1nm3AHbt2kVVJ56ejI6OpnPnzmzfvt1pbVC5x9k/X0oppa5ORFhzbA1jt47l8J4I+q9zp3FkChY3dwL69yfo4YdwDQrKLJ9mtTNj6Wri96zgwcdezPWwZozZKFeYzzUv3BCglFJKKZVjRISlR5by3bbvOLtrK303uFN/mx3jYiVgwECCHnkYt0KFspVftmYtyYs/pHv6UqwWd87HP4qff9DVd5KLNJzlsjJlyuhZM6WUUuoWSLYm8/eBv/l116+YHVH0W+9Ojd02jKcQMGgQgQ88gFuRItnqbI9cx5k579IsZRlW48axyoMo0ekFPPycE8xAw5lSSimlbnOxibH8vvt3/to1lWpb43l0uwdlDtqwFHQj8PEHCRg4ANeAgH8q2O0ciphN3NKvqHVhDSmmAFHlB1Ox+yhKFixy9R3dIhrOlFJKKXXbsdqtrIpdxR9Rf7B/8xLujhQ+22nB44Idt5JBBLzwJAH39MGSZaopks9xasUP2DeMo3R6DL74srHUA4T2epEq/oWd15lLaDhTSiml1G3jyPkjTN83nbk7plNh80nabrNQ/ogV3Nwo2KY1/n364NWgAcaSZUKKhOPEz38Pj+2/ESIpbJZKbKz0Jk26PEy9gj7O68xVaDhTSimlVJ639+xePlz/Aec2rKPVVuG93Qb3NDtu5coQ8EIf/Lp3y37pEiD5LGfnf4R35Di87FZmyF3E1xxM9w6dqO3tvCcAXI+Gs1vsjTfewMfHh1mzZvHxxx8THp79DtoJEyYQERHBl19+6aQWKqWUUnmH1W7lh+0/MG/2lzz2t42ip2zg5Yl/t0749+qFR61al095kXaB0ws/xyviS/xsScySJsSEDadPm7sI8S1w5R3lIRrOlFJKKZUn7T+3n5dXvESJ+dt5Y7HgVrgIhd99koLt22Hx8rq8QuIpji0cjc+2Hwm2nWOx1OVAzeF0b9eOYJ+8H8ou0nB2C7zzzjv8+OOPFCpUiJIlS1K3bl0Afv75Zx5++GGsVivjx4+nfv362erdf//9eHh4EBERwfnz5/nkk0/o3LmzM7qglFJK3TJptjR+2fUL360bzdA5dupvt+PTogXFPngfFz+/y8pbj+3g6Nz/UeTQDIqSzlLqEBM6jI4du9MqD1++vBoNZ7ls48aN/Pbbb0RGRmK1WqlTp05mOEtKSiIyMpLly5fz4IMPXnE+tOjoaNavX8/+/ftp2bIl+/btw8PD41Z3QymllMp1p5NPM2XPFH7f8zseMaf530wPAk6mEfLMMwQ98nD2Qf5AwsGNnJ31GqXOrCRE3Jnjdje2+kNp27wZLQrcvhHn9m35vzHnRTi+LWe3WaQGdHj/qqtXrFhBjx498HKcfu3atWvmunvvvReAu+66i/Pnz3Pu3LnL6t9zzz1YLBYqVqxIuXLl2L17N2FhYTnaBaWUUsqZdp3ZxS+7fmHu/tlUjE7jiUOFqbbJBVdvD4qP/wrvhg2yld+9ZzeJc16nztl5WPHmN9/7KNxqGJ3DquJiyRvPx/wv7qxwlsdcOoDxSs/wupEySiml1O3mbMpZZh+czcx9M0jdtoPmuy18t9cVz3N2jFc8Bdu1J2TEs7gVznjMktVmZ+7GKFKW/o/OF6ZhgBWF7qVwx5foV7akczuTw+6scHaNM1y55a677uL+++9n1KhRWK1WZs6cyZAhQwD4/fffadmyJStXrsTPzw+/K1xHnzJlCoMHD+bgwYMcOHCAypUr3+ouKKWUUjki3ZbOsphlzNg/g8g9y2myLZ3HtrsRctIG7i74Nm9CwU6d8GneHIunJ5Dx3MuFkfuInvMFPVOnE2QS2Fe0I4W6v0PzIuWc3KPccWeFMyeoU6cOffv2pVatWhQqVIh69eplrvPw8KB27dqkp6czfvz4K9YvVaoU9evX5/z584wZM0bHmymllLrtiAiLDi/isw2f4LftEB23uzFkdzoWmx3P2tXwf7oXvm3b4uLrm63e+p0H2Dfrf3S8MJ025gKnizTB3uVtKpSo46Se3BpGRJzdhhwTHh4uERER2Zbt2rWLqlWrOqlF/839999P586d6d27t7Oboq7idv75UkqpW2Hrsc1MnfwmQWv30ijKgm+iDYu/P/7du+PfuxcFKlTIVl5EiNwdxZE5/6Nl/F/4mmRiCrekSOdXcC0ZfpW93J6MMRtF5LJO6ZkzpZRSSuW4wxFL2fzNexTZdJh7k8FewI2CLVri16EDPq1aYXHPPsWFzS6sXL+e5GWf0SJpAbWMlYOFW+Pe9VVKlKjlpF44h4azPGzChAnOboJSSil1U84ln2XxB09TcfIGSrpCfL2KlOvzKEHN784cR5ZVqtXGokXz8dwwmrvSV2MzLhwq0ZUSnUdSvuideWVCw5lSSiml/rNUWyp/rBmH+7tjCD1g5XDd4tT8eAx1ila4Ynmx21m7dBYuK/9HR3skF4wX0ZUfokyn56joV/QWtz5v0XCmlFJKqZsWnxrP0cSjxCbGcuj8IbbO/JF+U0/hk27BvPAYbe9/4srTP4mwf82fpC35kEbpOzlr/DkY9hxl2j1JeU//W96PvEjDmVJKKaVuyNmUs7y2+jU2Ht9IQnoCAMHxQo81doZuFmxli1Pxi28oULHi5ZVt6ZxaP5mUpZ9SPjWK4wSzMXQUYV2fIqDAFZ6TeQfTcKaUUkqp6zoYf5DHFz3OiQsn6F6hO5XPeVL+7214LdkIxkLAoP4UenYElkunfEqKI3bR13hF/kCI7TQHpShzK7xK016PUfdKDy9XGs7ykjfeeIPvvvuOkJAQrFYr7777brbHPd0qe/fuZfjw4URFReHr60uFChUYPXo0R44c4aeffuKLL75g6dKluLu707hx41vePqWUUrfWhuMbGL5kOG64ML7wcwT+sorERYswnp4EDBxI4P2DcSuafZyY7cRuYud8TOHovyhOGmuoydEqo2jWoT/t/TSUXYuGszzmmWee4bnnnmPXrl00a9aMkydPYrnkQa85wWq14up6+eFPSUmhU6dOfPLJJ3Tp0gWApUuXcurUKcLDwwkPD89c5uPj45RwZrPZcHFxueX7VUqpO9Gfu6Yy/fe3ePigF42iBIl7i2Q/P4KfeIKAAf1xDQjIVv74rtUkLPiQ8nFLKSSuzHNridQfQpsWLWjkrrHjRuT8v/rqMj/99BM1a9akVq1aDBo0iOjoaFq1akXNmjW5++67OXz48GV1qlatiqurK6dPn6Z79+7UrVuX0NBQxo4dm1nGx8eHZ555htDQUO6++25OnToFwP79+2nfvj1169alWbNm7N69G8iY1Hbo0KE0aNCAkSNHsmzZMsLCwggLC6N27dokJCQwceJEGjVqlBnMAFq0aEH16tVZunQpnTt3Jjo6mjFjxvDpp58SFhbGihUrOHXqFL169aJevXrUq1ePVatWAVxxHwAfffQR9erVo2bNmrz++uuZ+/rll1+oX78+YWFhDBkyBJvNltnXZ599llq1arFmzZocPkJKKaUudXbPdhY80o0S/V7lxd/SabQ1DZ/6DSj28cdUWLyIkCcezwxmqelWVi/8k23vtaTI7x0odGYdMwr2Z1XX5XR6aTLd2rbGS4PZjRORfPOnbt26cqmdO3detuxW2r59u1SsWFFOnTolIiJnzpyRzp07y4QJE0RE5Pvvv5du3bqJiMjrr78uH330kYiIrF27VooWLSp2u13OnDkjIiJJSUkSGhoqp0+fFhERQH755RcREXnzzTfl8ccfFxGRVq1ayd69ezO307JlSxERGTx4sHTq1EmsVquIiHTu3FlWrlwpIiIJCQmSnp4uzzzzjHz22WdX7MuSJUukU6dOl7VVROTee++VFStWiIjIoUOHpEqVKlfdx7x58+SRRx4Ru90uNptNOnXqJMuWLZOdO3dK586dJS0tTUREhg0bJj/++GNmX3///feb/vxzm7N/vpRSKqcd2bVB5j/YSbZXqSKbqleRvx9sL2cXzhdbcvJlZdPTrbJ01q8S+UY9kdcLyunXS8nyH16R2OMnnNDy2w8QIVfIM3dUjP1g/Qfsjtudo9usEliFF+q/cNX1ixcvpk+fPgQHBwMQGBjImjVrmDZtGgCDBg1i5MiRmeU//fRTfvnlF3x9ffn9998xxvDFF18wffp0AI4cOUJUVBRBQUFYLBb69u0LwMCBA+nZsyeJiYmsXr2aPn36ZG4zNTU183WfPn0yLwk2adKEESNGMGDAAHr27EmJEiX+9eewcOFCdu7cmfn+/PnzJCYmXnEf8+fPZ/78+dSuXRuAxMREoqKi2Lp1Kxs3bsx8/mhycjKFChUCwMXFhV69ev3r9imllLq27duXsO/T96iw+giFXGBn63JUe+plOla8fPiK2O1ELp6M5+qPaW6P4qQlhL1136B826E0c798oll1c3ItnBljxgOdgZMiUv0K61sAfwEHHYumichbjnXtgc8BF2CciLyfW+3May6OObto6dKlLFy4kDVr1uDl5UWLFi1ISUm5Yl1jDHa7HX9/fyIjI69YxtvbO/P1iy++SKdOnZg9ezZNmjRh3rx5hIaGsmzZsptut91uZ+3atZc9mP1K+xARRo0axZAhQ7KVHT16NIMHD+a99967bPseHh46zkwppXJYuj2dJTtmEvvVF9RdcYJywJE21ag54k1qlbnsn26wprJ/5WTMyk+pbd3PUVOYbXX+j+odh1DItcAtb39+lZtnziYAXwI/XaPMChHpnHWBMcYF+ApoA8QAG4wxM0Rk55U2cDOudYYrt7Rq1YoePXowYsQIgoKCiIuLo3Hjxvz2228MGjSIX3/9lWbNml21fnx8PAEBAXh5ebF7927Wrl2buc5utzN16lT69evHxIkTadq0KQULFqRs2bJMmTKFPn36ICJs3bqVWrUufy7Z/v37qVGjBjVq1GDDhg3s3r2b/v3789577/H333/TqVMnAJYvX05gYGC2ur6+vpw/fz7zfdu2bRk9ejTPP/88AJGRkYSFhV1xH+3atePVV19lwIAB+Pj4EBsbi5ubG3fffTfdunXjmWeeoVChQsTFxZGQkEDp0qX/0zFQSimV3bmUc/yx63dif/mB9oviKZ4KZ1rVouYL71CzVPnshUWwHVpL7PIfCDj4N+UlkcMUYVX1/6N+1yEUc9dQltNyLZyJyHJjTJl/UbU+sE9EDgAYY34DugH/OZw5Q2hoKC+//DLNmzfHxcWF2rVrM3r0aB544AE++ugjQkJC+OGHH65av3379owZM4aqVatSuXJlGjZsmLnO29ub9evX8/bbb1OoUCF+//13AH799VeGDRvG22+/TXp6Ov369btiOPvss89YsmQJFouF0NBQOnToQIECBZg1axbDhw9n+PDhuLm5UbNmTT7//HNOnz6dWbdLly707t2bv/76i9GjR/PFF1/w+OOPU7NmTaxWK3fddRdjxoy56j527dpFo0aNgIzB/r/88gvVqlXj7bffpm3bttjtdtzc3Pjqq680nCmlVA4QEbad3saUPZM5MW8WfRel0jgOUmtXpuyr7xBaLTR7hbQLpK0cTeqGn/FNjiFYCrDStQFSsx9N2/ailKfHlXek/jOTMR4tlzaeEc5mXeOy5h9knB07CjwnIjuMMb2B9iLysKPcIKCBiDxxlX08CjwKUKpUqbqHDh3Ktn7Xrl1UrZo/H5zq4+NDYmKis5txR8vPP19KqfzhQvoF/j7wN1P2TkG27GLQMqh0xAalS1Bi1Mv4NG+e/TFLIpyO+AO3BS/jl3acFbbqbPJvR5WW99I6rAIulis8kkn9K8aYjSISfulyZ94QsAkoLSKJxpiOwJ/AFZ73cG0iMhYYCxAeHp57SVMppZS6jaTb0/lxx4+M2zaOwNhEHlntSZVdNiwhwRR643H8e/XCuLlllhcRNm+JxG3+C9RIWsdue0nGlfqc5q278nSZwGvsSeU0p4UzETmf5fVsY8zXxphgIBYomaVoCccydQk9a6aUUupKtp3axhtr3uD8/j28uLkQlTacx8XHEDRiBIGDBmLxzH5HZcS+Y+ya+n/ckzwZq3FhcenhVOn2HM8G+jqpB3c2p4UzY0wR4ISIiDGmPhkT4p4BzgEVjTFlyQhl/YD+zmqnUkopdbtISk9i9ObRrF38C33WuxK2y47FPY6ABx8g+JFHcPH3z1Y+ITmNGZO/p+n+Twi3nORw8Q4U6v0xrQL//dRK6r/Lzak0JgEtgGBjTAzwOuAGICJjgN7AMGOMFUgG+jkmZLMaY54A5pExlcZ4EdmRW+1USimlbndWu5X5B+cxb/IHNF16mq6HBOPrQeAj9xE4aCCuISGX1Vm7YT32OS8wwL6Jk15lSe4xnVKVWzmh9epSuXm35r3XWf8lGVNtXGndbGB2brRLKaWUyi9SbanM2D6FXRPH0GD1GYaeAgkOoNDzD+Pf9x5cfHyylRcR9kbt4fDcz7nrzGSsxp0jDV6lZNunwcXtKntRt9od9YQApZRSKj+IT41n1srvifv1VxpuSqJ6CqRVKEGR4UPx69IFi7t7tvKHo/dzYPlEgg79TQ3bLioDOwp3okL/jynpX8w5nVBXpeEsD3vnnXeYOHEiLi4uWCwWvv32Wxo0aECZMmWIiIjIfCTURY0bN2b16tVER0ezevVq+vfPGKoXGRnJ0aNH6dixozO6oZRS6j+yi53dcbtZEbOCLdsXU+eP7TTaaUcsBnvz+pR6+Cm86tTJNiWGNTWJzXPG47l9EtXSd1DKCNGuZYms+ARlmg8ktIROA5RXaTjLo9asWcOsWbPYtGkTBQoU4PTp06SlpV2zzurVqwGIjo5m4sSJ2cJZRETETYUzq9WKq6v+eCillDMlpSfxzZZvmLl/JucTTtN1nTB0rWDBgmVQTyo8/BRuhQtnq5N2Opp9sz+n2IEp1COBI5bibCo3hFJ3DaBM2ZpO6om6Gfqv7y3QvXt3jhw5QkpKCk8//TQPPfQQDz30EBERERhjePDBB3nmmWey1Tl27BjBwcEUKJDxWIxLz5JBxoPBe/bsSc+ePXnkkUcyJ6V98cUX2bVrF2FhYdx777189dVXJCcns3LlSkaNGkXnzp158skn2b59O+np6bzxxht069aNCRMmMG3aNBITE7HZbP/qGZtKKaVyxoqYFfzf2v/jeOIxHo6rQYu/0nA7cRbfdu0oPPJ53IoX/6ewCGn7lnJ8wecUP7mMyiKsL9AQ90ZDqdO8KyUtFud1RN00DWe3wPjx4wkMDCQ5OZl69epRt25dYmNj2b59OwDnzp27rE7btm156623qFSpEq1bt6Zv3740b948c31iYiL9+vXjvvvu47777stW9/333+fjjz9m1qxZABQuXJiIiAi+/DLj/ouXXnqJVq1aMX78eM6dO0f9+vVp3bo1AJs2bWLr1q2XPUtTKaXUrRGXEscny97m8Mp59DruR9OjxbDs30yBSpUo/MGneDds8E/h9BSSNv1G0vIvCb4Qhbf48qdPH4q3fpyGYTWzz/yvbht3VDg7/u67pO7anaPbLFC1CkVeeumaZb744gumT58OwJEjR0hLS+PAgQM8+eSTdOrUibZt215Wx8fHh40bN7JixQqWLFlC3759ef/997n//vsB6NatGyNHjmTAgAE33eb58+czY8YMPv74YwBSUlI4fPgwAG3atNFgppRSTnDy1CG2j/mAlKUr6HfUiouAcb+AZ1hFCg56AP/evTEXh5tcOEP8sq9w2TQeH+tZDtlL8lfIs4S2e5ieFYtqKLvN3VHhzBmWLl3KwoULWbNmDV5eXrRo0YLU1FS2bNnCvHnzGDNmDJMnT+bNN9+kS5cuAAwdOpShQ4fi4uJCixYtaNGiBTVq1ODHH3/MDGdNmjRh7ty59O/f/6a/hCLCH3/8QeXKlbMtX7duHd7e3jnSb6WUUtdmFzs7z+xkxcElpEz9k8bzj1I0GWLKeOM2uBclWrTHMywMi0eWB4zb7ZxcPhbv5W/jZ09gib02e8q8RssOvXmoaEHndUblqDsqnF3vDFduiI+PJyAgAC8vL3bv3s3atWs5ffo0drudXr16UblyZQYOHEjJkiWJjIzMrLdnzx4sFgsVK2Y8bjQyMpLSpUtnrn/rrbd46623ePzxx/n666+z7dPX15eEhISrvm/Xrh2jR49m9OjRGGPYvHkztWvXzqVPQCml1KXWH1vP6ytfpey6GPqusFMoHs5VLwnPPEXrxp2u+J/ugzvWYZ/5DOVTdrBeqrG5xst0bXM3Lf08r7AHdTu7o8KZM7Rv354xY8ZQtWpVKleuTMOGDYmNjaVFixbY7XYA3nvvvcvqJSYm8uSTT3Lu3DlcXV2pUKECY8eOzVbm888/58EHH2TkyJF8+OGHmctr1qyJi4sLtWrV4v7772fw4MG8//77hIWFMWrUKF599VWGDx9OzZo1sdvtlC1bNnN8mlJKqdyTbktn9ObRrJo3nmcXulD8mB3XyhUp+r8XqNKk8RVD2Y7oY8T8+Qatzk4hES/+rvAaDbs/Tn1fjyvsQeUHJuOJSflDeHi4REREZFu2a9cuqlbVuVxU7tCfL6XUjTpw7gBvzH+eutN30SZScA0pROHnn6dgp46YS+6mFBE27Iji8PzRNI//ixATz/ZCXSjZ92P8goo4qQcqpxljNopI+KXL9cyZUkoplYvSbelM3TuFVT99yGPz0yiYDEH33Ufwk0/h4pN9nK/NLqxas5oLK0bTMnkh9U060UFN8OzwEtUrNnVSD9StpuFMKaWUygWHzx/mj6g/WLd6Kt1mn+Xxg4Jr1cqUePsdPENDs5UVETasXIB16YfcZdtAKm4cLtWVUh2fo0zRak7qgXIWDWdKKaVUDrHZbSw8vJApe6ewJ2otfVcIr26xg6cHhV56hsABAzAuLtnq7N+0hIR5b1M/NYJ4fNlT5QkqdHqair6FnNQL5Wx3RDgTEZ3zReW4/DReUyn134gIK2JX8OnGT4k5EUX/zd4MX21wsRkCBg0geNgwXAMCslbg9M6lnJn9NpUvRHAWXzZWfIqaPZ6jspef8zqi8oR8H848PDw4c+YMQUFBGtBUjhERzpw5g4eH3i2l1J1u55mdfBLxCUd2rKXLHh9abPHE5dx5CnbsQMjw4biXKvVP4dQEzm+YSNKq7yiSHIWRgiwu9QThfZ6jbsGAq+9E3VHyfTgrUaIEMTExnDp1ytlNUfmMh4cHJUqUcHYzlFJOciThCONWfUbi7Ln03GGhbKwNXBLxadaM4MeG4Vkzy0PGT+wgefVYLNsmU9CeRIy9NKuKP0uD7o/RqvDlz05Wd7Z8H87c3NwoW7ass5uhlFIqnzh0/hA/rPsan5//ptdGG242cK1UjsAXeuHXuROuISH/FL5whuQ5r+C5fSJG3Jhpb0Rs+Xvp1qkrvUJ8nNcJlafl+3CmlFJK5YQD8Qf4bstYEmbOYsBiO35JgkfXjhS7/2E8Lp3v0G7n7KrvKbD0LdysF/jW1oXD1R7loTZ16K2hTF2HhjOllFLqGqLORvHd1u/YvXYODy0UKh2x4VqjGiVeexPPGtUvKx+zcy22mc9QOnkn6+xVWVFpFL3at6ZssD67WN0YDWdKKaXUFeyO283YTd9wZulCWm+3cP8eGxZ/P4q88zx+Pbpnm9VfRNi2bjFpKz6nduJyzlKQaWVepUH3x3guwMuJvVC3Iw1nSimllINd7Gw8sZFZ87/Ge+E67tkBfhcES4AfAQ/1JOjRR3EpWDCzfEpaOuvnTSQg8ltq2naQgBcRxQdRvscr9Awp7MSeqNuZhjOllFJ3vL1n9/L3vpnEzJlO02Vn6BcLdhcLXi3uIrhXH3yaNcO4uWWWP3c+gQ0zvqHCvh+4i6OcMIXYXO0FqnZ8jAY+/s7riMoXNJwppZS6I9nFzh9RfzB120SKL99L5w1C+zghvUggASMfILh7T1wDA7PVOXXqFNtmfEr1wxNpY85yyL0iUQ2+oEKL/hR2cbvKnpS6ORrOlFJK3XGizkbxfytep+ScLTy/3uB9wY5raFUKv/Yovm3aXPaIpSPR+zgw+1PqnPiDViaZPd51SW31PKXrtged4FzlMA1nSiml7hiptlS+3fItq2d/zyPzbBQ7Zce7WVOCHnkEr3r1sj1JRux2tq+aSdqasdS6sJriCNv9WxDS/gUqV23kxF6o/E7DmVJKqXwv3Z7OqthVfL3sA1rMOMTrWwVL0SIU++Y1fFu2zFY2+fxZdsz+ikJ7J1LDHss5fNhcYgDl2j1BzVJVnNQDdSfJtXBmjBkPdAZOishlE8EYYwYALwAGSACGicgWx7poxzIbYBWR8Nxqp1JKqfwpIS2BlbErWXJ4CREHVhAWeZ6RK8Ar1ULQIw8QPGwYFq9/prmwp15gx58fUWrXd4STyG7Xymyo+R412w+mnofOUaZundw8czYB+BL46SrrDwLNReSsMaYDMBZokGV9SxE5nYvtU0oplQ/tjtvNl5u/ZFXsSsofttJ+hxsDd6bjlmqnQO0wir3xJh6VK/1TwZbOoQXf4LP+U2rY44hwD6dA61eoXq9FtsucSt0quRbORGS5MabMNdavzvJ2LaBPkFZKKfWvnU4+zejNo5m9Yxpdtrrz3bYCeB9PxXgVwK9rD/x69sQzLOyfwGWzcnrtRGTpe5ROP8oWU4UdTT+jaauuWCwaypTz5JUxZw8Bc7K8F2C+MUaAb0VkrHOapZRSKq9Lsabw886f+SHyOxpvSmbsGjc84pPwDK+L/9O9KdiubbbLl2JLJ3rpj3it/ZTC6THsllKsCv2Utt0G4VVAp8NQzuf0cGaMaUlGOGuaZXFTEYk1xhQCFhhjdovI8qvUfxR4FKBUqVK53l6llFJ5Q0JaAtOjpvPzzp8os/EY/1vtgf9JK551a1Ho2WfxqlM7W/n09DS2zR1H4cjRlLUdZQ+lWVnxPZp0up/u/vqIJZV3ODWcGWNqAuOADiJy5uJyEYl1/H3SGDMdqA9cMZw5zqqNBQgPD5dcb7RSSimnOnL+CBN3T+SvvdMI3Z7IqI2eFD1ip0DFYoS8OQKfFtnHiqUkXyBy5jeU2PUddeQ4UZayLAn7hPrtB1HZw92JPVHqypwWzowxpYBpwCAR2ZtluTdgEZEEx+u2wFtOaqZSSqk84kD8AT7f+DlroxbTeit8HumGzxk7biUDCX7vFfy6dsk2eWxywlm2/fUp5fb9SEPOEeVaicgGr1KzVX8quliusSelnCs3p9KYBLQAgo0xMcDrgBuAiIwBXgOCgK8d/8O5OGVGYWC6Y5krMFFE5uZWO5VSSuVtqbZUxm0bx9TV39F9HTy81eCanI5XeBiBbw7Gp2XLbKHswunD7JnxCRUP/059ktjqXpvjd40gtHFnjEVDmcr7jEj+uRIYHh4uERERzm6GUkqpHLLu2Do+WvoGYQsO0TXCgpsdCnboSODgwXhWD81WNj5qNUfnfUqF04twETsRXk3wbvU8ofVaOKfxSl2HMWbjleZydfoNAUoppVRWIsK+c/v4cfM4rH/M4sU14J0kFOzYjpCnn8K9dOl/CtusxEVMIWn5aEpc2IERT5b5d6d42+HUD63pvE4o9R9oOFNKKeV0IsLOMztZcGgBSw4soOzqaHqushNyHjwbN6Lws8/iGfrPmbLUlAtEzRtL4W1jCLEe57wUYXrRp6nZeRitSxR1Yk+U+u80nCmllHKaA+cO8Nf+v5hzcA6n449y91bDS+tdKHjWjmtoVYo9+xzejRtnlt+6P4aji76i7tGJVOcc201FllccQYMOA+gR6OPEniiVczScKaWUuqXiU+OZc3AOM/bPYNvpbXilW3gwujSNF3vjGncez9o1CP7wMbybNsmcEuPAsdNETH6ftnETqWkusMerDscaPkNok85U1zsvVT6j4UwppdQtEZsYy5gtY/j7wN/YrGm0O12Mx6IqE7R+P6RE4VWvHsGfPIZXgwaZoSw+KZVFU76h/oHR3GNOczioCa6dX6dyuQbX2ZtSty8NZ0oppXLVyaSTjN06lj+i/qDEaXgjugwVNxyHuCNYChakYNeu+HXvnm1G/3SbnaXzplNs/Tv0ZD9HPStyrvMYSlVv48SeKHVraDhTSimVK+JS4hi/bTy/7fmN4sfS+XBLYUpsjAHX/fi2aE7BLl3wadECi/s/s/THJaayct5kim4fSxvZymlLMDHNPqFE8wdA5yhTdwgNZ0oppXLU6eTT/LTjJ37b8xvFYpJ5d3MIJSOPYfGOI+DRRwkcfB+ugYHZ6uyKjWPznAmEHfmRriaas5ZAomqOpEKn4Rh3byf1RCnn0HCmlFIqR5xKOsUPO35gyp4pBJ9M5e11QZTckoCl4AUCH3+cwPsG4eLnl63O4ZPnWDb5U5qf/JX+llOc9CjN8UYfU6TpfQS4FnBST5RyLg1nSiml/pP41Hi+3fotk/dMxvt8Oq9sKU6llYeweCYS9PRTBAwciIuvb7Y6ScnJrJjyBaH7xzLInOaYXw0SW/+PQjW66OVLdcfTcKaUUupfsdqtTN4zma+3fE3qhfM8t68SteYdgLQjBPS7l+DHH7vs8qVY09jy9xgKbR5NO04S7VmVuA5fUrRme3DcoanUnU7DmVJKqZu2OnY1H274kLiYfTwYVYxGG2wQtwPfNq0JGTGCAmXLZiufELub6IXfUjx6OmFylj0uFdnb8gMqNemhoUypS2g4U0opdUNsdhurjq5i0q6JnFy/kj5bPKi9QzD2GHzuuougRx7GK/yfZzhLWhIHV0zCvvEnKiRFUlUsbHQPJ7X2AzRp1w8XnTxWqSvScKaUUuqajiUeY/q+6czd9geVNhyn2zYLpY/ZsPha8B84iIAB/XEvVSqzvDXhFPv+/pSie36inCRwWAozr+gQSrV8iAaVKzuxJ0rdHjScKaWUuqKdZ3by9aYvObdyOS232nknClytgnuVSgQO6Ytfly5YvP+Z5iL11AEOzvyAMoenUYU01rjWI7nOEBq06kY7D/dr7EkplZWGM6WUUtkcv3CcMcs/wvrXXO7dBIHn7Rg/X/z7dcO/Zw88qlXLVj7h+D6OTnuVCifnUk4MKzxa4tF8OI0aNsVi0fFkSt0sDWdKKaUAuJB+gd9mf0jqb9Povs1KASsUaFCP4Hv749OqVbaZ/AHiTh1n/7Q3qXV0MqUxzCvYkyJtn6FV9dDMZ2MqpW6ehjOllLpDpdvT2Ru3l8hTkRxdv4xyv6+l8YF0rG4WPLt0pMQDQ/CoVOmyesfPnGPbtI+oH/MDdUlirV87Aju9ScfKVZzQC6XyHw1nSil1h1l6ZCk/bP+BHWd24HUuhf5L7XTeLiQVdMc2pD9V738C14CAy+pF79/D3nnfUOPEX7QxcezybYB3p7dpXLX+re+EUvmYhjOllLpDHL9wnPfXv8+iw4uo4FmKl/ZUpvLM7VhsFgIfGUylIUNx8bnkOZa2dA6smsqFteMJvbCBUsD+gvU42fo5qtZq55R+KJXfaThTSql8zma3MWn3JEZvHo3XBRsfxDWj4oI9WGM34dumNYVGjsS9ZMlsdewpCeyfM5qgbd9Rzh7HSQJYV/IBKrcfRsUSl1/qVErlHA1nSimVT4kIa46tYfSGz3DfsINXogKpsOMcWJfgWqMGxd55B++GDbPVSU44y+4Z/6Nc1AQqksB6U5OztV6nSft+NPL0cE5HlLrDaDhTSql8Jik9iRn7ZzB5+69UWXyAp9eDX6Idl0A7fgMG4NezJx6Vs5/9OnYshoN/f0r1mEnU5gIb3MJJaTSChs074KYz+St1S2k4U0qpfOJY4jF+2vkTf0ZNp+rOBJ5d6krgGTuejRsR1L8/PnfdhckyHUaa1c6aDRtIX/UlTRLm0tiksdm7Ce4tXyC87l06HYZSTqLhTCmlbnMXx5R9sfkLihxL5Z0VBSm6x457+RIUfn8UPs2aZit/MiGFOXNmUmLXOFra12E1Luwt3JGgNiOoXbG2k3qhlLpIw5lSSt3Gos5G8cbqNzhycAvPbypE9bUXcPFNJ/jVVwjo2xfj+s+v+dQL51g74zuCdk9ksDnABYsPR6oNoUT74VT3K+rEXiilstJwppRSt6E0Wxpjt45l0oZx9Fpv4aX1BoucInDQQIIfewwXP7/MsnJ0M7ELxxB44E+ak0KMe1nONHqboCYP4F3Ax4m9UEpdSa6GM2PMeKAzcFJEql9hvQE+BzoCScD9IrLJsW4w8Iqj6Nsi8mNutlUppW4H6bZ0/tz/Jz9uGkf1FTF8tdaFAhdSKdilCyFPP4V7iRL/FD4bzYU/R+B9aBHB4sYyt2YUajmU2o3bgo4nUyrPyu0zZxOAL4GfrrK+A1DR8acB8A3QwBgTCLwOhAMCbDTGzBCRs7ncXqWUypNSbalMi5rG5LXfUWvVCV7bYsH3vB3vJo0o9OyI7A8jt6aSuOQTCqz+BLFb+NT0J7jFUPrdVUPvvFTqNnBT4cwY4yUiSTdaXkSWG2PKXKNIN+AnERFgrTHG3xhTFGgBLBCROMd+FwDtgUk3016llLrdnUw6yawDs1i66AcarjzDWzvB1SZ4N2tM0IMP4t2oUbbyF/YsJmX6cIJSDjHH3oDdtV5icPvGBHq7X2UPSqm85obCmTGmMTAO8AFKGWNqAUNE5LH/uP/iwJEs72Mcy662XCml8r0L6RdYdHgRiyKn4b4sgsY7bIyKBfFwJ+CeXgQOGkiBcuX+qWC3cW7rHM4t/4YycSs5bS/E5JIf0KnnfXQI8nJeR5RS/8qNnjn7FGgHzAAQkS3GmLtyrVU3wRjzKPAoQKlSpZzcGqWU+veOJBzh+zVfcH7+AupvT+PRQ4JFwJQrTcjIvvj37oVLwYKZ5a3xx4leOAb/XRMJtp4gTfyZ6jeYKj1fZliZwk7siVLqv7jhy5oicuSSCQltObD/WCDrA91KOJbFknFpM+vypVdp11hgLEB4eLjkQJuUUuqWOpdyjm8jvyFu4kT6LrXimQb2YoUIebQ7BTt1wqNS9tn8E/evI3bux5Q/tYgK2FhnarCy4tPUaj2A3oX9ndMJpVSOudFwdsRxaVOMMW7A08CuHNj/DOAJY8xvZNwQEC8ix4wx84B3jTEBjnJtgVE5sD+llMozUqwp/LLrF+YvHMvAmYlUPCq4NQyn+DPP4VGzZvYZ+u02EiKnE7/kc0okbKWoeLKwYHe8mzxMw3oNdaC/UvnIjYazoWRMeVGcjLNa84HHr1fJGDOJjDNgwcaYGDLuwHQDEJExwGwyptHYR8ZUGg841sUZY/4P2ODY1FsXbw5QSqnbXVJ6ElP3TmXilgncteA4r60Dl4K+FPvoFQp27pw9lKUlkbD6e6yrvyYg7ShnJYQphR6nZucnaF+6mPM6oZTKNSbjRsn8ITw8XCIiIpzdDKWUuqIzyWeYtOVHtiz8jYp7E7kryg2/s2n49ehBoZHP4xoQ8E/h1ETiV4zBZe2X+FjPstFeia0lB9Gsy2AqFPG7+k6UUrcNY8xGEQm/dPmN3q1ZFngSKJO1joh0zakGKqVUfnXszCGWfPMKZu1mmkbbuNsK4uaKd3gdgh99NPt0GCnxnFv2FW7rx+Bni2elvTrby79Fh869eCDI23mdUErdMjd6WfNP4HtgJmDPtdYopVQ+Ep8azx/T3qXM6JnUjhMSCvvg1aMlRe/uiHf9+li8skxzYUvn2MLR+K37BH97AkvstdlTeQidO3ajaYBOh6HUneRGw1mKiHyRqy1RSql8IsWawu9bfuLs6K9pszaVpCAvvL56nap3X36xwWq1sWXRbxRd/w7FbLGslhpsr/oMXTt0oqWfhxNar5RythsNZ58bY14n40aA1IsLLz4HUymlFByIP8C8g/PYtHAi/f44Tf2zQI/21Hn5bVx8sl+SvJBqZfbCBZSNeIdw2Ua0Kc7cWl/QqO29NNbZ/JW6o91oOKsBDAJa8c9lTXG8V0qpO9aR80eYGz2XNZEzCV6/n/p7hWeOCFI4mFITPsK7YcNs5dNSU1k9+2c8t06gj2wjwfiyu/arVOz4FGXcNJQppW48nPUByolIWm42Rimlbhenk0/z+YI34O8l1N9j5/njGctdypcl4PFOBD1wPxbvf86W2c/FEDXnS4L2/kYLOcspl0IcrfUcxdo8QRXPgCvvRCl1R7rRcLYd8AdO5l5TlFIq7xMR5mycxL4vP6J3RAruVnAJrULggI74tm5NgbJls5c/e4ijM96iyMFpVBQhwq0uRxs/Qo3mvTEuN/yQFqXUHeRGfzP4A7uNMRvIPuZMp9JQSt0xjsbsZvH7T1F96RFK28C1Y2vKPvUc7qVLX1bWHn+UIzP+j2L7JxMsMN2tIwVbPEXrxvWxWMwVtq6UUhluNJy9nqutUEqpPMpqt7JtzwqOff8tRedtobYV4u6qTvgLH+BZrtxl5W0JJ4me8R4lon6hmNiY69YalxbP071RXVz1EUtKqRtwQ+FMRJbldkOUUiqvOJN8htVHV7N581xCpq+icWQqpe2wr25hwl54h9AaTS6rE3f8EEdmvU/lmKmUlXQWuTVHWrxIh0YNNJQppW7KNcOZMWaliDQ1xiSQcXdm5ipARKRgrrZOKaVugaT0JDad3MTao2tZe2wtSXt2032tnZ67BCwWkto2ouTjzxJaITRbPRFh+85txC/4mPpn/yYUO6u9WiFNR9CqUWNc9PKlUupfuGY4E5Gmjr99b01zlFLq1olNjOWD9R+wInYFVls6tQ+58OBmL8rutiGeBQgcfC9B9z+AW+FC2eqJCBu2bOXc3HdpmbwAgC0hnQluN5K7KlZ3RleUUvnIjT5b82cRGXS9ZUopdTuwi51Juyfx+abPcbXBC2caELpgP64HYnAJKUjg8OEE9OuLi79/tnoiwvptuzgz513uTpqDMbC/VG9KdX2Z8JDLbwpQSql/40ZvCMh2Lt8Y4wrUzfnmKKVU7jpw7gCvr36dyJObGXyyMp3nnEGOLaNAxQoEvvMOBbt0xuKefTLYjFC2h5NzP6TNhRm4GjsHSvagdI/XqBKkoUwplbOuN+ZsFPAS4GmMOX9xMZAGjM3ltimlVI5JSEvgl12/8N3W76h0xp0fV5XGc9tO3CtXptCbb+HdrBnGZB8jZrcLa1YuJHXVNzRJWYarsXOweGdKdn+DSoXKO6knSqn87npjzt4D3jPGvCcio25Rm5RSKsecSjrFL7t+YfKeyZj4BF7ZXJyqK2NwKWgIeeMN/Pv0xri4ZKuTlprKxrk/4rvle5rYd5OEB4fK9KF0h2eoUKSyk3qilLpT3OhUGqOMMcWB0lnriMjy3GqYUkr9F9Hx0UzYMYEZ+2cQHGdlxJ7ihK5NxaTGEjBwACGPP46Ln1+2Ohfiz7B95heU3fcTjYgj1lKU7TVfomqHoVTy9LvKnpRSKmfd6A0B7wP9gJ2AzbFYAA1nSqk85UjCEb6J/Ia/D8yixmELH+0IptiWo+AaS8EO7Ql+9FEKVKiQrU780X3sn/kxlY9NpwEpbHcP40SjD6jevBfFLS5X2ZNSSuWOG70hoAdQWURSr1tSKaWc4PiF44zd8i2bVk6j3j5h7AEfCsacxSUgiYBhQ/Hv1w+3QtmnxDi+YxWnF/6PqnGLqYGFjb4t8bt7ONVrN3NSL5RS6sbD2QHAjSzP1VRKqbzgeOJx/p78HomLFtMyykqf84AxeIaVwX/YsxTs3BlLgQKZ5cVmZf/KKciar6iYsg0v8WJZcF/KdhxBw/I6nkwp5Xw3Gs6SgEhjzCKyP/j8qVxplVJKXcfes3uZvvBLyo9bRONDdqzuLng0akxI2074tGiOa1BQtvLW5AR2zR1D8LbvqWA/RiwhLCr9DNU7P0GrkGAn9UIppS53o+FshuOPUko5jYiw7vg6fo4cT5GpK+m2VpAC7hR4YQiV730Qi4fHZXVSzh4jatYnlNo/iRoksMNSmb1hz1Kv/SDuvkJ5pZRythu9W/PH3G6IUkpdy8H4g7y37j0SV69iyHxDoTjBo2M7Sr70Cq7Bl5/5SozdyeFZH1L+2CxCxcr6Ag2h8ZPUv6sjFn3mpVIqD7vRuzUPkv3B5wCISLkcb5FSSmWRlJ7Et1u/ZdWCCfRZaafmPjtupUtR9OPX8W7c+LLyFw6s5djf71PuzFLKiysrfdoS0Go4DerUu2ySWaWUyotu9LJmeJbXHkAfIDDnm6OUUhlEhPmH5jP1j7dptfAM/3dQMP5+BI94iMDB92Ub5I8IKbvnc2behxQ/F0GIeDHbvz9lO4/g7ooVrr4TpZTKg270suaZSxZ9ZozZCLyW801SSt3J7GJnyZElzP/rc+r8vY8R0YL4+VLouUcJuPdeLN7e/xQWIXX7TBLmvU1w4h4sEsikwCFU7/wUncuXcF4nlFLqP7jRy5p1sry1kHEm7UbPuiml1HWl29OZc2A2y/78kkbzY7j/MFj9vAl+bihB/ftj8fL6p7AISTvnkDjnLQol7iLWXoQ/gkZQr8sQ7i1XxHmdUEqpHHCjAet/WV5bgWgyLm1ekzGmPfA54AKME5H3L1n/KdDS8dYLKCQi/o51NmCbY91hEel6g21VSt1GktKTmB41jQ1/jqXFolM8HAvWoIIEjxpG0D19sXh6/lNYhMTdizg/+w2KJWzjjD2EscHPEdZpCEPKF7r6TpRS6jZyo5c1W2Z9b4xxIeNxTnuvVsdR5iugDRADbDDGzBCRnVm2+0yW8k8CtbNsIllEwm6kfUqp28+ppFNM3Pkre2b+QodlFxhyHGyFAin82hP49+r5z5iy5HNwYCnx2+bA/sX4pZ8kQQL5JWQ4tbo8zqOlNZQppfKXa4YzY0xB4HGgOPAXsNDx/llgK/DrNarXB/aJyAHHtn4DupHxfM4ruRd4/WYar5S6/ew7u4+ftk/g5OwZdF9lpe1JQYoVosj/PYF/t24Yd/eMgrtnY1/5GcRGYBEbRjxZLTU4V/x+and6hIElNJQppfKn6505+xk4C6wBHgFeBgzQQ0Qir1O3OHAky/sYoMGVChpjSgNlgcVZFnsYYyLIuIz6voj8eZ39KaXysG2ntvHT+m9IX7iMLuuh2Bk7pnQJin7wBAU7dcK4On4dJZ8jacZzeO2awhGKMcPahV1e9Qhr1Jpe9coS5FPg2jtSSqnb3PXCWTkRqQFgjBkHHANKiUhKDrejHzBVRGxZlpUWkVhjTDlgsTFmm4jsv7SiMeZR4FGAUqVK5XCzlFL/hYiwIXYdC6Z+TKHlOxm4V3C3gmulChR+5TF827bFuLhklt254k+KLnuegtYzjLZ1Z0u5RxnQuCKPVQrBRSeOVUrdIa4XztIvvhARmzEm5iaCWSxQMsv7Eo5lV9KPjMulmUQk1vH3AWPMUjLGo10WzkRkLDAWIDw8/LKJcpVSt1a6LZ3IU5Gs27sYy++zqLXuND0TwerjgX+vLgT36o1HjRqZE8JabXb+Wr8X18Vv0C19Dgcozozq4+h+dweeDPS6zt6UUir/uV44q2WMOe94bQBPx3sDiIgUvEbdDUBFY0xZMkJZP6D/pYWMMVWAADIunV5cFgAkiUiqMSYYaAJ8eIN9UkrdYmm2NGYdmMWSw0uIPLKO5msv0H2tHa9USAivTOEBj+LfqjWWi+PJyDhTtixyD4fmfkbXlJn4mQvsLX8/pXq9w/1ePk7sjVJKOdc1w5mIuPzbDYuI1RjzBDCPjKk0xovIDmPMW0CEiFx8kHo/4DcRyXrWqyrwrTHGTsa8au9nvctTKZU3pNvTmbFvBt9u/ZaT54/Sc48/ny+z4nnOjsddTSk64lk8qlS5rN72nTs4NOsjWl6YTQuTyolirTCdXqZSifAr7EUppe4sJnsmur2Fh4dLRESEs5uhVL5ns9v4++DffBP5DbEJR+gbU5JuS5JwiT2JZ506FBrxDF7hlwQtu42DEXM4uXw8dRKWYgwcKtaRUl1G4VY01Cn9UEopZzLGbBSRy/5XqrP8K6VumIiwInYFH0d8zMFzB+h8uiQfLCuOW1Q0BSpWJOSbN/Bp0SL7A8bP7OfYsu9x3zGZsrZTBOHN9uJ9qNz9RcoXKuu8ziilVB6l4UwpdUP2nd3HRxEfsfroapqdK8Kbq8vhsSUKt+LFCfnwg4zpMFz+GQkhRzYQ//er+B9fQyExrDFhXKg6gkadBlHbx9eJPVFKqbxNw5lS6priUuL4OvJrZm2dTMs9bvywOwTv/TG4BAYS/PLL+Pe9J/tA/1N7OfXXKxSKmUe6FORLlwEENRlMt2Z18XLXXzlKKXU9+ptSKXVFCWkJ/LrjF9bMHU+jTUl8t8fgmpZEgcol8X/5Qfx69MDFxzuzvD3+GDF/vU7xA1PwEnfGufXDp8VwHm5QCQ+3f31vkVJK3XE0nCmlsklIjufvPz/m1JyZ1N6VSvMEwMsT/x5d8e/dG4/qof+MKbPbSdq7hBNLv6X48UUUEeEv9w64thjJ4IY1cXOxOLUvSil1O9JwppQC4OyhKDb/71W8Vm2l1gXB6mrBpVE9inXpjW/r1li8skwIm3CckyvG4xL5M0FpRwkQb+Z5dsSj2WN0a9RQZ/NXSqn/QMOZUne4w/GHWTHuLar8tIogGxyuUQjf7gOo1mlAtsuWAHHHD3Nq9juUOzyVQlhZZ6/G3JIPUaP1QLqULeKkHiilVP6i4UypO5CIsOnkJqasHUfV75dRf69wokIQhd/9P7rUbJmt7PmUdJZs3oNlzWhax0+jHDbmurcmqe4w2jZrQgNv96vsRSml1L+h4UypO8jFecrGbh2L26rNDJ0j+KQZvIYPpfkjj2ebCiMl3ca4RdtIX/U1D1lm4mOS2RXSjgKtX6Jz5ZrZ5zJTSimVYzScKXUHsIudRYcXMS5yLJ4bdtJ7sxuV99txr1KZ4h9+iEelStnKL9l+mK1/fUr/tKmEuJznXKnWmI5vEFqkhpN6oJRSdw4NZ0rlY1a7lTkH5zBx3VjKrjjAU5EWAs/acSnsR+CzAwkaPBiTZY6yI6fiWfz7Z7Q5NYGWJo5zRRtBp7fwL1nfib1QSqk7i4YzpfKhFGsKf+77k6nrvqfx/Fhe3Aru6YJneB0CBw7Et/XdGNd/vv7Hz5xjw4wx1Ij+gcHmOCf8qpPeZTz+FVteYy9KKaVyg4YzpfKRhLQEJu+ZzJRNP9J02WlejTC4WQ1+3bsRNHgwHpUrZyt/JOYwu2d+Ru3jU+li4onxrEBc258oXLsr6JgypZRyCg1nSt3mrHYra4+tZcb+GazYv4iW61N4d50FzyTBt0N7Qp56kgJlsz9gPGrnZk7O/x91z86lpElnj18j7HePoETNNhrKlFLKyTScKXWbOhB/gOlR0/n7wN8knj1J163ufL1B8Dhvx7tZEwo9MxyPatUyy1ttdtavXIDL6s+pl7KaUriys1BHSnV8jsplazqxJ0oppbLScKbUbebig8in7J2C/wXDo7uLErbCDUtSMt5NmhA8dAhe9epllo+/kMbKeb9RdPu3NLZvJwFvtpR5iPKdR1A7pLgTe6KUUupKNJwpdZtIt6UzcfdEvt3yLf7HE3l3X1nKrYqGtEP4tm1L0COP4Fk9NLP86fNJrJz5A5X3jqWTieaMJZjdtV6kYvvHqe1Z0HkdUUopdU0azpTK41JtqSw7sozRGz8ncHM0r233o/TudIzbQQp27ULQQw9ToNw/Y8pOnE1g7Z9jqBE9nu7mKCfcSxLb+GOKNxtMkKvO5q+UUnmdhjOl8qD41HiWxyxn8eHFbN+7gvAtyYzc4kLwGTuuhdwIGP40/n364BoUlFnnVNxZ1k/7nLAjP9PNnCbWsyLHm4+hSIN7wOJyjb0ppZTKSzScKZWHRJ2N4uOIj9l8eC119lhpvcuNh/enYbELHrVrEvTyIHzbtMG4uWXWSTx3mi3TPqLqoV/pZBKI9q7OqdafUbx2Z73zUimlbkMazpTKA9Jt6YzbPo7py8fQZ7WFJ3YLrsl2XIsG4PdwV/y6daVA+fLZ6qTFxbD3rw8oe2gyTUhhm3dDktu9QJlarZzUC6WUUjlBw5lSTrbj9A7eWzCKsNn7+GQTuLi549epC35du+FVLxxjsWQrnxC7h5hZ71Hh2EyqiJ01Xs0Jaf8CNWo1clIPlFJK5SQNZ0o5yfm084yPGEPcTz8xYo0dj3QI6NWL4CeewK1w4cvKH965nnPz3yf07GLK4cpi7/b43T2CpnXqYPTypVJK5RsazpS6xY4lHuOXbT9yaurvdF2eQlACeLRoRrHnRlKgQoVsZUWEHRuWYF3yAWHJawkUT5aF3Evx9s/S7pKySiml8gcNZ0rdIrvjdjNh2w+cnz2b3sttFD0rEFqJ0i++km3SWAC7Xdi4egEuyz+gTloE5/BhTamhVOo6glbBl59VU0oplX9oOFMqlx2IP8AXGz/n7OKF9F8OpU7asVQoR7F3nsOnZYtslyST02ysWz4bn7WfUM+6iXP4sqniU1Tr9iyNfPyd1gellFK3joYzpXLJyaSTfLPlG1atn8bD8+3U2G/HpWQJCn88nIIdO2QO9LfZhTVRJ9m34ndqHfmZFmYv50xBtlcbQeUuz1BHZ/NXSqk7Sq6GM2NMe+BzwAUYJyLvX7L+fuAjINax6EsRGedYNxh4xbH8bRH5MTfbqlROSUxLZPz28Uzc9hNt16byyUrB1c2dQi+NJODefplzlJ08n8KEpduxRP7KPdaZNLWc4ox7UQ7WeoXSrYdR3cPHyT1RSinlDLkWzowxLsBXQBsgBthgjJkhIjsvKfq7iDxxSd1A4HUgHBBgo6Pu2dxqr1L/VZotjcl7JvPt1m8ptP8snyzyIuCoFd82bSj88ku4FSkCQFKalZ8WbULWfsMQ5uFnLhAXUpu0Fh8TFNqFIJ3NXyml7mi5eeasPrBPRA4AGGN+A7oBl4azK2kHLBCROEfdBUB7YFIutVWpf80uduYcnMPoTV9QcFcsz233p9IWG65FfSjy9fv4tsqYFNZmF2at2kjiks8YZJuPp0kjuXwHaDGCwJL1rrMXpZRSd4rcDGfFgSNZ3scADa5Qrpcx5i5gL/CMiBy5St3iudVQpf6NdFs6S2OWMn7TtwSt3MXzke4Ui7Vh8bMSMGQIwY8+gsXbG4AtO3Zy5K+3aJ+6AFdj51yFrni3G4V3oSpO7oVSSqm8xtk3BMwEJolIqjFmCPAjcFPPnjHGPAo8ClCqVKmcb6FSl9gdt5s/9/3Jmk0zqLf+HM9sMfhesONevjiBb96HX9cuWDw9AUhKPMeGX96g/rFfqWbsHC3bk1JdXyIosKyTe6GUUiqvys1wFguUzPK+BP8M/AdARM5keTsO+DBL3RaX1F16pZ2IyFhgLEB4eLj8lwYrdTVptjT+2v8XU3f+jmfELtpuhq4H7IDBp3kzgu67D69Gjf6ZFsNuY9/8bwhY+xHNOce2gNaUv/dDShcuf839KKWUUrkZzjYAFY0xZckIW/2A/lkLGGOKisgxx9uuwC7H63nAu8aYAMf7tsCoXGyrUleUlJ7E1L1TmbXie6pHnOKZba74xduxhAQTMLQ3Ab1741Y8yxV3ES5s+5uE2a9SIeUA2yxVONZuPDUa3O28TiillLqt5Fo4ExGrMeYJMoKWCzBeRHYYY94CIkRkBvCUMaYrYAXigPsddeOMMf9HRsADeOvizQFK3QrxqfFM2fIzUX/8RL3NCbx5GMQYfBrXx79fX3xbtMicEgMAEc5unUPy/P+j2IWdnJLCTC3/Dp36DsWzgLNHDyillLqdGJH8cyUwPDxcIiIinN0MdRtKtiaz+eRm1h9bz4Ety6k8bw+Nd2Y8jNxeogiFe/fDr1tX3IoWzV5RhKOb55K28G3KJG0nRoJZXuQBwroMo1qJIOd0Riml1G3BGLNRRMIvXa7/pVd3tKizUfxv4/9Yd2wd5Q6n032d0G6vHZubCy7tW1P63gfwrF072yOWABBh75q/MMs/pmLKNo5LIH+VfI7aXZ+kfyF/p/RFKaVU/qDhTN2RktKT+GbLN/yy/ScaHSrAlxv9CNx7AoufH4GPDSBg4ABcAwMvq2ez2YhcOAm/DZ9RyRrFcYJYWOZZanV7mm4Bfk7oiVJKqfxGw5m6o4gICw8vZPTS96i+7gRjt3riczoB12I+BL30Ev69e2Hx8rqsXsyxE+xb9isl90ygrhwi1hRmVdXXCOsyjNZXKK+UUkr9WxrO1B1jT9wefp7xfxSZs4m3d4CbVfAKr0rAKwPwvfvu7AP8gSOnzrF9+TR89kyjXupaSph0jriUJLLOh9Ro9yDFXd2usiellFLq39NwpvK96Phovl/5GYV/ms+ALYKtgBsBPboRNHAgHpUrZysrImzYsJq4pd/Q4MISOphE4k1B9pXoQXCjQZQMbUbJS8efKaWUUjlIw5nKt45fOM6Yzd9wbvo0+i+24Z1m8L6/P8WHPYmLX/bxYWJNY/viSbDhO+qnbyMVNw4Xvhtrw4GE1GqPn4ueJVNKKXVraDhT+c6OMzuYuGsi29f9zQNz0qkcY8c1rAYl33wbj8qVspW1JZ5h/5zPCdz5MzUkjmMmhMjKz1Ct0+NULBjipB4opZS6k2k4U/lCuj2dRdELmbvse7zX76L+PsOAGBuWggUp8s5I/Hr0wFgsmeVTTh/iwMyPKHtoCpVIYb1LGLvrvEWDtv0o6qZnyZRSSjmPhjN1W4tJiGHOul9InTiVarsuMORsxnK3KpXwG9aagEEDcQ0IyCx/9tA2Yv9+n8on51BJhBUeLbA0fZomje/C1cVylb0opZRSt46GM3XbSbWlsvjwYqbv/oOAWWvou8KOu81gq12NQh16UrBlS9yKFctW58zBrRyb8SbV4hbhgRvL/LoS3GYELarXuHyCWaWUUsqJNJyp24Zd7EzcNZExW8cQcuAcjy2wUOKYHdfG9Sn95tu4lyx5WZ0z0ds5OuNNQs8swAN3loQMoFzXF2hdqpQTeqCUUkpdn4YzdVs4lniMV1e9yvaDaxkeUYiaq+y4hQRR+POX8W3b5rKzXyejIjg25yOqn5mHJ+4sDbmXit1HcXcJDWVKKaXyNg1nKk8TEWYdmMX7a9+lwZYUvlvmgWvCCQLvG0Twk0/i4uPzT2GblZi1U0ld9TXlk7bgK+6sDL6Hct1fplXJ0s7rhFJKKXUTNJypPCsuJY63177N3vXzeWOxJyWiU/CsXZsir76CR7VqmeUk6SzR87/Gd9sPlLCdIlZCmF/iCUI7P0bzosWd2AOllFLq5mk4U3lOXEocE3ZMYObmSXRbmszDG+24BrhT6N138eveLXNKjOTTh4ie9TGlo6dQlmTWmxqsD32RJh0G0tbHw8m9UEoppf4dDWcqzziTfIYfd/zIktUTuXttMp9vN7hZhYD+/Ql56p9Z/Y/ti+Tk3A+pdnouFUVYUaAZaQ2epPldrfBwc3FyL5RSSqn/RsOZcrrjF47z846f2D7/N9qsTeHDfQJurvh16Urg/YPxqJQxq//5syfZ+/NwwuP+xl/cWeXfFf/WOh2GUkqp/EXDmXKaA+cOMH77ePYvnUn/xel0PioQ4Efw4wMJuLcfrsHBGQVF2DZvPMXXvkmYJLCqcH8q9HiJFkUvnzpDKaWUut1pOFO3XOTJSL7f/j1Rm5cwaCkMiLJhCodQ+P+exK9rVywFCmSWPRMTxfGJj1MjaR17XCpysstvNAlr7LzGK6WUUrlMw5m6JexiZ0XMCsZvH8+BAxsZuMqVYZvtWLy9CB4xhMD7BmHx+GcQf3L8GXb/9RFVDoynjMDy8s/SsN8o3N31uZdKKaXyNw1nKlel29KZfXA2E3ZMIPFAFH03ejByC1jERsCggQQPG5bt2ZfnT8US9df7VImZTG1SWF+gMYXu+ZS7yldxYi+UUkqpW0fDmcoVF9IvMHXvVH7e+TPe+48zYKMX1bfbsbil49ejF0EPP4R7lkconY7ZR/SM96h+4i/CsLLBuzned4+kft0mTuyFUkopdetpOFM56mTSSX7d9Sszt04mdPt5Ru72puR+GxZfCHjkEQIHDcQ1JCSz/PF9mzn29/tUj1tALSDCvx0h7V+kYdVazuuEUkop5UQazlSOOBh/kAlbvufQohk03Wbj8yhwTbfjViqAgOeH4t/3nmyPWjq6fTlx8z6gesJKCkoB1of0pHTn52lUprITe6GUUko5n4Yz9Z/sPLOTH9d/g9tfi+kYIQQkChT0xb93J/y6dsUzLOyfOcisqRxa+Rvp68ZRIXkrXuLN0qIPUqXbszQpWsK5HVFKKaXyCA1n6qaJCBEnIpi46msKzVzHPZsFr1RwaxBOoYH34dO8ORZ398zyttMHODT/K4KjJlNaznNECrOw1FPU6j6cFkFBTuyJUkoplfdoOFM3zC52lhxewp9LvqbivF08sE1wtRm82ram8CND8awemq18yqEITs94jRJnVlFKLKx0qU9y2GCatu1Fa88CV9mLUkopdWfL1XBmjGkPfA64AONE5P1L1o8AHgaswCngQRE55FhnA7Y5ih4Wka652VZ1dem2dGbtn8ni2d8QvjiWoVECri749ehOoYcfwb106Wzlk47u5Nj0Vyl/aiFe4sNvPgMp1PwRmofXwsWij1lSSimlriXXwpkxxgX4CmgDxAAbjDEzRGRnlmKbgXARSTLGDAM+BPo61iWLSFhutU9dm4iwO243cw/MJmbWH9y14iyPHQWbrxfBQwcRNGDgP49Xckg4cZAj016j8omZFJYCTPcbSMlOI+lXufRV9qKUUkqpS+XmmbP6wD4ROQBgjPkN6AZkhjMRWZKl/FpgYC62R92AvWf3MvfgXBbvm0uZ1Yfotk5of1awFStE4VeH4N+zBxZPz2x1zhzezZFZ71Ht5CzKCywu2IPCnV+iR+WKTuqFUkopdfvKzXBWHDiS5X0M0OAa5R8C5mR572GMiSDjkuf7IvJnjrdQARljyZbHLOf7bd+z58hm2kXCyxst+Jy341atKoVeH4Jvm9YYF5ds9Q7vXEfcvA+ocW4xPriwzq8DIR1G0aZq6JV3pJRSSqnryhM3BBhjBgLhQPMsi0uLSKwxphyw2BizTUT2X6Huo8CjAKWyzDivri/dns6cg3MYv2088Yf30XurFyM3ueKalIp34wYEPfoIXg0a/DMVhsPu9fNJXfIxtZLXESQerC58L2U7P0+zUuWc1BOllFIq/8jNcBYLlMzyvoRjWTbGmNbAy0BzEUm9uFxEYh1/HzDGLAVqA5eFMxEZC4wFCA8Plxxsf74VnxrP9Kjp/LrrFwruPU7/SC+q77BjTBIF27Ul8KGH8AzNfvZL7DZ2L5+KWf05VdJ2cBZfVpcaQpUuI2gWUsRJPVFKKaXyn9wMZxuAisaYsmSEsn5A/6wFjDG1gW+B9iJyMsvyACBJRFKNMcFAEzJuFlD/wc4zO/lt92/MOfA3tXam8OJGT4odtmEpaAh48EEC+vfHrVixbHWsKRfYs/hnfDd+TVXbIY4SwupKI6nV5Qka+/o5qSdKKaVU/pVr4UxErMaYJ4B5ZEylMV5Edhhj3gIiRGQG8BHgA0xxXDq7OGVGVeBbY4wdsJAx5mznFXekrmt5zHLGbh3L1hOR3BXlxuh1BfCLTcK9dDABr43Av3t3LF5emeVjTp1l/5oZeO79i9DEVYSSwn5TipU13yW840MU8/BwYm+UUkqp/M2I5J8rgeHh4RIREeHsZuQZcSlxvL/+febun02Xg4H0Wm3HM+YM7uXLEzxsGAU7tM8c5J9mtbNg3kxcN/9Ao/S1FDTJxOPD7oCWUL0XdZp3xc3V5Tp7VEoppdSNMsZsFJHwS5fniRsCVM4SEeZFz+Ojle8QtukcP2z2wev4KQpUrEDwJy/h265dZigTu511i6fjvvpTOtm3ccF4E1usDQl1+lCsdnsauLpfZ29KKaWUykkazvKZk0kn+WjR63jOXM77myz4JFrxqF6SoBffxLdtG4zFklHQbmfv8t9h5Sc0tO7ljAlkX+1RlG//OJUK+Dq3E0oppdQdTMNZPnEg/gBTV32L22+z6b/Zikc6eDVrTPDDj+BVv17mdBhiS2f/kp/wWPc5ldIPEUNhImq8Tu0ujxHkrmPJlFJKKWfTcHYbExE2HN/AtBVjKDp9Le23Ci5icG/XmpJDn8SjcqXMsva0FHbP+5bAzV9TwX6c/ZRkYbV3aNrtEUoU0IeQK6WUUnmFhrPbkF3sLDq8iGmLv6LG7L0M3CEYiwXvHt0oNvRx3EuUyCwrtnR2zPiCIlu/pJrEsdNUYFftz2jUYSDl3d2c2AullFJKXYmGs9vIxRn9Z83/ivrzj/DEbgE3N/z796HQI4/iVrhwtvKxm+dh+/sFqlsPstUllN31P6Th3T2ppnddKqWUUnmWhrPbQLI1mRn7ZrB4zhiaLjrBM/sEu2cBgh4aSPD99+MaHJyt/IWT0Rz6bQTV4hYRSwhLwz6hWZcHcHGxOKkHSimllLpRGs7ysNPJp/lt1yS2zvmF1ssTGH5IsPt6E/zkgwQOHICLX/YZ+q0Jp4ma+RFl946nrMC8wg8Rfu9rtAjwd04HlFJKKXXTNJzlQVFno/hl+0+cmDODLqvTuPs42IP8CRn5CIF9+2Lx9s5WPuXMYQ7O+IAyh6ZQlVSWuzcjsPt7tKtWw0k9UEoppdS/peEsj0ixprDg0AKm75hMwcWb6LZOKHJWMKWKU+TtoRTs2hWLe/YJYRNjd3Jk5ntUOD6bimJnhUcLCrR4lqYNmmCxGCf1RCmllFL/hYYzJ9sTt4c/ov5gyY4ZNFqbwJBNhoKJdtyqVaXQG0PxbX135mz+F53auYK4BR9RMW45ZXFliW8ngto8S4uaNTPnM1NKKaXU7UnDmRMcSTjCvOh5zDk4h7MH99A1wvC/rYJbqh3vZk0JeughvBo0yB607HYOrpmGfeVnlE/ehpt4My9oIKU6DKdtxQrO64xSSimlcpSGs1skPjWev/b9xdzouWw7vY2KscKArQWputWOsVjw69SZwAcfzDZxLAB2O3uW/ITXmv9R1nqYoxLMvFLPUL3z43QoHOKcziillFIq12g4y2Xp9nSm7JnCN1u+4XzyWXrGFmP4+kL47j2KxdeK/wMPEDhoEG5FimSvaLezb/kk3Fd+SGVrNAdMCRZXe4fwTg/SztvLOZ1RSimlVK7TcJZLRIQVh5by6+z3KbAvhsfiCxG2LwBOHMGtVCkCX34Z/549LrvzErudg6umYFn+PhXSDxBNMZZUf59GXR6iXAH3K+9MKaWUUvmGhrMcJiLs/OlLTkz6maCYBIZbM5ZbfBLxDAsj4LV++LRocdkgf0lPIWrReLwjvqGs9TCHKMqiqm/TqNujlPHQZ18qpZRSdwoNZznEareybOtfXPi/j6m44xzpRVyI61CfWk174F2zFu6lS2Msl8/Qb71wlr2zv6DwzglUkjj2UoZFVd+mQddHKO3p4YSeKKWUUsqZNJz9R/Gp8fy570+2/TGO3n+eJiQNjjzQmsZPv4OvR8Gr1ks+fYgDMz+mzKEpVCOZCJdabK/7Hg3b9KaSmx4WpZRS6k6lKeBfOJ18msWHF7Po8CK2HVzHwIVpPLRVSKtQggr/+5KalStftW7c/giOz/mIiqcXUFmENR7NcG32NA0at9KJY5VSSiml4exGiQi/7vqVBYcWsC96E3Wj7LQ94MGT+6242AxBw4YQMmwYxv0Kg/bTkzkR8SfnV42jYmIE7uLBMv/uFG47nGahNW99Z5RSSimVZ2k4u0HGGOJ+/pm+kacpE23DiOBa1A/fe1vj3707HtWqZa9gt8HB5Zxb/ysFomZT2H4BkUDmFhtGlU5P0rpEced0RCmllFJ5moazm9D5UBC4eeA77G58W7emQNWqlz8u6cJpWPct6RETcEs6iUU8mUMDUqv2pnWHXrT30znKlFJKKXV1Gs5uQplx47B4XSVcnTuCrB6NfeOPuNhSWGarzRyXgZRu1IP77qqCv5fOUaaUUkqp69NwdhOuGMxO7sa28jPMtinYRfjT1oTJBXrRskUz3mhYCl8Pt1vfUKWUUkrdtjSc/RsisH8R6au+wu3gYtJxZ5L1bhYH3kP35g35pVYx3F0vn9NMKaWUUup6NJzdjPRk2Pp7RiiL28s58edHax/2l76H/i1rc3+F4MvHoCmllFJK3QQNZzdKhNQvm1Agfj977GWYYH8ME9qDB1tUoWrRq082q5RSSil1M3I1nBlj2gOfAy7AOBF5/5L1BYCfgLrAGaCviEQ71o0CHgJswFMiMi8323o9doGPUroRZfelQr12PNOsHMX9PZ3ZJKWUUkrlQ7kWzowxLsBXQBsgBthgjJkhIjuzFHsIOCsiFYwx/YAPgL7GmGpAPyAUKAYsNMZUEhFbbrX3eiwWQ/f7hlMywAs/Lx3kr5RSSqnckZuj1usD+0TkgIikAb8B3S4p0w340fF6KnC3yRi01Q34TURSReQgsM+xPaeqXtxPg5lSSimlclVuhrPiwJEs72Mcy65YRkSsQDwQdIN1lVJKKaXyndt+vgdjzKPGmAhjTMSpU6ec3RyllFJKqf8kN8NZLFAyy/sSjmVXLGOMcQX8yLgx4EbqAiAiY0UkXETCQ0JCcqjpSimllFLOkZvhbANQ0RhT1hjjTsYA/xmXlJkBDHa87g0sFhFxLO9njClgjCkLVATW52JblVJKKaXyhFy7W1NErMaYJ4B5ZEylMV5Edhhj3gIiRGQG8D3wszFmHxBHRoDDUW4ysBOwAo87805NpZRSSqlbxWScqMofwsPDJSIiwtnNUEoppZS6LmPMRhEJv3T5bX9DgFJKKaVUfqLhTCmllFIqD9FwppRSSimVh2g4U0oppZTKQ/LVDQHGmFPAoRzcZDBwOge3p3KGHpe8S49N3qTHJe/SY5M33arjUlpELpukNV+Fs5xmjIm40l0Uyrn0uORdemzyJj0ueZcem7zJ2cdFL2sqpZRSSuUhGs6UUkoppfIQDWfXNtbZDVBXpMcl79Jjkzfpccm79NjkTU49LjrmTCmllFIqD9EzZ0oppZRSeYiGsyswxrQ3xuwxxuwzxrzo7PbcaYwxJY0xS4wxO40xO4wxTzuWBxpjFhhjohx/BziWG2PMF47jtdUYU8e5PcjfjDEuxpjNxphZjvdljTHrHJ//78YYd8fyAo73+xzryzi14fmcMcbfGDPVGLPbGLPLGNNIvzPOZ4x5xvF7bLsxZpIxxkO/M85hjBlvjDlpjNmeZdlNf0eMMYMd5aOMMYNzo60azi5hjHEBvgI6ANWAe40x1ZzbqjuOFXhWRKoBDYHHHcfgRWCRiFQEFjneQ8axquj48yjwza1v8h3laWBXlvcfAJ+KSAXgLPCQY/lDwFnH8k8d5VTu+RyYKyJVgFpkHCP9zjiRMaY48BQQLiLVARegH/qdcZYJQPtLlt3Ud8QYEwi8DjQA6gOvXwx0OUnD2eXqA/tE5ICIpAG/Ad2c3KY7iogcE5FNjtcJZPwjU5yM4/Cjo9iPQHfH627AT5JhLeBvjCl6a1t9ZzDGlAA6AeMc7w3QCpjqKHLpcbl4vKYCdzvKqxxmjPED7gK+BxCRNBE5h35n8gJXwNMY4wp4AcfQ74xTiMhyIO6SxTf7HWkHLBCROBE5Cyzg8sD3n2k4u1xx4EiW9zGOZcoJHKf1awPrgMIicsyx6jhQ2PFaj9mt8xkwErA73gcB50TE6nif9bPPPC6O9fGO8irnlQVOAT84LjmPM8Z4o98ZpxKRWOBj4DAZoSwe2Ih+Z/KSm/2O3JLvjoYzlWcZY3yAP4DhInI+6zrJuM1YbzW+hYwxnYGTIrLR2W1Rl3EF6gDfiEht4AL/XJ4B9DvjDI7LXd3ICM/FAG9y4SyLyhl56Tui4exysUDJLO9LOJapW8gY40ZGMPtVRKY5Fp+4eOnF8fdJx3I9ZrdGE6CrMSaajMv9rcgY5+TvuGQD2T/7zOPiWO8HnLmVDb6DxAAxIrLO8X4qGWFNvzPO1Ro4KCKnRCQdmEbG90i/M3nHzX5Hbsl3R8PZ5TYAFR1307iTMXhzhpPbdEdxjLH4HtglIp9kWTUDuHhnzGDgryzL73PcXdMQiM9ymlrlEBEZJSIlRKQMGd+LxSIyAFgC9HYUu/S4XDxevR3l88T/SvMbETkOHDHGVHYsuhvYiX5nnO0w0NAY4+X4vXbxuOh3Ju+42e/IPKCtMSbAcWa0rWNZjtJJaK/AGNORjLE1LsB4EXnHuS26sxhjmgIrgG38M7bpJTLGnU0GSgGHgHtEJM7xS+9LMi4XJAEPiEjELW/4HcQY0wJ4TkQ6G2PKkXEmLRDYDAwUkVRjjAfwMxljBuOAfiJywElNzveMMWFk3KjhDhwAHiDjP+D6nXEiY8ybQF8y7kLfDDxMxhgl/c7cYsaYSUALIBg4QcZdl39yk98RY8yDZPybBPCOiPyQ423VcKaUUkoplXfoZU2llFJKqTxEw5lSSimlVB6i4UwppZRSKg/RcKaUUkoplYdoOFNKKaWUykM0nCmlbjvGGJsxJtIYs90YM8UY43WNsi2MMY2zvJ9gjOl9tfJZyiXmVHuzbDPMMVXPxfdvGGOey+n9KKVubxrOlFK3o2QRCROR6kAaMPQaZVsAja+x/lYKAzper5BS6s6m4UwpdbtbAVQwxnQxxqxzPPh7oTGmsDGmDBnB7RnHmbZmjjp3GWNWG2MO3OBZtOeNMRuMMVsdk4pijCljjNlljPnOGLPDGDPfGOPpWFfPUTbSGPOR4wyfO/AW0NexvK9j89WMMUsdbXkqpz8cpdTtR8OZUuq25Xj+YAcyniaxEmjoePD3b8BIEYkGxgCfOs60rXBULQo0BToD719nH22BikB9Ms581TXG3OVYXRH4SkRCgXNAL8fyH4AhIhIG2ABEJA14Dfjd0ZbfHWWrAO0c23/d8VxZpdQdzPX6RZRSKs/xNMZEOl6vIONZrJWB3x0PL3YHDl6j/p8iYgd2GmMKX2dfbR1/Njve+5ARyg6T8VDri+3YCJQxxvgDviKyxrF8Ihkh8Gr+FpFUINUYcxIoTMaDzJVSdygNZ0qp21Gy46xUJmPMaOATEZnhePbnG9eon5q16nX2ZYD3ROTbS/ZX5pLt2ADP62zrem2xob+Xlbrj6WVNpVR+4QfEOl4PzrI8AfD9D9udBzxojPEBMMYUN8YUulphETkHJBhjGjgW9cvBtiil7gAazpRS+cUbwBRjzEbgdJblM4Eel9wQcMNEZD4ZlybXGGO2AVO5fsB6CPjOcenVG4h3LF9Cxg0AWW8IUEqpbIyIOLsNSimVrxhjfEQk0fH6RaCoiDzt5GYppW4TOrZBKaVyXidjzCgyfsceAu53bnOUUrcTPXOmlFJKKZWH6JgzpZRSSqk8RMOZUkoppVQeouFMKaWUUioP0XCmlFJKKZWHaDhTSimllMpDNJwppZRSSuUh/w8lHpCtWvBwwAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "seed_idx = list(range (10, max_seeds +1, 10))\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "\n", + "for i in range(len(data)):\n", + " plt.plot(seed_idx, time_algo_cu[i], label = names[i])\n", + "\n", + "\n", + "plt.title('Runtime vs. Number of Seeds')\n", + "plt.xlabel('Number of Seeds')\n", + "plt.ylabel('Runtime')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4094" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del time_algo_cu\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 3: Multi-seed versus Sequential\n", + "This test uses a single files since sequential execution is slow" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/coPapersCiteseer.mtx...\n", + "\t434,102 nodes, 16,036,720 edges\n" + ] + } + ], + "source": [ + "G = read_and_create('./data/coPapersCiteseer.mtx')\n", + "nodes = G.nodes().to_array().tolist()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "rw_depth = 4\n", + "max_seeds = 100\n", + "num_nodes = G.number_of_nodes()\n", + "runtime_seq = [0] * max_seeds" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# sequenctial = so also get a single random seed\n", + "for i in range (max_seeds) :\n", + " for j in range(i):\n", + " seeds = random.sample(nodes, 1)\n", + " t = run_rw(G, seeds, rw_depth)\n", + " runtime_seq[i] = runtime_seq[i] + t" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "runtime = [None] * max_seeds\n", + "\n", + "for i in range (max_seeds) :\n", + " seeds = random.sample(nodes, i+1)\n", + " t = run_rw(G, seeds, rw_depth)\n", + " runtime[i] = t" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "seed_idx = list(range(1,max_seeds +1))\n", + "\n", + "\n", + "plt.figure(figsize=(7,4))\n", + "plt.plot(seed_idx, runtime, label = \"multiple seeds\")\n", + "plt.plot(seed_idx, runtime_seq, label = \"sequential\")\n", + "\n", + "\n", + "plt.title('Runtime vs. Number of Seeds')\n", + "plt.xlabel('Number of Seeds')\n", + "plt.ylabel('Runtime')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----\n", + "Copyright (c) 2021, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/cugraph_benchmarks/random_walk_perf.ipynb b/notebooks/cugraph_benchmarks/random_walk_perf.ipynb new file mode 100644 index 000000000..738298767 --- /dev/null +++ b/notebooks/cugraph_benchmarks/random_walk_perf.ipynb @@ -0,0 +1,621 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Walk Performance\n", + "# Skip notebook test¶ \n", + "\n", + "Random walk performance is governed by the length of the paths to find, the number of seeds, and the size or structure of the graph.\n", + "This benchmark will use several test graphs of increasingly larger sizes. While not even multiples in scale, the four test graphs should give an indication of how well Random Walk performs as data size increases. \n", + "\n", + "### Test Data\n", + "Users must run the _dataPrep.sh_ script before running this notebook so that the test files are downloaded\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "| ---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the modules\n", + "import cugraph\n", + "import cudf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# system and other\n", + "import gc\n", + "import os\n", + "import time\n", + "import random\n", + "\n", + "# MTX file reader\n", + "from scipy.io import mmread\n", + "\n", + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import matplotlib\n", + "except ModuleNotFoundError:\n", + " os.system('pip install matplotlib')\n", + "\n", + "import matplotlib.pyplot as plt; plt.rcdefaults()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import pybind11\n", + "except ModuleNotFoundError:\n", + " os.system('pip install pybind11')\n", + " \n", + "import pybind11" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import walker\n", + "except ModuleNotFoundError:\n", + " os.system('pip install graph-walker')\n", + "\n", + "import walker" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Test File\n", + "data = {\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the data and create a graph" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Data reader - the file format is MTX, so we will use the reader from SciPy\n", + "def read_data(datafile):\n", + " print('Reading ' + str(datafile) + '...')\n", + " M = mmread(datafile).asfptype()\n", + "\n", + " _gdf = cudf.DataFrame()\n", + " _gdf['src'] = M.row\n", + " _gdf['dst'] = M.col\n", + " _gdf['wt'] = 1.0\n", + " \n", + " return _gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def create_cu_ugraph(_df):\n", + " _g = cugraph.Graph()\n", + " _g.from_cudf_edgelist(_df, source='src', destination='dst', edge_attr='wt', renumber=False)\n", + " return _g" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def create_nx_ugraph(_df):\n", + " _gnx = nx.from_pandas_edgelist(_df, source='src', target='dst', edge_attr='wt', create_using=nx.Graph)\n", + " return _gnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the call to RandomWalk\n", + "We are only interested in the runtime, so throw away the results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def run_cu_rw(_G, _seeds, _depth):\n", + " t1 = time.time()\n", + " _, _ = cugraph.random_walks(_G, _seeds, _depth)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def run_wk_rw(_G, _seeds, _depth):\n", + " t1 = time.time()\n", + " _ = walker.random_walks(_G, n_walks=1, walk_len=_depth, start_nodes=_seeds)\n", + " t2 = time.time() - t1\n", + " return t2 \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 1: Runtime versus path depth" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=2.23s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=2.48s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=2.02s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=2.31s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=2.01s\n", + "update i\n", + "Reading ./data/dblp-2010.mtx...\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=4.21s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=4.03s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=3.59s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=3.95s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=3.68s\n", + "update i\n", + "Reading ./data/coPapersCiteseer.mtx...\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=59.64s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=49.43s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=47.45s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=54.66s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=46.96s\n", + "update i\n", + "Reading ./data/as-Skitter.mtx...\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=53.14s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=44.36s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=46.38s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=41.96s\n", + "\tcuGraph\n", + "\tWalkerRandom walks - T=53.18s\n", + "update i\n" + ] + } + ], + "source": [ + "# some parameters\n", + "max_depth = 6\n", + "num_seeds = 100\n", + "\n", + "# arrays to capture performance gains\n", + "names = []\n", + "\n", + "# Two dimension data\n", + "time_algo_cu = [] # will be two dimensional\n", + "time_algo_wk = [] # will be two dimensional\n", + "\n", + "i = 0\n", + "for k,v in data.items():\n", + " time_algo_cu.append([])\n", + " time_algo_wk.append([])\n", + " \n", + " # Saved the file Name\n", + " names.append(k)\n", + "\n", + " # read data\n", + " gdf = read_data(v)\n", + " pdf = gdf.to_pandas()\n", + " \n", + " # Create the Graphs\n", + " Gcg = create_cu_ugraph(gdf)\n", + " Gnx = create_nx_ugraph(pdf)\n", + " \n", + " num_nodes = Gcg.number_of_nodes()\n", + " nodes = Gcg.nodes().to_array().tolist()\n", + "\n", + " seeds = random.sample(nodes, num_seeds)\n", + "\n", + " for j in range (2, max_depth+1) :\n", + " print(\"\\tcuGraph\")\n", + " tc = run_cu_rw(Gcg, seeds, j)\n", + " time_algo_cu[i].append(tc)\n", + " \n", + " print(\"\\tWalker\", end='')\n", + " tw = run_wk_rw(Gnx, seeds, j)\n", + " time_algo_wk[i].append(tw)\n", + "\n", + " # update i\n", + " i = i + 1\n", + " print(\"update i\")\n", + " \n", + " del Gcg\n", + " del Gnx\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "seed_idx = list(range(2,max_depth +1))\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "\n", + "for i in range(len(data)):\n", + " plt.plot(seed_idx, time_algo_cu[i], label = (str(names[i] + \"-cuGraph\")))\n", + "\n", + " plt.plot(seed_idx, time_algo_wk[i], label = (str(names[i] + \"-walker\")), linestyle='-.')\n", + "\n", + "\n", + "plt.title(f'Runtime vs. Path Length ({num_seeds} Seeds)')\n", + "plt.xlabel('Path length')\n", + "plt.ylabel('Runtime')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5164" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del time_algo_cu\n", + "del time_algo_wk\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 2: Runtime Speedup versus number of seeds\n", + "The number of seeds will be increased over a range in increments of 50. \n", + "The runtime will be the sum of runtime per increment. Increaing number of seeds by 1 would make for very long execution times " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\t.Random walks - T=2.28s\n", + ".Random walks - T=2.29s\n", + ".Random walks - T=2.28s\n", + ".Random walks - T=2.21s\n", + ".Random walks - T=1.95s\n", + ".Random walks - T=2.38s\n", + ".Random walks - T=2.00s\n", + ".Random walks - T=2.19s\n", + ".Random walks - T=1.99s\n", + ".Random walks - T=2.40s\n", + ".Random walks - T=1.95s\n", + ".Random walks - T=2.17s\n", + ".Random walks - T=1.95s\n", + ".Random walks - T=2.39s\n", + ".Random walks - T=1.97s\n", + ".Random walks - T=2.23s\n", + ".Random walks - T=2.63s\n", + ".Random walks - T=4.08s\n", + ".Random walks - T=3.44s\n", + ".Random walks - T=3.77s\n", + " \n", + "Reading ./data/dblp-2010.mtx...\n", + "\t.Random walks - T=6.61s\n", + ".Random walks - T=6.57s\n", + ".Random walks - T=6.48s\n", + ".Random walks - T=6.69s\n", + ".Random walks - T=6.11s\n", + ".Random walks - T=6.18s\n", + ".Random walks - T=4.98s\n", + ".Random walks - T=5.64s\n", + ".Random walks - T=3.83s\n", + ".Random walks - T=4.28s\n", + ".Random walks - T=4.34s\n", + ".Random walks - T=4.14s\n", + ".Random walks - T=3.79s\n", + ".Random walks - T=4.37s\n", + ".Random walks - T=4.00s\n", + ".Random walks - T=3.66s\n", + ".Random walks - T=4.01s\n", + ".Random walks - T=3.67s\n", + ".Random walks - T=4.32s\n", + ".Random walks - T=3.70s\n", + " \n", + "Reading ./data/coPapersCiteseer.mtx...\n", + "\t.Random walks - T=56.64s\n", + ".Random walks - T=52.26s\n", + ".Random walks - T=45.66s\n", + ".Random walks - T=48.81s\n", + ".Random walks - T=56.16s\n", + ".Random walks - T=56.73s\n", + ".Random walks - T=45.43s\n", + ".Random walks - T=44.96s\n", + ".Random walks - T=51.77s\n", + ".Random walks - T=58.39s\n", + ".Random walks - T=43.35s\n", + ".Random walks - T=42.89s\n", + ".Random walks - T=57.96s\n", + ".Random walks - T=45.03s\n", + ".Random walks - T=64.27s\n", + ".Random walks - T=52.57s\n", + ".Random walks - T=46.91s\n", + ".Random walks - T=55.62s\n", + ".Random walks - T=46.85s\n", + ".Random walks - T=44.84s\n", + " \n", + "Reading ./data/as-Skitter.mtx...\n", + "\t.Random walks - T=51.36s\n", + ".Random walks - T=52.06s\n", + ".Random walks - T=44.91s\n", + ".Random walks - T=49.73s\n", + ".Random walks - T=47.45s\n", + ".Random walks - T=52.21s\n", + ".Random walks - T=47.65s\n", + ".Random walks - T=45.49s\n", + ".Random walks - T=47.84s\n", + ".Random walks - T=43.48s\n", + ".Random walks - T=45.67s\n", + ".Random walks - T=45.75s\n", + ".Random walks - T=55.03s\n", + ".Random walks - T=46.39s\n", + ".Random walks - T=50.64s\n", + ".Random walks - T=43.87s\n", + ".Random walks - T=40.98s\n", + ".Random walks - T=49.42s\n", + ".Random walks - T=51.94s\n", + ".Random walks - T=49.28s\n", + " \n" + ] + } + ], + "source": [ + "# some parameters\n", + "rw_depth = 4\n", + "max_seeds = 1000\n", + "\n", + "# arrays to capture performance gains\n", + "names = []\n", + "\n", + "# Two dimension data\n", + "time_algo_cu = [] # will be two dimensional\n", + "time_algo_wk = [] # will be two dimensional\n", + "perf = [] # will be two dimensional\n", + "\n", + "i = 0\n", + "for k,v in data.items():\n", + " time_algo_cu.append([])\n", + " time_algo_wk.append([])\n", + " perf.append([])\n", + " \n", + " # Saved the file Name\n", + " names.append(k)\n", + "\n", + " # read data\n", + " gdf = read_data(v)\n", + " pdf = gdf.to_pandas()\n", + " \n", + " # Create the Graphs\n", + " Gcg = create_cu_ugraph(gdf)\n", + " Gnx = create_nx_ugraph(pdf)\n", + " \n", + " num_nodes = Gcg.number_of_nodes()\n", + " nodes = Gcg.nodes().to_array().tolist()\n", + " \n", + " print('\\t', end='')\n", + " for j in range (50, max_seeds +1, 50) :\n", + " print('.', end='')\n", + " seeds = random.sample(nodes, j)\n", + " tc = run_cu_rw(Gcg, seeds, rw_depth)\n", + " tw = run_wk_rw(Gnx, seeds, rw_depth)\n", + " \n", + " time_algo_cu[i].append(tc)\n", + " time_algo_wk[i].append(tw) \n", + " perf[i].append(tw/tc)\n", + " \n", + "\n", + " # update i\n", + " i = i + 1\n", + " print(\" \")\n", + " \n", + " del Gcg\n", + " del Gnx\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "seed_idx = list(range (50, max_seeds +1, 50))\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "\n", + "for i in range(len(data)):\n", + " plt.plot(seed_idx, perf[i], label = names[i] )\n", + "\n", + "plt.title('Speedup vs. Number of Seeds')\n", + "plt.xlabel('Number of Seeds')\n", + "plt.ylabel('Speedup')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3786" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del time_algo_cu\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----\n", + "Copyright (c) 2021, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_dev", + "language": "python", + "name": "cugraph_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index 3c6da55ab..a6eeeb65c 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -22,6 +22,7 @@ "| Triangle Counting | X | |\n", "\n", "### Test Data\n", + "Users must run the _dataPrep.sh_ script before running this notebook so that the test files are downloaded\n", "\n", "| File Name | Num of Vertices | Num of Edges |\n", "| ---------------------- | --------------: | -----------: |\n", @@ -594,7 +595,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.8" } }, "nbformat": 4, From e526bcbc42693d485050cb5386014c90aa76d9c6 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Mon, 10 May 2021 08:53:26 -0500 Subject: [PATCH 141/384] Bindings for RMAT (#1573) Bindings for RMAT Bindings for the list of RMAT graphs includes some quick fixes to address some sphinx doc-building warnings closes #1473 Authors: - Joseph Nke (https://github.com/jnke2016) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Andrei Schaffer (https://github.com/aschaffer) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/1573 --- docs/cugraph/source/api.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/cugraph/source/api.rst b/docs/cugraph/source/api.rst index e2c2c19cf..adcf33d11 100644 --- a/docs/cugraph/source/api.rst +++ b/docs/cugraph/source/api.rst @@ -10,7 +10,7 @@ Structure Graph ----- -.. autoclass:: cugraph.structure.graph.Graph +.. autoclass:: cugraph.structure.graph_classes.Graph :members: :undoc-members: @@ -143,7 +143,7 @@ Core Number .. automodule:: cugraph.cores.core_number :members: :undoc-members: - + K-Core ------ @@ -196,7 +196,7 @@ Pagerank (MG) .. automodule:: cugraph.dask.link_analysis.pagerank :members: pagerank - :undoc-members: + :undoc-members: Link Prediction From 0b2175c152794039fc69d8a05b5115b926d5fc2b Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Tue, 25 May 2021 12:04:52 -0400 Subject: [PATCH 142/384] =?UTF-8?q?address=20'ValueError:=20Series=20conta?= =?UTF-8?q?ins=20NULL=20values'=20from=20from=5Fcudf=5Fedge=E2=80=A6=20(#1?= =?UTF-8?q?610)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `G.from_cudf_edgelist` after the `cudf.read_csv` fails with the following error - ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in 1 t_start_build_sg = time.perf_counter() 2 G = cugraph.DiGraph() ----> 3 G.from_cudf_edgelist(e_list, source='src', destination='dst') 4 t_stop_build_sg = time.perf_counter() /opt/conda/envs/rapids/lib/python3.7/site-packages/cugraph/structure/graph_classes.py in from_cudf_edgelist(self, input_df, source, destination, edge_attr, renumber) 127 destination=destination, 128 edge_attr=edge_attr, --> 129 renumber=renumber) 130 131 def from_cudf_adjlist(self, offset_col, index_col, value_col=None): /opt/conda/envs/rapids/lib/python3.7/site-packages/cugraph/structure/graph_implementation/simpleGraph.py in __from_edgelist(self, input_df, source, destination, edge_attr, renumber) 171 source_col, dest_col = symmetrize( 172 source_col, dest_col, multi=self.properties.multi_edge, --> 173 symmetrize=not self.properties.directed) 174 175 self.edgelist = simpleGraphImpl.EdgeList(source_col, dest_col, /opt/conda/envs/rapids/lib/python3.7/site-packages/cugraph/structure/symmetrize.py in symmetrize(source_col, dest_col, value_col, multi, symmetrize) 200 ) 201 csg.null_check(source_col) --> 202 csg.null_check(dest_col) 203 if value_col is not None: 204 if isinstance(value_col, cudf.Series): /opt/conda/envs/rapids/lib/python3.7/site-packages/cugraph/structure/graph_classes.py in null_check(col) 23 def null_check(col): 24 if col.null_count != 0: ---> 25 raise ValueError("Series contains NULL values") 26 27 ValueError: Series contains NULL values ``` the header from the input is turning into NULLs - ``` $ head soc-LiveJournal1.txt # Directed graph (each unordered pair of nodes is saved once): soc-LiveJournal1.txt # Directed LiveJournal friednship social network # Nodes: 4847571 Edges: 68993773 # FromNodeId ToNodeId 0 1 0 2 0 3 ``` `e_list` ||src|dst |---|---|--- |0|0|NA |1|0|NA |2|0|NA |3|0|0 |4|0|1 ... this issue has existed since at least 0.16 and may warrant further test coverage for cudf's read_csv. Authors: - Matthew Farrellee (https://github.com/mattf) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/1610 --- notebooks/demo/batch_betweenness.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/demo/batch_betweenness.ipynb b/notebooks/demo/batch_betweenness.ipynb index e2ad83ff1..885d26c95 100644 --- a/notebooks/demo/batch_betweenness.ipynb +++ b/notebooks/demo/batch_betweenness.ipynb @@ -138,7 +138,7 @@ "outputs": [], "source": [ "t_start_read_sg = time.perf_counter()\n", - "e_list = cudf.read_csv(input_data_path, delimiter='\\t', names=['src', 'dst'], dtype=['int32', 'int32'])\n", + "e_list = cudf.read_csv(input_data_path, delimiter='\\t', names=['src', 'dst'], dtype=['int32', 'int32'], comment='#')\n", "t_stop_read_sg = time.perf_counter()" ] }, From f551f9dc6826a09d0518392857383512c4b1cebb Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Fri, 4 Jun 2021 15:46:46 -0500 Subject: [PATCH 143/384] Made call to cugraph.random_walks() ignore the unused return value (#1648) Made call to cugraph.random_walks() ignore the unused return value. Manually tested in browser with jupyterlab. Author: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/1648 --- notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb b/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb index be50c0754..65cf9fb59 100644 --- a/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb @@ -124,7 +124,8 @@ "source": [ "def run_rw(_G, _seeds, _depth):\n", " t1 = time.time()\n", - " _, _ = cugraph.random_walks(_G, _seeds, _depth)\n", + " # cugraph.random_walks() returns a 3-tuple, which is being ignored here.\n", + " cugraph.random_walks(_G, _seeds, _depth)\n", " t2 = time.time() - t1\n", " return t2" ] @@ -450,7 +451,7 @@ "metadata": {}, "outputs": [], "source": [ - "# sequenctial = so also get a single random seed\n", + "# sequential = so also get a single random seed\n", "for i in range (max_seeds) :\n", " for j in range(i):\n", " seeds = random.sample(nodes, 1)\n", From 40fa3018bd9762b35fde1f4cb30d7e76bf541859 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 9 Jun 2021 14:06:45 -0700 Subject: [PATCH 144/384] REL v21.06.00 release --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index a4633d04f..66c0cbc6b 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -80,9 +80,9 @@ # built documents. # # The short X.Y version. -version = '0.20' +version = '21.06' # The full version, including alpha/beta/rc tags. -release = '0.20.0' +release = '21.06.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From fefc22008401f9b609e3d48d5d154b5c449fe6de Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 15 Jul 2021 16:59:20 -0400 Subject: [PATCH 145/384] DOC v21.10 Updates --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index a4633d04f..f2d60f53b 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -80,9 +80,9 @@ # built documents. # # The short X.Y version. -version = '0.20' +version = '21.10' # The full version, including alpha/beta/rc tags. -release = '0.20.0' +release = '21.10.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 6fbfab3c8f0af75edb53836d87cc33dce7e1aa0d Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 20 Jul 2021 14:54:36 -0400 Subject: [PATCH 146/384] Doc updates (#1719) Added generator to API docs Removed reference to 11.2 environment Updated list of algorithms Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/1719 --- docs/cugraph/source/api.rst | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/docs/cugraph/source/api.rst b/docs/cugraph/source/api.rst index adcf33d11..a07044ecf 100644 --- a/docs/cugraph/source/api.rst +++ b/docs/cugraph/source/api.rst @@ -133,6 +133,12 @@ Connected Components :members: :undoc-members: +Connected Components (MG) +-------------------- + +.. automodule:: cugraph.dask.components.connectivity + :members: + :undoc-members: Cores ===== @@ -294,6 +300,18 @@ Maximum Spanning Tree :noindex: +Generator +========= + + RMAT + --------------------- + +.. automodule:: cugraph.generators +:members: rmat +:undoc-members: +:noindex: + + DASK MG Helper functions =========================== From 386fb964fdd6ca12789bd6f4802463b567f5f6c1 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Thu, 29 Jul 2021 09:09:02 -0400 Subject: [PATCH 147/384] Docs for RMAT (#1735) Fixed indentations so that docs appear correctly Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/1735 --- docs/cugraph/source/api.rst | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/docs/cugraph/source/api.rst b/docs/cugraph/source/api.rst index a07044ecf..81c5e3e4e 100644 --- a/docs/cugraph/source/api.rst +++ b/docs/cugraph/source/api.rst @@ -302,14 +302,13 @@ Maximum Spanning Tree Generator ========= - - RMAT - --------------------- + +RMAT +--------------------- .. automodule:: cugraph.generators -:members: rmat -:undoc-members: -:noindex: + :members: rmat + :undoc-members: DASK MG Helper functions From 2419b7cee3d0a3cacf91ddd96641be52e2e20950 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 4 Aug 2021 11:47:59 -0700 Subject: [PATCH 148/384] REL v21.08.00 release --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 66c0cbc6b..1198e2213 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -80,9 +80,9 @@ # built documents. # # The short X.Y version. -version = '21.06' +version = '21.08' # The full version, including alpha/beta/rc tags. -release = '21.06.00' +release = '21.08.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 76764424164905ea676075fd4291dca9f7f4975a Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Fri, 6 Aug 2021 13:28:43 -0700 Subject: [PATCH 149/384] REL v21.08.01 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 1198e2213..e570fa56c 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -82,7 +82,7 @@ # The short X.Y version. version = '21.08' # The full version, including alpha/beta/rc tags. -release = '21.08.00' +release = '21.08.01' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 64b9be60fb2059e550d9a58372990cf689276581 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Mon, 9 Aug 2021 09:54:26 -0400 Subject: [PATCH 150/384] updating notebooks (#1761) updating notebooks Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/1761 --- notebooks/demo/uvm.ipynb | 60 +++++++++++++++++++++++----------------- 1 file changed, 35 insertions(+), 25 deletions(-) diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index 8fa2b08b6..08b04a99c 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -20,11 +20,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Data\n", + "### Data\n", "We will be analyzing **1.47 billion social relations** on 41.7 million user profiles from the Twitter dataset. The CSV file is 26GB and was collected in :
\n", "*What is Twitter, a social network or a news media? Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010.*
\n", "\n", - "Notice that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. While we are not limited by the device memory size in this case, the whole system (host+device memory) should still have at least 80GB of memory available" + "__Notice__ that the memory requirement to read this 26GB dataset is already bigger than the memory of a single GPU. While we are not limited by the device memory size in this case, the whole system (host+device memory) should still have at least 84GB of memory available. Additionally, the more that host memory is used, the slower the process will be. \n", + "\n", + "If you run into memory issues (kernel crashing), then you can limit the amount of data loaded using the _nrows_ option with _read_csv_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize RMM\n", + "RAPIDS Memory Manager (RMM) is a central place for all device memory allocations in RAPIDS libraries. Using RMM in python code is straightforward. Simply `import rmm` and configure RMM options to be used before making any call to a RAPIDS library." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import rmm " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Set RMM to allocate all memory as managed memory (cudaMallocManaged underlying allocator)\n", + "rmm.mr.set_current_device_resource(rmm.mr.ManagedMemoryResource())\n", + "assert(rmm.is_initialized())" ] }, { @@ -43,7 +73,6 @@ "source": [ "# Import needed libraries. We recommend using cugraph_dev env through conda\n", "import time\n", - "import rmm \n", "import cudf \n", "import cugraph " ] @@ -105,25 +134,6 @@ "input_data_path = data_dir+fn" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize RMM\n", - "RAPIDS Memory Manager (RMM) is a central place for all device memory allocations in RAPIDS libraries. Using RMM in python code is straightforward. Simply `import rmm` and configure RMM options to be used before making any call to a RAPIDS library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Set RMM to allocate all memory as managed memory (cudaMallocManaged underlying allocator)\n", - "rmm.reinitialize(managed_memory=True)\n", - "assert(rmm.is_initialized())" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -280,9 +290,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "cugraph_dev", "language": "python", - "name": "python3" + "name": "cugraph_dev" }, "language_info": { "codemirror_mode": { @@ -294,7 +304,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.10" } }, "nbformat": 4, From 82f50386b1a028b15ebc2e5e88dfbeb41ca113f6 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Mon, 16 Aug 2021 09:29:59 -0700 Subject: [PATCH 151/384] REL v21.08.02 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index e570fa56c..98b3aeec5 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -82,7 +82,7 @@ # The short X.Y version. version = '21.08' # The full version, including alpha/beta/rc tags. -release = '21.08.01' +release = '21.08.02' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 9c9ef95e0d9c5f5d100de36da5c3cb3aef1be63c Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Tue, 17 Aug 2021 07:01:00 -0700 Subject: [PATCH 152/384] REL v21.08.03 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 98b3aeec5..2b4c8bdb1 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -82,7 +82,7 @@ # The short X.Y version. version = '21.08' # The full version, including alpha/beta/rc tags. -release = '21.08.02' +release = '21.08.03' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 0e83e8fa722282ed2af8a8404bfcd2525c29310e Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Mon, 23 Aug 2021 14:56:13 -0500 Subject: [PATCH 153/384] Change python docs to pydata theme (#1785) This PR changes docs theme from sphinx_rtd_theme to use pydata-theme: https://pydata-sphinx-theme.readthedocs.io/en/latest/ Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/1785 --- .gitignore | 3 + docs/cugraph/Makefile | 5 +- docs/cugraph/README.md | 21 +- docs/cugraph/requirement.txt | 4 - docs/cugraph/source/_static/copybutton.css | 42 --- .../source/_static/copybutton_pydocs.js | 65 ---- docs/cugraph/source/_static/example_mod.js | 61 ---- docs/cugraph/source/_static/params.css | 21 ++ docs/cugraph/source/api.rst | 324 ------------------ docs/cugraph/source/api_docs/centrality.rst | 29 ++ docs/cugraph/source/api_docs/community.rst | 81 +++++ docs/cugraph/source/api_docs/components.rst | 25 ++ docs/cugraph/source/api_docs/cores.rst | 21 ++ .../source/{ => api_docs}/dask-cugraph.rst | 0 docs/cugraph/source/api_docs/generator.rst | 14 + .../source/api_docs/helper_functions.rst | 16 + docs/cugraph/source/api_docs/index.rst | 27 ++ docs/cugraph/source/api_docs/layout.rst | 14 + .../source/api_docs/linear_assignment.rst | 13 + .../cugraph/source/api_docs/link_analysis.rst | 29 ++ .../source/api_docs/link_prediction.rst | 34 ++ docs/cugraph/source/api_docs/sampling.rst | 13 + docs/cugraph/source/api_docs/structure.rst | 68 ++++ docs/cugraph/source/api_docs/traversal.rst | 49 +++ docs/cugraph/source/api_docs/tree.rst | 22 ++ .../source/{ => basics}/cugraph_blogs.rst | 0 .../source/{ => basics}/cugraph_intro.md | 0 .../source/{ => basics}/cugraph_ref.rst | 0 docs/cugraph/source/basics/index.rst | 12 + .../source/{ => basics}/nx_transition.rst | 4 +- docs/cugraph/source/conf.py | 22 +- docs/cugraph/source/index.rst | 10 +- 32 files changed, 525 insertions(+), 524 deletions(-) delete mode 100644 docs/cugraph/requirement.txt delete mode 100644 docs/cugraph/source/_static/copybutton.css delete mode 100644 docs/cugraph/source/_static/copybutton_pydocs.js delete mode 100644 docs/cugraph/source/_static/example_mod.js delete mode 100644 docs/cugraph/source/api.rst create mode 100644 docs/cugraph/source/api_docs/centrality.rst create mode 100644 docs/cugraph/source/api_docs/community.rst create mode 100644 docs/cugraph/source/api_docs/components.rst create mode 100644 docs/cugraph/source/api_docs/cores.rst rename docs/cugraph/source/{ => api_docs}/dask-cugraph.rst (100%) create mode 100644 docs/cugraph/source/api_docs/generator.rst create mode 100644 docs/cugraph/source/api_docs/helper_functions.rst create mode 100644 docs/cugraph/source/api_docs/index.rst create mode 100644 docs/cugraph/source/api_docs/layout.rst create mode 100644 docs/cugraph/source/api_docs/linear_assignment.rst create mode 100644 docs/cugraph/source/api_docs/link_analysis.rst create mode 100644 docs/cugraph/source/api_docs/link_prediction.rst create mode 100644 docs/cugraph/source/api_docs/sampling.rst create mode 100644 docs/cugraph/source/api_docs/structure.rst create mode 100644 docs/cugraph/source/api_docs/traversal.rst create mode 100644 docs/cugraph/source/api_docs/tree.rst rename docs/cugraph/source/{ => basics}/cugraph_blogs.rst (100%) rename docs/cugraph/source/{ => basics}/cugraph_intro.md (100%) rename docs/cugraph/source/{ => basics}/cugraph_ref.rst (100%) create mode 100644 docs/cugraph/source/basics/index.rst rename docs/cugraph/source/{ => basics}/nx_transition.rst (99%) diff --git a/.gitignore b/.gitignore index c2498b35c..3afdcbccc 100644 --- a/.gitignore +++ b/.gitignore @@ -81,3 +81,6 @@ python/_external_repositories/ # created by Dask tests python/dask-worker-space + +# Sphinx docs & build artifacts +docs/cugraph/source/api_docs/api/* diff --git a/docs/cugraph/Makefile b/docs/cugraph/Makefile index 9c35aa6fc..330c06bed 100644 --- a/docs/cugraph/Makefile +++ b/docs/cugraph/Makefile @@ -4,7 +4,7 @@ # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build -SPHINXPROJ = cuGraph +SPHINXPROJ = cugraph SOURCEDIR = source BUILDDIR = build IMGDIR = images @@ -18,5 +18,4 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - cp -r $(SOURCEDIR)/$(IMGDIR) $(BUILDDIR)/html + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docs/cugraph/README.md b/docs/cugraph/README.md index 199e743cd..734867af9 100644 --- a/docs/cugraph/README.md +++ b/docs/cugraph/README.md @@ -1,25 +1,28 @@ # Building Documentation -A basic python environment with packages listed in `./requirement.txt` is -enough to build the docs. Doc generation does not get run by default +All prerequisite for building docs are in the cugraph development conda environment. +[See build instructions](../../SOURCEBUILD.md) on how to create the development conda environment -## Get additional dependency +## Steps to follow: -```bash -pip install -r requirement.txt -``` +In order to build the docs, we need the conda dev environment from cugraph and we need to build cugraph from source. + +1. Create a conda env and build cugraph from source. The dependencies to build rapids from source are installed in that conda environment, and then rapids is built and installed into the same environment. + +2. Once cugraph is built from source, navigate to `../docs/cugraph/`. If you have your documentation written and want to turn it into HTML, run makefile: -## Run makefile: ```bash +# most be in the /docs/cugraph directory make html ``` -Outputs to `build/html/index.html` +This should run Sphinx in your shell, and outputs to `build/html/index.html` + ## View docs web page by opening HTML in browser: -First navigate to `/build/html/` folder, i.e., `cd build/html` and then run the following command: +First navigate to `/build/html/` folder, and then run the following command: ```bash python -m http.server diff --git a/docs/cugraph/requirement.txt b/docs/cugraph/requirement.txt deleted file mode 100644 index f636fd879..000000000 --- a/docs/cugraph/requirement.txt +++ /dev/null @@ -1,4 +0,0 @@ -sphinx -sphinx_rtd_theme -numpydoc -ipython \ No newline at end of file diff --git a/docs/cugraph/source/_static/copybutton.css b/docs/cugraph/source/_static/copybutton.css deleted file mode 100644 index 5eef6e366..000000000 --- a/docs/cugraph/source/_static/copybutton.css +++ /dev/null @@ -1,42 +0,0 @@ -/* This contains code with copyright by the scikit-learn project, subject to -the license in /thirdparty/LICENSES/LICENSE.scikit_learn */ - -/* copybutton */ -/* Adds "Show/Hide Output" button to Examples */ - -.copybutton { - cursor: pointer; - position: absolute; - top: 0px; - right: 0px; - border: 1px solid rgb(221, 221, 221); - color: rgb(221, 221, 221); - font-family: monospace; - padding-left: 0.2rem; - padding-right: 0.2rem; -} - -div.highlight:hover span.copybutton::after { - background: #3F556B; - border-radius: 0.25rem; - color: white; - content: attr(title); - padding: 0.25rem; - position: absolute; - z-index: 98; - width: 100px; - font-size: 0.7rem; - top: 0; - right: 0; -} - -/* copy buttonn */ -div.highlight:hover span.copybutton { - background-color: #3F556B; - color: white; -} - -div.highlight:hover span.copybutton:hover { - background-color: #20252B; -} - diff --git a/docs/cugraph/source/_static/copybutton_pydocs.js b/docs/cugraph/source/_static/copybutton_pydocs.js deleted file mode 100644 index cec05777e..000000000 --- a/docs/cugraph/source/_static/copybutton_pydocs.js +++ /dev/null @@ -1,65 +0,0 @@ -$(document).ready(function() { - /* Add a [>>>] button on the top-right corner of code samples to hide - * the >>> and ... prompts and the output and thus make the code - * copyable. */ - var div = $('.highlight-python .highlight,' + - '.highlight-python3 .highlight,' + - '.highlight-pycon .highlight,' + - '.highlight-default .highlight'); - var pre = div.find('pre'); - - // get the styles from the current theme - pre.parent().parent().css('position', 'relative'); - var hide_text = 'Hide the prompts and output'; - var show_text = 'Show the prompts and output'; - var border_width = pre.css('border-top-width'); - var border_style = pre.css('border-top-style'); - var border_color = pre.css('border-top-color'); - var button_styles = { - 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', - 'border-color': border_color, 'border-style': border_style, - 'border-width': border_width, 'text-size': '75%', - 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '1.5em', - 'border-radius': '0 3px 0 0', - 'transition': "0.5s" - } - - // create and add the button to all the code blocks that contain >>> - div.each(function(index) { - var jthis = $(this); - if (jthis.find('.gp').length > 0) { - var button = $('>>>'); - button.css(button_styles) - button.attr('title', hide_text); - button.data('hidden', 'false'); - jthis.prepend(button); - } - // tracebacks (.gt) contain bare text elements that need to be - // wrapped in a span to work with .nextUntil() (see later) - jthis.find('pre:has(.gt)').contents().filter(function() { - return ((this.nodeType == 3) && (this.data.trim().length > 0)); - }).wrap(''); - }); - - // define the behavior of the button when it's clicked - $('.copybutton').click(function(e){ - e.preventDefault(); - var button = $(this); - if (button.data('hidden') === 'false') { - // hide the code output - button.parent().find('.go, .gp, .gt').hide(); - button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); - button.css('text-decoration', 'line-through'); - button.attr('title', show_text); - button.data('hidden', 'true'); - } else { - // show the code output - button.parent().find('.go, .gp, .gt').show(); - button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); - button.css('text-decoration', 'none'); - button.attr('title', hide_text); - button.data('hidden', 'false'); - } - }); -}); - diff --git a/docs/cugraph/source/_static/example_mod.js b/docs/cugraph/source/_static/example_mod.js deleted file mode 100644 index 77dc618a8..000000000 --- a/docs/cugraph/source/_static/example_mod.js +++ /dev/null @@ -1,61 +0,0 @@ -// This contains code with copyright by the scikit-learn project, subject to -// the license in /thirdparty/LICENSES/LICENSE.scikit_learn - -$(document).ready(function () { - /* Add a [>>>] button on the top-right corner of code samples to hide - * the >>> and ... prompts and the output and thus make the code - * copyable. */ - var div = $('.highlight-python .highlight,' + - '.highlight-python3 .highlight,' + - '.highlight-pycon .highlight,' + - '.highlight-default .highlight') - var pre = div.find('pre'); - - // get the styles from the current theme - pre.parent().parent().css('position', 'relative'); - var hide_text = 'Hide prompts and outputs'; - var show_text = 'Show prompts and outputs'; - - // create and add the button to all the code blocks that contain >>> - div.each(function (index) { - var jthis = $(this); - if (jthis.find('.gp').length > 0) { - var button = $('>>>'); - button.attr('title', hide_text); - button.data('hidden', 'false'); - jthis.prepend(button); - } - // tracebacks (.gt) contain bare text elements that need to be - // wrapped in a span to work with .nextUntil() (see later) - jthis.find('pre:has(.gt)').contents().filter(function () { - return ((this.nodeType == 3) && (this.data.trim().length > 0)); - }).wrap(''); - }); - - // define the behavior of the button when it's clicked - $('.copybutton').click(function (e) { - e.preventDefault(); - var button = $(this); - if (button.data('hidden') === 'false') { - // hide the code output - button.parent().find('.go, .gp, .gt').hide(); - button.next('pre') - .find('.gt') - .nextUntil('.gp, .go') - .css('visibility', 'hidden'); - button.css('text-decoration', 'line-through'); - button.attr('title', show_text); - button.data('hidden', 'true'); - } else { - // show the code output - button.parent().find('.go, .gp, .gt').show(); - button.next('pre') - .find('.gt') - .nextUntil('.gp, .go') - .css('visibility', 'visible'); - button.css('text-decoration', 'none'); - button.attr('title', hide_text); - button.data('hidden', 'false'); - } - }); -}); \ No newline at end of file diff --git a/docs/cugraph/source/_static/params.css b/docs/cugraph/source/_static/params.css index dc5cb9640..b57bcdb7c 100644 --- a/docs/cugraph/source/_static/params.css +++ b/docs/cugraph/source/_static/params.css @@ -7,3 +7,24 @@ margin: 0.5em; content: ":"; } + +:root { + + --pst-color-active-navigation: 114, 83, 237; + --pst-color-navbar-link: 77, 77, 77; + --pst-color-navbar-link-hover: var(--pst-color-active-navigation); + --pst-color-navbar-link-active: var(--pst-color-active-navigation); + --pst-color-sidebar-link: 77, 77, 77; + --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); + --pst-color-sidebar-link-active: var(--pst-color-active-navigation); + --pst-color-sidebar-expander-background-hover: 244, 244, 244; + --pst-color-sidebar-caption: 77, 77, 77; + --pst-color-toc-link: 119, 117, 122; + --pst-color-toc-link-hover: var(--pst-color-active-navigation); + --pst-color-toc-link-active: var(--pst-color-active-navigation); + +} + +.special-table td, .special-table th { + border: 1px solid #dee2e6; +} \ No newline at end of file diff --git a/docs/cugraph/source/api.rst b/docs/cugraph/source/api.rst deleted file mode 100644 index 81c5e3e4e..000000000 --- a/docs/cugraph/source/api.rst +++ /dev/null @@ -1,324 +0,0 @@ -~~~~~~~~~~~~~~~~~~~~~ -cuGraph API Reference -~~~~~~~~~~~~~~~~~~~~~ - - - -Structure -========= - -Graph ------ - -.. autoclass:: cugraph.structure.graph_classes.Graph - :members: - :undoc-members: - - -Symmetrize ----------- - -.. automodule:: cugraph.structure.symmetrize - :members: - :undoc-members: - -Conversion from Other Formats ------------------------------ - -.. automodule:: cugraph.structure.convert_matrix - :members: - :undoc-members: - - -Centrality -========== - -Betweenness Centrality ----------------------- - -.. automodule:: cugraph.centrality.betweenness_centrality - :members: - :undoc-members: - -Katz Centrality ---------------- - -.. automodule:: cugraph.centrality.katz_centrality - :members: - :undoc-members: - - -Katz Centrality (MG) --------------------- - -.. automodule:: cugraph.dask.centrality.katz_centrality - :members: - :undoc-members: - -Community -========= - -EgoNet ------------------------------------- - -.. automodule:: cugraph.community.egonet - :members: - :undoc-members: - -Ensemble clustering for graphs (ECG) ------------------------------------- - -.. automodule:: cugraph.community.ecg - :members: - :undoc-members: - -K-Truss -------- - -.. automodule:: cugraph.community.ktruss_subgraph - :members: - :undoc-members: - -Leiden -------- - -.. automodule:: cugraph.community.leiden - :members: - :undoc-members: - -Louvain -------- - -.. automodule:: cugraph.community.louvain - :members: - :undoc-members: - -Louvain (MG) ------------- - -.. automodule:: cugraph.dask.community.louvain - :members: - :undoc-members: - - -Spectral Clustering -------------------- - -.. automodule:: cugraph.community.spectral_clustering - :members: - :undoc-members: - -Subgraph Extraction -------------------- - -.. automodule:: cugraph.community.subgraph_extraction - :members: - :undoc-members: - -Triangle Counting ------------------ - -.. automodule:: cugraph.community.triangle_count - :members: - :undoc-members: - - -Components -========== - -Connected Components --------------------- - -.. automodule:: cugraph.components.connectivity - :members: - :undoc-members: - -Connected Components (MG) --------------------- - -.. automodule:: cugraph.dask.components.connectivity - :members: - :undoc-members: - -Cores -===== - -Core Number ------------ - -.. automodule:: cugraph.cores.core_number - :members: - :undoc-members: - -K-Core ------- - -.. automodule:: cugraph.cores.k_core - :members: - :undoc-members: - - -Layout -====== - -Force Atlas 2 -------------- - -.. automodule:: cugraph.layout.force_atlas2 - :members: - :undoc-members: - - -Linear Assignment -================= - -Hungarian -------------- - -.. automodule:: cugraph.linear_assignment.hungarian - :members: - :undoc-members: - - -Link Analysis -============= - -HITS ---------- - -.. automodule:: cugraph.link_analysis.hits - :members: - :undoc-members: - -Pagerank ---------- - -.. automodule:: cugraph.link_analysis.pagerank - :members: - :undoc-members: - -Pagerank (MG) -------------- - -.. automodule:: cugraph.dask.link_analysis.pagerank - :members: pagerank - :undoc-members: - - -Link Prediction -=============== - -Jaccard Coefficient -------------------- - -.. automodule:: cugraph.link_prediction.jaccard - :members: - :undoc-members: - -.. automodule:: cugraph.link_prediction.wjaccard - :members: - :undoc-members: - -Overlap Coefficient -------------------- - -.. automodule:: cugraph.link_prediction.overlap - :members: - :undoc-members: - -.. automodule:: cugraph.link_prediction.woverlap - :members: - :undoc-members: - - -Sampling -======== - -Random Walks ------------- - -.. automodule:: cugraph.sampling.random_walks - :members: - :undoc-members: - - -Traversal -========= - -Breadth-first-search --------------------- - -.. automodule:: cugraph.traversal.bfs - :members: - :undoc-members: - -Breadth-first-search (MG) -------------------------- - -.. automodule:: cugraph.dask.traversal.bfs - :members: - :undoc-members: - -Single-source-shortest-path ---------------------------- - -.. automodule:: cugraph.traversal.sssp - :members: - :undoc-members: - -Single-source-shortest-path (MG) --------------------------------- - -.. automodule:: cugraph.dask.traversal.sssp - :members: - :undoc-members: - -Traveling-salesperson-problem ------------------------------ - -.. automodule:: cugraph.traversal.traveling_salesperson - :members: - :undoc-members: - - -Tree -========= - -Minimum Spanning Tree ---------------------- - -.. automodule:: cugraph.tree.minimum_spanning_tree - :members: minimum_spanning_tree - :undoc-members: - -Maximum Spanning Tree ---------------------- - -.. automodule:: cugraph.tree.minimum_spanning_tree - :members: maximum_spanning_tree - :undoc-members: - :noindex: - - -Generator -========= - -RMAT ---------------------- - -.. automodule:: cugraph.generators - :members: rmat - :undoc-members: - - -DASK MG Helper functions -=========================== - -.. automodule:: cugraph.comms.comms - :members: initialize, destroy - :undoc-members: - :member-order: bysource - -.. automodule:: cugraph.dask.common.read_utils - :members: get_chunksize - :undoc-members: diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/centrality.rst new file mode 100644 index 000000000..2f43354ea --- /dev/null +++ b/docs/cugraph/source/api_docs/centrality.rst @@ -0,0 +1,29 @@ +========== +Centrality +========== +.. currentmodule:: cugraph + + + +Betweenness Centrality +---------------------- +.. autosummary:: + :toctree: api/ + + cugraph.centrality.betweenness_centrality.betweenness_centrality + cugraph.centrality.betweenness_centrality.edge_betweenness_centrality + +Katz Centrality +--------------- +.. autosummary:: + :toctree: api/ + + cugraph.centrality.katz_centrality.katz_centrality + +Katz Centrality (MG) +-------------------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.centrality.katz_centrality.call_katz_centrality + cugraph.dask.centrality.katz_centrality.katz_centrality diff --git a/docs/cugraph/source/api_docs/community.rst b/docs/cugraph/source/api_docs/community.rst new file mode 100644 index 000000000..7f7752ae4 --- /dev/null +++ b/docs/cugraph/source/api_docs/community.rst @@ -0,0 +1,81 @@ +========= +Community +========= +.. currentmodule:: cugraph + + + +EgoNet +------ +.. autosummary:: + :toctree: api/ + + cugraph.community.egonet.batched_ego_graphs + cugraph.community.egonet.ego_graph + +Ensemble clustering for graphs (ECG) +------------------------------------ +.. autosummary:: + :toctree: api/ + + cugraph.community.ecg.ecg + + +K-Truss +------- +.. autosummary:: + :toctree: api/ + + cugraph.community.ktruss_subgraph.k_truss + cugraph.community.ktruss_subgraph.ktruss_subgraph + +Leiden +------ +.. autosummary:: + :toctree: api/ + + cugraph.community.leiden.leiden + + +Louvain +------- +.. autosummary:: + :toctree: api/ + + cugraph.community.louvain.louvain + + +Louvain (MG) +------------ +.. autosummary:: + :toctree: api/ + + cugraph.dask.community.louvain.call_louvain + cugraph.community.ktruss_subgraph.ktruss_subgraph + +Spectral Clustering +------------------- +.. autosummary:: + :toctree: api/ + + cugraph.community.spectral_clustering.analyzeClustering_edge_cut + cugraph.community.spectral_clustering.analyzeClustering_modularity + cugraph.community.spectral_clustering.analyzeClustering_ratio_cut + cugraph.community.spectral_clustering.spectralBalancedCutClustering + cugraph.community.spectral_clustering.spectralModularityMaximizationClustering + + +Subgraph Extraction +------------------- +.. autosummary:: + :toctree: api/ + + cugraph.community.subgraph_extraction.subgraph + + +Triangle Counting +----------------- +.. autosummary:: + :toctree: api/ + + cugraph.community.triangle_count.triangles diff --git a/docs/cugraph/source/api_docs/components.rst b/docs/cugraph/source/api_docs/components.rst new file mode 100644 index 000000000..0f7d3a9c2 --- /dev/null +++ b/docs/cugraph/source/api_docs/components.rst @@ -0,0 +1,25 @@ +========== +Components +========== +.. currentmodule:: cugraph + + + +Connected Components +-------------------- +.. autosummary:: + :toctree: api/ + + cugraph.components.connectivity.connected_components + cugraph.components.connectivity.strongly_connected_components + cugraph.components.connectivity.weakly_connected_components + + +Connected Components (MG) +------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.components.connectivity.call_wcc + cugraph.dask.components.connectivity.weakly_connected_components + diff --git a/docs/cugraph/source/api_docs/cores.rst b/docs/cugraph/source/api_docs/cores.rst new file mode 100644 index 000000000..344725914 --- /dev/null +++ b/docs/cugraph/source/api_docs/cores.rst @@ -0,0 +1,21 @@ +===== +Cores +===== +.. currentmodule:: cugraph + + + +Core Number +----------- +.. autosummary:: + :toctree: api/ + + cugraph.cores.core_number.core_number + + +K-Core +------ +.. autosummary:: + :toctree: api/ + + cugraph.centrality.katz_centrality.katz_centrality diff --git a/docs/cugraph/source/dask-cugraph.rst b/docs/cugraph/source/api_docs/dask-cugraph.rst similarity index 100% rename from docs/cugraph/source/dask-cugraph.rst rename to docs/cugraph/source/api_docs/dask-cugraph.rst diff --git a/docs/cugraph/source/api_docs/generator.rst b/docs/cugraph/source/api_docs/generator.rst new file mode 100644 index 000000000..e89fe25e8 --- /dev/null +++ b/docs/cugraph/source/api_docs/generator.rst @@ -0,0 +1,14 @@ +========= +Generator +========= +.. currentmodule:: cugraph + + + +RMAT +---- +.. autosummary:: + :toctree: api/ + + cugraph.generators.rmat + diff --git a/docs/cugraph/source/api_docs/helper_functions.rst b/docs/cugraph/source/api_docs/helper_functions.rst new file mode 100644 index 000000000..f8a3f0495 --- /dev/null +++ b/docs/cugraph/source/api_docs/helper_functions.rst @@ -0,0 +1,16 @@ +======================== +DASK MG Helper functions +======================== +.. currentmodule:: cugraph + + + +Methods +------- +.. autosummary:: + :toctree: api/ + + cugraph.comms.comms.initialize + cugraph.comms.comms.destroy + cugraph.dask.common.read_utils.get_chunksize + diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst new file mode 100644 index 000000000..974217dfe --- /dev/null +++ b/docs/cugraph/source/api_docs/index.rst @@ -0,0 +1,27 @@ +============= +API reference +============= + +This page provides a list of all publicly accessible modules, methods and classes through +``cugraph.*`` namespace. + +.. toctree:: + :maxdepth: 2 + :caption: API Documentation + + structure + centrality + community + components + cores + layout + linear_assignment + link_analysis + link_prediction + sampling + traversal + tree + generator + helper_functions + dask-cugraph.rst + diff --git a/docs/cugraph/source/api_docs/layout.rst b/docs/cugraph/source/api_docs/layout.rst new file mode 100644 index 000000000..fc81df722 --- /dev/null +++ b/docs/cugraph/source/api_docs/layout.rst @@ -0,0 +1,14 @@ +====== +Layout +====== +.. currentmodule:: cugraph + + + +Force Atlas 2 +------------- +.. autosummary:: + :toctree: api/ + + cugraph.layout.force_atlas2.force_atlas2 + diff --git a/docs/cugraph/source/api_docs/linear_assignment.rst b/docs/cugraph/source/api_docs/linear_assignment.rst new file mode 100644 index 000000000..147597879 --- /dev/null +++ b/docs/cugraph/source/api_docs/linear_assignment.rst @@ -0,0 +1,13 @@ +================= +Linear Assignment +================= +.. currentmodule:: cugraph + + + +Hungarian +--------- +.. autosummary:: + :toctree: api/ + + cugraph.linear_assignment.hungarian diff --git a/docs/cugraph/source/api_docs/link_analysis.rst b/docs/cugraph/source/api_docs/link_analysis.rst new file mode 100644 index 000000000..809fd3647 --- /dev/null +++ b/docs/cugraph/source/api_docs/link_analysis.rst @@ -0,0 +1,29 @@ +============= +Link Analysis +============= +.. currentmodule:: cugraph + + + +HITS +---- +.. autosummary:: + :toctree: api/ + + cugraph.link_analysis.hits.hits + + +Pagerank +-------- +.. autosummary:: + :toctree: api/ + + cugraph.centrality.katz_centrality.katz_centrality + +Pagerank (MG) +------------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.link_analysis.pagerank.pagerank + diff --git a/docs/cugraph/source/api_docs/link_prediction.rst b/docs/cugraph/source/api_docs/link_prediction.rst new file mode 100644 index 000000000..0ecfde58d --- /dev/null +++ b/docs/cugraph/source/api_docs/link_prediction.rst @@ -0,0 +1,34 @@ +=============== +Link Prediction +=============== +.. currentmodule:: cugraph + + + +Jaccard Coefficient +------------------- +.. autosummary:: + :toctree: api/ + + cugraph.link_prediction.jaccard.jaccard + cugraph.link_prediction.jaccard.jaccard_coefficient + cugraph.link_prediction.wjaccard.jaccard_w + + +Overlap Coefficient +------------------- +.. autosummary:: + :toctree: api/ + + cugraph.link_prediction.overlap.overlap + cugraph.link_prediction.overlap.overlap_coefficient + cugraph.link_prediction.woverlap.overlap_w + + +Pagerank (MG) +------------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.link_analysis.pagerank.pagerank + diff --git a/docs/cugraph/source/api_docs/sampling.rst b/docs/cugraph/source/api_docs/sampling.rst new file mode 100644 index 000000000..de7f707fb --- /dev/null +++ b/docs/cugraph/source/api_docs/sampling.rst @@ -0,0 +1,13 @@ +======== +Sampling +======== +.. currentmodule:: cugraph + + + +Random Walks +------------ +.. autosummary:: + :toctree: api/ + + cugraph.sampling.random_walks.random_walks diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst new file mode 100644 index 000000000..e1781eda7 --- /dev/null +++ b/docs/cugraph/source/api_docs/structure.rst @@ -0,0 +1,68 @@ +========= +Structure +========= +.. currentmodule:: cugraph.structure.graph_classes + +Constructor +----------- +.. autosummary:: + :toctree: api/ + + Graph + + +Methods +------- +.. autosummary:: + :toctree: api/ + + Graph.add_internal_vertex_id + Graph.add_nodes_from + Graph.clear + Graph.from_cudf_adjlist + Graph.from_cudf_edgelist + Graph.from_dask_cudf_edgelist + Graph.from_numpy_array + Graph.from_numpy_matrix + Graph.from_pandas_adjacency + Graph.from_pandas_edgelist + Graph.has_isolated_vertices + Graph.is_bipartite + Graph.is_directed + Graph.is_multigraph + Graph.is_multipartite + Graph.is_renumbered + Graph.is_weighted + Graph.lookup_internal_vertex_id + Graph.to_directed + Graph.to_undirected + Graph.unrenumber + +.. currentmodule:: cugraph + +Symmetrize +---------- +.. autosummary:: + :toctree: api/ + + cugraph.structure.symmetrize.symmetrize + cugraph.structure.symmetrize.symmetrize_ddf + cugraph.structure.symmetrize.symmetrize_df + + +Conversion from Other Formats +----------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.structure.convert_matrix.from_adjlist + cugraph.structure.convert_matrix.from_cudf_edgelist + cugraph.structure.convert_matrix.from_edgelist + cugraph.structure.convert_matrix.from_numpy_array + cugraph.structure.convert_matrix.from_numpy_matrix + cugraph.structure.convert_matrix.from_pandas_adjacency + cugraph.structure.convert_matrix.from_pandas_edgelist + cugraph.structure.convert_matrix.to_numpy_array + cugraph.structure.convert_matrix.to_numpy_matrix + cugraph.structure.convert_matrix.to_pandas_adjacency + cugraph.structure.convert_matrix.to_pandas_edgelist \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/traversal.rst b/docs/cugraph/source/api_docs/traversal.rst new file mode 100644 index 000000000..6a11a4a13 --- /dev/null +++ b/docs/cugraph/source/api_docs/traversal.rst @@ -0,0 +1,49 @@ +========= +Traversal +========= +.. currentmodule:: cugraph + + + +Breadth-first-search +-------------------- +.. autosummary:: + :toctree: api/ + + cugraph.traversal.bfs.bfs + cugraph.traversal.bfs.bfs_edges + +Breadth-first-search (MG) +------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.traversal.bfs.bfs + cugraph.dask.traversal.bfs.call_bfs + + +Single-source-shortest-path +--------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.traversal.sssp.filter_unreachable + cugraph.traversal.sssp.shortest_path + cugraph.traversal.sssp.shortest_path_length + cugraph.traversal.sssp.sssp + +Single-source-shortest-path (MG) +-------------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.traversal.sssp.call_sssp + cugraph.dask.traversal.sssp.sssp + + +Traveling-salesperson-problem +----------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.traversal.traveling_salesperson.traveling_salesperson diff --git a/docs/cugraph/source/api_docs/tree.rst b/docs/cugraph/source/api_docs/tree.rst new file mode 100644 index 000000000..7952c4fa3 --- /dev/null +++ b/docs/cugraph/source/api_docs/tree.rst @@ -0,0 +1,22 @@ +==== +Tree +==== +.. currentmodule:: cugraph + + + +Minimum Spanning Tree +--------------------- +.. autosummary:: + :toctree: api/ + + cugraph.tree.minimum_spanning_tree.minimum_spanning_tree + + +Maximum Spanning Tree +--------------------- +.. autosummary:: + :toctree: api/ + + cugraph.tree.minimum_spanning_tree.maximum_spanning_tree + diff --git a/docs/cugraph/source/cugraph_blogs.rst b/docs/cugraph/source/basics/cugraph_blogs.rst similarity index 100% rename from docs/cugraph/source/cugraph_blogs.rst rename to docs/cugraph/source/basics/cugraph_blogs.rst diff --git a/docs/cugraph/source/cugraph_intro.md b/docs/cugraph/source/basics/cugraph_intro.md similarity index 100% rename from docs/cugraph/source/cugraph_intro.md rename to docs/cugraph/source/basics/cugraph_intro.md diff --git a/docs/cugraph/source/cugraph_ref.rst b/docs/cugraph/source/basics/cugraph_ref.rst similarity index 100% rename from docs/cugraph/source/cugraph_ref.rst rename to docs/cugraph/source/basics/cugraph_ref.rst diff --git a/docs/cugraph/source/basics/index.rst b/docs/cugraph/source/basics/index.rst new file mode 100644 index 000000000..9260169ee --- /dev/null +++ b/docs/cugraph/source/basics/index.rst @@ -0,0 +1,12 @@ +====== +Basics +====== + + +.. toctree:: + :maxdepth: 2 + + cugraph_intro.md + cugraph_blogs.rst + cugraph_ref.rst + nx_transition.rst diff --git a/docs/cugraph/source/nx_transition.rst b/docs/cugraph/source/basics/nx_transition.rst similarity index 99% rename from docs/cugraph/source/nx_transition.rst rename to docs/cugraph/source/basics/nx_transition.rst index 6c57ab89c..3d116162c 100644 --- a/docs/cugraph/source/nx_transition.rst +++ b/docs/cugraph/source/basics/nx_transition.rst @@ -40,7 +40,7 @@ While module can be assigned any name can be used, we picked cnx to reduce the amount of text to be changed. The text highlighted in yellow indicates changes. -.. image:: ./images/Nx_Cg_1.png +.. image:: ../images/Nx_Cg_1.png :width: 600 It is that easy. All algorithms in cuGraph support a NetworkX graph object as @@ -191,7 +191,7 @@ If your code follows the above model of inserting one element at a time, the we Now, if your code bulk loads the data from Pandas, then RAPIDS can accelerate that process by orders of magnitude. -.. image:: ./images/Nx_Cg_2.png +.. image:: ../images/Nx_Cg_2.png :width: 600 The above cuGraph code will create cuGraph.Graph object and not a NetworkX.Graph object. diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index f2d60f53b..7857a4f0a 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -53,6 +53,7 @@ "IPython.sphinxext.ipython_directive", "nbsphinx", "recommonmark", + "sphinx_copybutton", ] @@ -72,7 +73,7 @@ # General information about the project. project = 'cugraph' -copyright = '2019-2020, NVIDIA' +copyright = '2019-2021, NVIDIA' author = 'NVIDIA' # The version info for the project you're documenting, acts as replacement for @@ -109,7 +110,7 @@ # a list of builtin themes. # -html_theme = 'sphinx_rtd_theme' +html_theme = 'pydata_sphinx_theme' # on_rtd is whether we are on readthedocs.org on_rtd = os.environ.get('READTHEDOCS', None) == 'True' @@ -118,16 +119,24 @@ # only import and set the theme if we're building docs locally # otherwise, readthedocs.org uses their theme by default, # so no need to specify it - import sphinx_rtd_theme - html_theme = 'sphinx_rtd_theme' - html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + import pydata_sphinx_theme + html_theme = 'pydata_sphinx_theme' + html_theme_path = pydata_sphinx_theme.get_html_theme_path() +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # -# html_theme_options = {} +html_theme_options = { + "external_links": [], + "github_url": "https://github.com/rapidsai/cugraph", + "twitter_url": "https://twitter.com/rapidsai", + "show_toc_level": 1, + "navbar_align": "right", +} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, @@ -202,7 +211,6 @@ def setup(app): - app.add_css_file('copybutton.css') app.add_css_file('params.css') app.add_css_file('references.css') diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 93184f40b..6eae4ceed 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -2,15 +2,11 @@ Welcome to cugraph's documentation! =================================== .. toctree:: - :maxdepth: 4 + :maxdepth: 2 :caption: Contents: - cugraph_intro.md - api.rst - dask-cugraph.rst - cugraph_blogs.rst - cugraph_ref.rst - nx_transition.rst + basics/index + api_docs/index Indices and tables ================== From 1e56ddacfb49bb2b0da0a9c465b24eff7efcc686 Mon Sep 17 00:00:00 2001 From: Paul Taylor Date: Fri, 10 Sep 2021 08:24:33 -0500 Subject: [PATCH 154/384] Add new new raft symlink path to .gitignore (#1808) Authors: - Paul Taylor (https://github.com/trxcllnt) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/1808 --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 3afdcbccc..a2f8cab42 100644 --- a/.gitignore +++ b/.gitignore @@ -76,7 +76,8 @@ datasets/* cpp/doxygen/html # Raft symlink -python/cugraph/raft +python/cugraph/cugraph/raft +python/pylibcugraph/pylibcugraph/raft python/_external_repositories/ # created by Dask tests From baffbbd36401b880c67fcfbf279fba2f4da9c992 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Fri, 10 Sep 2021 11:06:06 -0400 Subject: [PATCH 155/384] remove tsp implementation from 21.10 (#1812) TSP implementation being removed Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Brad Rees (https://github.com/BradReesWork) - Seunghwa Kang (https://github.com/seunghwak) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/1812 --- docs/cugraph/source/api_docs/traversal.rst | 8 -------- 1 file changed, 8 deletions(-) diff --git a/docs/cugraph/source/api_docs/traversal.rst b/docs/cugraph/source/api_docs/traversal.rst index 6a11a4a13..2a6d6873f 100644 --- a/docs/cugraph/source/api_docs/traversal.rst +++ b/docs/cugraph/source/api_docs/traversal.rst @@ -39,11 +39,3 @@ Single-source-shortest-path (MG) cugraph.dask.traversal.sssp.call_sssp cugraph.dask.traversal.sssp.sssp - - -Traveling-salesperson-problem ------------------------------ -.. autosummary:: - :toctree: api/ - - cugraph.traversal.traveling_salesperson.traveling_salesperson From d4f4dcde641d70615827ebf78ef88853e55f2985 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Fri, 10 Sep 2021 10:07:15 -0500 Subject: [PATCH 156/384] Changed pylibcugraph connected_components APIs to use duck typing for CAI inputs, added doc placeholders (#1810) * Changed API to use duck-typing on `__cuda_array_interface__` types instead of requiring users to access and pass the `__cuda_array_interface__` object in directly. * Added initial placeholder for pylibcugraph docs and docstrings to cython file * Updated tests to not access the `__cuda_array_interface__` attr directly, added fixtures for easier code reuse, added test for invalid inputs, added FIXMEs to track tech debt in how return values are checked. Tested API changes by running new unit tests, and tested docs by running `./build.sh docs` and inspecting the html locally in a browser. NOTE: since this package is still limited to nightly releases (ie. it has not been part of a release yet), it is using the "non-breaking" label even though the user API has changed. attn: @BradReesWork and @raydouglass for the `pylibcugraph` placeholder docs in this PR ![image](https://user-images.githubusercontent.com/3039903/132440516-f3310756-d080-49bf-898a-0bc6581525eb.png) ![image](https://user-images.githubusercontent.com/3039903/132440640-920cf86a-6971-45bb-8d9d-c93229c762c4.png) Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/1810 --- docs/cugraph/source/api_docs/index.rst | 2 +- docs/cugraph/source/api_docs/pylibcugraph.rst | 15 +++++++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) create mode 100644 docs/cugraph/source/api_docs/pylibcugraph.rst diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 974217dfe..9da23c396 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -24,4 +24,4 @@ This page provides a list of all publicly accessible modules, methods and classe generator helper_functions dask-cugraph.rst - + pylibcugraph.rst diff --git a/docs/cugraph/source/api_docs/pylibcugraph.rst b/docs/cugraph/source/api_docs/pylibcugraph.rst new file mode 100644 index 000000000..ffc2e84e3 --- /dev/null +++ b/docs/cugraph/source/api_docs/pylibcugraph.rst @@ -0,0 +1,15 @@ +~~~~~~~~~~~~~~~~~~~~~~ +pylibcugraph +~~~~~~~~~~~~~~~~~~~~~~ + +pylibcugraph + +.. currentmodule:: pylibcugraph + +Methods +------- +.. autosummary:: + :toctree: api/ + + pylibcugraph.components.strongly_connected_components + pylibcugraph.components.weakly_connected_components From 1882ca4933b093c27b4706390fe0b9a3dce0c278 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 14 Sep 2021 17:50:22 -0400 Subject: [PATCH 157/384] updated to new doc theme (#1793) Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/1793 --- docs/cugraph/source/api_docs/structure.rst | 36 ++++++++++++-------- docs/cugraph/source/basics/cugraph_blogs.rst | 5 --- docs/cugraph/source/index.rst | 4 +++ 3 files changed, 26 insertions(+), 19 deletions(-) diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst index e1781eda7..9f43079b7 100644 --- a/docs/cugraph/source/api_docs/structure.rst +++ b/docs/cugraph/source/api_docs/structure.rst @@ -1,31 +1,40 @@ -========= -Structure -========= -.. currentmodule:: cugraph.structure.graph_classes +============= +Graph Classes +============= +.. currentmodule:: cugraph -Constructor ------------ +Constructors +------------ .. autosummary:: :toctree: api/ Graph + MultiGraph + BiPartiteGraph -Methods -------- +Adding Data +----------- .. autosummary:: :toctree: api/ - Graph.add_internal_vertex_id - Graph.add_nodes_from - Graph.clear + Graph.from_cudf_adjlist Graph.from_cudf_edgelist Graph.from_dask_cudf_edgelist - Graph.from_numpy_array - Graph.from_numpy_matrix Graph.from_pandas_adjacency Graph.from_pandas_edgelist + Graph.from_numpy_array + Graph.from_numpy_matrix + Graph.add_internal_vertex_id + Graph.add_nodes_from + Graph.clear + +Checks +------ +.. autosummary:: + :toctree: api/ + Graph.has_isolated_vertices Graph.is_bipartite Graph.is_directed @@ -38,7 +47,6 @@ Methods Graph.to_undirected Graph.unrenumber -.. currentmodule:: cugraph Symmetrize ---------- diff --git a/docs/cugraph/source/basics/cugraph_blogs.rst b/docs/cugraph/source/basics/cugraph_blogs.rst index cbbc93a1b..35db20ff4 100644 --- a/docs/cugraph/source/basics/cugraph_blogs.rst +++ b/docs/cugraph/source/basics/cugraph_blogs.rst @@ -48,8 +48,3 @@ Other BLOGS * `Where should I walk `_ * `Where really are the parking spots? `_ * `Accelerating Single Cell Genomic Analysis using RAPIDS `_ - - - - -Copyright (c) 2020, NVIDIA CORPORATION. diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 6eae4ceed..6b2d62fbd 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -1,5 +1,9 @@ Welcome to cugraph's documentation! =================================== +RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates +into the RAPIDS data science ecosystem and allows the data scientist to +easily call graph algorithms using data stored in GPU DataFrames, +NetworkX Graphs, or even CuPy or SciPy sparse Matrices. .. toctree:: :maxdepth: 2 From f1b773da53310e8ec5b74254d64cb0132af13c54 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Thu, 16 Sep 2021 12:23:11 -0700 Subject: [PATCH 158/384] REL v21.08.04 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 2b4c8bdb1..9523bbd59 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -82,7 +82,7 @@ # The short X.Y version. version = '21.08' # The full version, including alpha/beta/rc tags. -release = '21.08.03' +release = '21.08.04' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 3014802e4f7adf28b689000088076df13e5199b1 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 16 Sep 2021 16:44:55 -0400 Subject: [PATCH 159/384] DOC v21.12 Updates --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 7857a4f0a..e9ccab365 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '21.10' +version = '21.12' # The full version, including alpha/beta/rc tags. -release = '21.10.00' +release = '21.12.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From f6a25b22b60e469c24ace53b9cedaa26ee309b87 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 6 Oct 2021 12:07:37 -0700 Subject: [PATCH 160/384] REL v21.10.00 release From e8dafa948105c54f72ed75d8934796c9f5e377f3 Mon Sep 17 00:00:00 2001 From: AJ Schmidt Date: Thu, 4 Nov 2021 10:03:47 -0400 Subject: [PATCH 161/384] DOC v22.02 Updates --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index e9ccab365..f0c0dc441 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '21.12' +version = '22.02' # The full version, including alpha/beta/rc tags. -release = '21.12.00' +release = '22.02.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From aea45228fc2c39325ec54c77e430e45836742d28 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Mon, 8 Nov 2021 15:45:27 -0500 Subject: [PATCH 162/384] add new demo notebook for louvain (#1927) Added a demonstration notebook for running MNMG Louvain Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/1927 --- notebooks/demo/mg_louvain.ipynb | 403 ++++++++++++++++++++++++++++++++ 1 file changed, 403 insertions(+) create mode 100644 notebooks/demo/mg_louvain.ipynb diff --git a/notebooks/demo/mg_louvain.ipynb b/notebooks/demo/mg_louvain.ipynb new file mode 100644 index 000000000..1ce2150e6 --- /dev/null +++ b/notebooks/demo/mg_louvain.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple GPU Louvain in cuGraph\n", + "#### Author : Chuck Hastings\n", + "\n", + "In this notebook, we will show how to use multiple GPUs in cuGraph to compute the Louvain partitions and global modularity score for a dataset.\n", + "\n", + "This notebook was tested using RAPIDS 21.12 and CUDA 11.4. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", + "\n", + "\n", + "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multi GPU Louvain with cuGraph\n", + "### Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries. We recommend using the [cugraph_dev](https://github.com/rapidsai/cugraph/tree/branch-21.12/conda/environments) env through conda\n", + "from dask.distributed import Client, wait\n", + "from dask_cuda import LocalCUDACluster\n", + "import cugraph.comms as Comms\n", + "import cugraph.dask as dask_cugraph\n", + "import cugraph\n", + "import dask_cudf\n", + "import time\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Hollywood dataset is in our S3 bucket and zipped. \n", + "1. We'll need to create a folder for our data in the `/data` folder\n", + "1. Download the zipped data into that folder from S3 (it will take some time as it it 6GB)\n", + "1. Decompress the zipped data for use (it will take some time as it it 26GB)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your data file, ../data/hollywood.csv, already exists\n" + ] + } + ], + "source": [ + "import urllib.request\n", + "import os\n", + "\n", + "data_dir = '../data/'\n", + "if not os.path.exists(data_dir):\n", + " print('creating data directory')\n", + " os.system('mkdir ../data')\n", + "\n", + "# download the Hollywood dataset\n", + "base_url = 'https://rapidsai-data.s3.us-east-2.amazonaws.com/cugraph/benchmark/'\n", + "fn = 'hollywood.csv'\n", + "comp = '.gz'\n", + "\n", + "if not os.path.isfile(data_dir+fn):\n", + " if not os.path.isfile(data_dir+fn+comp):\n", + " print(f'Downloading {base_url+fn+comp} to {data_dir+fn+comp}')\n", + " urllib.request.urlretrieve(base_url+fn+comp, data_dir+fn+comp)\n", + " print(f'Decompressing {data_dir+fn+comp}...')\n", + " os.system('gunzip '+data_dir+fn+comp)\n", + " print(f'{data_dir+fn+comp} decompressed!')\n", + "else:\n", + " print(f'Your data file, {data_dir+fn}, already exists')\n", + "\n", + "# File path, assuming Notebook directory\n", + "input_data_path = data_dir+fn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize multi-GPU environment\n", + "Before we get started, we need to setup a Dask local cluster of workers to execute our work and a client to coordinate and schedule work for that cluster. As we see below, we can initiate a cluster and client using only 3 lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n", + "distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n" + ] + } + ], + "source": [ + "cluster = LocalCUDACluster()\n", + "client = Client(cluster)\n", + "Comms.initialize(p2p=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data from disk\n", + "cuGraph depends on cudf for data loading and the initial DataFrame creation. The CSV data file contains an edge list, which represents the connection of a vertex to another. The source to destination pairs is what is known as Coordinate Format (COO). In this test case, the data is just two columns. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Start ETL timer\n", + "t_start = time.time()\n", + "\n", + "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", + "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", + "\n", + "# Multi-GPU CSV reader\n", + "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a graph\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read, load and renumber: 121.63822555541992 s\n" + ] + } + ], + "source": [ + "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", + "G = cugraph.DiGraph()\n", + "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')\n", + "\n", + "# Print time\n", + "print(\"Read, load and renumber: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Call Louvain algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Louvain: 8.262660264968872 s\n" + ] + } + ], + "source": [ + "# Start Pagerank timer\n", + "t_start = time.time()\n", + "\n", + "# Get the Louvain partition assignments for each vertex and the global modularity score.\n", + "(louvain_df, modularity) = dask_cugraph.louvain(G)\n", + "\n", + "# Print time\n", + "print(\"Louvain: \", time.time()-t_start, \"s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It was that easy! Louvain should take 5-10 seconds to run on this 1.5GB input with two GPUs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display subset of the Louvain result\n", + "\n", + "For now just display the louvain result" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 97afba96a56ebe5f075d68449de71906c8d1bfb9 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Thu, 2 Dec 2021 16:41:17 -0500 Subject: [PATCH 163/384] 22.02 dep fix (#1974) address cuDF deprecation of "to_array". some cleanup Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Joseph Nke (https://github.com/jnke2016) URL: https://github.com/rapidsai/cugraph/pull/1974 --- .gitignore | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/.gitignore b/.gitignore index a2f8cab42..fa7580040 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ __pycache__ .cache .coverage .vscode +.lock *.swp *.pytest_cache DartConfiguration.tcl @@ -32,6 +33,9 @@ dist/ cugraph.egg-info/ python/build python/cugraph/bindings/*.cpp + +## pylibcugraph build directories & artifacts +python/pylibcugraph/pylibcugraph.egg-info ## Patching *.diff @@ -82,6 +86,9 @@ python/_external_repositories/ # created by Dask tests python/dask-worker-space +python/cugraph/dask-worker-space +python/cugraph/cugraph/dask-worker-space +python/cugraph/cugraph/tests/dask-worker-space # Sphinx docs & build artifacts docs/cugraph/source/api_docs/api/* From 3bf5d085bdb1c65662e3e4f9cfab33f77789c2d4 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 8 Dec 2021 19:15:12 +0000 Subject: [PATCH 164/384] REL v21.12.00 release From 96b9c11839ed8c58363206c4aed7f685ea636a24 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 13 Jan 2022 10:38:10 -0500 Subject: [PATCH 165/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index f0c0dc441..c763f268f 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '22.02' +version = '22.04' # The full version, including alpha/beta/rc tags. -release = '22.02.00' +release = '22.04.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 428bb12d27d8c20876ab93dc6644b7d3da7a3b4d Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 25 Jan 2022 10:19:49 -0500 Subject: [PATCH 166/384] updated for release 22.02 (#2034) Updated the README with latest algorithm info and update the release benchmark notebook to include additional comparisons Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2034 --- notebooks/cugraph_benchmarks/release.ipynb | 623 ++++++++++++++++----- 1 file changed, 498 insertions(+), 125 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index a6eeeb65c..7d1c82468 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -4,22 +4,64 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Release Benchmarking\n", "# Skip notebook test\n", + "(this notebook is not executed as part of the RAPIDS cuGraph CI process. Execution could take a few hours)\n", + "\n", + "---\n", + "\n", + "# Release Benchmarking\n", "\n", "With every release, RAPIDS publishes a release slide deck that includes the current performance state of cuGraph. \n", "This notebook, starting with release 0.15, runs all the various algorithms to computes the performance gain. \n", "\n", + "\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Timing \n", + "When looking at the overall workflow, NetworkX and cuGraph do things differently. For example, NetworkX spends a lot of time creating the graph data structure. cuGraph on the other hand does a lazy creation of the data structure when an algorithm is called. \n", + "\n", + "To further complicate the comparison problem, NetworkX does not always return the answer. In some cases it returns a generator that is then called to get the data. \n", + "\n", + "This benchmark will measure time from an analyst perspective, how long does it take to create the graph and run an algorithm. \n", + "\n", + "__What is not timed__: Reading the data

\n", + "__What is timed__: (1) creating a Graph, (2) running the algorithm (3) run any generators\n", + "\n", + "\n", + "Notes:\n", + "* Since this is clean test data, we do not need to renumber the data.\n", + "* use default arguments in most cases\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "### Algorithms\n", - "| Algorithm | Graph | DiGraph |\n", - "| ------------------------| -------- | ----------- |\n", - "| BFS | X | |\n", - "| SSSP | X | |\n", - "| PageRank | | X |\n", - "| WCC | | X |\n", - "| Betweenness Centrality | X | |\n", - "| Louvain | X | |\n", - "| Triangle Counting | X | |\n", + "| Algorithm | Type | Graph | DiGraph | Notes\n", + "| ------------------------|---------------|------ | ------- |-------------\n", + "| Katz | Centrality | X | | \n", + "| Betweenness Centrality | Centrality | X | | Estimated, k = 100\n", + "| Betweenness Centrality | Centrality | X | | Estimated, k = 0.1% \n", + "| Louvain | Community | X | | Uses python-louvain for comparison\n", + "| Triangle Counting | Community | X | |\n", + "| WCC | Components | | X | Nx requires directed and returns a generator \n", + "| Core Number | Core | X | | \n", + "| PageRank | Link Analysis | | X |\n", + "| Jaccard | Similarity | X | |\n", + "| BFS | Traversal | X | | No depth limit \n", + "| SSSP | Traversal | X | | \n", + "\n", "\n", "### Test Data\n", "Users must run the _dataPrep.sh_ script before running this notebook so that the test files are downloaded\n", @@ -32,27 +74,25 @@ "| as-Skitter | 1,696,415 | 22,190,596 |\n", "\n", "\n", - "Notebook Credits\n", - "\n", - " Original Authors: Bradley Rees\n", - " Last Edit: 10/06/2020\n", - " \n", - "RAPIDS Versions: 0.16\n", "\n", - "Test Hardware\n", - " GV100 32G, CUDA 10.2\n", - " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", - " 32GB system memory\n", - " " + "### Notes\n", + "* Running Betweenness Centrality on the full graph is prohibited using NetworkX\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Timing \n", - "What is not timed: Reading the data

\n", - "What is timmed: (1) creating a Graph, (2) running the algorithm" + "Notebook Credits\n", + "\n", + " \n", + "| Author | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|------------------|-----------------|------------------------|\n", + "| Brad Rees | 10/06/2020 | created | 0.16 | GV100, CUDA 10.2 |\n", + "| Brad Rees | 01/20/2022 | updated | 22.02 | Quadro A6000 CUDA 11.5 |\n", + "\n", + "\n", + "\n" ] }, { @@ -73,6 +113,7 @@ "import os\n", "import time\n", "import numpy as np\n", + "import math\n", "\n", "# rapids\n", "import cugraph\n", @@ -99,24 +140,36 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "try: \n", - " import matplotlib\n", - "except ModuleNotFoundError:\n", - " os.system('pip install matplotlib')\n", - "\n", - "import matplotlib.pyplot as plt; plt.rcdefaults()\n" + "### Define the test data" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### Define the test data" + "# Test Files\n", + "# set the data argument for full test or quick test\n", + "\n", + "data_full = {\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " 'dblp' : './data/dblp-2010.mtx',\n", + " 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", + " 'as-Skitter' : './data/as-Skitter.mtx'\n", + "}\n", + "\n", + "# for quick testing\n", + "data_quick = {\n", + " 'karate' : './data/karate.mtx',\n", + "}\n", + "\n", + "\n", + "\n", + "data = data_full\n" ] }, { @@ -125,18 +178,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Test File\n", - "# data = {\n", - "# 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", - "# 'dblp' : './data/dblp-2010.mtx',\n", - "# 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n", - "# 'as-Skitter' : './data/as-Skitter.mtx'\n", - "#}\n", - "\n", - "# for quick testing\n", - "data = {\n", - " 'polbooks' : './data/polbooks.mtx', \n", - "}\n" + "# Get the data - will auto skip if files exists\n", + "!./dataPrep.sh" ] }, { @@ -171,8 +214,8 @@ "source": [ "## Create Graph functions\n", "There are two types of graphs created:\n", - "Directed Graphs - calls to create_xx_digraph\n", - "Undirected Graphs - calls to create_xx_ugraph <- fully syemmeterized " + "* Directed Graphs - calls to create_xx_digraph\n", + "* Undirected Graphs - calls to create_xx_ugraph <- fully syemmeterized " ] }, { @@ -193,12 +236,12 @@ "\n", "# cuGraph\n", "def create_cu_digraph(_df):\n", - " _g = cugraph.DiGraph()\n", + " _g = cugraph.Graph(directed=True)\n", " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", " return _g\n", "\n", "def create_cu_ugraph(_df):\n", - " _g = cugraph.Graph()\n", + " _g = cugraph.Graph(directed=False)\n", " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", " return _g" ] @@ -207,7 +250,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### BFS" + "## Algorithm Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Katz" ] }, { @@ -216,17 +266,17 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_bfs(_df):\n", + "def nx_katz(_df, alpha):\n", " t1 = time.time()\n", " _G = create_nx_ugraph(_df)\n", - " _ = nx.bfs_edges(_G, 1)\n", + " _ = nx.katz_centrality(_G, alpha)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_bfs(_df):\n", + "def cu_katz(_df, alpha):\n", " t1 = time.time()\n", " _G = create_cu_ugraph(_df)\n", - " _ = cugraph.bfs(_G, 1)\n", + " _ = cugraph.katz_centrality(_G, alpha)\n", " t2 = time.time() - t1\n", " return t2" ] @@ -235,7 +285,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### SSSP" + "### Betweenness Centrality" ] }, { @@ -244,17 +294,17 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_sssp(_df):\n", + "def nx_bc(_df, _k):\n", " t1 = time.time()\n", " _G = create_nx_ugraph(_df)\n", - " _ = nx.shortest_path(_G, 1)\n", + " _ = nx.betweenness_centrality(_G, k=_k)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_sssp(_df):\n", + "def cu_bc(_df, _k):\n", " t1 = time.time()\n", - " _G = create_cu_ugraph(_df) \n", - " _ = cugraph.sssp(_G, 1)\n", + " _G = create_cu_ugraph(_df)\n", + " _ = cugraph.betweenness_centrality(_G, k=_k)\n", " t2 = time.time() - t1\n", " return t2" ] @@ -263,7 +313,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### PageRank" + "### Louvain" ] }, { @@ -272,17 +322,55 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_pagerank(_df):\n", + "def nx_louvain(_df):\n", " t1 = time.time()\n", - " _G = create_nx_digraph(_df)\n", - " _ = nx.pagerank(_G)\n", + " _G = create_nx_ugraph(_df)\n", + " parts = community.best_partition(_G)\n", + " \n", + " # Calculating modularity scores for comparison \n", + " _ = community.modularity(parts, _G) \n", + " \n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_pagerank(_df):\n", + "def cu_louvain(_df):\n", " t1 = time.time()\n", - " _G = create_cu_digraph(_df)\n", - " _ = cugraph.pagerank(_G)\n", + " _G = create_cu_ugraph(_df)\n", + " _,_ = cugraph.louvain(_G)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Triangle Counting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_tc(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " nx_count = nx.triangles(_G)\n", + " \n", + " # To get the number of triangles, we would need to loop through the array and add up each count\n", + " count = 0\n", + " for key, value in nx_count.items():\n", + " count = count + value \n", + " \n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_tc(_df):\n", + " t1 = time.time()\n", + " _G = create_cu_ugraph(_df)\n", + " _ = cugraph.triangles(_G)\n", " t2 = time.time() - t1\n", " return t2" ] @@ -303,7 +391,13 @@ "def nx_wcc(_df):\n", " t1 = time.time()\n", " _G = create_nx_digraph(_df)\n", - " _ = nx.weakly_connected_components(_G)\n", + " gen = nx.weakly_connected_components(_G)\n", + "\n", + " list_of_digraphs = []\n", + "\n", + " for subgraph in gen:\n", + " list_of_digraphs.append(nx.subgraph(_G, subgraph))\n", + " \n", " t2 = time.time() - t1\n", " return t2\n", "\n", @@ -319,7 +413,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Betweenness Centrality (vertex)" + "### Core Number" ] }, { @@ -328,17 +422,22 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_bc(_df):\n", + "def nx_core_num(_df):\n", " t1 = time.time()\n", " _G = create_nx_ugraph(_df)\n", - " _ = nx.betweenness_centrality(_G, k=100)\n", + " nx_count = nx.core_number(_G)\n", + " \n", + " count = 0\n", + " for key, value in nx_count.items():\n", + " count = count + value \n", + " \n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_bc(_df):\n", + "def cu_core_num(_df):\n", " t1 = time.time()\n", - " _G = create_cu_ugraph(_df)\n", - " _ = cugraph.betweenness_centrality(_G, k=100)\n", + " _G = create_cu_ugraph(_df) \n", + " _ = cugraph.core_number(_G)\n", " t2 = time.time() - t1\n", " return t2" ] @@ -347,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Louvain" + "### PageRank" ] }, { @@ -356,21 +455,45 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_louvain(_df):\n", + "def nx_pagerank(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_digraph(_df)\n", + " _ = nx.pagerank(_G)\n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_pagerank(_df):\n", + " t1 = time.time()\n", + " _G = create_cu_digraph(_df)\n", + " _ = cugraph.pagerank(_G)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Jaccard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_jaccard(_df):\n", " t1 = time.time()\n", " _G = create_nx_ugraph(_df)\n", - " parts = community.best_partition(_G)\n", - " \n", - " # Calculating modularity scores for comparison \n", - " _ = community.modularity(parts, _G) \n", - " \n", + " _ = nx.jaccard_coefficient(_G)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_louvain(_df):\n", + "def cu_jaccard(_df):\n", " t1 = time.time()\n", " _G = create_cu_ugraph(_df)\n", - " _,_ = cugraph.louvain(_G)\n", + " _ = cugraph.jaccard_coefficient(_G)\n", " t2 = time.time() - t1\n", " return t2" ] @@ -379,7 +502,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Triangle Counting" + "### BFS" ] }, { @@ -388,23 +511,17 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_tc(_df):\n", + "def nx_bfs(_df):\n", " t1 = time.time()\n", " _G = create_nx_ugraph(_df)\n", - " nx_count = nx.triangles(_G)\n", - " \n", - " # To get the number of triangles, we would need to loop through the array and add up each count\n", - " count = 0\n", - " for key, value in nx_count.items():\n", - " count = count + value \n", - " \n", + " _ = nx.bfs_edges(_G, 1)\n", " t2 = time.time() - t1\n", " return t2\n", "\n", - "def cu_tc(_df):\n", + "def cu_bfs(_df):\n", " t1 = time.time()\n", " _G = create_cu_ugraph(_df)\n", - " _ = cugraph.triangles(_G)\n", + " _ = cugraph.bfs(_G, 1)\n", " t2 = time.time() - t1\n", " return t2" ] @@ -413,7 +530,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Benchmark Functions" + "### SSSP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_sssp(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = nx.shortest_path(_G, 1)\n", + " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_sssp(_df):\n", + " t1 = time.time()\n", + " _G = create_cu_ugraph(_df) \n", + " _ = cugraph.sssp(_G, 1)\n", + " t2 = time.time() - t1\n", + " return t2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# Benchmark" ] }, { @@ -436,6 +590,7 @@ "# This cell might not be needed\n", "v = './data/preferentialAttachment.mtx'\n", "gdf = read_data(v)\n", + "pdf = gdf.to_pandas()\n", "print(f\"\\tGDF Size {len(gdf)}\")\n", "\n", "g = create_cu_ugraph(gdf)\n", @@ -443,12 +598,30 @@ "print(f\"\\tcugraph Size {g.number_of_edges()}\")\n", "print(f\"\\tcugraph Order {g.number_of_vertices()}\")\n", "\n", + "gnx = create_nx_ugraph(pdf)\n", + "\n", "# clean up what we just created\n", "del gdf\n", + "del pdf\n", "del g\n", + "del gnx\n", "gc.collect()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -457,13 +630,14 @@ "source": [ "# arrays to capture performance gains\n", "names = []\n", + "algos = []\n", "\n", "# Two dimension data\n", "time_algo_cu = [] # will be two dimensional\n", "time_algo_nx = [] # will be two dimensional\n", "perf = []\n", "\n", - "\n", + "algos.append(\" \")\n", "\n", "i = 0\n", "for k,v in data.items():\n", @@ -479,76 +653,188 @@ " pdf = gdf.to_pandas()\n", " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", "\n", - " # BFS\n", - " print(\"\\tBFS\")\n", - " tx = nx_bfs(pdf)\n", - " tc = cu_bfs(gdf)\n", + " # prep\n", + " tmp_g = create_cu_ugraph(gdf)\n", + " deg = tmp_g.degree()\n", + " deg_max = deg['degree'].max()\n", "\n", + " alpha = 1 / deg_max\n", + " num_nodes = tmp_g.number_of_vertices()\n", + " \n", + " del tmp_g\n", + " del deg\n", + " \n", + " \n", + " #----- Algorithm order is same as defined at top ----\n", + " \n", + " # Katz \n", + " print(\"\\tKatz \", end = '')\n", + " if i == 0: \n", + " algos.append(\"Katz\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_katz(pdf, alpha)\n", + " print(\"c.\", end='')\n", + " tc = cu_katz(gdf, alpha)\n", + " print(\"\")\n", + " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", " gc.collect()\n", " \n", - " # SSSP\n", - " print(\"\\tSSSP\")\n", - " tx = nx_sssp(pdf)\n", - " tc = cu_sssp(gdf)\n", - "\n", + " \n", + " # BC\n", + " print(\"\\tBC k=100 \", end='')\n", + " if i == 0:\n", + " algos.append(\"BC Estimate fixed\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_bc(pdf, 100)\n", + " print(\"c.\", end='')\n", + " tc = cu_bc(gdf, 100)\n", + " print(\" \")\n", + " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", " gc.collect()\n", + " \n", "\n", - " # PageRank\n", - " print(\"\\tPageRank\") \n", - " tx = nx_pagerank(pdf)\n", - " tc = cu_pagerank(gdf)\n", + " # Louvain\n", + " print(\"\\tLouvain \", end='')\n", + " if i == 0:\n", + " algos.append(\"Louvain\")\n", "\n", + " print(\"n.\", end='')\n", + " tx = nx_louvain(pdf)\n", + " print(\"c.\", end='')\n", + " tc = cu_louvain(gdf)\n", + " print(\" \")\n", + " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", " gc.collect()\n", + " \n", + " # TC\n", + " print(\"\\tTC \", end='')\n", + " if i == 0:\n", + " algos.append(\"TC\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_tc(pdf)\n", + " print(\"c.\", end='')\n", + " tc = cu_tc(gdf)\n", + " print(\" \")\n", + " \n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + " gc.collect()\n", + "\n", "\n", " # WCC\n", - " print(\"\\tWCC\")\n", + " print(\"\\tWCC \", end='')\n", + " if i == 0:\n", + " algos.append(\"WCC\")\n", + "\n", + " print(\"n.\", end='')\n", " tx = nx_wcc(pdf)\n", + " print(\"c.\", end='')\n", " tc = cu_wcc(gdf)\n", + " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", " gc.collect()\n", + " \n", + " # Core Number\n", + " print(\"\\tCore Number \", end='')\n", + " if i == 0:\n", + " algos.append(\"Core Number\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_core_num(pdf)\n", + " print(\"c.\", end='')\n", + " tc = cu_core_num(gdf)\n", + " print(\" \")\n", + " \n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + " gc.collect() \n", "\n", - " # BC\n", - " print(\"\\tBC\")\n", - " tx = nx_bc(pdf)\n", - " tc = cu_bc(gdf)\n", + " \n", + " # PageRank\n", + " print(\"\\tPageRank \", end='')\n", + " if i == 0:\n", + " algos.append(\"PageRank\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_pagerank(pdf)\n", + " print(\"c.\", end='')\n", + " tc = cu_pagerank(gdf)\n", + " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", " gc.collect()\n", + " \n", + " \n", + " # Jaccard\n", + " print(\"\\tJaccard \", end='')\n", + " if i == 0:\n", + " algos.append(\"Jaccard\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_jaccard(pdf)\n", + " print(\"c.\", end='')\n", + " tc = cu_jaccard(gdf)\n", + " print(\" \")\n", + " \n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + " gc.collect()\n", + " \n", "\n", - " # Louvain\n", - " print(\"\\tLouvain\")\n", - " tx = nx_louvain(pdf)\n", - " tc = cu_louvain(gdf)\n", + " # BFS\n", + " print(\"\\tBFS \", end='')\n", + " if i == 0:\n", + " algos.append(\"BFS\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_bfs(pdf)\n", + " print(\"c.\", end='')\n", + " tc = cu_bfs(gdf)\n", + " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", " gc.collect()\n", + " \n", + " \n", + " # SSSP\n", + " print(\"\\tSSSP \", end='')\n", + " if i == 0:\n", + " algos.append(\"SSP\")\n", "\n", - " # TC\n", - " print(\"\\tTC\")\n", - " tx = nx_tc(pdf)\n", - " tc = cu_tc(gdf)\n", + " print(\"n.\", end='')\n", + " tx = nx_sssp(pdf)\n", + " print(\"c.\", end='')\n", + " tc = cu_sssp(gdf)\n", + " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", " perf[i].append(tx/tc)\n", " gc.collect()\n", "\n", + " # increament count\n", " i = i + 1\n", "\n" ] @@ -560,6 +846,93 @@ "outputs": [], "source": [ "#Print results\n", + "print(algos)\n", + "\n", + "for i in range(num_datasets):\n", + " print(f\"{names[i]}\")\n", + " print(f\"{perf[i]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The following section is to rerun portions of the benchamrks if needed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# arrays to capture performance gains\n", + "names = []\n", + "algos = []\n", + "\n", + "# Two dimension data\n", + "time_algo_cu = [] # will be two dimensional\n", + "time_algo_nx = [] # will be two dimensional\n", + "perf = []\n", + "\n", + "algos.append(\" \")\n", + "\n", + "i = 0\n", + "for k,v in data.items():\n", + " time_algo_cu.append([])\n", + " time_algo_nx.append([])\n", + " perf.append([])\n", + " \n", + " # Saved the file Name\n", + " names.append(k)\n", + "\n", + " # read data\n", + " gdf = read_data(v)\n", + " pdf = gdf.to_pandas()\n", + " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", + "\n", + " # prep\n", + " tmp_g = create_cu_ugraph(gdf)\n", + " deg = tmp_g.degree()\n", + " deg_max = deg['degree'].max()\n", + "\n", + " alpha = 1 / deg_max\n", + " num_nodes = tmp_g.number_of_vertices()\n", + " \n", + " del tmp_g\n", + " del deg\n", + " \n", + " \n", + " #----- Algorithm order is same as defined at top ----\n", + " \n", + " # testing BC with large k values\n", + " # BC - Estimate\n", + " print(\"\\tBC k = 0.1% (x 0.001) \", end='')\n", + " if i == 0:\n", + " algos.append(\"BC Estimate percent\")\n", + " \n", + " k = math.ceil(num_nodes * 0.001)\n", + " \n", + " print(\"n.\", end='')\n", + " tx = nx_bc(pdf, k)\n", + " print(\"c.\", end='')\n", + " tc = cu_bc(gdf, k)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf[i].append(tx/tc)\n", + " gc.collect() \n", + " \n", + " # increament count\n", + " i = i + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Print results\n", + "print(algos)\n", + "\n", "for i in range(num_datasets):\n", " print(f\"{names[i]}\")\n", " print(f\"{perf[i]}\")" @@ -595,7 +968,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.12" } }, "nbformat": 4, From 3762c755a42b4dfbb8251a7f13a6c3f1f446c95d Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Wed, 26 Jan 2022 18:48:54 -0800 Subject: [PATCH 167/384] Initial automated doctest, all current examples now pass, other documentation edits (#2014) Closes #2012 and #2022 Authors: - https://github.com/betochimas Approvers: - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2014 --- docs/cugraph/source/api_docs/centrality.rst | 2 +- docs/cugraph/source/api_docs/community.rst | 2 +- docs/cugraph/source/api_docs/cores.rst | 2 +- docs/cugraph/source/api_docs/link_analysis.rst | 2 +- docs/cugraph/source/api_docs/link_prediction.rst | 8 -------- docs/cugraph/source/api_docs/structure.rst | 11 +++++++++-- docs/cugraph/source/api_docs/traversal.rst | 1 - docs/cugraph/source/conf.py | 7 ++++--- 8 files changed, 17 insertions(+), 18 deletions(-) diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/centrality.rst index 2f43354ea..81c4577fa 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/centrality.rst @@ -25,5 +25,5 @@ Katz Centrality (MG) .. autosummary:: :toctree: api/ - cugraph.dask.centrality.katz_centrality.call_katz_centrality cugraph.dask.centrality.katz_centrality.katz_centrality + cugraph.dask.centrality.katz_centrality.call_katz_centrality diff --git a/docs/cugraph/source/api_docs/community.rst b/docs/cugraph/source/api_docs/community.rst index 7f7752ae4..98292e6fb 100644 --- a/docs/cugraph/source/api_docs/community.rst +++ b/docs/cugraph/source/api_docs/community.rst @@ -51,7 +51,7 @@ Louvain (MG) :toctree: api/ cugraph.dask.community.louvain.call_louvain - cugraph.community.ktruss_subgraph.ktruss_subgraph + cugraph.dask.community.louvain.louvain Spectral Clustering ------------------- diff --git a/docs/cugraph/source/api_docs/cores.rst b/docs/cugraph/source/api_docs/cores.rst index 344725914..2f0761597 100644 --- a/docs/cugraph/source/api_docs/cores.rst +++ b/docs/cugraph/source/api_docs/cores.rst @@ -18,4 +18,4 @@ K-Core .. autosummary:: :toctree: api/ - cugraph.centrality.katz_centrality.katz_centrality + cugraph.cores.k_core.k_core diff --git a/docs/cugraph/source/api_docs/link_analysis.rst b/docs/cugraph/source/api_docs/link_analysis.rst index 809fd3647..162163afd 100644 --- a/docs/cugraph/source/api_docs/link_analysis.rst +++ b/docs/cugraph/source/api_docs/link_analysis.rst @@ -18,7 +18,7 @@ Pagerank .. autosummary:: :toctree: api/ - cugraph.centrality.katz_centrality.katz_centrality + cugraph.link_analysis.pagerank.pagerank Pagerank (MG) ------------- diff --git a/docs/cugraph/source/api_docs/link_prediction.rst b/docs/cugraph/source/api_docs/link_prediction.rst index 0ecfde58d..f1c713f68 100644 --- a/docs/cugraph/source/api_docs/link_prediction.rst +++ b/docs/cugraph/source/api_docs/link_prediction.rst @@ -24,11 +24,3 @@ Overlap Coefficient cugraph.link_prediction.overlap.overlap_coefficient cugraph.link_prediction.woverlap.overlap_w - -Pagerank (MG) -------------- -.. autosummary:: - :toctree: api/ - - cugraph.dask.link_analysis.pagerank.pagerank - diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst index 9f43079b7..7b4e58aec 100644 --- a/docs/cugraph/source/api_docs/structure.rst +++ b/docs/cugraph/source/api_docs/structure.rst @@ -45,7 +45,6 @@ Checks Graph.lookup_internal_vertex_id Graph.to_directed Graph.to_undirected - Graph.unrenumber Symmetrize @@ -73,4 +72,12 @@ Conversion from Other Formats cugraph.structure.convert_matrix.to_numpy_array cugraph.structure.convert_matrix.to_numpy_matrix cugraph.structure.convert_matrix.to_pandas_adjacency - cugraph.structure.convert_matrix.to_pandas_edgelist \ No newline at end of file + cugraph.structure.convert_matrix.to_pandas_edgelist + +Other +----------------------------- +.. autosummary:: + :toctree: api/ + + Graph.unrenumber + cugraph.structure.hypergraph.hypergraph \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/traversal.rst b/docs/cugraph/source/api_docs/traversal.rst index 2a6d6873f..3eb32e2cf 100644 --- a/docs/cugraph/source/api_docs/traversal.rst +++ b/docs/cugraph/source/api_docs/traversal.rst @@ -21,7 +21,6 @@ Breadth-first-search (MG) cugraph.dask.traversal.bfs.bfs cugraph.dask.traversal.bfs.call_bfs - Single-source-shortest-path --------------------------- .. autosummary:: diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index f0c0dc441..c4963bdde 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # -# Copyright (c) 2018-2021, NVIDIA CORPORATION. +# Copyright (c) 2018-2022, NVIDIA CORPORATION. # # pygdf documentation build configuration file, created by # sphinx-quickstart on Wed May 3 10:59:22 2017. @@ -73,7 +73,7 @@ # General information about the project. project = 'cugraph' -copyright = '2019-2021, NVIDIA' +copyright = '2019-2022, NVIDIA' author = 'NVIDIA' # The version info for the project you're documenting, acts as replacement for @@ -120,8 +120,9 @@ # otherwise, readthedocs.org uses their theme by default, # so no need to specify it import pydata_sphinx_theme + import sphinx_rtd_theme html_theme = 'pydata_sphinx_theme' - html_theme_path = pydata_sphinx_theme.get_html_theme_path() + html_theme_path = sphinx_rtd_theme.get_html_theme_path() # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False From 4219fe2ceda9d8e3cfe49dcf1fc3deda5c3c9d61 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 2 Feb 2022 20:27:05 +0000 Subject: [PATCH 168/384] REL v22.02.00 release From 5187df494aaa902b7fcedfd8fa966830d465d3e8 Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Fri, 18 Feb 2022 18:40:37 -0800 Subject: [PATCH 169/384] API Doc Namespace Edits + SimpleGraphImpl methods (#2086) Increases consistency between the API docs and the namespace in which the method can be called from, reducing potential confusion. Ex: cugraph.centrality.betweenness_centrality.betweenness_centrality can now be found as cugraph.betweenness_centrality. Note: both are valid ways of calling the same method. Includes methods and attributes of a graph object once data is loaded (from SimpleGraphImpl), such as number_of_vertices, has_edge, get_two_hops_neighbors, etc. Ready for review. Authors: - https://github.com/betochimas Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2086 --- docs/cugraph/source/api_docs/centrality.rst | 6 +-- docs/cugraph/source/api_docs/community.rst | 28 +++++++------- docs/cugraph/source/api_docs/components.rst | 6 +-- docs/cugraph/source/api_docs/cores.rst | 4 +- .../source/api_docs/graph_implementation.rst | 38 +++++++++++++++++++ docs/cugraph/source/api_docs/index.rst | 1 + docs/cugraph/source/api_docs/layout.rst | 2 +- .../source/api_docs/linear_assignment.rst | 3 +- .../cugraph/source/api_docs/link_analysis.rst | 4 +- .../source/api_docs/link_prediction.rst | 21 +++++++--- docs/cugraph/source/api_docs/pylibcugraph.rst | 4 +- docs/cugraph/source/api_docs/sampling.rst | 2 +- docs/cugraph/source/api_docs/structure.rst | 34 +++++++++-------- docs/cugraph/source/api_docs/traversal.rst | 12 +++--- 14 files changed, 108 insertions(+), 57 deletions(-) create mode 100644 docs/cugraph/source/api_docs/graph_implementation.rst diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/centrality.rst index 81c4577fa..4001328ee 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/centrality.rst @@ -10,15 +10,15 @@ Betweenness Centrality .. autosummary:: :toctree: api/ - cugraph.centrality.betweenness_centrality.betweenness_centrality - cugraph.centrality.betweenness_centrality.edge_betweenness_centrality + cugraph.betweenness_centrality + cugraph.edge_betweenness_centrality Katz Centrality --------------- .. autosummary:: :toctree: api/ - cugraph.centrality.katz_centrality.katz_centrality + cugraph.katz_centrality Katz Centrality (MG) -------------------- diff --git a/docs/cugraph/source/api_docs/community.rst b/docs/cugraph/source/api_docs/community.rst index 98292e6fb..23db12c44 100644 --- a/docs/cugraph/source/api_docs/community.rst +++ b/docs/cugraph/source/api_docs/community.rst @@ -10,15 +10,15 @@ EgoNet .. autosummary:: :toctree: api/ - cugraph.community.egonet.batched_ego_graphs - cugraph.community.egonet.ego_graph + cugraph.batched_ego_graphs + cugraph.ego_graph Ensemble clustering for graphs (ECG) ------------------------------------ .. autosummary:: :toctree: api/ - cugraph.community.ecg.ecg + cugraph.ecg K-Truss @@ -26,15 +26,15 @@ K-Truss .. autosummary:: :toctree: api/ - cugraph.community.ktruss_subgraph.k_truss - cugraph.community.ktruss_subgraph.ktruss_subgraph + cugraph.k_truss + cugraph.ktruss_subgraph Leiden ------ .. autosummary:: :toctree: api/ - cugraph.community.leiden.leiden + cugraph.leiden Louvain @@ -42,7 +42,7 @@ Louvain .. autosummary:: :toctree: api/ - cugraph.community.louvain.louvain + cugraph.louvain Louvain (MG) @@ -58,11 +58,11 @@ Spectral Clustering .. autosummary:: :toctree: api/ - cugraph.community.spectral_clustering.analyzeClustering_edge_cut - cugraph.community.spectral_clustering.analyzeClustering_modularity - cugraph.community.spectral_clustering.analyzeClustering_ratio_cut - cugraph.community.spectral_clustering.spectralBalancedCutClustering - cugraph.community.spectral_clustering.spectralModularityMaximizationClustering + cugraph.analyzeClustering_edge_cut + cugraph.analyzeClustering_modularity + cugraph.analyzeClustering_ratio_cut + cugraph.spectralBalancedCutClustering + cugraph.spectralModularityMaximizationClustering Subgraph Extraction @@ -70,7 +70,7 @@ Subgraph Extraction .. autosummary:: :toctree: api/ - cugraph.community.subgraph_extraction.subgraph + cugraph.subgraph Triangle Counting @@ -78,4 +78,4 @@ Triangle Counting .. autosummary:: :toctree: api/ - cugraph.community.triangle_count.triangles + cugraph.triangles diff --git a/docs/cugraph/source/api_docs/components.rst b/docs/cugraph/source/api_docs/components.rst index 0f7d3a9c2..cf2f09efa 100644 --- a/docs/cugraph/source/api_docs/components.rst +++ b/docs/cugraph/source/api_docs/components.rst @@ -10,9 +10,9 @@ Connected Components .. autosummary:: :toctree: api/ - cugraph.components.connectivity.connected_components - cugraph.components.connectivity.strongly_connected_components - cugraph.components.connectivity.weakly_connected_components + cugraph.connected_components + cugraph.strongly_connected_components + cugraph.weakly_connected_components Connected Components (MG) diff --git a/docs/cugraph/source/api_docs/cores.rst b/docs/cugraph/source/api_docs/cores.rst index 2f0761597..0e48c584b 100644 --- a/docs/cugraph/source/api_docs/cores.rst +++ b/docs/cugraph/source/api_docs/cores.rst @@ -10,7 +10,7 @@ Core Number .. autosummary:: :toctree: api/ - cugraph.cores.core_number.core_number + cugraph.core_number K-Core @@ -18,4 +18,4 @@ K-Core .. autosummary:: :toctree: api/ - cugraph.cores.k_core.k_core + cugraph.k_core diff --git a/docs/cugraph/source/api_docs/graph_implementation.rst b/docs/cugraph/source/api_docs/graph_implementation.rst new file mode 100644 index 000000000..e96c49c91 --- /dev/null +++ b/docs/cugraph/source/api_docs/graph_implementation.rst @@ -0,0 +1,38 @@ +==================== +Graph Implementation +==================== +.. currentmodule:: cugraph.structure.graph_implementation.simpleGraphImpl + + +Graph Implementation +------------------------- +.. autosummary:: + :toctree: api/ + + view_edge_list + delete_edge_list + view_adj_list + view_transposed_adj_list + delete_adj_list + + enable_batch + batch_adjlists + batch_edgelists + batch_enabled + batch_transposed_adjlists + + get_two_hop_neighbors + number_of_vertices + number_of_nodes + number_of_edges + in_degree + out_degree + degree + degrees + has_edge + has_node + has_self_loop + edges + nodes + neighbors + vertex_column_size \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 9da23c396..e7f94d7e1 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -10,6 +10,7 @@ This page provides a list of all publicly accessible modules, methods and classe :caption: API Documentation structure + graph_implementation centrality community components diff --git a/docs/cugraph/source/api_docs/layout.rst b/docs/cugraph/source/api_docs/layout.rst index fc81df722..b3943e4d3 100644 --- a/docs/cugraph/source/api_docs/layout.rst +++ b/docs/cugraph/source/api_docs/layout.rst @@ -10,5 +10,5 @@ Force Atlas 2 .. autosummary:: :toctree: api/ - cugraph.layout.force_atlas2.force_atlas2 + cugraph.force_atlas2 diff --git a/docs/cugraph/source/api_docs/linear_assignment.rst b/docs/cugraph/source/api_docs/linear_assignment.rst index 147597879..383bb4d43 100644 --- a/docs/cugraph/source/api_docs/linear_assignment.rst +++ b/docs/cugraph/source/api_docs/linear_assignment.rst @@ -10,4 +10,5 @@ Hungarian .. autosummary:: :toctree: api/ - cugraph.linear_assignment.hungarian + cugraph.hungarian + cugraph.dense_hungarian diff --git a/docs/cugraph/source/api_docs/link_analysis.rst b/docs/cugraph/source/api_docs/link_analysis.rst index 162163afd..c19a36aed 100644 --- a/docs/cugraph/source/api_docs/link_analysis.rst +++ b/docs/cugraph/source/api_docs/link_analysis.rst @@ -10,7 +10,7 @@ HITS .. autosummary:: :toctree: api/ - cugraph.link_analysis.hits.hits + cugraph.hits Pagerank @@ -18,7 +18,7 @@ Pagerank .. autosummary:: :toctree: api/ - cugraph.link_analysis.pagerank.pagerank + cugraph.pagerank Pagerank (MG) ------------- diff --git a/docs/cugraph/source/api_docs/link_prediction.rst b/docs/cugraph/source/api_docs/link_prediction.rst index f1c713f68..7c7b34f2f 100644 --- a/docs/cugraph/source/api_docs/link_prediction.rst +++ b/docs/cugraph/source/api_docs/link_prediction.rst @@ -10,9 +10,9 @@ Jaccard Coefficient .. autosummary:: :toctree: api/ - cugraph.link_prediction.jaccard.jaccard - cugraph.link_prediction.jaccard.jaccard_coefficient - cugraph.link_prediction.wjaccard.jaccard_w + cugraph.jaccard + cugraph.jaccard_coefficient + cugraph.jaccard_w Overlap Coefficient @@ -20,7 +20,16 @@ Overlap Coefficient .. autosummary:: :toctree: api/ - cugraph.link_prediction.overlap.overlap - cugraph.link_prediction.overlap.overlap_coefficient - cugraph.link_prediction.woverlap.overlap_w + cugraph.overlap + cugraph.overlap_coefficient + cugraph.overlap_w + +Sorensen Coefficient +-------------------- +.. autosummary:: + :toctree: api/ + + cugraph.sorensen + cugraph.sorensen_coefficient + cugraph.sorensen_w diff --git a/docs/cugraph/source/api_docs/pylibcugraph.rst b/docs/cugraph/source/api_docs/pylibcugraph.rst index ffc2e84e3..9317274cf 100644 --- a/docs/cugraph/source/api_docs/pylibcugraph.rst +++ b/docs/cugraph/source/api_docs/pylibcugraph.rst @@ -11,5 +11,5 @@ Methods .. autosummary:: :toctree: api/ - pylibcugraph.components.strongly_connected_components - pylibcugraph.components.weakly_connected_components + pylibcugraph.strongly_connected_components + pylibcugraph.weakly_connected_components diff --git a/docs/cugraph/source/api_docs/sampling.rst b/docs/cugraph/source/api_docs/sampling.rst index de7f707fb..6255c981a 100644 --- a/docs/cugraph/source/api_docs/sampling.rst +++ b/docs/cugraph/source/api_docs/sampling.rst @@ -10,4 +10,4 @@ Random Walks .. autosummary:: :toctree: api/ - cugraph.sampling.random_walks.random_walks + cugraph.random_walks diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst index 7b4e58aec..f2d497814 100644 --- a/docs/cugraph/source/api_docs/structure.rst +++ b/docs/cugraph/source/api_docs/structure.rst @@ -29,6 +29,7 @@ Adding Data Graph.add_internal_vertex_id Graph.add_nodes_from Graph.clear + Graph.unrenumber Checks ------ @@ -52,9 +53,9 @@ Symmetrize .. autosummary:: :toctree: api/ - cugraph.structure.symmetrize.symmetrize - cugraph.structure.symmetrize.symmetrize_ddf - cugraph.structure.symmetrize.symmetrize_df + cugraph.symmetrize + cugraph.symmetrize_ddf + cugraph.symmetrize_df Conversion from Other Formats @@ -62,22 +63,23 @@ Conversion from Other Formats .. autosummary:: :toctree: api/ - cugraph.structure.convert_matrix.from_adjlist - cugraph.structure.convert_matrix.from_cudf_edgelist - cugraph.structure.convert_matrix.from_edgelist - cugraph.structure.convert_matrix.from_numpy_array - cugraph.structure.convert_matrix.from_numpy_matrix - cugraph.structure.convert_matrix.from_pandas_adjacency - cugraph.structure.convert_matrix.from_pandas_edgelist - cugraph.structure.convert_matrix.to_numpy_array - cugraph.structure.convert_matrix.to_numpy_matrix - cugraph.structure.convert_matrix.to_pandas_adjacency - cugraph.structure.convert_matrix.to_pandas_edgelist + cugraph.from_adjlist + cugraph.from_cudf_edgelist + cugraph.from_edgelist + cugraph.from_numpy_array + cugraph.from_numpy_matrix + cugraph.from_pandas_adjacency + cugraph.from_pandas_edgelist + cugraph.to_numpy_array + cugraph.to_numpy_matrix + cugraph.to_pandas_adjacency + cugraph.to_pandas_edgelist Other ----------------------------- .. autosummary:: :toctree: api/ - Graph.unrenumber - cugraph.structure.hypergraph.hypergraph \ No newline at end of file + cugraph.hypergraph + cugraph.structure.shuffle + cugraph.structure.NumberMap diff --git a/docs/cugraph/source/api_docs/traversal.rst b/docs/cugraph/source/api_docs/traversal.rst index 3eb32e2cf..d1736799e 100644 --- a/docs/cugraph/source/api_docs/traversal.rst +++ b/docs/cugraph/source/api_docs/traversal.rst @@ -10,8 +10,8 @@ Breadth-first-search .. autosummary:: :toctree: api/ - cugraph.traversal.bfs.bfs - cugraph.traversal.bfs.bfs_edges + cugraph.bfs + cugraph.bfs_edges Breadth-first-search (MG) ------------------------- @@ -26,10 +26,10 @@ Single-source-shortest-path .. autosummary:: :toctree: api/ - cugraph.traversal.sssp.filter_unreachable - cugraph.traversal.sssp.shortest_path - cugraph.traversal.sssp.shortest_path_length - cugraph.traversal.sssp.sssp + cugraph.filter_unreachable + cugraph.shortest_path + cugraph.shortest_path_length + cugraph.sssp Single-source-shortest-path (MG) -------------------------------- From 146e1e7e05ed847d0687d0f9f9a26e0299be3dc3 Mon Sep 17 00:00:00 2001 From: "Corey J. Nolet" Date: Thu, 24 Feb 2022 16:36:34 -0500 Subject: [PATCH 170/384] Automatically clone raft when the raft pinned tag changes (#2087) Now that RAFT has official conda packages, it'll be much easier to work locally on cugraph features that rely on the RAFT nightly. Working on features that span raft and cugraph should be fairly straightforward to do locally as well, because CPM provides the `CPM_raft_SOURCE` variable that can passed into the `cmake` call to tell the build to use a local working copy of RAFT. The raft conda packages are now ready to add to the CI conda dependencies and once added, the updating the raft pinned tag in `get_raft.cmake` will no longer work. The option added in this PR will force clone a local version of the pinned tag (both locally in CI) and set the `CPM_raft_SOURCE` to that path. Authors: - Corey J. Nolet (https://github.com/cjnolet) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2087 --- .gitignore | 5 ----- 1 file changed, 5 deletions(-) diff --git a/.gitignore b/.gitignore index fa7580040..485b71cac 100644 --- a/.gitignore +++ b/.gitignore @@ -79,11 +79,6 @@ datasets/* ## Doxygen cpp/doxygen/html -# Raft symlink -python/cugraph/cugraph/raft -python/pylibcugraph/pylibcugraph/raft -python/_external_repositories/ - # created by Dask tests python/dask-worker-space python/cugraph/dask-worker-space From 4efbd9927527987a56f0be28f658409e580e7a74 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 9 Mar 2022 13:38:52 -0500 Subject: [PATCH 171/384] updated release performance notebook to also measure using Nx as imput (#2083) Update the release notebook to produce speedup metrics of using a NetworkX object as the input Graph. Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2083 --- notebooks/cugraph_benchmarks/release.ipynb | 342 ++++++++++++++++++--- 1 file changed, 294 insertions(+), 48 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index 7d1c82468..e9d887a04 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Skip notebook test\n", - "(this notebook is not executed as part of the RAPIDS cuGraph CI process. Execution could take a few hours)\n", + "(this notebook is not executed as part of the RAPIDS cuGraph CI process. Execution will take a few hours)\n", "\n", "---\n", "\n", @@ -52,7 +52,6 @@ "| ------------------------|---------------|------ | ------- |-------------\n", "| Katz | Centrality | X | | \n", "| Betweenness Centrality | Centrality | X | | Estimated, k = 100\n", - "| Betweenness Centrality | Centrality | X | | Estimated, k = 0.1% \n", "| Louvain | Community | X | | Uses python-louvain for comparison\n", "| Triangle Counting | Community | X | |\n", "| WCC | Components | | X | Nx requires directed and returns a generator \n", @@ -76,7 +75,7 @@ "\n", "\n", "### Notes\n", - "* Running Betweenness Centrality on the full graph is prohibited using NetworkX\n" + "* Running Betweenness Centrality on the full graph is prohibited using NetworkX. Anything over k=100 can explode runtime to days\n" ] }, { @@ -86,10 +85,11 @@ "Notebook Credits\n", "\n", " \n", - "| Author | Date | Update | cuGraph Version | Test Hardware |\n", - "| --------------|------------|------------------|-----------------|------------------------|\n", - "| Brad Rees | 10/06/2020 | created | 0.16 | GV100, CUDA 10.2 |\n", - "| Brad Rees | 01/20/2022 | updated | 22.02 | Quadro A6000 CUDA 11.5 |\n", + "| Author | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|---------------------|-----------------|------------------------|\n", + "| Brad Rees | 10/06/2020 | created | 0.16 | GV100, CUDA 10.2 |\n", + "| Brad Rees | 01/20/2022 | updated | 22.02 | Quadro A6000 CUDA 11.5 |\n", + "| Brad Rees | 01/20/2022 | added perf w/Nx obj | 22.02 | Quadro A6000 CUDA 11.5 |\n", "\n", "\n", "\n" @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,8 @@ "\n", "# for quick testing\n", "data_quick = {\n", - " 'karate' : './data/karate.mtx',\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx', \n", + " #'karate' : './data/karate.mtx',\n", "}\n", "\n", "\n", @@ -174,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -220,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -278,6 +279,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.katz_centrality(_G, alpha)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_katz_nx(_df, alpha):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.katz_centrality(_G, alpha)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -290,11 +298,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def nx_bc(_df, _k):\n", + " print(f\" k = {_k}\", end=' ')\n", " t1 = time.time()\n", " _G = create_nx_ugraph(_df)\n", " _ = nx.betweenness_centrality(_G, k=_k)\n", @@ -306,6 +315,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.betweenness_centrality(_G, k=_k)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_bc_nx(_df, _k):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.betweenness_centrality(_G, k=_k)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -318,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -338,6 +354,13 @@ " _G = create_cu_ugraph(_df)\n", " _,_ = cugraph.louvain(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_louvain_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _,_ = cugraph.louvain(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -350,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -372,6 +395,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.triangles(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_tc_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.triangles(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -384,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -406,6 +436,13 @@ " _G = create_cu_digraph(_df) \n", " _ = cugraph.weakly_connected_components(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_wcc_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_digraph(_df) \n", + " _ = cugraph.weakly_connected_components(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -418,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -439,6 +476,13 @@ " _G = create_cu_ugraph(_df) \n", " _ = cugraph.core_number(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_core_num_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df) \n", + " _ = cugraph.core_number(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -451,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -467,6 +511,13 @@ " _G = create_cu_digraph(_df)\n", " _ = cugraph.pagerank(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_pagerank_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_digraph(_df)\n", + " _ = cugraph.pagerank(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -479,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -495,6 +546,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.jaccard_coefficient(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_jaccard_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.jaccard_coefficient(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -507,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -523,6 +581,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.bfs(_G, 1)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_bfs_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.bfs(_G, 1)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -535,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -551,6 +616,13 @@ " _G = create_cu_ugraph(_df) \n", " _ = cugraph.sssp(_G, 1)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_sssp_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df) \n", + " _ = cugraph.sssp(_G, 1)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -572,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -582,9 +654,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tGDF Size 999970\n", + "\tcugraph Size 499985\n", + "\tcugraph Order 100000\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# do a simple pass just to get all the libraries initiallized\n", "# This cell might not be needed\n", @@ -624,26 +717,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tdata in gdf 999970 and data in pandas 999970\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n", + "Reading ./data/dblp-2010.mtx...\n", + "\tdata in gdf 1615400 and data in pandas 1615400\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n", + "Reading ./data/coPapersCiteseer.mtx...\n", + "\tdata in gdf 32073440 and data in pandas 32073440\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n", + "Reading ./data/as-Skitter.mtx...\n", + "\tdata in gdf 22190596 and data in pandas 22190596\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n" + ] + } + ], "source": [ "# arrays to capture performance gains\n", "names = []\n", "algos = []\n", "\n", - "# Two dimension data\n", - "time_algo_cu = [] # will be two dimensional\n", - "time_algo_nx = [] # will be two dimensional\n", + "# Two dimension data [file, perf]\n", + "time_algo_nx = [] # NetworkX\n", + "time_algo_cu = [] # cuGraph\n", + "time_algo_cx = [] # cuGraph\n", "perf = []\n", + "perf_cu_nx = []\n", "\n", "algos.append(\" \")\n", "\n", "i = 0\n", "for k,v in data.items():\n", - " time_algo_cu.append([])\n", " time_algo_nx.append([])\n", + " time_algo_cu.append([])\n", + " time_algo_cx.append([])\n", " perf.append([])\n", + " perf_cu_nx.append([])\n", " \n", " # Saved the file Name\n", " names.append(k)\n", @@ -667,7 +819,7 @@ " \n", " #----- Algorithm order is same as defined at top ----\n", " \n", - " # Katz \n", + " #-- Katz \n", " print(\"\\tKatz \", end = '')\n", " if i == 0: \n", " algos.append(\"Katz\")\n", @@ -676,32 +828,44 @@ " tx = nx_katz(pdf, alpha)\n", " print(\"c.\", end='')\n", " tc = cu_katz(gdf, alpha)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_katz_nx(pdf, alpha)\n", " print(\"\")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", " \n", - " # BC\n", + " #-- BC\n", " print(\"\\tBC k=100 \", end='')\n", " if i == 0:\n", " algos.append(\"BC Estimate fixed\")\n", "\n", + " k = 100\n", + " if k > num_nodes:\n", + " k = num_nodes\n", + " \n", " print(\"n.\", end='')\n", - " tx = nx_bc(pdf, 100)\n", + " tx = nx_bc(pdf, k)\n", " print(\"c.\", end='')\n", - " tc = cu_bc(gdf, 100)\n", + " tc = cu_bc(gdf, k)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_bc_nx(pdf, k)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", "\n", - " # Louvain\n", + " #-- Louvain\n", " print(\"\\tLouvain \", end='')\n", " if i == 0:\n", " algos.append(\"Louvain\")\n", @@ -710,14 +874,18 @@ " tx = nx_louvain(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_louvain(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_louvain_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", - " # TC\n", + " #-- TC\n", " print(\"\\tTC \", end='')\n", " if i == 0:\n", " algos.append(\"TC\")\n", @@ -726,15 +894,19 @@ " tx = nx_tc(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_tc(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_tc_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", "\n", "\n", - " # WCC\n", + " #-- WCC\n", " print(\"\\tWCC \", end='')\n", " if i == 0:\n", " algos.append(\"WCC\")\n", @@ -743,14 +915,18 @@ " tx = nx_wcc(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_wcc(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_wcc_nx(pdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", - " # Core Number\n", + " #-- Core Number\n", " print(\"\\tCore Number \", end='')\n", " if i == 0:\n", " algos.append(\"Core Number\")\n", @@ -759,15 +935,19 @@ " tx = nx_core_num(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_core_num(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_core_num_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", - " gc.collect() \n", + " perf_cu_nx[i].append(tx/tcx)\n", + " gc.collect()\n", "\n", " \n", - " # PageRank\n", + " #-- PageRank\n", " print(\"\\tPageRank \", end='')\n", " if i == 0:\n", " algos.append(\"PageRank\")\n", @@ -776,15 +956,19 @@ " tx = nx_pagerank(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_pagerank(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_pagerank_nx(pdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", " \n", - " # Jaccard\n", + " #-- Jaccard\n", " print(\"\\tJaccard \", end='')\n", " if i == 0:\n", " algos.append(\"Jaccard\")\n", @@ -793,15 +977,19 @@ " tx = nx_jaccard(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_jaccard(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_jaccard_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", "\n", - " # BFS\n", + " #-- BFS\n", " print(\"\\tBFS \", end='')\n", " if i == 0:\n", " algos.append(\"BFS\")\n", @@ -810,15 +998,19 @@ " tx = nx_bfs(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_bfs(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_bfs_nx(pdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", " \n", - " # SSSP\n", + " #-- SSSP\n", " print(\"\\tSSSP \", end='')\n", " if i == 0:\n", " algos.append(\"SSP\")\n", @@ -827,11 +1019,15 @@ " tx = nx_sssp(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_sssp(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_sssp(gdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", "\n", " # increament count\n", @@ -841,9 +1037,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", + "preferentialAttachment\n", + "[531.5083672005737, 713.2091362850375, 5404.871910284401, 378.8714209575697, 70.72927362986779, 76.7855488460584, 369.8407050216162, 100.5242813104882, 4.378275208783683, 162.25090274331285]\n", + "dblp\n", + "[263.7293255857766, 1558.1884974132756, 622.282663375948, 142.59215746942343, 88.19197528607465, 116.20873484788576, 460.3603070425029, 121.2753931555862, 38.357311342936576, 186.59598139534884]\n", + "coPapersCiteseer\n", + "[2270.9739290077137, 2436.608230097977, 1057.486186959562, 2300.0750485569533, 233.7511393615408, 246.97401172398136, 853.279646090366, 112.73241487709193, 233.03646005823012, 267.6295226503849]\n", + "as-Skitter\n", + "[1037.8761480097223, 7091.970778835749, 1475.2901788933452, 3950.5098941991014, 228.11245124984197, 245.35607830599636, 547.8739313855568, 98.81747467270701, 222.20389619850272, 326.73959437931654]\n" + ] + } + ], "source": [ "#Print results\n", "print(algos)\n", @@ -853,6 +1065,40 @@ " print(f\"{perf[i]}\")" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "------------\n", + "\n", + "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", + "preferentialAttachment\n", + "[4.003601795825807, 93.08063351361315, 204.47865340368915, 2.7097492953036797, 0.8666907616338123, 1.2867491927399868, 1.9752328393020735, 0.1240993236960286, 0.7203119096547798, 179.41122927863145]\n", + "dblp\n", + "[4.885862057048338, 122.87364779256156, 31.02199520144101, 2.180625966164683, 1.4324289723528034, 1.3579940932619847, 2.348198022642711, 0.11145559203860335, 0.7265996313793216, 202.49957600312277]\n", + "coPapersCiteseer\n", + "[8.158465907417547, 49.369787236543914, 9.28701234947398, 12.067711445592089, 0.8232363804394461, 1.3752921888176353, 2.8151237928186994, 0.08727548257474192, 0.798480184263566, 322.0841686740297]\n", + "as-Skitter\n", + "[3.66123817526995, 631.2091494372795, 20.617572936160165, 34.49437773532841, 0.9264442180418646, 4.828647431829589, 2.2476285215684615, 0.12267649645919085, 0.8469181921567667, 431.6171964493335]\n" + ] + } + ], + "source": [ + "#Print results\n", + "print(\"\\n------------\\n\")\n", + "print(algos)\n", + "\n", + "for i in range(num_datasets):\n", + " print(f\"{names[i]}\")\n", + " print(f\"{perf_cu_nx[i]}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, From 414a3c98534a9c214ccf49b32f171b77bf262459 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 17 Mar 2022 12:36:27 -0400 Subject: [PATCH 172/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 6595593a4..f060f10f6 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '22.04' +version = '22.06' # The full version, including alpha/beta/rc tags. -release = '22.04.00' +release = '22.06.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From afbc588d3602a620e763c2045f71f6a2b3861acd Mon Sep 17 00:00:00 2001 From: Mike McCarty Date: Tue, 29 Mar 2022 09:20:51 -0400 Subject: [PATCH 173/384] Updated random_walk_benchmark notebook for API change in cudf (#2164) Updated random_walk_benchmark notebook for API change in cudf; to_array -> to_numpy Authors: - Mike McCarty (https://github.com/mmccarty) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2164 --- .../random_walk_benchmark.ipynb | 52 +++++++++---------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb b/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb index 65cf9fb59..95b4e87dd 100644 --- a/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/random_walk_benchmark.ipynb @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -183,7 +183,7 @@ " G = read_and_create(v)\n", " \n", " num_nodes = G.number_of_nodes()\n", - " nodes = G.nodes().to_array().tolist()\n", + " nodes = G.nodes().to_numpy().tolist()\n", "\n", " seeds = random.sample(nodes, num_seeds)\n", "\n", @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -209,7 +209,7 @@ "list" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -220,12 +220,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -255,16 +255,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "12979" + "4156" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -334,7 +334,7 @@ " G = read_and_create(v)\n", " \n", " num_nodes = G.number_of_nodes()\n", - " nodes = G.nodes().to_array().tolist()\n", + " nodes = G.nodes().to_numpy().tolist()\n", " \n", " print('\\t', end='')\n", " for j in range (10, max_seeds +1, 10) :\n", @@ -352,12 +352,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -387,16 +387,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4094" + "3711" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -430,12 +430,12 @@ ], "source": [ "G = read_and_create('./data/coPapersCiteseer.mtx')\n", - "nodes = G.nodes().to_array().tolist()\n" + "nodes = G.nodes().to_numpy().tolist()\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -447,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -461,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -475,12 +475,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -523,9 +523,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -537,7 +537,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.13" } }, "nbformat": 4, From 9cf462eb8c5e991a23b3493cfae1fa081a50b6fb Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 29 Mar 2022 11:24:12 -0400 Subject: [PATCH 174/384] 22.04 Update docs (#2171) Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2171 --- docs/cugraph/source/basics/cugraph_blogs.rst | 30 ++++++++++++++++---- docs/cugraph/source/basics/cugraph_intro.md | 5 ++++ 2 files changed, 30 insertions(+), 5 deletions(-) diff --git a/docs/cugraph/source/basics/cugraph_blogs.rst b/docs/cugraph/source/basics/cugraph_blogs.rst index 35db20ff4..4d5447879 100644 --- a/docs/cugraph/source/basics/cugraph_blogs.rst +++ b/docs/cugraph/source/basics/cugraph_blogs.rst @@ -7,8 +7,12 @@ these blog posts provide deeper dives into features from cuGraph. Here, we've selected just a few that are of particular interest to cuGraph users: -BLOGS -============== +BLOGS & Conferences +==================== +2018 +------- + * `GTC18 Fall - RAPIDS: Benchmarking Graph Analytics on the DGX-2 `_ + 2019 ------- @@ -16,7 +20,8 @@ BLOGS * `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ * `RAPIDS cuGraph : multi-GPU PageRank `_ * `Similarity in graphs: Jaccard versus the Overlap Coefficient `_ - + * `GTC19 Spring - Accelerating Graph Algorithms with RAPIDS `_ + * `GTC19 Fall - Multi-Node Multi-GPU Machine Learning and Graph Analytics with RAPIDS `_ 2020 ------ @@ -24,10 +29,19 @@ BLOGS * `Tackling Large Graphs with RAPIDS cuGraph and CUDA Unified Memory on GPUs `_ * `RAPIDS cuGraph adds NetworkX and DiGraph Compatibility `_ * `Large Graph Visualization with RAPIDS cuGraph `_ + * `GTC 20 Fall - cuGraph Goes Big `_ 2021 ------ - * + * `GTC 21 - State of RAPIDS cuGraph and what's comming next `_ + + +2022 +------ + * `GTC: State of cuGraph (video & slides) `_ + * `GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides) `_ + + @@ -38,7 +52,13 @@ Media Academic Papers =============== -* S. Kang, A. Fender, J. Eaton, B. Rees: Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters. In IEEE HPEC, Sep. 2020 + + * S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020 + + * Hricik, T., Bader, D., & Green, O. (2020, September). `Using RAPIDS AI to accelerate graph data science workflows`. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE. + + * Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504). + Other BLOGS diff --git a/docs/cugraph/source/basics/cugraph_intro.md b/docs/cugraph/source/basics/cugraph_intro.md index 142395fb7..068412950 100644 --- a/docs/cugraph/source/basics/cugraph_intro.md +++ b/docs/cugraph/source/basics/cugraph_intro.md @@ -67,3 +67,8 @@ documentation we will mostly use the terms __Node__ and __Edge__ to better match NetworkX preferred term use, as well as other Python-based tools. At the CUDA/C layer, we favor the mathematical terms of __Vertex__ and __Edge__. +# Roadmap +GitHub does not provide a robust project management interface, and so a roadmap turns into simply a projection of when work will be completed and not a complete picture of everything that needs to be done. To capture the work that requires multiple steps, issues are labels as “EPIC” and include multiple subtasks that could span multiple releases. The EPIC will be in the release where work in expected to be completed. A better roadmap is being worked an image of the roadmap will be posted when ready. + + * GitHub Project Board: https://github.com/rapidsai/cugraph/projects/28 + \ No newline at end of file From 9e7f9b49960725631da2df0663648da78319ffc9 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 6 Apr 2022 17:09:05 +0000 Subject: [PATCH 175/384] REL v22.04.00 release From a8ea2392d88241ee5644b9f76ba60abd108ba8bd Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Fri, 15 Apr 2022 09:32:20 -0400 Subject: [PATCH 176/384] Updating issue template and missing docs (#2211) Templates * added acceptance criterias * dropped enhancement as an issue type Doc * adding missing references * adding sampling Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/2211 --- docs/cugraph/source/api_docs/sampling.rst | 2 ++ docs/cugraph/source/basics/cugraph_ref.rst | 19 +++++++++++++++++++ 2 files changed, 21 insertions(+) diff --git a/docs/cugraph/source/api_docs/sampling.rst b/docs/cugraph/source/api_docs/sampling.rst index 6255c981a..d633e94b7 100644 --- a/docs/cugraph/source/api_docs/sampling.rst +++ b/docs/cugraph/source/api_docs/sampling.rst @@ -11,3 +11,5 @@ Random Walks :toctree: api/ cugraph.random_walks + cugraph.ego_graph + cugraph.experimental.dask.uniform_neighborhood_sampling \ No newline at end of file diff --git a/docs/cugraph/source/basics/cugraph_ref.rst b/docs/cugraph/source/basics/cugraph_ref.rst index e0f113eab..f099752c5 100644 --- a/docs/cugraph/source/basics/cugraph_ref.rst +++ b/docs/cugraph/source/basics/cugraph_ref.rst @@ -28,6 +28,13 @@ Betweenness Centrality Katz +- Katz, L. (1953). *A new status index derived from sociometric analysis*. Psychometrika, 18(1), 39-43. +- Foster, K.C., Muth, S.Q., Potterat, J.J. et al. *A faster Katz status score algorithm*. Computational & Mathematical Organization Theory (2001) 7: 275. + + + +K-Truss + - J. Cohen, *Trusses: Cohesive subgraphs for social network analysis* National security agency technical report, 2008 - O. Green, J. Fox, E. Kim, F. Busato, et al. *Quickly Finding a Truss in a Haystack* IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038 - O. Green, P. Yalamanchili, L.M. Munguia, *Fast Triangle Counting on GPU* Irregular Applications: Architectures and Algorithms (IA3), 2014 @@ -36,6 +43,18 @@ Hungarian Algorithm - Date, K., & Nagi, R. (2016). GPU-accelerated Hungarian algorithms for the Linear Assignment Problem. Parallel Computing, 57, 52-72. + +Leiden + +- Traag, V. A., Waltman, L., & Van Eck, N. J. (2019). *From Louvain to Leiden: guaranteeing well-connected communities*. Scientific reports, 9(1), 1-12. + +Louvain + +- VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre. *Fast unfolding of community hierarchies in large networks*. J Stat Mech P10008 (2008) + + + + | | From 40910784c2dca73002cdb1059953259a1a8b45da Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Fri, 29 Apr 2022 20:19:37 -0700 Subject: [PATCH 177/384] Python code cleanup across docs, wrappers, testing (#2194) This PR incorporates suggested changes from issue #2037, opened back in 22.02, as well as cleaning up documentation and testing code from 22.04 work. Specifically, this PR does the following: - Removes worker helpers from MNMG algos in the online documentation (e.g. `call_katz_centrality`, `call_louvain`, `call_wcc`) - Adds helpers from comms such as `get_comms`, `get_session_id`, `get_2D_partition`, is_initialized that were previously missing - Adds helpers from NumberMap such as `from_internal_vertex_id`, `to_internal_vertex_id`, `renumber`, `set_renumbered_col_names` - Add MNMG Hits and Node2vec to docs - Added docstring descriptions and examples to methods that were missing them - Fixed docstring examples in `egonet.py`, `node2vec.py` - Adds `pylibcugraph` docstring examples to the list of examples tested in `cugraph/tests/test_doctests.py` - Adds support for MG Dask docstring example testing, tested in newly created `cugraph/tests/dask/test_mg_doctests.py` This PR addresses the items from and closes https://github.com/rapidsai/cugraph/issues/2227. This PR also closes #2193. Authors: - https://github.com/betochimas Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2194 --- docs/cugraph/source/api_docs/centrality.rst | 1 - docs/cugraph/source/api_docs/community.rst | 1 - docs/cugraph/source/api_docs/components.rst | 1 - .../source/api_docs/helper_functions.rst | 8 +++++++ .../cugraph/source/api_docs/link_analysis.rst | 7 +++++++ docs/cugraph/source/api_docs/sampling.rst | 9 +++++++- docs/cugraph/source/api_docs/structure.rst | 21 ++++++++++++++++++- docs/cugraph/source/api_docs/traversal.rst | 2 -- 8 files changed, 43 insertions(+), 7 deletions(-) diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/centrality.rst index 4001328ee..c9c1e24b5 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/centrality.rst @@ -26,4 +26,3 @@ Katz Centrality (MG) :toctree: api/ cugraph.dask.centrality.katz_centrality.katz_centrality - cugraph.dask.centrality.katz_centrality.call_katz_centrality diff --git a/docs/cugraph/source/api_docs/community.rst b/docs/cugraph/source/api_docs/community.rst index 23db12c44..d35ac657f 100644 --- a/docs/cugraph/source/api_docs/community.rst +++ b/docs/cugraph/source/api_docs/community.rst @@ -50,7 +50,6 @@ Louvain (MG) .. autosummary:: :toctree: api/ - cugraph.dask.community.louvain.call_louvain cugraph.dask.community.louvain.louvain Spectral Clustering diff --git a/docs/cugraph/source/api_docs/components.rst b/docs/cugraph/source/api_docs/components.rst index cf2f09efa..8616c8742 100644 --- a/docs/cugraph/source/api_docs/components.rst +++ b/docs/cugraph/source/api_docs/components.rst @@ -20,6 +20,5 @@ Connected Components (MG) .. autosummary:: :toctree: api/ - cugraph.dask.components.connectivity.call_wcc cugraph.dask.components.connectivity.weakly_connected_components diff --git a/docs/cugraph/source/api_docs/helper_functions.rst b/docs/cugraph/source/api_docs/helper_functions.rst index f8a3f0495..249ea9c14 100644 --- a/docs/cugraph/source/api_docs/helper_functions.rst +++ b/docs/cugraph/source/api_docs/helper_functions.rst @@ -12,5 +12,13 @@ Methods cugraph.comms.comms.initialize cugraph.comms.comms.destroy + cugraph.comms.comms.is_initialized + cugraph.comms.comms.get_comms + cugraph.comms.comms.get_workers + cugraph.comms.comms.get_session_id + cugraph.comms.comms.get_2D_partition + cugraph.comms.comms.get_default_handle + cugraph.comms.comms.get_handle + cugraph.comms.comms.get_worker_id cugraph.dask.common.read_utils.get_chunksize diff --git a/docs/cugraph/source/api_docs/link_analysis.rst b/docs/cugraph/source/api_docs/link_analysis.rst index c19a36aed..b84dd2ccb 100644 --- a/docs/cugraph/source/api_docs/link_analysis.rst +++ b/docs/cugraph/source/api_docs/link_analysis.rst @@ -12,6 +12,13 @@ HITS cugraph.hits +HITS (MG) +--------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.link_analysis.hits.hits + Pagerank -------- diff --git a/docs/cugraph/source/api_docs/sampling.rst b/docs/cugraph/source/api_docs/sampling.rst index d633e94b7..8a6cc5f22 100644 --- a/docs/cugraph/source/api_docs/sampling.rst +++ b/docs/cugraph/source/api_docs/sampling.rst @@ -12,4 +12,11 @@ Random Walks cugraph.random_walks cugraph.ego_graph - cugraph.experimental.dask.uniform_neighborhood_sampling \ No newline at end of file + cugraph.experimental.dask.uniform_neighborhood_sampling + +Node2Vec +--------- +.. autosummary:: + :toctree: api/ + + cugraph.node2vec diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst index f2d497814..04e836ff3 100644 --- a/docs/cugraph/source/api_docs/structure.rst +++ b/docs/cugraph/source/api_docs/structure.rst @@ -75,6 +75,26 @@ Conversion from Other Formats cugraph.to_pandas_adjacency cugraph.to_pandas_edgelist +NumberMap +----------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.structure.NumberMap + cugraph.structure.NumberMap.MultiGPU + cugraph.structure.NumberMap.SingleGPU + cugraph.structure.NumberMap.from_internal_vertex_id + cugraph.structure.NumberMap.to_internal_vertex_id + cugraph.structure.NumberMap.add_internal_vertex_id + cugraph.structure.NumberMap.compute_vals + cugraph.structure.NumberMap.compute_vals_types + cugraph.structure.NumberMap.generate_unused_column_name + cugraph.structure.NumberMap.renumber + cugraph.structure.NumberMap.renumber_and_segment + cugraph.structure.NumberMap.set_renumbered_col_names + cugraph.structure.NumberMap.unrenumber + cugraph.structure.NumberMap.vertex_column_size + Other ----------------------------- .. autosummary:: @@ -82,4 +102,3 @@ Other cugraph.hypergraph cugraph.structure.shuffle - cugraph.structure.NumberMap diff --git a/docs/cugraph/source/api_docs/traversal.rst b/docs/cugraph/source/api_docs/traversal.rst index d1736799e..675bf89f3 100644 --- a/docs/cugraph/source/api_docs/traversal.rst +++ b/docs/cugraph/source/api_docs/traversal.rst @@ -19,7 +19,6 @@ Breadth-first-search (MG) :toctree: api/ cugraph.dask.traversal.bfs.bfs - cugraph.dask.traversal.bfs.call_bfs Single-source-shortest-path --------------------------- @@ -36,5 +35,4 @@ Single-source-shortest-path (MG) .. autosummary:: :toctree: api/ - cugraph.dask.traversal.sssp.call_sssp cugraph.dask.traversal.sssp.sssp From 9b4f4db8da32c300280d8ad7baae2891b89a6ac9 Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Tue, 3 May 2022 17:14:16 -0700 Subject: [PATCH 178/384] Allow `cugraph` to be imported in an SG env for SG algorithms (#2241) This PR aims to directly resolve #2113, and allows for cugraph to be imported without errors in environments with only a single GPU What this PR does: - Moves `comms` module into the `dask` module, renaming imports across `cugraph` accordingly - Adds mock class in case ucp is not available or usable in SG-only environments (note ucp requires libnuma.so) - Renames `tests/dask` to `tests/mg` to better reflect the fact that those tests are for mg algs and utils - Separates symmetrize testing into `tests/mg` and `tests` for separation of SG and MG testing Authors: - https://github.com/betochimas Approvers: - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/2241 --- docs/cugraph/source/api_docs/dask-cugraph.rst | 2 +- .../source/api_docs/helper_functions.rst | 20 +++++++++---------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/cugraph/source/api_docs/dask-cugraph.rst b/docs/cugraph/source/api_docs/dask-cugraph.rst index 51487bfbf..9ccc429a7 100644 --- a/docs/cugraph/source/api_docs/dask-cugraph.rst +++ b/docs/cugraph/source/api_docs/dask-cugraph.rst @@ -20,7 +20,7 @@ Example from dask.distributed import Client, wait from dask_cuda import LocalCUDACluster - import cugraph.comms as Comms + import cugraph.dask.comms as Comms import cugraph.dask as dask_cugraph cluster = LocalCUDACluster() diff --git a/docs/cugraph/source/api_docs/helper_functions.rst b/docs/cugraph/source/api_docs/helper_functions.rst index 249ea9c14..08585d264 100644 --- a/docs/cugraph/source/api_docs/helper_functions.rst +++ b/docs/cugraph/source/api_docs/helper_functions.rst @@ -10,15 +10,15 @@ Methods .. autosummary:: :toctree: api/ - cugraph.comms.comms.initialize - cugraph.comms.comms.destroy - cugraph.comms.comms.is_initialized - cugraph.comms.comms.get_comms - cugraph.comms.comms.get_workers - cugraph.comms.comms.get_session_id - cugraph.comms.comms.get_2D_partition - cugraph.comms.comms.get_default_handle - cugraph.comms.comms.get_handle - cugraph.comms.comms.get_worker_id + cugraph.dask.comms.comms.initialize + cugraph.dask.comms.comms.destroy + cugraph.dask.comms.comms.is_initialized + cugraph.dask.comms.comms.get_comms + cugraph.dask.comms.comms.get_workers + cugraph.dask.comms.comms.get_session_id + cugraph.dask.comms.comms.get_2D_partition + cugraph.dask.comms.comms.get_default_handle + cugraph.dask.comms.comms.get_handle + cugraph.dask.comms.comms.get_worker_id cugraph.dask.common.read_utils.get_chunksize From 075011011339173438ebe0c861bfd9d3ff6f5391 Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Tue, 10 May 2022 10:50:41 -0700 Subject: [PATCH 179/384] Add Degree Centrality to cugraph (#2259) This PR adds Degree Centrality to the cugraph software stack, and closes #2145 when merged. Authors: - https://github.com/betochimas Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2259 --- docs/cugraph/source/api_docs/centrality.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/centrality.rst index c9c1e24b5..aad5e355f 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/centrality.rst @@ -26,3 +26,10 @@ Katz Centrality (MG) :toctree: api/ cugraph.dask.centrality.katz_centrality.katz_centrality + +Degree Centrality +--------------- +.. autosummary:: + :toctree: api/ + + cugraph.degree_centrality From 8f3bf0955f5c3306ddfc361b36984e629ed1256a Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 19 May 2022 11:04:06 -0400 Subject: [PATCH 180/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index f060f10f6..02c69f0a5 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '22.06' +version = '22.08' # The full version, including alpha/beta/rc tags. -release = '22.06.00' +release = '22.08.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 1b3ca0b1b1f2806c154f564560560b0ac4a73261 Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Fri, 20 May 2022 08:40:42 -0700 Subject: [PATCH 181/384] Branch 22.06 bug fixes + update imports (#2261) This PR: 1. Updates reference code in the html documentation to reflect recent changes from 22.02 and 22.04 (closes #2248 ) 2. Updates the default value of max_depth within node2vec, so that a call without a default max_depth value still yields a result (closes #2228) 3. Casts a list of start_vertices to int32 type. (closes #2229) 4. Remove experimental from list of imports from within pylibcugraph Authors: - https://github.com/betochimas Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) - Joseph Nke (https://github.com/jnke2016) URL: https://github.com/rapidsai/cugraph/pull/2261 --- docs/cugraph/source/api_docs/dask-cugraph.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/api_docs/dask-cugraph.rst b/docs/cugraph/source/api_docs/dask-cugraph.rst index 9ccc429a7..e015b5996 100644 --- a/docs/cugraph/source/api_docs/dask-cugraph.rst +++ b/docs/cugraph/source/api_docs/dask-cugraph.rst @@ -37,7 +37,7 @@ Example names=['src', 'dst'], dtype=['int32', 'int32']) - G = cugraph.DiGraph() + G = cugraph.Graph(directed=True) G.from_dask_cudf_edgelist(e_list, source='src', destination='dst') # now run PageRank From 0b66fa6cbb2e719cc39a0ddc40abae6d5ceed319 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Fri, 20 May 2022 15:22:41 -0400 Subject: [PATCH 182/384] Implement eigenvector centrality (#2287) This PR implements Eigenvector Centrality in C++ using the graph primitives. It also provides the C API implementation. There are unit tests for C++ and C both SG and MG. Partially addresses #2146 Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/2287 --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 485b71cac..8ae12d147 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,7 @@ __pycache__ .lock *.swp *.pytest_cache +*~ DartConfiguration.tcl .DS_Store From 5bba2b3f4a3da16fbec450e906475986bf83a02a Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 23 May 2022 12:41:17 -0400 Subject: [PATCH 183/384] Migrate SG and MG BFS to pylibcugraph (#2284) Closes #2270 Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Rick Ratzel (https://github.com/rlratzel) - https://github.com/betochimas URL: https://github.com/rapidsai/cugraph/pull/2284 --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 8ae12d147..44a1471fe 100644 --- a/.gitignore +++ b/.gitignore @@ -88,3 +88,4 @@ python/cugraph/cugraph/tests/dask-worker-space # Sphinx docs & build artifacts docs/cugraph/source/api_docs/api/* + From 985634e468a983e9ac867b77ec000d0b178d6177 Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Fri, 27 May 2022 07:18:31 -0700 Subject: [PATCH 184/384] Add Eigenvector Centrality to pylibcugraph, cugraph APIs (#2255) This PR: 1. Adds Eigenvector Centrality to the pylibcugraph and cugraph software stacks, which started from https://github.com/rapidsai/cugraph/pull/2180 and is followed up by future PRs in order to close https://github.com/rapidsai/cugraph/issues/2146 2. Minor improvements to pylibcugraph Katz Centrality 3. Added functionality to `test_doctests.py` so that certain docstrings can be skipped on different architecture configs (such as ktruss in CUDA 11.4) 4. Added undirected/directed versions of graph example used in C tests in `datasets` 5. Removed cugraph copy of warning wrapper from pylibcugraph and have it call the pylibcugraph version 6. Testing for both Python eigenvector centrality wrappers This PR is identical to #2243, just that the name of the branch is different Authors: - https://github.com/betochimas - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2255 --- docs/cugraph/source/api_docs/centrality.rst | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/centrality.rst index aad5e355f..92f712523 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/centrality.rst @@ -33,3 +33,17 @@ Degree Centrality :toctree: api/ cugraph.degree_centrality + +Eigenvector Centrality +---------------------- +.. autosummary:: + :toctree: api/ + + cugraph.centrality.eigenvector_centrality + +Eigenvector Centrality (MG) +--------------------------- +.. autosummary:: + :toctree: api/ + + cugraph.dask.centrality.eigenvector_centrality.eigenvector_centrality From 3985a5aee0fc7be00fbd94873954e92601c3b871 Mon Sep 17 00:00:00 2001 From: betochimas <97180625+betochimas@users.noreply.github.com> Date: Wed, 1 Jun 2022 06:52:19 -0700 Subject: [PATCH 185/384] Refactored SG `hits` and MG `katz_centrality` (#2276) This PR: 1. Refactors SG `hits` with the updated pylibcugraph implementation 2. Refactors MG `katz_centrality` with the updated pylibcugraph implementation, adding support for multiple arguments 3. Improves support within `test_doctests.py` to ignore certain docstring examples based on the build architecture (such as ktruss in CUDA 11.4) 4. Passing MG `katz_centrality` testing This PR also closes #2025. Authors: - https://github.com/betochimas Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Joseph Nke (https://github.com/jnke2016) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2276 --- docs/cugraph/source/api_docs/pylibcugraph.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/cugraph/source/api_docs/pylibcugraph.rst b/docs/cugraph/source/api_docs/pylibcugraph.rst index 9317274cf..93efec37f 100644 --- a/docs/cugraph/source/api_docs/pylibcugraph.rst +++ b/docs/cugraph/source/api_docs/pylibcugraph.rst @@ -11,5 +11,12 @@ Methods .. autosummary:: :toctree: api/ + pylibcugraph.eigenvector_centrality + pylibcugraph.katz_centrality pylibcugraph.strongly_connected_components pylibcugraph.weakly_connected_components + pylibcugraph.pagerank + pylibcugraph.hits + pylibcugraph.node2vec + pylibcugraph.bfs + pylibcugraph.sssp From f1170aa8dce9b8121b8a513b714efa33238e716a Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Fri, 3 Jun 2022 08:10:20 -0500 Subject: [PATCH 186/384] fix 'comms' import in the api_docs (#2327) This PR fixes the `comms` import in the API docs. Authors: Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Brad Rees (https://github.com/BradReesWork) --- docs/cugraph/source/api_docs/dask-cugraph.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/api_docs/dask-cugraph.rst b/docs/cugraph/source/api_docs/dask-cugraph.rst index e015b5996..4eee5b36a 100644 --- a/docs/cugraph/source/api_docs/dask-cugraph.rst +++ b/docs/cugraph/source/api_docs/dask-cugraph.rst @@ -20,7 +20,7 @@ Example from dask.distributed import Client, wait from dask_cuda import LocalCUDACluster - import cugraph.dask.comms as Comms + import cugraph.dask.comms.comms as Comms import cugraph.dask as dask_cugraph cluster = LocalCUDACluster() From 7c4d22e31fca9af6ee2162cc5bd9dfb115109651 Mon Sep 17 00:00:00 2001 From: Ralph Liu <106174412+oorliu@users.noreply.github.com> Date: Mon, 6 Jun 2022 12:04:06 -0400 Subject: [PATCH 187/384] DOC Fix for Renumber-2.ipynb (#2335) Fixed issue #2178. Updated to the correct number of unique IPs. Added code to confirm cuGraph results using CuPy. Authors: - Ralph Liu (https://github.com/oorliu) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2335 --- notebooks/demo/batch_betweenness.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/demo/batch_betweenness.ipynb b/notebooks/demo/batch_betweenness.ipynb index 885d26c95..854621eb3 100644 --- a/notebooks/demo/batch_betweenness.ipynb +++ b/notebooks/demo/batch_betweenness.ipynb @@ -16,7 +16,7 @@ "metadata": {}, "source": [ "## Introduction\n", - "Betweennes Centrality can be slow to compute on large graphs, in order to speed up the process we can leverage multiple GPUs.\n", + "Betweenness Centrality can be slow to compute on large graphs, in order to speed up the process we can leverage multiple GPUs.\n", "In this notebook we will showcase how it would have been done with a Single GPU approach, then we will show how it can be done using multiple GPUs." ] }, From ae37de37d7711790d3da168c8eb31058f2e0a4e7 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Tue, 7 Jun 2022 20:28:28 +0000 Subject: [PATCH 188/384] REL v22.06.00 release From 1831dbbd3637f8db2160ce36cf5d5959443fa50f Mon Sep 17 00:00:00 2001 From: Ralph Liu <106174412+oorliu@users.noreply.github.com> Date: Wed, 8 Jun 2022 12:03:59 -0400 Subject: [PATCH 189/384] Release notebook: Nx Generators & Adding Perf_counter (#2341) There was an issue where some Nx methods return a generator and not a full answer. These functions (WCC, Jaccard, and BFS) were identified and fixed for the purpose of getting more accurate ("apples-to-apples") comparisons against cuGraph methods. Next, `time.time()` was replaced with `time.perf_counter()` to get appropriate interval timings. Authors: - Ralph Liu (https://github.com/oorliu) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2341 --- notebooks/cugraph_benchmarks/release.ipynb | 322 +++++++-------------- 1 file changed, 98 insertions(+), 224 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index e9d887a04..4693265d0 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -90,6 +90,7 @@ "| Brad Rees | 10/06/2020 | created | 0.16 | GV100, CUDA 10.2 |\n", "| Brad Rees | 01/20/2022 | updated | 22.02 | Quadro A6000 CUDA 11.5 |\n", "| Brad Rees | 01/20/2022 | added perf w/Nx obj | 22.02 | Quadro A6000 CUDA 11.5 |\n", + "| Ralph Liu | 06/01/2022 | Fix: Generators | 22.06 | Tesla V100, CUDA 11.5 |\n", "\n", "\n", "\n" @@ -104,14 +105,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# system and other\n", "import gc\n", "import os\n", - "import time\n", + "from time import perf_counter\n", "import numpy as np\n", "import math\n", "\n", @@ -128,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -169,13 +170,13 @@ "}\n", "\n", "\n", - "\n", - "data = data_full\n" + "# TODO: Was set to quick for test\n", + "data = data_quick\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -263,29 +264,29 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_katz(_df, alpha):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = nx.katz_centrality(_G, alpha)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_katz(_df, alpha):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", " _ = cugraph.katz_centrality(_G, alpha)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_katz_nx(_df, alpha):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = cugraph.katz_centrality(_G, alpha)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -298,30 +299,30 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_bc(_df, _k):\n", " print(f\" k = {_k}\", end=' ')\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = nx.betweenness_centrality(_G, k=_k)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_bc(_df, _k):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", " _ = cugraph.betweenness_centrality(_G, k=_k)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_bc_nx(_df, _k):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = cugraph.betweenness_centrality(_G, k=_k)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -334,33 +335,33 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_louvain(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " parts = community.best_partition(_G)\n", " \n", " # Calculating modularity scores for comparison \n", " _ = community.modularity(parts, _G) \n", " \n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_louvain(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", " _,_ = cugraph.louvain(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_louvain_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _,_ = cugraph.louvain(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -373,12 +374,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_tc(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " nx_count = nx.triangles(_G)\n", " \n", @@ -387,21 +388,21 @@ " for key, value in nx_count.items():\n", " count = count + value \n", " \n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_tc(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", " _ = cugraph.triangles(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_tc_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = cugraph.triangles(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -414,12 +415,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_wcc(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_digraph(_df)\n", " gen = nx.weakly_connected_components(_G)\n", "\n", @@ -428,21 +429,21 @@ " for subgraph in gen:\n", " list_of_digraphs.append(nx.subgraph(_G, subgraph))\n", " \n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_wcc(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_digraph(_df) \n", " _ = cugraph.weakly_connected_components(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_wcc_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_digraph(_df) \n", " _ = cugraph.weakly_connected_components(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -455,12 +456,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_core_num(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " nx_count = nx.core_number(_G)\n", " \n", @@ -468,21 +469,21 @@ " for key, value in nx_count.items():\n", " count = count + value \n", " \n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_core_num(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df) \n", " _ = cugraph.core_number(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_core_num_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df) \n", " _ = cugraph.core_number(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -495,29 +496,29 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_pagerank(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_digraph(_df)\n", " _ = nx.pagerank(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_pagerank(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_digraph(_df)\n", " _ = cugraph.pagerank(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_pagerank_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_digraph(_df)\n", " _ = cugraph.pagerank(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -530,29 +531,30 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_jaccard(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", - " _ = nx.jaccard_coefficient(_G)\n", - " t2 = time.time() - t1\n", + " nj = nx.jaccard_coefficient(_G)\n", + " nj_list = list(nj) # gen -> list\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_jaccard(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", " _ = cugraph.jaccard_coefficient(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_jaccard_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = cugraph.jaccard_coefficient(_G)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -565,29 +567,30 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_bfs(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", - " _ = nx.bfs_edges(_G, 1)\n", - " t2 = time.time() - t1\n", + " nb = nx.bfs_edges(_G, 1) \n", + " nb_list = list(nb) # gen -> list\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_bfs(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", " _ = cugraph.bfs(_G, 1)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_bfs_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = cugraph.bfs(_G, 1)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, @@ -600,39 +603,32 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_sssp(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = nx.shortest_path(_G, 1)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_sssp(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df) \n", " _ = cugraph.sssp(_G, 1)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_sssp_nx(_df):\n", - " t1 = time.time()\n", + " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df) \n", " _ = cugraph.sssp(_G, 1)\n", - " t2 = time.time() - t1\n", + " t2 = perf_counter() - t1\n", " return t2" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -644,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -654,32 +650,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading ./data/preferentialAttachment.mtx...\n", - "\tGDF Size 999970\n", - "\tcugraph Size 499985\n", - "\tcugraph Order 100000\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# do a simple pass just to get all the libraries initiallized\n", + "# do a simple pass just to get all the libraries initialized\n", "# This cell might not be needed\n", "v = './data/preferentialAttachment.mtx'\n", "gdf = read_data(v)\n", @@ -706,75 +681,6 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading ./data/preferentialAttachment.mtx...\n", - "\tdata in gdf 999970 and data in pandas 999970\n", - "\tKatz n.c.cx.\n", - "\tBC k=100 n. k = 100 c.cx. \n", - "\tLouvain n.c.cx. \n", - "\tTC n.c.cx. \n", - "\tWCC n.c.cx. \n", - "\tCore Number n.c.cx. \n", - "\tPageRank n.c.cx. \n", - "\tJaccard n.c.cx. \n", - "\tBFS n.c.cx. \n", - "\tSSSP n.c.cx. \n", - "Reading ./data/dblp-2010.mtx...\n", - "\tdata in gdf 1615400 and data in pandas 1615400\n", - "\tKatz n.c.cx.\n", - "\tBC k=100 n. k = 100 c.cx. \n", - "\tLouvain n.c.cx. \n", - "\tTC n.c.cx. \n", - "\tWCC n.c.cx. \n", - "\tCore Number n.c.cx. \n", - "\tPageRank n.c.cx. \n", - "\tJaccard n.c.cx. \n", - "\tBFS n.c.cx. \n", - "\tSSSP n.c.cx. \n", - "Reading ./data/coPapersCiteseer.mtx...\n", - "\tdata in gdf 32073440 and data in pandas 32073440\n", - "\tKatz n.c.cx.\n", - "\tBC k=100 n. k = 100 c.cx. \n", - "\tLouvain n.c.cx. \n", - "\tTC n.c.cx. \n", - "\tWCC n.c.cx. \n", - "\tCore Number n.c.cx. \n", - "\tPageRank n.c.cx. \n", - "\tJaccard n.c.cx. \n", - "\tBFS n.c.cx. \n", - "\tSSSP n.c.cx. \n", - "Reading ./data/as-Skitter.mtx...\n", - "\tdata in gdf 22190596 and data in pandas 22190596\n", - "\tKatz n.c.cx.\n", - "\tBC k=100 n. k = 100 c.cx. \n", - "\tLouvain n.c.cx. \n", - "\tTC n.c.cx. \n", - "\tWCC n.c.cx. \n", - "\tCore Number n.c.cx. \n", - "\tPageRank n.c.cx. \n", - "\tJaccard n.c.cx. \n", - "\tBFS n.c.cx. \n", - "\tSSSP n.c.cx. \n" - ] - } - ], "source": [ "# arrays to capture performance gains\n", "names = []\n", @@ -1037,25 +943,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", - "preferentialAttachment\n", - "[531.5083672005737, 713.2091362850375, 5404.871910284401, 378.8714209575697, 70.72927362986779, 76.7855488460584, 369.8407050216162, 100.5242813104882, 4.378275208783683, 162.25090274331285]\n", - "dblp\n", - "[263.7293255857766, 1558.1884974132756, 622.282663375948, 142.59215746942343, 88.19197528607465, 116.20873484788576, 460.3603070425029, 121.2753931555862, 38.357311342936576, 186.59598139534884]\n", - "coPapersCiteseer\n", - "[2270.9739290077137, 2436.608230097977, 1057.486186959562, 2300.0750485569533, 233.7511393615408, 246.97401172398136, 853.279646090366, 112.73241487709193, 233.03646005823012, 267.6295226503849]\n", - "as-Skitter\n", - "[1037.8761480097223, 7091.970778835749, 1475.2901788933452, 3950.5098941991014, 228.11245124984197, 245.35607830599636, 547.8739313855568, 98.81747467270701, 222.20389619850272, 326.73959437931654]\n" - ] - } - ], + "outputs": [], "source": [ "#Print results\n", "print(algos)\n", @@ -1067,28 +957,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "------------\n", - "\n", - "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", - "preferentialAttachment\n", - "[4.003601795825807, 93.08063351361315, 204.47865340368915, 2.7097492953036797, 0.8666907616338123, 1.2867491927399868, 1.9752328393020735, 0.1240993236960286, 0.7203119096547798, 179.41122927863145]\n", - "dblp\n", - "[4.885862057048338, 122.87364779256156, 31.02199520144101, 2.180625966164683, 1.4324289723528034, 1.3579940932619847, 2.348198022642711, 0.11145559203860335, 0.7265996313793216, 202.49957600312277]\n", - "coPapersCiteseer\n", - "[8.158465907417547, 49.369787236543914, 9.28701234947398, 12.067711445592089, 0.8232363804394461, 1.3752921888176353, 2.8151237928186994, 0.08727548257474192, 0.798480184263566, 322.0841686740297]\n", - "as-Skitter\n", - "[3.66123817526995, 631.2091494372795, 20.617572936160165, 34.49437773532841, 0.9264442180418646, 4.828647431829589, 2.2476285215684615, 0.12267649645919085, 0.8469181921567667, 431.6171964493335]\n" - ] - } - ], + "outputs": [], "source": [ "#Print results\n", "print(\"\\n------------\\n\")\n", @@ -1199,10 +1070,13 @@ } ], "metadata": { + "interpreter": { + "hash": "f708a36acfaef0acf74ccd43dfb58100269bf08fb79032a1e0a6f35bd9856f51" + }, "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "Python 3.8.10 ('base')", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1214,7 +1088,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.10" } }, "nbformat": 4, From b0dfaea1dd6d7b15d060f42ddd31eb43825d2a74 Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Wed, 22 Jun 2022 12:47:46 -0500 Subject: [PATCH 190/384] Switch `language` from `None` to `"en"` in docs build (#2368) Needed for Sphinx 5 compatibility. Should fix the following warning occurring in doc builds. ``` Invalid configuration value found: 'language = None'. Update your configuration to a valid langauge code. Falling back to 'en' (English). ``` xref: https://github.com/sphinx-doc/sphinx/pull/10481 Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Ray Douglass (https://github.com/raydouglass) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2368 --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 02c69f0a5..9833c1bd8 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -90,7 +90,7 @@ # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = 'en' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. From 111834110619acb675d14aa7a40638b432b07f83 Mon Sep 17 00:00:00 2001 From: Charles Blackmon-Luca <20627856+charlesbluca@users.noreply.github.com> Date: Wed, 29 Jun 2022 14:28:30 -0400 Subject: [PATCH 191/384] Update multi-GPU example to include data generation (#2345) This PR updates the multi-GPU example in the docs to include input data generation using `rmat`, as well as resolving the missing / unnecessary imports. Authors: - Charles Blackmon-Luca (https://github.com/charlesbluca) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2345 --- docs/cugraph/source/api_docs/dask-cugraph.rst | 48 ++++++++++++++----- 1 file changed, 35 insertions(+), 13 deletions(-) diff --git a/docs/cugraph/source/api_docs/dask-cugraph.rst b/docs/cugraph/source/api_docs/dask-cugraph.rst index 4eee5b36a..e671d4666 100644 --- a/docs/cugraph/source/api_docs/dask-cugraph.rst +++ b/docs/cugraph/source/api_docs/dask-cugraph.rst @@ -18,36 +18,58 @@ Example .. code-block:: python - from dask.distributed import Client, wait + import dask_cudf + from dask.distributed import Client from dask_cuda import LocalCUDACluster - import cugraph.dask.comms.comms as Comms + + import cugraph import cugraph.dask as dask_cugraph + import cugraph.dask.comms.comms as Comms + from cugraph.generators.rmat import rmat + input_data_path = "input_data.csv" + + # cluster initialization cluster = LocalCUDACluster() client = Client(cluster) Comms.initialize(p2p=True) - # Helper function to set the reader chunk size to automatically get one partition per GPU + # helper function to generate random input data + input_data = rmat( + scale=5, + num_edges=400, + a=0.30, + b=0.65, + c=0.05, + seed=456, + clip_and_flip=False, + scramble_vertex_ids=False, + create_using=None, + ) + input_data.to_csv(input_data_path, index=False) + + # helper function to set the reader chunk size to automatically get one partition per GPU chunksize = dask_cugraph.get_chunksize(input_data_path) - # Multi-GPU CSV reader - e_list = dask_cudf.read_csv(input_data_path, - chunksize = chunksize, - delimiter=' ', - names=['src', 'dst'], - dtype=['int32', 'int32']) + # multi-GPU CSV reader + e_list = dask_cudf.read_csv( + input_data_path, + chunksize=chunksize, + names=['src', 'dst'], + dtype=['int32', 'int32'], + ) - G = cugraph.Graph(directed=True) + # create graph from input data + G = cugraph.DiGraph() G.from_dask_cudf_edgelist(e_list, source='src', destination='dst') - # now run PageRank + # run PageRank pr_df = dask_cugraph.pagerank(G, tol=1e-4) - # All done, clean up + # cluster clean up Comms.destroy() client.close() cluster.close() | - From 76a79f776185dd4557f7be8cf971e1a7d8d0efd0 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Tue, 12 Jul 2022 16:29:14 +0000 Subject: [PATCH 192/384] REL v22.06.01 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index f060f10f6..faf77e1d9 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -83,7 +83,7 @@ # The short X.Y version. version = '22.06' # The full version, including alpha/beta/rc tags. -release = '22.06.00' +release = '22.06.01' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 1d572a02bf0fd7b38a06605fd24d3b9bc841ef46 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Fri, 22 Jul 2022 10:51:14 -0400 Subject: [PATCH 193/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 9833c1bd8..45bd4d4da 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '22.08' +version = '22.10' # The full version, including alpha/beta/rc tags. -release = '22.08.00' +release = '22.10.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 39973448f41d06f9d41bead49b86776bb0074ad7 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Fri, 29 Jul 2022 08:18:22 -0500 Subject: [PATCH 194/384] Update cugraph python build (#2378) This PR updates cugraph python build to use scikit-build instead of setuptools. Scikit-build leverages cmake to build the python extension closes #2333 Authors: - Joseph Nke (https://github.com/jnke2016) - Chuck Hastings (https://github.com/ChuckHastings) - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Brad Rees (https://github.com/BradReesWork) - Sevag H (https://github.com/sevagh) - Rick Ratzel (https://github.com/rlratzel) - Vyas Ramasubramani (https://github.com/vyasr) URL: https://github.com/rapidsai/cugraph/pull/2378 --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 44a1471fe..7bd759bbc 100644 --- a/.gitignore +++ b/.gitignore @@ -29,11 +29,15 @@ junit-cugraph.xml test-results ## Python build directories & artifacts +dask-worker-space/ htmlcov dist/ cugraph.egg-info/ python/build python/cugraph/bindings/*.cpp +wheels/ +_skbuild/ +cufile.log ## pylibcugraph build directories & artifacts python/pylibcugraph/pylibcugraph.egg-info From 1d9582d56261efdce55ae673dd2399502a0e703e Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Fri, 29 Jul 2022 12:19:23 -0500 Subject: [PATCH 195/384] Fix issues with day & night modes in python docs (#2471) Fixes similar issue found in: https://github.com/rapidsai/cudf/pull/11400/ Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2471 --- docs/cugraph/source/_static/custom.js | 22 +++++ docs/cugraph/source/_static/custom_styles.css | 89 +++++++++++++++++++ docs/cugraph/source/_static/params.css | 30 ------- docs/cugraph/source/conf.py | 3 +- 4 files changed, 113 insertions(+), 31 deletions(-) create mode 100644 docs/cugraph/source/_static/custom.js create mode 100644 docs/cugraph/source/_static/custom_styles.css delete mode 100644 docs/cugraph/source/_static/params.css diff --git a/docs/cugraph/source/_static/custom.js b/docs/cugraph/source/_static/custom.js new file mode 100644 index 000000000..567a07a7c --- /dev/null +++ b/docs/cugraph/source/_static/custom.js @@ -0,0 +1,22 @@ +// Copyright (c) 2022, NVIDIA CORPORATION. + +function update_switch_theme_button() { + current_theme = document.documentElement.dataset.mode; + if (current_theme == "light") { + document.getElementById("theme-switch").title = "Switch to auto theme"; + } else if (current_theme == "auto") { + document.getElementById("theme-switch").title = "Switch to dark theme"; + } else { + document.getElementById("theme-switch").title = "Switch to light theme"; + } +} + +$(document).ready(function() { + var observer = new MutationObserver(function(mutations) { + update_switch_theme_button(); + }) + observer.observe(document.documentElement, { + attributes: true, + attributeFilter: ['data-theme'] + }); +}); diff --git a/docs/cugraph/source/_static/custom_styles.css b/docs/cugraph/source/_static/custom_styles.css new file mode 100644 index 000000000..31db0633f --- /dev/null +++ b/docs/cugraph/source/_static/custom_styles.css @@ -0,0 +1,89 @@ +/* Mirrors the change in: + * https://github.com/sphinx-doc/sphinx/pull/5976 + * which is not showing up in our theme. + */ + .classifier:before { + font-style: normal; + margin: 0.5em; + content: ":"; +} + +/* Fix for text wrap in sphinx tables: +* https://rackerlabs.github.io/docs-rackspace/tools/rtd-tables.html +*/ +@media screen and (min-width: 767px) { + + .wy-table-responsive table td { + /* !important prevents the common CSS stylesheets from overriding + this as on RTD they are loaded after this stylesheet */ + white-space: normal !important; + } + + .wy-table-responsive { + overflow: visible !important; + } +} + + +table.io-supported-types-table { + text-align: center +} + +table.io-supported-types-table thead{ + text-align: center !important; +} + +:root { + +--pst-color-active-navigation: 114, 83, 237; +--pst-color-navbar-link: 77, 77, 77; +--pst-color-navbar-link-hover: var(--pst-color-active-navigation); +--pst-color-navbar-link-active: var(--pst-color-active-navigation); +--pst-color-sidebar-link: 77, 77, 77; +--pst-color-sidebar-link-hover: var(--pst-color-active-navigation); +--pst-color-sidebar-link-active: var(--pst-color-active-navigation); +--pst-color-sidebar-expander-background-hover: 244, 244, 244; +--pst-color-sidebar-caption: 77, 77, 77; +--pst-color-toc-link: 119, 117, 122; +--pst-color-toc-link-hover: var(--pst-color-active-navigation); +--pst-color-toc-link-active: var(--pst-color-active-navigation); + +} + +/* Used to make special-table scrollable when it overflows */ +.special-table-wrapper { + width: 100%; + overflow: auto !important; +} + +.special-table td, .special-table th { + border: 1px solid #dee2e6; +} + +/* Needed to resolve https://github.com/executablebooks/jupyter-book/issues/1611 */ +.output.text_html { + overflow: auto; +} + +html[data-theme="light"] { + --pst-color-primary: rgb(19, 6, 84); + --pst-color-text-base: rgb(51, 51, 51); + + --pst-color-primary: rgb(19, 6, 84); + + --pst-color-link: rgb(0, 91, 129); + --pst-color-secondary: rgb(227, 46, 0); + --pst-table-background-color: transparent; +} + + +html[data-theme="dark"] { + --pst-color-primary: rgb(221, 221, 221); + --pst-color-inline-code: rgb(248, 6, 204); + --pst-table-background-color: var(--pst-color-text-muted); + +} + +div.cell_output table{ + background: var(--pst-table-background-color); +} diff --git a/docs/cugraph/source/_static/params.css b/docs/cugraph/source/_static/params.css deleted file mode 100644 index b57bcdb7c..000000000 --- a/docs/cugraph/source/_static/params.css +++ /dev/null @@ -1,30 +0,0 @@ -/* Mirrors the change in: - * https://github.com/sphinx-doc/sphinx/pull/5976 - * which is not showing up in our theme. - */ -.classifier:before { - font-style: normal; - margin: 0.5em; - content: ":"; -} - -:root { - - --pst-color-active-navigation: 114, 83, 237; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - -} - -.special-table td, .special-table th { - border: 1px solid #dee2e6; -} \ No newline at end of file diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 9833c1bd8..1f6dc19a3 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -212,7 +212,8 @@ def setup(app): - app.add_css_file('params.css') + app.add_css_file('custom_styles.css') + app.add_js_file('custom.js') app.add_css_file('references.css') From 9d287d8d1b60a95b0bb6c9f4d431e74fc6e38653 Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Tue, 2 Aug 2022 14:10:00 -0500 Subject: [PATCH 196/384] Centralize common `css` & `js` code in docs (#2472) This PR will utilize the common `css` & `js` code being merged here: https://github.com/rapidsai/docs/pull/286 Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/2472 --- docs/cugraph/source/_static/custom.js | 22 ----- docs/cugraph/source/_static/custom_styles.css | 89 ------------------- docs/cugraph/source/conf.py | 6 +- 3 files changed, 3 insertions(+), 114 deletions(-) delete mode 100644 docs/cugraph/source/_static/custom.js delete mode 100644 docs/cugraph/source/_static/custom_styles.css diff --git a/docs/cugraph/source/_static/custom.js b/docs/cugraph/source/_static/custom.js deleted file mode 100644 index 567a07a7c..000000000 --- a/docs/cugraph/source/_static/custom.js +++ /dev/null @@ -1,22 +0,0 @@ -// Copyright (c) 2022, NVIDIA CORPORATION. - -function update_switch_theme_button() { - current_theme = document.documentElement.dataset.mode; - if (current_theme == "light") { - document.getElementById("theme-switch").title = "Switch to auto theme"; - } else if (current_theme == "auto") { - document.getElementById("theme-switch").title = "Switch to dark theme"; - } else { - document.getElementById("theme-switch").title = "Switch to light theme"; - } -} - -$(document).ready(function() { - var observer = new MutationObserver(function(mutations) { - update_switch_theme_button(); - }) - observer.observe(document.documentElement, { - attributes: true, - attributeFilter: ['data-theme'] - }); -}); diff --git a/docs/cugraph/source/_static/custom_styles.css b/docs/cugraph/source/_static/custom_styles.css deleted file mode 100644 index 31db0633f..000000000 --- a/docs/cugraph/source/_static/custom_styles.css +++ /dev/null @@ -1,89 +0,0 @@ -/* Mirrors the change in: - * https://github.com/sphinx-doc/sphinx/pull/5976 - * which is not showing up in our theme. - */ - .classifier:before { - font-style: normal; - margin: 0.5em; - content: ":"; -} - -/* Fix for text wrap in sphinx tables: -* https://rackerlabs.github.io/docs-rackspace/tools/rtd-tables.html -*/ -@media screen and (min-width: 767px) { - - .wy-table-responsive table td { - /* !important prevents the common CSS stylesheets from overriding - this as on RTD they are loaded after this stylesheet */ - white-space: normal !important; - } - - .wy-table-responsive { - overflow: visible !important; - } -} - - -table.io-supported-types-table { - text-align: center -} - -table.io-supported-types-table thead{ - text-align: center !important; -} - -:root { - ---pst-color-active-navigation: 114, 83, 237; ---pst-color-navbar-link: 77, 77, 77; ---pst-color-navbar-link-hover: var(--pst-color-active-navigation); ---pst-color-navbar-link-active: var(--pst-color-active-navigation); ---pst-color-sidebar-link: 77, 77, 77; ---pst-color-sidebar-link-hover: var(--pst-color-active-navigation); ---pst-color-sidebar-link-active: var(--pst-color-active-navigation); ---pst-color-sidebar-expander-background-hover: 244, 244, 244; ---pst-color-sidebar-caption: 77, 77, 77; ---pst-color-toc-link: 119, 117, 122; ---pst-color-toc-link-hover: var(--pst-color-active-navigation); ---pst-color-toc-link-active: var(--pst-color-active-navigation); - -} - -/* Used to make special-table scrollable when it overflows */ -.special-table-wrapper { - width: 100%; - overflow: auto !important; -} - -.special-table td, .special-table th { - border: 1px solid #dee2e6; -} - -/* Needed to resolve https://github.com/executablebooks/jupyter-book/issues/1611 */ -.output.text_html { - overflow: auto; -} - -html[data-theme="light"] { - --pst-color-primary: rgb(19, 6, 84); - --pst-color-text-base: rgb(51, 51, 51); - - --pst-color-primary: rgb(19, 6, 84); - - --pst-color-link: rgb(0, 91, 129); - --pst-color-secondary: rgb(227, 46, 0); - --pst-table-background-color: transparent; -} - - -html[data-theme="dark"] { - --pst-color-primary: rgb(221, 221, 221); - --pst-color-inline-code: rgb(248, 6, 204); - --pst-table-background-color: var(--pst-color-text-muted); - -} - -div.cell_output table{ - background: var(--pst-table-background-color); -} diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 1f6dc19a3..99f789c94 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -212,9 +212,9 @@ def setup(app): - app.add_css_file('custom_styles.css') - app.add_js_file('custom.js') - app.add_css_file('references.css') + app.add_css_file("https://docs.rapids.ai/assets/css/custom.css") + app.add_js_file("https://docs.rapids.ai/assets/js/custom.js") + app.add_css_file("references.css") source_suffix = ['.rst', '.md'] From 48c9678e197796994a5f8ced15b9efa29b07d4b9 Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Thu, 4 Aug 2022 14:39:43 -0500 Subject: [PATCH 197/384] Defer loading of `custom.js` (#2506) This PR switches the loading of `custom.js` to defer because we will need the entire page to be loading until the methods in this script can even execute correctly. xref: https://github.com/rapidsai/cudf/pull/11465 Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2506 --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 99f789c94..ac4ab5e5d 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -213,7 +213,7 @@ def setup(app): app.add_css_file("https://docs.rapids.ai/assets/css/custom.css") - app.add_js_file("https://docs.rapids.ai/assets/js/custom.js") + app.add_js_file("https://docs.rapids.ai/assets/js/custom.js", loading_method="defer") app.add_css_file("references.css") From c30237fe93bf037d5d30a2583883eb29dafc3b4b Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Wed, 7 Sep 2022 09:33:16 -0400 Subject: [PATCH 198/384] Refactor Sampling, Structure and Traversal Notebooks (#2628) - Moves notebooks under algorithms - Adds README's - Tests functionality - Updates format to match other notebooks Fixed doc error in edge_betweenness_centrality call reported in issue #2519 closes #2610 closes #2611 closes #2612 closes #2519 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) - Alex Barghi (https://github.com/alexbarghi-nv) URL: https://github.com/rapidsai/cugraph/pull/2628 --- notebooks/cugraph_benchmarks/release.ipynb | 130 +++++++++++++++------ 1 file changed, 97 insertions(+), 33 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index 4693265d0..8ba3c82eb 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -171,12 +171,12 @@ "\n", "\n", "# TODO: Was set to quick for test\n", - "data = data_quick\n" + "data = data_full\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -374,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -415,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -456,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -531,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -539,14 +539,13 @@ " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " nj = nx.jaccard_coefficient(_G)\n", - " nj_list = list(nj) # gen -> list\n", " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_jaccard(_df):\n", " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", - " _ = cugraph.jaccard_coefficient(_G)\n", + " _ = list(cugraph.jaccard_coefficient(_G))\n", " t2 = perf_counter() - t1\n", " return t2\n", "\n", @@ -567,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -603,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -640,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -650,9 +649,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tGDF Size 999970\n", + "\tcugraph Size 499985\n", + "\tcugraph Order 100000\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# do a simple pass just to get all the libraries initialized\n", "# This cell might not be needed\n", @@ -678,9 +698,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tdata in gdf 999970 and data in pandas 999970\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n" + ] + } + ], "source": [ "# arrays to capture performance gains\n", "names = []\n", @@ -943,9 +982,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", + "preferentialAttachment\n", + "[145.30643917185208, 236.64781352415596, 4385.897320647833, 135.74325850737782, 34.89773706999491, 35.98962606492319, 148.4826107413348, 34.04823126698779, 41.64064391168044, 39.70684495453922]\n" + ] + } + ], "source": [ "#Print results\n", "print(algos)\n", @@ -957,9 +1006,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "------------\n", + "\n", + "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", + "preferentialAttachment\n", + "[3.4762573984859073, 36.25720333229164, 134.55004466061635, 2.4608888173507344, 0.9039513041939133, 1.117330592113951, 2.4959144857253164, 0.09156219712459993, 1.006714469868979, 41.36659009325271]\n" + ] + } + ], "source": [ "#Print results\n", "print(\"\\n------------\\n\")\n", @@ -1070,11 +1132,8 @@ } ], "metadata": { - "interpreter": { - "hash": "f708a36acfaef0acf74ccd43dfb58100269bf08fb79032a1e0a6f35bd9856f51" - }, "kernelspec": { - "display_name": "Python 3.8.10 ('base')", + "display_name": "Python 3.9.13 ('cugraph_dev')", "language": "python", "name": "python3" }, @@ -1088,7 +1147,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "cee8a395f2f0c5a5bcf513ae8b620111f4346eff6dc64e1ea99c951b2ec68604" + } } }, "nbformat": 4, From dbc29df69e11d57fefb42b591b1d66b0cf8719a4 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Mon, 12 Sep 2022 08:56:33 -0400 Subject: [PATCH 199/384] Removed reference to hard limit of 2 billion vertices for dask cugraph (#2680) Closes #2476 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2680 --- docs/cugraph/source/api_docs/dask-cugraph.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/api_docs/dask-cugraph.rst b/docs/cugraph/source/api_docs/dask-cugraph.rst index e671d4666..772d3abc4 100644 --- a/docs/cugraph/source/api_docs/dask-cugraph.rst +++ b/docs/cugraph/source/api_docs/dask-cugraph.rst @@ -2,7 +2,7 @@ Multi-GPU with cuGraph ~~~~~~~~~~~~~~~~~~~~~~ -cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_. The maximum graph size is currently limited to 2 Billion vertices (to be waived in the next versions). +cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_. Distributed graph analytics =========================== From b1b11e7ef52048ea5a32d1b9e36b6f9389a86a33 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Mon, 12 Sep 2022 09:35:49 -0400 Subject: [PATCH 200/384] updated list of conferences (#2672) closes #2664 Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2672 --- docs/cugraph/source/basics/cugraph_blogs.rst | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/docs/cugraph/source/basics/cugraph_blogs.rst b/docs/cugraph/source/basics/cugraph_blogs.rst index 4d5447879..2d3e751a4 100644 --- a/docs/cugraph/source/basics/cugraph_blogs.rst +++ b/docs/cugraph/source/basics/cugraph_blogs.rst @@ -39,10 +39,8 @@ BLOGS & Conferences 2022 ------ * `GTC: State of cuGraph (video & slides) `_ - * `GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides) `_ - - - + * `GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides) `_ + * `KDD Tutorial on Accelerated GNN Training with DGL/PyG and cuGraph `_ Media @@ -59,12 +57,16 @@ Academic Papers * Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504). + * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press Other BLOGS ======================== -* `4 graph algorithms on steroids for data scientists with cugraph - `_ +* `4 graph algorithms on steroids for data scientists with cugraph `_ * `Where should I walk `_ * `Where really are the parking spots? `_ * `Accelerating Single Cell Genomic Analysis using RAPIDS `_ +* `Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms `_ +* `Dev Blog Repost: Similarity in Graphs: Jaccard Versus the Overlap Coefficient `_ + + From 2259787660fa427baed8892200df29b60b0f984f Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Thu, 22 Sep 2022 11:16:17 -0400 Subject: [PATCH 201/384] adds mechanism to skip notebook directories for different run types (#2693) closes #2622 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/2693 --- notebooks/cugraph_benchmarks/SKIP_CI_TESTING | 0 notebooks/demo/SKIP_CI_TESTING | 0 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 notebooks/cugraph_benchmarks/SKIP_CI_TESTING create mode 100644 notebooks/demo/SKIP_CI_TESTING diff --git a/notebooks/cugraph_benchmarks/SKIP_CI_TESTING b/notebooks/cugraph_benchmarks/SKIP_CI_TESTING new file mode 100644 index 000000000..e69de29bb diff --git a/notebooks/demo/SKIP_CI_TESTING b/notebooks/demo/SKIP_CI_TESTING new file mode 100644 index 000000000..e69de29bb From 694ec7df3b25de1c4409295a072bd5cb59532353 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Fri, 23 Sep 2022 11:37:24 -0400 Subject: [PATCH 202/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 05def8649..e0552e3d7 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '22.10' +version = '22.12' # The full version, including alpha/beta/rc tags. -release = '22.10.00' +release = '22.12.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From ff19c461013294b2a154c818d36df02e1128a4de Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Fri, 23 Sep 2022 13:45:52 -0400 Subject: [PATCH 203/384] fix Comms import (#2717) closes #2563 Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2717 --- notebooks/demo/mg_pagerank.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index db1f69ccb..7c32c0520 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -43,7 +43,7 @@ "# Import needed libraries. We recommend using cugraph_dev env through conda\n", "from dask.distributed import Client, wait\n", "from dask_cuda import LocalCUDACluster\n", - "import cugraph.comms as Comms\n", + "from cugraph.dask.comms import comms as Comms\n", "import cugraph.dask as dask_cugraph\n", "import cugraph\n", "import dask_cudf\n", @@ -162,7 +162,7 @@ "outputs": [], "source": [ "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", - "G = cugraph.DiGraph()\n", + "G = cugraph.Graph(directed=True)\n", "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')\n", "\n", "# Print time\n", @@ -295,9 +295,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "cugraph_dev", "language": "python", - "name": "python3" + "name": "cugraph_dev" }, "language_info": { "codemirror_mode": { @@ -309,7 +309,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.13" } }, "nbformat": 4, From 726e459623136577df6dbe551bf9594fc939f873 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 4 Oct 2022 10:55:46 -0400 Subject: [PATCH 204/384] Adding new classes to api docs (#2754) updating api docs for new classes Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2754 --- docs/cugraph/source/api_docs/community.rst | 2 +- docs/cugraph/source/api_docs/graph_implementation.rst | 4 ---- docs/cugraph/source/api_docs/sampling.rst | 2 +- docs/cugraph/source/api_docs/structure.rst | 1 + 4 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/cugraph/source/api_docs/community.rst b/docs/cugraph/source/api_docs/community.rst index d35ac657f..94eb1a49c 100644 --- a/docs/cugraph/source/api_docs/community.rst +++ b/docs/cugraph/source/api_docs/community.rst @@ -77,4 +77,4 @@ Triangle Counting .. autosummary:: :toctree: api/ - cugraph.triangles + cugraph.triangle_count diff --git a/docs/cugraph/source/api_docs/graph_implementation.rst b/docs/cugraph/source/api_docs/graph_implementation.rst index e96c49c91..9d67c21c6 100644 --- a/docs/cugraph/source/api_docs/graph_implementation.rst +++ b/docs/cugraph/source/api_docs/graph_implementation.rst @@ -16,10 +16,6 @@ Graph Implementation delete_adj_list enable_batch - batch_adjlists - batch_edgelists - batch_enabled - batch_transposed_adjlists get_two_hop_neighbors number_of_vertices diff --git a/docs/cugraph/source/api_docs/sampling.rst b/docs/cugraph/source/api_docs/sampling.rst index 8a6cc5f22..bab8aee6b 100644 --- a/docs/cugraph/source/api_docs/sampling.rst +++ b/docs/cugraph/source/api_docs/sampling.rst @@ -12,7 +12,7 @@ Random Walks cugraph.random_walks cugraph.ego_graph - cugraph.experimental.dask.uniform_neighborhood_sampling + cugraph.uniform_neighbor_sample Node2Vec --------- diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst index 04e836ff3..ab097156c 100644 --- a/docs/cugraph/source/api_docs/structure.rst +++ b/docs/cugraph/source/api_docs/structure.rst @@ -13,6 +13,7 @@ Constructors BiPartiteGraph + Adding Data ----------- .. autosummary:: From e42938554f1cf2f355b0f5c0f9212876ed59ba0c Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Wed, 12 Oct 2022 17:56:42 +0000 Subject: [PATCH 205/384] REL v22.10.00 release From 8411283c2bd770c914f9fbebb63df5f7378a36c0 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 12 Oct 2022 14:56:01 -0500 Subject: [PATCH 206/384] Format Python code with black (#2778) closes https://github.com/rapidsai/cugraph/issues/2527 I also added `.pre-commit-config.yaml` to make this easier to use and match versions (for example, I kept using a newer flake8 locally). This uses the versions of black and flake8 that are in the `rapids` environment in the docker container. There's a lot more we can do with pre-commit, but I didn't want to be overzealous in this PR. _After_ this PR is merged, we should add a `.git-blame-ignore-revs` file so that I won't get blamed for everything :) . See: - https://black.readthedocs.io/en/stable/guides/introducing_black_to_your_project.html#avoiding-ruining-git-blame - https://docs.github.com/en/repositories/working-with-files/using-files/viewing-a-file#ignore-commits-in-the-blame-view Once this is merged, existing PRs should probably run `black` (version 22.3.0) on their branch before merging 22.12 branch into their branch. **This changes the maximum line length to 88, which is the default for black** Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/2778 --- .pre-commit-config.yaml | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..61d21fcba --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,31 @@ +## https://pre-commit.com/ +# +# Before first use: `pre-commit install` +# To run: `pre-commit run --all-files` +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: check-added-large-files + - id: debug-statements + - id: mixed-line-ending + - repo: https://github.com/psf/black + rev: 22.3.0 + hooks: + - id: black + language_version: python3 + exclude: versioneer.py + args: [--target-version=py38] + files: ^python/ + - repo: https://github.com/PyCQA/flake8 + rev: 3.8.4 + hooks: + - id: flake8 + args: [--config=python/.flake8] + files: ^python/ + - repo: https://github.com/asottile/yesqa + rev: v1.3.0 + hooks: + - id: yesqa + additional_dependencies: + - flake8==3.8.4 From d025a53e591986487388b3e7ed34ded980eb0229 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Fri, 14 Oct 2022 09:52:43 -0400 Subject: [PATCH 207/384] Updated Release Notebook for changes in latest cuGraph release (#2800) - [X] Changed call to triangle count algorithm - [x] Added transpose parameter to graph building - [x] Fixed handling of small graphs (only used for testing) closes #2790 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2800 --- notebooks/cugraph_benchmarks/release.ipynb | 167 ++++++++------------- 1 file changed, 60 insertions(+), 107 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index 8ba3c82eb..32265684a 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -91,6 +91,7 @@ "| Brad Rees | 01/20/2022 | updated | 22.02 | Quadro A6000 CUDA 11.5 |\n", "| Brad Rees | 01/20/2022 | added perf w/Nx obj | 22.02 | Quadro A6000 CUDA 11.5 |\n", "| Ralph Liu | 06/01/2022 | Fix: Generators | 22.06 | Tesla V100, CUDA 11.5 |\n", + "| Don Acosta | 10/12/2022 | Fix triangles and transposed graphs | 22.12 nightly | Tesla A6000, CUDA 11.5 |\n", "\n", "\n", "\n" @@ -105,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -165,8 +166,8 @@ "\n", "# for quick testing\n", "data_quick = {\n", - " 'preferentialAttachment' : './data/preferentialAttachment.mtx', \n", - " #'karate' : './data/karate.mtx',\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n", + " #'karate' : './data/karate.mtx',\n", "}\n", "\n", "\n", @@ -176,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -222,29 +223,45 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# NetworkX\n", "def create_nx_digraph(_df):\n", - " _gnx = nx.from_pandas_edgelist(_df, source='src', target='dst', edge_attr=None, create_using=nx.DiGraph)\n", + " _gnx = nx.from_pandas_edgelist(_df,\n", + " source='src',\n", + " target='dst',\n", + " edge_attr=None,\n", + " create_using=nx.DiGraph)\n", " return _gnx\n", "\n", "def create_nx_ugraph(_df):\n", - " _gnx = nx.from_pandas_edgelist(_df, source='src', target='dst', edge_attr=None, create_using=nx.Graph)\n", + " _gnx = nx.from_pandas_edgelist(_df,\n", + " source='src',\n", + " target='dst',\n", + " edge_attr=None,\n", + " create_using=nx.Graph)\n", " return _gnx\n", "\n", "\n", "# cuGraph\n", - "def create_cu_digraph(_df):\n", + "def create_cu_digraph(_df, transpose=False):\n", " _g = cugraph.Graph(directed=True)\n", - " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", + " _g.from_cudf_edgelist(_df,\n", + " source='src',\n", + " destination='dst',\n", + " renumber=False,\n", + " store_transposed=transpose)\n", " return _g\n", "\n", - "def create_cu_ugraph(_df):\n", + "def create_cu_ugraph(_df,transpose=False):\n", " _g = cugraph.Graph(directed=False)\n", - " _g.from_cudf_edgelist(_df, source='src', destination='dst', renumber=False)\n", + " _g.from_cudf_edgelist(_df,\n", + " source='src',\n", + " destination='dst',\n", + " renumber=False,\n", + " store_transposed=transpose)\n", " return _g" ] }, @@ -264,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +294,7 @@ "\n", "def cu_katz(_df, alpha):\n", " t1 = perf_counter()\n", - " _G = create_cu_ugraph(_df)\n", + " _G = create_cu_ugraph(_df, transpose=True)\n", " _ = cugraph.katz_centrality(_G, alpha)\n", " t2 = perf_counter() - t1\n", " return t2\n", @@ -299,12 +316,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nx_bc(_df, _k):\n", - " print(f\" k = {_k}\", end=' ')\n", " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", " _ = nx.betweenness_centrality(_G, k=_k)\n", @@ -335,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -374,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -394,14 +410,14 @@ "def cu_tc(_df):\n", " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", - " _ = cugraph.triangles(_G)\n", + " _ = cugraph.triangle_count(_G)\n", " t2 = perf_counter() - t1\n", " return t2\n", "\n", "def cu_tc_nx(_df):\n", " t1 = perf_counter()\n", " _G = create_nx_ugraph(_df)\n", - " _ = cugraph.triangles(_G)\n", + " _ = cugraph.triangle_count(_G)\n", " t2 = perf_counter() - t1\n", " return t2" ] @@ -415,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -456,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -509,7 +525,7 @@ "\n", "def cu_pagerank(_df):\n", " t1 = perf_counter()\n", - " _G = create_cu_digraph(_df)\n", + " _G = create_cu_digraph(_df, transpose=True)\n", " _ = cugraph.pagerank(_G)\n", " t2 = perf_counter() - t1\n", " return t2\n", @@ -531,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -545,7 +561,7 @@ "def cu_jaccard(_df):\n", " t1 = perf_counter()\n", " _G = create_cu_ugraph(_df)\n", - " _ = list(cugraph.jaccard_coefficient(_G))\n", + " _ = cugraph.jaccard_coefficient(_G)\n", " t2 = perf_counter() - t1\n", " return t2\n", "\n", @@ -566,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -639,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -649,30 +665,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading ./data/preferentialAttachment.mtx...\n", - "\tGDF Size 999970\n", - "\tcugraph Size 499985\n", - "\tcugraph Order 100000\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# do a simple pass just to get all the libraries initialized\n", "# This cell might not be needed\n", @@ -698,28 +693,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading ./data/preferentialAttachment.mtx...\n", - "\tdata in gdf 999970 and data in pandas 999970\n", - "\tKatz n.c.cx.\n", - "\tBC k=100 n. k = 100 c.cx. \n", - "\tLouvain n.c.cx. \n", - "\tTC n.c.cx. \n", - "\tWCC n.c.cx. \n", - "\tCore Number n.c.cx. \n", - "\tPageRank n.c.cx. \n", - "\tJaccard n.c.cx. \n", - "\tBFS n.c.cx. \n", - "\tSSSP n.c.cx. \n" - ] - } - ], + "outputs": [], "source": [ "# arrays to capture performance gains\n", "names = []\n", @@ -792,8 +768,7 @@ "\n", " k = 100\n", " if k > num_nodes:\n", - " k = num_nodes\n", - " \n", + " k = int(num_nodes)\n", " print(\"n.\", end='')\n", " tx = nx_bc(pdf, k)\n", " print(\"c.\", end='')\n", @@ -976,25 +951,16 @@ " gc.collect()\n", "\n", " # increament count\n", + " \n", " i = i + 1\n", "\n" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", - "preferentialAttachment\n", - "[145.30643917185208, 236.64781352415596, 4385.897320647833, 135.74325850737782, 34.89773706999491, 35.98962606492319, 148.4826107413348, 34.04823126698779, 41.64064391168044, 39.70684495453922]\n" - ] - } - ], + "outputs": [], "source": [ "#Print results\n", "print(algos)\n", @@ -1006,22 +972,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "------------\n", - "\n", - "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", - "preferentialAttachment\n", - "[3.4762573984859073, 36.25720333229164, 134.55004466061635, 2.4608888173507344, 0.9039513041939133, 1.117330592113951, 2.4959144857253164, 0.09156219712459993, 1.006714469868979, 41.36659009325271]\n" - ] - } - ], + "outputs": [], "source": [ "#Print results\n", "print(\"\\n------------\\n\")\n", @@ -1036,7 +989,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## The following section is to rerun portions of the benchamrks if needed" + "## The following section is to rerun portions of the benchmarks if needed" ] }, { @@ -1122,7 +1075,7 @@ "metadata": {}, "source": [ "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "Copyright (c) 2020-2022, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -1151,7 +1104,7 @@ }, "vscode": { "interpreter": { - "hash": "cee8a395f2f0c5a5bcf513ae8b620111f4346eff6dc64e1ea99c951b2ec68604" + "hash": "5ad9d2119cd61fde5a45f3586cbfbdeb97f4ed8f77c9b94c9482dc6d8640b46a" } } }, From e23e2c4e12a431ace2a3a42af3588963c931e2f5 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 25 Oct 2022 09:05:23 -0400 Subject: [PATCH 208/384] removed docs from cugraph build defaults and updated docs clean (#2831) removed docs from default cugraph build behavior added additional generated directory to docs clean target closes #2789 closes #2812 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2831 --- docs/cugraph/Makefile | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/cugraph/Makefile b/docs/cugraph/Makefile index 330c06bed..32237aa2c 100644 --- a/docs/cugraph/Makefile +++ b/docs/cugraph/Makefile @@ -9,11 +9,16 @@ SOURCEDIR = source BUILDDIR = build IMGDIR = images +gen_doc_dirs = build source/api_docs/api # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -.PHONY: help Makefile +.PHONY: help Makefile clean + +# clean to remove all the generated documentation files in build and source +clean: + rm -rf $(gen_doc_dirs) # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). From f7a874ff07debcf691802f0dfe695a4c9dcf022d Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 26 Oct 2022 15:41:47 -0400 Subject: [PATCH 209/384] Create a page on why we do not support cascading (#2842) Created a markdown file that explains the cuGraph stance on method cascading. This is in response to both issues, direct messages, and stack overflow questions. It might be easier to review the page by looking at: https://github.com/BradReesWork/cugraph/blob/22.12-cascade-doc/docs/cugraph/source/basics/cugraph_cascading.md Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Don Acosta (https://github.com/acostadon) URL: https://github.com/rapidsai/cugraph/pull/2842 --- .../source/basics/cugraph_cascading.md | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 docs/cugraph/source/basics/cugraph_cascading.md diff --git a/docs/cugraph/source/basics/cugraph_cascading.md b/docs/cugraph/source/basics/cugraph_cascading.md new file mode 100644 index 000000000..7795e9e00 --- /dev/null +++ b/docs/cugraph/source/basics/cugraph_cascading.md @@ -0,0 +1,53 @@ + +# Method Cascading and cuGraph + +BLUF: cuGraph does not support method cascading + +[Method Cascading](https://en.wikipedia.org/wiki/Method_cascading) is a popular, and useful, functional programming concept and is a great way to make code more readable. Python supports method cascading ... _for the most part_. There are a number of Python built-in classes that do not support cascading. + +An example, from cuDF, is a sequence of method calls for loading data and then finding the largest values from a subset of the data (yes there are other ways this could be done): + +``` +gdf = cudf.from_pandas(df).query(‘val > 200’).nlargest(‘va’3) +``` + +cuGraph does not support method cascading for two main reasons: (1) the object-oriented nature of the Graph data object leverages in-place methods, and (2) the fact that algorithms operate on graphs rather than graphs running algorithms. + +## Graph Data Objects +cuGraph follows an object-oriented design for the Graph objects. Users create a Graph and can then add data to object, but every add method call returns `None`. + +_Why Inplace methods?_
+cuGraph focuses on the big graph problems where there are 10s of millions to trillions of edges (Giga bytes to Terabytes of data). At that scale, creating a copy of the data becomes memory inefficient. + +_Why not return `self` rather than `None`?_
+It would be simple to modify the methods to return `self` rather than `None`, however it opens the methods to misinterpretation. Consider the following code: + +``` +# cascade flow - makes sense +G = cugraph.Graph().from_cudf_edgelist(df) + +# non-cascaded code can be confusing +G = cugraph.Graph() +G2 = G.from_cudf_edgelist(df) +G3 = G.from_cudf_edgelist(df2) +``` +The confusion with the non-cascade code is that G, G1, and G3 are all the same object with the same data. Users could be confused since it is not obvious that changing G3 would also change both G2 and G. To prevent confusion, cuGraph has opted to not return `self`. + +_Why not add a flag "return_self" to the methods?_
+``` +# cascade flow - makes sense +G = cugraph.Graph().from_cudf_edgelist(df, return_self=True) +``` +The fact that a developer would explicitly add a "return_self" flag to the method indicates that the developer is aware that the method returns None. It is just as easy for the developer to use a non-cascading workflow. + +### Algorithms +Algorithms operate on graph objects. +``` +cugraph.pagerank(G) and not G.pagerank() +``` +This pattern allows cuGraph to maintain a particular object-oriented model, where Graph objects simply maintain graph data, and algorithm functions operate independently on Graph objects. While this model has benefits that simplify the overall design and its usability in the majority of use cases, it does mean that the developer cannot cascade graph creation into an algorithm call. + +``` +# will not work +G = cugraph.Graph().from_cudf_edgelist(df).pagerank() +``` From 2a40977fb0ee6a297f2a25c8ac290b614d5f9399 Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Fri, 4 Nov 2022 19:51:53 +0000 Subject: [PATCH 210/384] REL v22.10.01 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 05def8649..8edee0af7 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -83,7 +83,7 @@ # The short X.Y version. version = '22.10' # The full version, including alpha/beta/rc tags. -release = '22.10.00' +release = '22.10.01' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 9d0ec692e7cf394414bb796e27d06812fb61221a Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 10 Nov 2022 12:57:22 -0500 Subject: [PATCH 211/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index e0552e3d7..a9e3e2d6c 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '22.12' +version = '23.02' # The full version, including alpha/beta/rc tags. -release = '22.12.00' +release = '23.02.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 4052151347fd6e32223a5f7e6bd369b50464a470 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Mon, 14 Nov 2022 13:56:07 -0500 Subject: [PATCH 212/384] Add ProperyGraph to doc generation and update docstrings (#2826) closes #2683 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/2826 --- .../source/api_docs/graph_implementation.rst | 2 +- docs/cugraph/source/api_docs/index.rst | 1 + .../source/api_docs/property_graph.rst | 29 +++++++++++++++++++ 3 files changed, 31 insertions(+), 1 deletion(-) create mode 100644 docs/cugraph/source/api_docs/property_graph.rst diff --git a/docs/cugraph/source/api_docs/graph_implementation.rst b/docs/cugraph/source/api_docs/graph_implementation.rst index 9d67c21c6..f6dc38a27 100644 --- a/docs/cugraph/source/api_docs/graph_implementation.rst +++ b/docs/cugraph/source/api_docs/graph_implementation.rst @@ -5,7 +5,7 @@ Graph Implementation Graph Implementation -------------------------- +-------------------- .. autosummary:: :toctree: api/ diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index e7f94d7e1..aca8e0eff 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -11,6 +11,7 @@ This page provides a list of all publicly accessible modules, methods and classe structure graph_implementation + property_graph centrality community components diff --git a/docs/cugraph/source/api_docs/property_graph.rst b/docs/cugraph/source/api_docs/property_graph.rst new file mode 100644 index 000000000..ce1901d42 --- /dev/null +++ b/docs/cugraph/source/api_docs/property_graph.rst @@ -0,0 +1,29 @@ +==================== +Property Graph +==================== +.. currentmodule:: cugraph.structure.property_graph + + +Property Graph +------------------------- +.. autosummary:: + :toctree: api/ + + EXPERIMENTAL__PropertySelection + EXPERIMENTAL__PropertyGraph + EXPERIMENTAL__PropertyGraph.add_edge_data + EXPERIMENTAL__PropertyGraph.add_vertex_data + EXPERIMENTAL__PropertyGraph.annotate_dataframe + EXPERIMENTAL__PropertyGraph.edge_props_to_graph + EXPERIMENTAL__PropertyGraph.extract_subgraph + EXPERIMENTAL__PropertyGraph.get_edge_data + EXPERIMENTAL__PropertyGraph.get_num_edges + EXPERIMENTAL__PropertyGraph.get_num_vertices + EXPERIMENTAL__PropertyGraph.get_vertex_data + EXPERIMENTAL__PropertyGraph.get_vertices + EXPERIMENTAL__PropertyGraph.has_duplicate_edges + EXPERIMENTAL__PropertyGraph.is_multigraph + EXPERIMENTAL__PropertyGraph.renumber_edges_by_type + EXPERIMENTAL__PropertyGraph.renumber_vertices_by_type + EXPERIMENTAL__PropertyGraph.select_edges + EXPERIMENTAL__PropertyGraph.select_vertices \ No newline at end of file From d4670b1fc9d2fb16b0e58b9960f6eebfe74f6ca2 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Thu, 17 Nov 2022 10:23:49 -0500 Subject: [PATCH 213/384] Update gitignore to Exclude Egg Files (#2948) Resolves #2947 Excludes egg files from git. Previously, only egg files in a certain directory were excluded. This broadens the exclusion to the whole repository. Authors: - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/2948 --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 7bd759bbc..a5a375667 100644 --- a/.gitignore +++ b/.gitignore @@ -32,7 +32,7 @@ test-results dask-worker-space/ htmlcov dist/ -cugraph.egg-info/ +*.egg-info/ python/build python/cugraph/bindings/*.cpp wheels/ From c508c65b9486336a9950f51699fe4a394ca6f690 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Mon, 28 Nov 2022 19:58:36 -0500 Subject: [PATCH 214/384] cuGraph Readme pages and Documentation API structure refactoring (#2894) Refactoring the API and adding new landing pages for each cuGraph component Please just go to: https://github.com/acostadon/cugraph/tree/README_issue_2663 to visualize the changes closes #2663 Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) - Vibhu Jawa (https://github.com/VibhuJawa) - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Brad Rees (https://github.com/BradReesWork) - Taurean Dyer (https://github.com/taureandyernv) URL: https://github.com/rapidsai/cugraph/pull/2894 --- docs/cugraph/source/basics/cugraph_blogs.rst | 55 +++++++++++--------- 1 file changed, 29 insertions(+), 26 deletions(-) diff --git a/docs/cugraph/source/basics/cugraph_blogs.rst b/docs/cugraph/source/basics/cugraph_blogs.rst index 2d3e751a4..368dbcce4 100644 --- a/docs/cugraph/source/basics/cugraph_blogs.rst +++ b/docs/cugraph/source/basics/cugraph_blogs.rst @@ -1,5 +1,5 @@ -cuGraph BLOGS and Presentations +cuGraph Blogs and Presentations ************************************************ The RAPIDS team blogs at https://medium.com/rapids-ai, and many of @@ -7,21 +7,17 @@ these blog posts provide deeper dives into features from cuGraph. Here, we've selected just a few that are of particular interest to cuGraph users: -BLOGS & Conferences +Blogs & Conferences ==================== -2018 -------- - * `GTC18 Fall - RAPIDS: Benchmarking Graph Analytics on the DGX-2 `_ - +2022 +------ + * `GTC: State of cuGraph (video & slides) `_ + * `GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides) `_ + * `KDD Tutorial on Accelerated GNN Training with DGL/PyG and cuGraph `_ -2019 -------- - * `RAPIDS cuGraph `_ - * `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ - * `RAPIDS cuGraph : multi-GPU PageRank `_ - * `Similarity in graphs: Jaccard versus the Overlap Coefficient `_ - * `GTC19 Spring - Accelerating Graph Algorithms with RAPIDS `_ - * `GTC19 Fall - Multi-Node Multi-GPU Machine Learning and Graph Analytics with RAPIDS `_ +2021 +------ + * `GTC 21 - State of RAPIDS cuGraph and what's comming next `_ 2020 ------ @@ -31,16 +27,19 @@ BLOGS & Conferences * `Large Graph Visualization with RAPIDS cuGraph `_ * `GTC 20 Fall - cuGraph Goes Big `_ -2021 ------- - * `GTC 21 - State of RAPIDS cuGraph and what's comming next `_ +2019 +------- + * `RAPIDS cuGraph `_ + * `RAPIDS cuGraph — The vision and journey to version 1.0 and beyond `_ + * `RAPIDS cuGraph : multi-GPU PageRank `_ + * `Similarity in graphs: Jaccard versus the Overlap Coefficient `_ + * `GTC19 Spring - Accelerating Graph Algorithms with RAPIDS `_ + * `GTC19 Fall - Multi-Node Multi-GPU Machine Learning and Graph Analytics with RAPIDS `_ +2018 +------- + * `GTC18 Fall - RAPIDS: Benchmarking Graph Analytics on the DGX-2 `_ -2022 ------- - * `GTC: State of cuGraph (video & slides) `_ - * `GTC: Scaling and Validating Louvain in cuGraph against Massive Graphs (video & slides) `_ - * `KDD Tutorial on Accelerated GNN Training with DGL/PyG and cuGraph `_ Media @@ -51,16 +50,16 @@ Media Academic Papers =============== + * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press + * S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020 * Hricik, T., Bader, D., & Green, O. (2020, September). `Using RAPIDS AI to accelerate graph data science workflows`. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE. * Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504). - * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press - -Other BLOGS +Other Blogs ======================== * `4 graph algorithms on steroids for data scientists with cugraph `_ * `Where should I walk `_ @@ -69,4 +68,8 @@ Other BLOGS * `Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms `_ * `Dev Blog Repost: Similarity in Graphs: Jaccard Versus the Overlap Coefficient `_ - +RAPIDS Event Notebooks +====================== +* `KDD 2022 Notebook that demonstates using cuDF for ETL/data cleaning and XGBoost for training a fraud predection model. `_ +* `SciPy 22 Notebook comparing cuGraph to NetworkX `_ +* `KDD 2020 Tutorial Notebooks - Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS `_ From 55f40abd2ea0cf36d6cc2bff11b686e281f64f0e Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 30 Nov 2022 13:06:53 -0600 Subject: [PATCH 215/384] Updates experimental warning wrapper and PropertyGraph docs for correct experimental namespace name (#3007) closes #3006 * Updated experimental warning wrapper to also pass-through __doc__ attr * Updated PropertyGraph doc .rst file to use the proper experimental namespace. Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3007 --- .../source/api_docs/property_graph.rst | 38 +++++++++---------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/docs/cugraph/source/api_docs/property_graph.rst b/docs/cugraph/source/api_docs/property_graph.rst index ce1901d42..f3653c085 100644 --- a/docs/cugraph/source/api_docs/property_graph.rst +++ b/docs/cugraph/source/api_docs/property_graph.rst @@ -1,7 +1,7 @@ ==================== Property Graph ==================== -.. currentmodule:: cugraph.structure.property_graph +.. currentmodule:: cugraph.experimental Property Graph @@ -9,21 +9,21 @@ Property Graph .. autosummary:: :toctree: api/ - EXPERIMENTAL__PropertySelection - EXPERIMENTAL__PropertyGraph - EXPERIMENTAL__PropertyGraph.add_edge_data - EXPERIMENTAL__PropertyGraph.add_vertex_data - EXPERIMENTAL__PropertyGraph.annotate_dataframe - EXPERIMENTAL__PropertyGraph.edge_props_to_graph - EXPERIMENTAL__PropertyGraph.extract_subgraph - EXPERIMENTAL__PropertyGraph.get_edge_data - EXPERIMENTAL__PropertyGraph.get_num_edges - EXPERIMENTAL__PropertyGraph.get_num_vertices - EXPERIMENTAL__PropertyGraph.get_vertex_data - EXPERIMENTAL__PropertyGraph.get_vertices - EXPERIMENTAL__PropertyGraph.has_duplicate_edges - EXPERIMENTAL__PropertyGraph.is_multigraph - EXPERIMENTAL__PropertyGraph.renumber_edges_by_type - EXPERIMENTAL__PropertyGraph.renumber_vertices_by_type - EXPERIMENTAL__PropertyGraph.select_edges - EXPERIMENTAL__PropertyGraph.select_vertices \ No newline at end of file + PropertySelection + PropertyGraph + PropertyGraph.add_edge_data + PropertyGraph.add_vertex_data + PropertyGraph.annotate_dataframe + PropertyGraph.edge_props_to_graph + PropertyGraph.extract_subgraph + PropertyGraph.get_edge_data + PropertyGraph.get_num_edges + PropertyGraph.get_num_vertices + PropertyGraph.get_vertex_data + PropertyGraph.get_vertices + PropertyGraph.has_duplicate_edges + PropertyGraph.is_multigraph + PropertyGraph.renumber_edges_by_type + PropertyGraph.renumber_vertices_by_type + PropertyGraph.select_edges + PropertyGraph.select_vertices From 7827cb2cf0dabfb623509d4647a8c8127eb4c86d Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Wed, 30 Nov 2022 11:49:06 -0800 Subject: [PATCH 216/384] Add wheel builds (#2964) This PR adds wheel building to cugraph. Also resolves #3004. Authors: - Vyas Ramasubramani (https://github.com/vyasr) - Sevag H (https://github.com/sevagh) - Paul Taylor (https://github.com/trxcllnt) Approvers: - Joseph (https://github.com/jolorunyomi) - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/2964 --- .gitignore | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index a5a375667..530ccffca 100644 --- a/.gitignore +++ b/.gitignore @@ -36,17 +36,18 @@ dist/ python/build python/cugraph/bindings/*.cpp wheels/ +wheelhouse/ _skbuild/ cufile.log ## pylibcugraph build directories & artifacts python/pylibcugraph/pylibcugraph.egg-info - + ## Patching *.diff *.orig *.rej - + ## C++ build directories & artifacts CMakeFiles/ Debug @@ -54,7 +55,7 @@ build/ cpp/build/ cpp/include/cugraph/ipc_generated/*.h cpp/thirdparty/googletest/ - + ## Eclipse IDE .project .cproject @@ -92,4 +93,3 @@ python/cugraph/cugraph/tests/dask-worker-space # Sphinx docs & build artifacts docs/cugraph/source/api_docs/api/* - From 07911e0bbaaeba9ae1fe1d58b0f705f8ab2a750d Mon Sep 17 00:00:00 2001 From: gpuCI <38199262+GPUtester@users.noreply.github.com> Date: Fri, 9 Dec 2022 18:15:23 +0000 Subject: [PATCH 217/384] REL v22.12.00 release From a611453b8283b92e4c962b2cabfaf90bc78a1a0d Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Mon, 12 Dec 2022 19:23:52 -0500 Subject: [PATCH 218/384] redo cuGraph main docs (#3060) closes #2877 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3060 --- docs/cugraph/source/api_docs/c_and_cpp.rst | 4 ++ docs/cugraph/source/api_docs/cugraph_dgl.rst | 14 +++++++ docs/cugraph/source/api_docs/cugraph_pyg.rst | 13 ++++++ .../api_docs/cugraph_service_client.rst | 21 ++++++++++ .../api_docs/cugraph_service_server.rst | 13 ++++++ docs/cugraph/source/api_docs/cugraph_top.rst | 25 ++++++++++++ docs/cugraph/source/api_docs/index.rst | 27 ++++--------- docs/cugraph/source/api_docs/pylibcugraph.rst | 6 +-- docs/cugraph/source/basics/coming_soon.md | 6 +++ docs/cugraph/source/basics/cugraph_toc.md | 13 ++++++ docs/cugraph/source/images/Stack2.png | Bin 0 -> 457436 bytes docs/cugraph/source/index.rst | 38 ++++++++++++++++-- 12 files changed, 154 insertions(+), 26 deletions(-) create mode 100644 docs/cugraph/source/api_docs/c_and_cpp.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_dgl.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_pyg.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_service_client.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_service_server.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_top.rst create mode 100644 docs/cugraph/source/basics/coming_soon.md create mode 100644 docs/cugraph/source/basics/cugraph_toc.md create mode 100644 docs/cugraph/source/images/Stack2.png diff --git a/docs/cugraph/source/api_docs/c_and_cpp.rst b/docs/cugraph/source/api_docs/c_and_cpp.rst new file mode 100644 index 000000000..a3a88e8f6 --- /dev/null +++ b/docs/cugraph/source/api_docs/c_and_cpp.rst @@ -0,0 +1,4 @@ +CuGraph C and C++ API Links +=========================== + +`CuGraph C and C++ API <../basics/coming_soon.md>`_ \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph_dgl.rst b/docs/cugraph/source/api_docs/cugraph_dgl.rst new file mode 100644 index 000000000..f1f508754 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_dgl.rst @@ -0,0 +1,14 @@ +~~~~~~~~~~~~~~~~~~~~~~~~~~ +cugraph-dgl API Refrerence +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +cugraph-dgl + +.. currentmodule:: cugraph_dgl + +Methods +------- +.. autosummary:: + :toctree: api/ + + cugraph_dgl.cugraph_storage_from_heterograph diff --git a/docs/cugraph/source/api_docs/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph_pyg.rst new file mode 100644 index 000000000..0184a4a07 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_pyg.rst @@ -0,0 +1,13 @@ +~~~~~~~~~~~~~~~~~~~~~~~~~ +cugraph-pyg API Reference +~~~~~~~~~~~~~~~~~~~~~~~~~ + +cugraph-pyg + +.. currentmodule:: cugraph_pyg + +.. autosummary:: + :toctree: api/ + + cugraph_pyg.loader.dispatch.call_cugraph_algorithm + cugraph_pyg.sampler.EXPERIMENTAL__CuGraphSampler diff --git a/docs/cugraph/source/api_docs/cugraph_service_client.rst b/docs/cugraph/source/api_docs/cugraph_service_client.rst new file mode 100644 index 000000000..8947bc83b --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_service_client.rst @@ -0,0 +1,21 @@ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +cugraph-service-client API Reference +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +cugraph-service + +.. currentmodule:: cugraph-service + +.. autosummary:: + :toctree: api/ + + cugraph_service_client.client.RunAsyncioThread + cugraph_service_client.cugraph_service_thrift + cugraph_service_client.defaults + cugraph_service_client.exceptions + cugraph_service_client.remote_graph_utils + cugraph_service_client.remote_graph + cugraph_service_client.types + + + diff --git a/docs/cugraph/source/api_docs/cugraph_service_server.rst b/docs/cugraph/source/api_docs/cugraph_service_server.rst new file mode 100644 index 000000000..7e90759ef --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_service_server.rst @@ -0,0 +1,13 @@ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +cugraph-service-server API Reference +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +cugraph-service + +.. currentmodule:: cugraph-service + +.. autosummary:: + :toctree: api/ + + cugraph_service_server.cugraph_handler + diff --git a/docs/cugraph/source/api_docs/cugraph_top.rst b/docs/cugraph/source/api_docs/cugraph_top.rst new file mode 100644 index 000000000..1b920a841 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_top.rst @@ -0,0 +1,25 @@ +~~~~~~~~~~~~~~~~~~~~~ +cugraph API Reference +~~~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 2 + :caption: API Documentation + + structure + graph_implementation + property_graph + centrality + community + components + cores + layout + linear_assignment + link_analysis + link_prediction + sampling + traversal + tree + generator + helper_functions + dask-cugraph.rst diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index aca8e0eff..a8d025451 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -1,5 +1,4 @@ -============= -API reference +Python API reference ============= This page provides a list of all publicly accessible modules, methods and classes through @@ -7,23 +6,11 @@ This page provides a list of all publicly accessible modules, methods and classe .. toctree:: :maxdepth: 2 - :caption: API Documentation + :caption: Python API Documentation - structure - graph_implementation - property_graph - centrality - community - components - cores - layout - linear_assignment - link_analysis - link_prediction - sampling - traversal - tree - generator - helper_functions - dask-cugraph.rst + cugraph_top.rst pylibcugraph.rst + cugraph_dgl.rst + cugraph_pyg.rst + cugraph_service_client.rst + cugraph_service_server.rst diff --git a/docs/cugraph/source/api_docs/pylibcugraph.rst b/docs/cugraph/source/api_docs/pylibcugraph.rst index 93efec37f..7b63cd7dc 100644 --- a/docs/cugraph/source/api_docs/pylibcugraph.rst +++ b/docs/cugraph/source/api_docs/pylibcugraph.rst @@ -1,6 +1,6 @@ -~~~~~~~~~~~~~~~~~~~~~~ -pylibcugraph -~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ +pylibcugraph API reference +~~~~~~~~~~~~~~~~~~~~~~~~~~ pylibcugraph diff --git a/docs/cugraph/source/basics/coming_soon.md b/docs/cugraph/source/basics/coming_soon.md new file mode 100644 index 000000000..6cf15a954 --- /dev/null +++ b/docs/cugraph/source/basics/coming_soon.md @@ -0,0 +1,6 @@ + + + + +This part of the cuGraph documentation is currently under development. \ No newline at end of file diff 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zx$#I%-oPcgW&dG%vHdyCa@xu17S<3ky$A2BPgJgaC>UCTP}b&`8!rCkDh6KyNA zhLmV3(rLE>wgw$^)HJ#{$Hc4DF8k#sHf&4|j`fkJ4qgVb&A)uYx~Q}0cidhJ{jjnA z_jO;f1Y8!^JQV(;fcCeuz1@&bzSy z7QBy%(agT-AZ=}x|8KK8t0+zWIKfuwfE;9tn#N)%Ag86H`^r-M8|nTg;h=*I^afhB zZhn)NwMsBMcp_2uJHBKsT43GL-qbo`kPB0k#6HB4K0W6ptSPGsoW+kf;+RRDf73!YGc@c#e(+y|Ot;E5RTZwnj&|CR@SOPMbklk zet_TLJp)gCq8oGJKYQZ;!Q+2JoBxByzvBBL0Ox-K{D1KH4c&2wul^4ne@+?fWAXpN zvBOJOpBWLtg*Sdi=)W9OA40U#&;UfT1@E-|<_)kh7i5)x|^!2U=F6 z0e1Bx*u3i_QOh7N;Fgu;vll;rE6#r2i*q*NdN=36kGzfc%2GLDV3(}YcS^SIUY1l) zAOrzP53=2l&Kjx{gI2>lqb$7!tV2ry`5x@m2d^Q0$qMt$p|AZ;tXA?E+KCehVMP%+<6bN-`80!oN-b91HSSXsOq^C!= za)=$=^`*KAN=-Cria&J~{oBF>F15C{o(dM#ey68aVr|;d(INU(*#7&l?MF#Iu9+1( z_|4(*);OLgzoDUFM`x#@bSYr5*}1v=hbIZX zNy+^MLNtIS;+-\n", + "__What is timed__: (1) creating a Graph, (2) running the algorithm (3) run any generators\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "| Algorithm | Type | Undirected Graph | Directed Graph | Notes\n", + "| ------------------------|---------------|------ | ------- |-------------\n", + "| Katz | Centrality | X | | \n", + "| Betweenness Centrality | Centrality | X | | Estimated, k = 100\n", + "| Louvain | Community | X | | Uses python-louvain for comparison\n", + "| Triangle Counting | Community | X | |\n", + "| Core Number | Core | X | |\n", + "| PageRank | Link Analysis | | X |\n", + "| Jaccard | Similarity | X | |\n", + "| BFS | Traversal | X | | No depth limit\n", + "| SSSP | Traversal | X | | \n", + "\n", + "\n", + "### Test Data\n", + "Data is generated using a Recursive MATrix (R-MAT) graph generation algorithm. \n", + "The generator specifics are documented [here](https://docs.rapids.ai/api/cugraph/stable/api_docs/generator.html)\n", + "\n", + "\n", + "\n", + "### Notes\n", + "* Running Betweenness Centrality on the full graph is prohibitive using NetworkX. Anything over k=100 can explode runtime to days\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Import Modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# system and other\n", + "import gc\n", + "import os\n", + "from time import perf_counter\n", + "import numpy as np\n", + "import math\n", + "\n", + "# rapids\n", + "import cugraph\n", + "import cudf\n", + "\n", + "# NetworkX libraries\n", + "import networkx as nx\n", + "\n", + "# RMAT data generator\n", + "from cugraph.generators import rmat\n", + "from cugraph.structure import NumberMap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import community\n", + "except ModuleNotFoundError:\n", + " os.system('pip install python-louvain')\n", + " import community" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine the scale of the test data\n", + "RMAT generates graph where the number of vertices is a power of 2 and the number of edges is based on an edge factor times the number vertices.\n", + "\n", + "Since RMAT tends to generate about 50% isolated vertices, those vertices are dropped from the graph data. Hence the number of vertices is closer to (2 ** scale) / 2\n", + "\n", + "\n", + "| Scale | Vertices (est) | Edges |\n", + "| ------|----------------|--------|\n", + "| 10 | 512 | 16,384 | \n", + "| 11 | 1,024 | 32,768| \n", + "| 12 | 2,048 | 65,536| \n", + "| 13 | 4,096 | 131,072| \n", + "| 14 | 8,192 | 262,144| \n", + "| 15 | 16,384 | 524,288 | \n", + "| 16 | 32,768 | 1,048,576 | \n", + "| 17 | 65,536 | 2,097,152 | \n", + "| 18 | 131,072 | 4,194,304 | \n", + "| 19 | 262,144 | 8,388,608 | \n", + "| 20 | 524,288 | 16,777,216 | \n", + "| 21 | 1,048,576 | 33,554,432 | \n", + "| 22 | 2,097,152 | 67,108,864 | \n", + "| 23 | 4,194,304 | 134,217,728 | \n", + "| 24 | 8,388,608 | 268,435,456 | \n", + "| 25 | 16,777,216 | 536,870,912 | \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test Data Sizes\n", + "# Here you can create an array of test data sizes. Then set the \"data\" variable to the array you want\n", + "# the dictionary format is 'name' : scale\n", + "\n", + "\n", + "# These scales are used by R-MAT to determine the number of vertices/edges in the synthetic data graph.\n", + "data_full = {\n", + " 'data_scale_10' : 10,\n", + " 'data_scale_12' : 12,\n", + " 'data_scale_14' : 14,\n", + " 'data_scale_16' : 16,\n", + " 'data_scale_18' : 18,\n", + " 'data_scale_20' : 20,\n", + "}\n", + "\n", + "# for quick testing\n", + "data_quick = {\n", + " 'data_scale_9' : 9,\n", + " 'data_scale_10' : 10,\n", + " 'data_scale_11' : 11,\n", + "}\n", + "\n", + "\n", + "# Which dataset is to be used\n", + "data = data_full\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate data\n", + "The data is generated once for each size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data generator \n", + "# The result is an edgelist of the size determined by the scale and edge factor\n", + "def generate_data(scale, edgefactor=16):\n", + " _gdf = rmat(\n", + " scale,\n", + " (2 ** scale) * edgefactor,\n", + " 0.57,\n", + " 0.19,\n", + " 0.19,\n", + " 42,\n", + " clip_and_flip=False,\n", + " scramble_vertex_ids=True,\n", + " create_using=None, # return edgelist instead of Graph instance\n", + " mg=False # determines whether generated data will be used on one or multiple GPUs\n", + " )\n", + "\n", + " clean_coo = NumberMap.renumber(_gdf, src_col_names=\"src\", dst_col_names=\"dst\")[0]\n", + " clean_coo.rename(columns={\"renumbered_src\": \"src\", \"renumbered_dst\": \"dst\"}, inplace=True)\n", + " print(f'Generated a dataframe of {len(clean_coo)} edges')\n", + " return clean_coo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Graph functions\n", + "There are two types of graphs created:\n", + "* Directed Graphs - calls to create_nx_digraph, create_cu_directed_graph.\n", + "* Undirected Graphs - calls to create_xx_ugraph <- fully symmeterized" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX\n", + "def create_nx_graph(_df, directed=False):\n", + " t1 = perf_counter()\n", + " if directed:\n", + " g_type = nx.DiGraph\n", + " else:\n", + " g_type = nx.Graph\n", + " \n", + " _gnx = nx.from_pandas_edgelist(_df,\n", + " source='src',\n", + " target='dst',\n", + " edge_attr=None,\n", + " create_using=g_type)\n", + " t2 = perf_counter() - t1\n", + "\n", + " return _gnx, t2\n", + "\n", + "\n", + "# cuGraph\n", + "def create_cu_graph(_df,transpose=False, directed=False):\n", + " t1 = perf_counter()\n", + " _g = cugraph.Graph(directed=directed)\n", + " _g.from_cudf_edgelist(_df,\n", + " source='src',\n", + " destination='dst',\n", + " edge_attr=None,\n", + " renumber=False,\n", + " store_transposed=transpose)\n", + " t2 = perf_counter() - t1\n", + "\n", + " return _g, t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Algorithm Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Katz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_katz(_G, alpha):\n", + " t1 = perf_counter()\n", + " _ = nx.katz_centrality(_G, alpha)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_katz(_G, alpha):\n", + " t1 = perf_counter()\n", + " _ = cugraph.katz_centrality(_G, alpha)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Betweenness Centrality" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_bc(_G, _k):\n", + " t1 = perf_counter()\n", + " _ = nx.betweenness_centrality(_G, k=_k)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_bc(_G, _k):\n", + " t1 = perf_counter()\n", + " _ = cugraph.betweenness_centrality(_G, k=_k)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Louvain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_louvain(_G):\n", + " t1 = perf_counter()\n", + " parts = community.best_partition(_G)\n", + " \n", + " # Calculating modularity scores for comparison\n", + " _ = community.modularity(parts, _G)\n", + " \n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_louvain(_G):\n", + " t1 = perf_counter()\n", + " _,_ = cugraph.louvain(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Triangle Counting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_tc(_G):\n", + " t1 = perf_counter()\n", + " nx_count = nx.triangles(_G)\n", + "\n", + " # To get the number of triangles, we would need to loop through the array and add up each count\n", + " count = 0\n", + " for key, value in nx_count.items():\n", + " count = count + value\n", + " \n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_tc(_G):\n", + " t1 = perf_counter()\n", + " _ = cugraph.triangle_count(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Core Number" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_core_num(_G):\n", + " t1 = perf_counter()\n", + " _G.remove_edges_from(nx.selfloop_edges(_G))\n", + " nx_count = nx.core_number(_G)\n", + " \n", + " count = 0\n", + " for key, value in nx_count.items():\n", + " count = count + value\n", + " \n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_core_num(_G):\n", + " t1 = perf_counter()\n", + " _ = cugraph.core_number(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PageRank" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_pagerank(_G):\n", + " t1 = perf_counter()\n", + " _ = nx.pagerank(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2 \n", + "\n", + "def cu_pagerank(_G):\n", + " t1 = perf_counter()\n", + " _ = cugraph.pagerank(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Jaccard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_jaccard(_G):\n", + " t1 = perf_counter()\n", + " nj = nx.jaccard_coefficient(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_jaccard(_G):\n", + " t1 = perf_counter()\n", + " _ = cugraph.jaccard_coefficient(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BFS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_bfs(_G):\n", + " seed = 0\n", + " t1 = perf_counter()\n", + " nb = nx.bfs_edges(_G, seed)\n", + " nb_list = list(nb) # gen -> list\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_bfs(_G):\n", + " seed = 0\n", + " t1 = perf_counter()\n", + " _ = cugraph.bfs(_G, seed)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SSSP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_sssp(_G):\n", + " seed = 0\n", + " t1 = perf_counter()\n", + " _ = nx.shortest_path(_G, seed)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_sssp(_G):\n", + " seed = 0\n", + " t1 = perf_counter()\n", + " _ = cugraph.sssp(_G, seed)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# Benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# number of datasets\n", + "num_datasets = len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# arrays to capture performance gains\n", + "names = []\n", + "algos = []\n", + "graph_create_cu = []\n", + "graph_create_nx = []\n", + "\n", + "# Two dimension data [file, perf]\n", + "time_algo_nx = [] # NetworkX\n", + "time_algo_cu = [] # cuGraph\n", + "perf = []\n", + "perf_algo = []\n", + "\n", + "algos.append(\" \")\n", + "\n", + "i = 0\n", + "for k,v in data.items():\n", + " # init all the 2-d arrays\n", + " time_algo_nx.append([])\n", + " time_algo_cu.append([])\n", + " perf.append([])\n", + " perf_algo.append([])\n", + "\n", + " # Saved the file Name\n", + " names.append(k)\n", + "\n", + " # generate data\n", + " print(\"------------------------------\")\n", + " print(f'Creating Graph of Scale = {v}')\n", + "\n", + " gdf = generate_data(v)\n", + " pdf = gdf.to_pandas()\n", + " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", + "\n", + " # create the graphs\n", + " g_cu, tcu = create_cu_graph(gdf)\n", + " g_nx, tnx = create_nx_graph(pdf)\n", + " graph_create_cu.append(tcu)\n", + " graph_create_nx.append(tnx)\n", + " del gdf, pdf\n", + "\n", + " # prep\n", + " deg = g_cu.degree()\n", + " deg_max = deg['degree'].max()\n", + "\n", + " alpha = 1 / deg_max\n", + " num_nodes = g_cu.number_of_vertices()\n", + "\n", + " del deg\n", + " gc.collect()\n", + "\n", + " #----- Algorithm order is same as defined at top ----\n", + "\n", + " #-- Katz \n", + " print(\"\\tKatz \", end = '')\n", + " if i == 0: \n", + " algos.append(\"Katz\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_katz(g_nx, alpha)\n", + " print(\"c.\", end='')\n", + " tc = cu_katz(g_cu, alpha)\n", + " print(\"\")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- BC\n", + " print(\"\\tBC k=100 \", end='')\n", + " if i == 0:\n", + " algos.append(\"BC Estimate fixed\")\n", + "\n", + " k = 100\n", + " if k > num_nodes:\n", + " k = int(num_nodes)\n", + " print(\"n.\", end='')\n", + " tx = nx_bc(g_nx, k)\n", + " print(\"c.\", end='')\n", + " tc = cu_bc(g_cu, k)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- Louvain\n", + " print(\"\\tLouvain \", end='')\n", + " if i == 0:\n", + " algos.append(\"Louvain\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_louvain(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_louvain(g_cu)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- TC\n", + " print(\"\\tTC \", end='')\n", + " if i == 0:\n", + " algos.append(\"TC\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_tc(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_tc(g_cu)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- Core Number\n", + " print(\"\\tCore Number \", end='')\n", + " if i == 0:\n", + " algos.append(\"Core Number\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_core_num(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_core_num(g_cu)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- PageRank\n", + " print(\"\\tPageRank \", end='')\n", + " if i == 0:\n", + " algos.append(\"PageRank\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_pagerank(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_pagerank(g_cu)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- Jaccard\n", + " print(\"\\tJaccard \", end='')\n", + " if i == 0:\n", + " algos.append(\"Jaccard\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_jaccard(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_jaccard(g_cu)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- BFS\n", + " print(\"\\tBFS \", end='')\n", + " if i == 0:\n", + " algos.append(\"BFS\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_bfs(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_bfs(g_cu)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " #-- SSSP\n", + " print(\"\\tSSSP \", end='')\n", + " if i == 0:\n", + " algos.append(\"SSP\")\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_sssp(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_sssp(g_cu)\n", + " print(\" \")\n", + "\n", + " time_algo_nx[i].append(tx)\n", + " time_algo_cu[i].append(tc)\n", + " perf_algo[i].append ( (tx/tc) )\n", + " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + "\n", + " # increament count\n", + " i = i + 1\n", + " \n", + " del g_cu, g_nx\n", + " gc.collect()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Print results\n", + "print(algos)\n", + "\n", + "for i in range(num_datasets):\n", + " print(f\"{names[i]}\")\n", + " print(f\"{perf[i]}\")\n", + " print(f\"{perf_algo[i]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Print results\n", + "print(\"\\n------------------------------\")\n", + "print(\"\\tAlgorithm Run times (NX then cuGraph)\\n\")\n", + "\n", + "print(algos)\n", + "for i in range(num_datasets):\n", + " print(f\"{names[i]}\")\n", + " print(f\"{time_algo_nx[i]}\")\n", + " print(f\"{time_algo_cu[i]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cudfdev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 15:55:03) \n[GCC 10.4.0]" + }, + "vscode": { + "interpreter": { + "hash": "587ff963ecd34554a9da41c94362e2baa062d9a57502e220f049e10816826984" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 302b4ed39c2e7322cdbb0b1443d29c8f3e645976 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 31 Jan 2023 12:47:33 -0500 Subject: [PATCH 226/384] Created notebook for running louvain algorithm on a Multi-GPU Property Graph (#3130) resolves #2609 This notebook specifically creates a multi-GPU Property graph. Might need to create a single-GPU later but this shows use of the Property Graph interface. Also updated the mg_louvain notebook to function with change to Comms. Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Jim Scott (https://github.com/kingmesal) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3130 --- notebooks/demo/mg_louvain.ipynb | 190 +++----------------- notebooks/demo/mg_property_graph.ipynb | 229 +++++++++++++++++++++++++ 2 files changed, 257 insertions(+), 162 deletions(-) create mode 100644 notebooks/demo/mg_property_graph.ipynb diff --git a/notebooks/demo/mg_louvain.ipynb b/notebooks/demo/mg_louvain.ipynb index 1ce2150e6..b2b348fd2 100644 --- a/notebooks/demo/mg_louvain.ipynb +++ b/notebooks/demo/mg_louvain.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -9,7 +10,7 @@ "\n", "In this notebook, we will show how to use multiple GPUs in cuGraph to compute the Louvain partitions and global modularity score for a dataset.\n", "\n", - "This notebook was tested using RAPIDS 21.12 and CUDA 11.4. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", + "This notebook was tested using RAPIDS 23.02 and CUDA 11.5. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", "\n", "\n", "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." @@ -25,14 +26,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import needed libraries. We recommend using the [cugraph_dev](https://github.com/rapidsai/cugraph/tree/branch-21.12/conda/environments) env through conda\n", "from dask.distributed import Client, wait\n", "from dask_cuda import LocalCUDACluster\n", - "import cugraph.comms as Comms\n", + "from cugraph.dask.comms import comms as Comms\n", "import cugraph.dask as dask_cugraph\n", "import cugraph\n", "import dask_cudf\n", @@ -58,19 +59,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your data file, ../data/hollywood.csv, already exists\n" - ] - } - ], + "outputs": [], "source": [ "import urllib.request\n", "import os\n", @@ -109,18 +102,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n", - "distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n" - ] - } - ], + "outputs": [], "source": [ "cluster = LocalCUDACluster()\n", "client = Client(cluster)\n", @@ -137,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "tags": [] }, @@ -150,7 +134,7 @@ "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", "\n", "# Multi-GPU CSV reader\n", - "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n" + "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])" ] }, { @@ -162,22 +146,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read, load and renumber: 121.63822555541992 s\n" - ] - } - ], + "outputs": [], "source": [ "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", - "G = cugraph.DiGraph()\n", + "G = cugraph.Graph()\n", "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')\n", "\n", "# Print time\n", @@ -193,20 +169,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "scrolled": true, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Louvain: 8.262660264968872 s\n" - ] - } - ], + "outputs": [], "source": [ "# Start Pagerank timer\n", "t_start = time.time()\n", @@ -236,127 +204,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "scrolled": true, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " partition vertex\n", - "0 1 484873\n", - "1 22951 410343\n", - "2 1 486216\n", - "3 22970 98213\n", - "4 22970 100890\n", - "... ... ...\n", - "569610 1571 1077778\n", - "569611 0 629654\n", - "569612 33 963203\n", - "569613 27308 1077821\n", - "569614 0 239788\n", - "\n", - "[1139905 rows x 2 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "louvain_df.compute()\n" + "louvain_df.compute()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -366,11 +226,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "___\n", - "Copyright (c) 2021, NVIDIA CORPORATION.\n", + "Copyright (c) 2021-2023, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -381,7 +242,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "cudfdev", "language": "python", "name": "python3" }, @@ -395,7 +256,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 15:55:03) \n[GCC 10.4.0]" + }, + "vscode": { + "interpreter": { + "hash": "587ff963ecd34554a9da41c94362e2baa062d9a57502e220f049e10816826984" + } } }, "nbformat": 4, diff --git a/notebooks/demo/mg_property_graph.ipynb b/notebooks/demo/mg_property_graph.ipynb new file mode 100644 index 000000000..e8db6f727 --- /dev/null +++ b/notebooks/demo/mg_property_graph.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-GPU Property Graph\n", + "\n", + "This notebook loads data into a cudf_dask dataframe, uses it to populate an MultiGPU Property Graph.\n", + "It then runs the Louvain algorithm on that graph and annotates the MG Property Graph with the results of the algorithm.\n", + "\n", + "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "|---------------|------------|------------------|-----------------|-----------------------|\n", + "| Don Acosta | 01/30/2023 | created | 23.02 nightly | 2xA6000 CUDA 11.7 |\n", + "\n", + "\n", + "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-GPU Property Graph\n", + "### Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries. We recommend using the [cugraph_dev](https://github.com/rapidsai/cugraph/tree/branch-23.02/conda/environments) env through conda\n", + "from dask.distributed import Client, wait\n", + "from dask_cuda import LocalCUDACluster\n", + "from cugraph.dask.comms import comms as Comms\n", + "import cugraph.dask as dask_cugraph\n", + "import cugraph\n", + "import dask_cudf\n", + "import time\n", + "import urllib.request\n", + "import os" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code pulls the datafile from the rapids S3 bucket and decompresses it. This will not be necessary when the Datasets API supports decompression and direct loading into a dask edgelist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def get_data_file():\n", + "\n", + " data_dir = '../data/'\n", + " if not os.path.exists(data_dir):\n", + " print('creating data directory')\n", + " os.system('mkdir ../data')\n", + "\n", + " # download the Hollywood dataset\n", + " base_url = 'https://rapidsai-data.s3.us-east-2.amazonaws.com/cugraph/benchmark/'\n", + " fn = 'hollywood.csv'\n", + " comp = '.gz'\n", + "\n", + " if not os.path.isfile(data_dir+fn):\n", + " if not os.path.isfile(data_dir+fn+comp):\n", + " print(f'Downloading {base_url+fn+comp} to {data_dir+fn+comp}')\n", + " urllib.request.urlretrieve(base_url+fn+comp, data_dir+fn+comp)\n", + " print(f'Decompressing {data_dir+fn+comp}...')\n", + " os.system('gunzip '+data_dir+fn+comp)\n", + " print(f'{data_dir+fn+comp} decompressed!')\n", + " else:\n", + " print(f'Your data file, {data_dir+fn}, already exists')\n", + "\n", + " # File path, assuming Notebook directory\n", + " return (data_dir+fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize multi-GPU environment\n", + "Before we get started, we need to setup a Dask local cluster of workers to execute our work and a client to coordinate and schedule work for that cluster. As we see below, we can initiate a cluster and client using only 3 lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster = LocalCUDACluster()\n", + "client = Client(cluster)\n", + "Comms.initialize(p2p=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data from disk\n", + "cuGraph depends on cudf for data loading and the initial DataFrame creation. The CSV data file contains an edge list, which represents the connection of a vertex to another. The source to destination pairs is what is known as Coordinate Format (COO). In this test case, the data is just two columns. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Start ETL timer\n", + "t_start = time.time()\n", + "\n", + "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", + "input_data_path = get_data_file()\n", + "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", + "\n", + "# Multi-GPU CSV reader\n", + "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a Multi-GPU Property Graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cugraph.experimental import MGPropertyGraph\n", + "pG = MGPropertyGraph()\n", + "pG.add_edge_data(e_list,vertex_col_names=[\"src\", \"dst\"])\n", + "# removes the original edgelist to free up GPU memory\n", + "del e_list\n", + "pG.get_num_edges()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(louvain_df, modularity) = dask_cugraph.louvain(pG.extract_subgraph(create_using=cugraph.Graph))\n", + "pG.add_vertex_data(louvain_df, vertex_col_name=\"vertex\")\n", + "pG.get_vertex_data().compute().sort_index(axis=1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Shut down the multi-GPU Environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Comms.destroy()\n", + "client.close()\n", + "cluster.close()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2023, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cudfdev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "587ff963ecd34554a9da41c94362e2baa062d9a57502e220f049e10816826984" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f4d2d7147f67232a799e0b465e17f42294748318 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 9 Feb 2023 16:21:07 -0500 Subject: [PATCH 227/384] REL v23.02.00 release From 9ff83a8d03fde6c5c7b9c0a6fcb282e8172ac887 Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Fri, 24 Feb 2023 15:30:11 -0800 Subject: [PATCH 228/384] Add dfg as a pre-commit hook. (#3294) This change allows local and remote runs to handle calls to dfg identically, and removes the need for a separate CI check. Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3294 --- .pre-commit-config.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a075beea3..499192ac0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,3 +51,8 @@ repos: language: python pass_filenames: false additional_dependencies: [gitpython] + - repo: https://github.com/rapidsai/dependency-file-generator + rev: v1.4.0 + hooks: + - id: rapids-dependency-file-generator + args: ["--clean"] From 79ac27a67e534d00b4b149f31445fbfdb9765e08 Mon Sep 17 00:00:00 2001 From: Jordan Jacobelli Date: Mon, 27 Feb 2023 16:50:15 +0100 Subject: [PATCH 229/384] Update datasets download URL (#3299) Update datasets download URL to reduce latency and costs Authors: - Jordan Jacobelli (https://github.com/jjacobelli) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3299 --- notebooks/demo/mg_louvain.ipynb | 2 +- notebooks/demo/mg_pagerank.ipynb | 4 ++-- notebooks/demo/mg_property_graph.ipynb | 2 +- notebooks/demo/uvm.ipynb | 4 ++-- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/notebooks/demo/mg_louvain.ipynb b/notebooks/demo/mg_louvain.ipynb index b2b348fd2..3ff14ce69 100644 --- a/notebooks/demo/mg_louvain.ipynb +++ b/notebooks/demo/mg_louvain.ipynb @@ -74,7 +74,7 @@ " os.system('mkdir ../data')\n", "\n", "# download the Hollywood dataset\n", - "base_url = 'https://rapidsai-data.s3.us-east-2.amazonaws.com/cugraph/benchmark/'\n", + "base_url = 'https://data.rapids.ai/cugraph/benchmark/'\n", "fn = 'hollywood.csv'\n", "comp = '.gz'\n", "\n", diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index 7c32c0520..5e3f05210 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -84,7 +84,7 @@ " os.system('mkdir ../data')\n", "\n", "# download the Twitter dataset\n", - "base_url = 'https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/'\n", + "base_url = 'https://data.rapids.ai/cugraph/benchmark/'\n", "fn = 'twitter-2010.csv'\n", "comp = '.gz'\n", "if not os.path.isfile(data_dir+fn):\n", @@ -239,7 +239,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now use the [map](https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/twitter-2010-ids.csv.gz) to convert Vertex ID into to Twitter's numeric ID. The user name can also be retrieved using the [TwitterID](https://tweeterid.com/) web app.
\n", + "We can now use the [map](https://data.rapids.ai/cugraph/benchmark/twitter-2010-ids.csv.gz) to convert Vertex ID into to Twitter's numeric ID. The user name can also be retrieved using the [TwitterID](https://tweeterid.com/) web app.
\n", "The table below shows more information on our *Top 3*. Notice that this ranking is much better at capturing network influence compared the number of followers for instance. Further analysis of this dataset was published [here](https://doi.org/10.1145/1772690.1772751).\n", "\n", "| Vertex ID\t| Twitter ID\t| User name\t| Description |\n", diff --git a/notebooks/demo/mg_property_graph.ipynb b/notebooks/demo/mg_property_graph.ipynb index e8db6f727..0a5aa5df8 100644 --- a/notebooks/demo/mg_property_graph.ipynb +++ b/notebooks/demo/mg_property_graph.ipynb @@ -70,7 +70,7 @@ " os.system('mkdir ../data')\n", "\n", " # download the Hollywood dataset\n", - " base_url = 'https://rapidsai-data.s3.us-east-2.amazonaws.com/cugraph/benchmark/'\n", + " base_url = 'https://data.rapids.ai/cugraph/benchmark/'\n", " fn = 'hollywood.csv'\n", " comp = '.gz'\n", "\n", diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index 08b04a99c..d4d9caeba 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -117,7 +117,7 @@ "outputs": [], "source": [ "# download the Twitter dataset\n", - "base_url = 'https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/'\n", + "base_url = 'https://data.rapids.ai/cugraph/benchmark/'\n", "fn = 'twitter-2010.csv'\n", "comp = '.gz'\n", "if not os.path.isfile(data_dir+fn):\n", @@ -252,7 +252,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now use the [map](https://s3.us-east-2.amazonaws.com/rapidsai-data/cugraph/benchmark/twitter-2010-ids.csv.gz) to convert Vertex ID into to Twitter's numeric ID. The user name can also be retrieved using the [TwitterID](https://tweeterid.com/) web app.
\n", + "We can now use the [map](https://data.rapids.ai/cugraph/benchmark/twitter-2010-ids.csv.gz) to convert Vertex ID into to Twitter's numeric ID. The user name can also be retrieved using the [TwitterID](https://tweeterid.com/) web app.
\n", "The table below shows more information on our *Top 3*. Notice that this ranking is much better at capturing network influence compared the number of followers for instance. Further analysis of this dataset was published [here](https://doi.org/10.1145/1772690.1772751).\n", "\n", "| Vertex ID\t| Twitter ID\t| User name\t| Description |\n", From a4b86015239ec353e6ace1d82f55891d211dda73 Mon Sep 17 00:00:00 2001 From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com> Date: Tue, 28 Feb 2023 11:49:57 -0600 Subject: [PATCH 230/384] Add docs build job (#3157) The PR adds a `docs_build` process to the PR and Build workflows for this repository. The generated docs are synced to s3 for only the build workflows. Authors: - Jake Awe (https://github.com/AyodeAwe) - AJ Schmidt (https://github.com/ajschmidt8) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) - Vibhu Jawa (https://github.com/VibhuJawa) URL: https://github.com/rapidsai/cugraph/pull/3157 --- .gitignore | 2 ++ docs/cugraph/source/api_docs/centrality.rst | 2 +- docs/cugraph/source/api_docs/cugraph_pyg.rst | 1 - docs/cugraph/source/api_docs/index.rst | 2 +- docs/cugraph/source/conf.py | 17 ----------------- 5 files changed, 4 insertions(+), 20 deletions(-) diff --git a/.gitignore b/.gitignore index 530ccffca..4115f2285 100644 --- a/.gitignore +++ b/.gitignore @@ -93,3 +93,5 @@ python/cugraph/cugraph/tests/dask-worker-space # Sphinx docs & build artifacts docs/cugraph/source/api_docs/api/* +_html +_text diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/centrality.rst index 92f712523..9b634cb6d 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/centrality.rst @@ -28,7 +28,7 @@ Katz Centrality (MG) cugraph.dask.centrality.katz_centrality.katz_centrality Degree Centrality ---------------- +----------------- .. autosummary:: :toctree: api/ diff --git a/docs/cugraph/source/api_docs/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph_pyg.rst index b2772450c..7e25b3547 100644 --- a/docs/cugraph/source/api_docs/cugraph_pyg.rst +++ b/docs/cugraph/source/api_docs/cugraph_pyg.rst @@ -10,6 +10,5 @@ cugraph-pyg :toctree: api/ cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore - cugraph_pyg.loader.dispatch.call_cugraph_algorithm cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index a8d025451..0e7598af0 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -1,5 +1,5 @@ Python API reference -============= +==================== This page provides a list of all publicly accessible modules, methods and classes through ``cugraph.*`` namespace. diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 4d39e7d8d..4d11d4ef9 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -1,6 +1,3 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# # Copyright (c) 2018-2023, NVIDIA CORPORATION. # # pygdf documentation build configuration file, created by @@ -27,8 +24,6 @@ # is relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. sys.path.insert(0, os.path.abspath('sphinxext')) -sys.path.insert(0, os.path.abspath('../../python')) -sys.path.insert(0, os.path.abspath('../..')) from github_link import make_linkcode_resolve # noqa @@ -112,18 +107,6 @@ html_theme = 'pydata_sphinx_theme' -# on_rtd is whether we are on readthedocs.org -on_rtd = os.environ.get('READTHEDOCS', None) == 'True' - -if not on_rtd: - # only import and set the theme if we're building docs locally - # otherwise, readthedocs.org uses their theme by default, - # so no need to specify it - import pydata_sphinx_theme - import sphinx_rtd_theme - html_theme = 'pydata_sphinx_theme' - html_theme_path = sphinx_rtd_theme.get_html_theme_path() - # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False From d020a414c6bfd21e2f9a41dfd6cf271719611618 Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Wed, 1 Mar 2023 06:27:23 -0800 Subject: [PATCH 231/384] Stop using versioneer to manage versions (#3298) This PR replaces usage of versioneer with hard-coded version numbers in setup.py and init.py. Since cugraph needs to manage versions across a wide range of file types (CMake, C++, Sphinx and doxygen docs, etc), versioneer cannot be relied on as a single source of truth and therefore does not allow us to single-source our versioning to the Git repo as is intended. Additionally, since the primary means of installing raft is via conda packages (or now, pip packages), information from the package manager tends to be far more informative than the version strings for troubleshooting and debugging purposes. Conversely, the nonstandard version strings that it produces tend to be problematic for other tools, which at best will ignore such versions but at worst will simply fail. This PR also replaces usage of an environment variable to set the package name for wheels in setup.py, instead moving the renaming logic into the same sed script used to update package versions. This change makes setup.py essentially static, paving the way for migration to pyproject.toml. Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3298 --- .pre-commit-config.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 499192ac0..585659aaa 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -14,7 +14,6 @@ repos: hooks: - id: black language_version: python3 - exclude: versioneer.py args: [--target-version=py38] files: ^python/ - repo: https://github.com/PyCQA/flake8 From 2c2f2647512512cbb730847987d4cd6abd622980 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Fri, 10 Mar 2023 10:05:50 -0500 Subject: [PATCH 232/384] Refactoring tests (#3292) Redoing the organization of the python tests. Merging both SG and MG together and using tag to specify runs organizing tests by graph function **MG Test File Renaming** - MG test files now end with "_mg" rather than starting with "test_mg". This will make it easier to see that there are test pairs, SG and MG, since a listing will now group files together. **mg pytest marker** - created a pytest marker called "mg" to identify MG test and allow CI/users to either include or skip - `pytest -m mg` to run all mg tests - `pytest -m "not mg"` to skip all MG tests **sg marker** test only SG code pytest -m sg Added markers for "sg" and "ci" for future use Fixed a number of test issues closes #1481 Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Ray Douglass (https://github.com/raydouglass) - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) - Joseph Nke (https://github.com/jnke2016) URL: https://github.com/rapidsai/cugraph/pull/3292 --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 4115f2285..6ddf97c44 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,7 @@ __pycache__ *~ DartConfiguration.tcl .DS_Store +rmm_log.txt # Unit test / coverage reports htmlcov/ From 9e854b2ac87d1f69933070730c64e0688b5f5835 Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Wed, 15 Mar 2023 20:04:36 -0400 Subject: [PATCH 233/384] Migrate as much as possible to pyproject.toml (#3317) This moves as much packaging information and linter configuration out of setup.[py|cfg] files into pyproject.toml with the aim of migrating to more modern Python builders in the future. Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Ray Douglass (https://github.com/raydouglass) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3317 --- .flake8 | 25 +++++++++++++++++++++++++ .pre-commit-config.yaml | 2 +- 2 files changed, 26 insertions(+), 1 deletion(-) create mode 100644 .flake8 diff --git a/.flake8 b/.flake8 new file mode 100644 index 000000000..1d917da6b --- /dev/null +++ b/.flake8 @@ -0,0 +1,25 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. + +[flake8] +filename = *.py, *.pyx, *.pxd, *.pxi +exclude = __init__.py, *.egg, build, docs, .git +force-check = True +max-line-length = 88 +ignore = + # line break before binary operator + W503, + # whitespace before : + E203 +per-file-ignores = + # Rules ignored only in Cython: + # E211: whitespace before '(' (used in multi-line imports) + # E225: Missing whitespace around operators (breaks cython casting syntax like ) + # E226: Missing whitespace around arithmetic operators (breaks cython pointer syntax like int*) + # E227: Missing whitespace around bitwise or shift operator (Can also break casting syntax) + # E275: Missing whitespace after keyword (Doesn't work with Cython except?) + # E402: invalid syntax (works for Python, not Cython) + # E999: invalid syntax (works for Python, not Cython) + # W504: line break after binary operator (breaks lines that end with a pointer) + *.pyx: E211, E225, E226, E227, E275, E402, E999, W504 + *.pxd: E211, E225, E226, E227, E275, E402, E999, W504 + *.pxi: E211, E225, E226, E227, E275, E402, E999, W504 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 585659aaa..a5d5a00ba 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -20,7 +20,7 @@ repos: rev: 6.0.0 hooks: - id: flake8 - args: ["--config=setup.cfg"] + args: ["--config=.flake8"] files: python/.*$ types: [file] types_or: [python] # TODO: Enable [python, cython] From fdfafb57035fc2327c43c85309d1c62396bd6e7d Mon Sep 17 00:00:00 2001 From: Jordan Jacobelli Date: Thu, 16 Mar 2023 15:49:23 +0100 Subject: [PATCH 234/384] Update datasets download URL (#3305) Some URLs were not updated by https://github.com/rapidsai/cugraph/pull/3299. This PR is fixing this and removing the `datasets/get_test_data.sh` from the ignored files Authors: - Jordan Jacobelli (https://github.com/jjacobelli) - AJ Schmidt (https://github.com/ajschmidt8) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3305 --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 6ddf97c44..3fda9f8a0 100644 --- a/.gitignore +++ b/.gitignore @@ -72,6 +72,7 @@ cpp/thirdparty/googletest/ ## Datasets datasets/* !datasets/cyber.csv +!datasets/get_test_data.sh !datasets/karate-data.csv !datasets/karate_undirected.csv !datasets/karate-disjoint.csv From f832eb3beb4d9223ac1aa61bae304ebd856e16e9 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Mon, 20 Mar 2023 11:38:13 -0400 Subject: [PATCH 235/384] Doc fix and change to Louvain notebook (#3224) Fixed to errors in the cuGraph documentation and addressed an error in my earlier commit on the mg_louvain notebook. Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3224 --- docs/cugraph/source/index.rst | 3 ++- notebooks/demo/mg_louvain.ipynb | 13 +++++++++---- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 5aba992ff..f1012c0fb 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -19,7 +19,8 @@ General CuGraph Information - `CuGraph Intro <./basics/cugraph_intro.md>`_ - `Blogs and Presentation <./basics/cugraph_blogs.rst>`_ - `How-to Guides <./basics/coming_soon.md>`_ - - `Performance <./basics/coming_soon.md>`_ + - `Performance <./basics/coming_soon.md>`_ + - `References <./basics/cugraph_ref.rst>`_ CuGraph Development and Contributing - `Getting cuGraph Packages <./basics/coming_soon.md>`_ diff --git a/notebooks/demo/mg_louvain.ipynb b/notebooks/demo/mg_louvain.ipynb index 3ff14ce69..a23c8eadb 100644 --- a/notebooks/demo/mg_louvain.ipynb +++ b/notebooks/demo/mg_louvain.ipynb @@ -6,7 +6,12 @@ "metadata": {}, "source": [ "# Multiple GPU Louvain in cuGraph\n", - "#### Author : Chuck Hastings\n", + "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "|---------------|------------|------------------|-----------------|-----------------------|\n", + "| Chuck Hastings| 11/08/2021 | created | 21.10 nightly | |\n", + "| Don Acosta | 01/30/2023 | updated | 23.02 nightly | 2xA6000 CUDA 11.7 |\n", + "\n", "\n", "In this notebook, we will show how to use multiple GPUs in cuGraph to compute the Louvain partitions and global modularity score for a dataset.\n", "\n", @@ -152,8 +157,8 @@ }, "outputs": [], "source": [ - "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", - "G = cugraph.Graph()\n", + "# Create an undirected graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", + "G = cugraph.Graph(directed=False)\n", "G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')\n", "\n", "# Print time\n", @@ -256,7 +261,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 15:55:03) \n[GCC 10.4.0]" + "version": "3.10.8" }, "vscode": { "interpreter": { From 36663a03912096d6fde080ebd3b4b217d719e2a0 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Thu, 23 Mar 2023 14:55:19 -0400 Subject: [PATCH 236/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 4d11d4ef9..983584839 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -76,9 +76,9 @@ # built documents. # # The short X.Y version. -version = '23.04' +version = '23.06' # The full version, including alpha/beta/rc tags. -release = '23.04.00' +release = '23.06.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 7c2bf1dffa1938c49a80810286622aee2b2987ca Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Sun, 2 Apr 2023 16:49:49 -0400 Subject: [PATCH 237/384] New cugraph site structure (#3343) resolves #3297 new website structure with some migrations. Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) - Alex Barghi (https://github.com/alexbarghi-nv) URL: https://github.com/rapidsai/cugraph/pull/3343 --- docs/cugraph/source/api_docs/cugraph_dgl.rst | 2 +- docs/cugraph/source/dev_resources/API.rst | 5 ++ docs/cugraph/source/dev_resources/index.rst | 10 +++ .../source/graph_support/DGL_support.md | 55 ++++++++++++ .../source/graph_support/PyG_support.md | 3 + .../source/graph_support/algorithms.md | 85 ++++++++++++++++++ .../source/graph_support/compatibility.rst | 8 ++ .../source/graph_support/cugraph_service.rst | 9 ++ .../graph_support/cugraphops_support.rst | 10 +++ .../source/graph_support/datastores.rst | 11 +++ .../source/graph_support/feature_stores.md | 0 .../source/graph_support/gnn_support.rst | 12 +++ .../source/graph_support/graph_algorithms.rst | 9 ++ docs/cugraph/source/graph_support/index.rst | 13 +++ .../source/graph_support/knowledge_stores.md | 0 .../source/graph_support/pg_example.png | Bin 0 -> 242398 bytes .../source/graph_support/property_graph.md | 54 +++++++++++ .../graph_support/wholegraph_support.rst | 0 docs/cugraph/source/index.rst | 47 ++-------- .../source/installation/getting_cugraph.md | 58 ++++++++++++ docs/cugraph/source/installation/index.rst | 9 ++ .../{basics => references}/cugraph_ref.rst | 0 docs/cugraph/source/references/datasets.rst | 0 docs/cugraph/source/references/index.rst | 9 ++ docs/cugraph/source/references/licenses.rst | 0 docs/cugraph/source/releases/index.rst | 5 ++ docs/cugraph/source/tutorials/blogs.rst | 8 ++ .../source/tutorials/community_resources.md | 2 + .../{basics => tutorials}/cugraph_blogs.rst | 0 .../source/tutorials/cugraph_notebooks.md | 77 ++++++++++++++++ .../cugraph/source/tutorials/how_to_guides.md | 9 ++ docs/cugraph/source/tutorials/index.rst | 12 +++ 32 files changed, 483 insertions(+), 39 deletions(-) create mode 100644 docs/cugraph/source/dev_resources/API.rst create mode 100644 docs/cugraph/source/dev_resources/index.rst create mode 100644 docs/cugraph/source/graph_support/DGL_support.md create mode 100644 docs/cugraph/source/graph_support/PyG_support.md create mode 100644 docs/cugraph/source/graph_support/algorithms.md create mode 100644 docs/cugraph/source/graph_support/compatibility.rst create mode 100644 docs/cugraph/source/graph_support/cugraph_service.rst create mode 100644 docs/cugraph/source/graph_support/cugraphops_support.rst create mode 100644 docs/cugraph/source/graph_support/datastores.rst create mode 100644 docs/cugraph/source/graph_support/feature_stores.md create mode 100644 docs/cugraph/source/graph_support/gnn_support.rst create mode 100644 docs/cugraph/source/graph_support/graph_algorithms.rst create mode 100644 docs/cugraph/source/graph_support/index.rst create mode 100644 docs/cugraph/source/graph_support/knowledge_stores.md create mode 100644 docs/cugraph/source/graph_support/pg_example.png create mode 100644 docs/cugraph/source/graph_support/property_graph.md create mode 100644 docs/cugraph/source/graph_support/wholegraph_support.rst create mode 100644 docs/cugraph/source/installation/getting_cugraph.md create mode 100644 docs/cugraph/source/installation/index.rst rename docs/cugraph/source/{basics => references}/cugraph_ref.rst (100%) create mode 100644 docs/cugraph/source/references/datasets.rst create mode 100644 docs/cugraph/source/references/index.rst create mode 100644 docs/cugraph/source/references/licenses.rst create mode 100644 docs/cugraph/source/releases/index.rst create mode 100644 docs/cugraph/source/tutorials/blogs.rst create mode 100644 docs/cugraph/source/tutorials/community_resources.md rename docs/cugraph/source/{basics => tutorials}/cugraph_blogs.rst (100%) create mode 100644 docs/cugraph/source/tutorials/cugraph_notebooks.md create mode 100644 docs/cugraph/source/tutorials/how_to_guides.md create mode 100644 docs/cugraph/source/tutorials/index.rst diff --git a/docs/cugraph/source/api_docs/cugraph_dgl.rst b/docs/cugraph/source/api_docs/cugraph_dgl.rst index 9a8b3ba6c..98e101f99 100644 --- a/docs/cugraph/source/api_docs/cugraph_dgl.rst +++ b/docs/cugraph/source/api_docs/cugraph_dgl.rst @@ -1,5 +1,5 @@ ~~~~~~~~~~~~~~~~~~~~~~~~~~ -cugraph-dgl API Refrerence +cugraph-dgl API Reference ~~~~~~~~~~~~~~~~~~~~~~~~~~ cugraph-dgl diff --git a/docs/cugraph/source/dev_resources/API.rst b/docs/cugraph/source/dev_resources/API.rst new file mode 100644 index 000000000..409e307fd --- /dev/null +++ b/docs/cugraph/source/dev_resources/API.rst @@ -0,0 +1,5 @@ +=== +API +=== + +https://docs.rapids.ai/api/cugraph/nightly/api_docs/index.html \ No newline at end of file diff --git a/docs/cugraph/source/dev_resources/index.rst b/docs/cugraph/source/dev_resources/index.rst new file mode 100644 index 000000000..8568772b3 --- /dev/null +++ b/docs/cugraph/source/dev_resources/index.rst @@ -0,0 +1,10 @@ +=================== +Developer Resources +=================== + + +.. toctree:: + :maxdepth: 3 + + https://docs.rapids.ai/maintainers + API.rst \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/DGL_support.md b/docs/cugraph/source/graph_support/DGL_support.md new file mode 100644 index 000000000..4db2a4d77 --- /dev/null +++ b/docs/cugraph/source/graph_support/DGL_support.md @@ -0,0 +1,55 @@ +# cugraph_dgl + +## Description + +[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.DGLGraph) class, which uses cugraph for storing graph structure and node/edge feature data. Using cugraph as the backend allows DGL users to access a collection of GPU accelerated algorithms for graph analytics, such as centrality computation and community detection. + +## Conda + +Install and update cugraph-dgl and the required dependencies using the command: + +``` +conda install mamba -n base -c conda-forge +mamba install cugraph-dgl -c rapidsai-nightly -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam +``` + +## Build from Source + +### Create the conda development environment +``` +mamba env create -n cugraph_dgl_dev --file conda/cugraph_dgl_dev_11.6.yml +``` + +### Install in editable mode +``` +pip install -e . +``` + +### Run tests + +``` +pytest tests/* +``` + + +## Usage +```diff + ++from cugraph_dgl.convert import cugraph_storage_from_heterograph ++cugraph_g = cugraph_storage_from_heterograph(dgl_g) + +sampler = dgl.dataloading.NeighborSampler( + [15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label']) + +train_dataloader = dgl.dataloading.DataLoader( +- dgl_g, ++ cugraph_g, +train_idx, +sampler, +device=device, +batch_size=1024, +shuffle=True, +drop_last=False, +num_workers=0) +``` + diff --git a/docs/cugraph/source/graph_support/PyG_support.md b/docs/cugraph/source/graph_support/PyG_support.md new file mode 100644 index 000000000..42d4d1c55 --- /dev/null +++ b/docs/cugraph/source/graph_support/PyG_support.md @@ -0,0 +1,3 @@ +# cugraph_pyg + +[RAPIDS](https://rapids.ai) cugraph_pyg enables the ability to use cugraph graph storage and sampling with PyTorch Geometric (PyG). PyG users will have access to cuGraph through the PyG GraphStore, FeatureStore, and Sampler interfaces. \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md new file mode 100644 index 000000000..fa2e7cc95 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms.md @@ -0,0 +1,85 @@ +# List of Supported and Planned Algorithms + +## Supported Graph + +| Type | Description | +| ---------- | ----------------------------------------------------------- | +| Graph | A directed or undirected Graph (use directed={True, False}) | +| Multigraph | A Graph with multiple edges between a vertex pair | +| | | + +ALL Algorithms support Graphs and MultiGraph (directed and undirected) + +--- + +
+ +# Supported Algorithms + +_Italic_ algorithms are planned for future releases. + +Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called MNMG). + +| Category | Algorithm | Scale | Notes | +| ----------------- | ---------------------------------- | ------------------- | --------------------------------------------------------------- | +| Centrality | | | | +| | Katz | __Multi-GPU__ | | +| | Betweenness Centrality | Single-GPU | MG planned for 23.02 | +| | Edge Betweenness Centrality | Single-GPU | MG planned for 23.02 | +| | Eigenvector Centrality | __Multi-GPU__ | | +| | Degree Centrality | __Multi-GPU__ | Python only | +| Community | | | | +| | Leiden | Single-GPU | MG planned for 23.02 | +| | Louvain | __Multi-GPU__ | | +| | Ensemble Clustering for Graphs | Single-GPU | | +| | Spectral-Clustering - Balanced Cut | Single-GPU | | +| | Spectral-Clustering - Modularity | Single-GPU | | +| | Subgraph Extraction | Single-GPU | | +| | Triangle Counting | __Multi-GPU__ | | +| | K-Truss | Single-GPU | | +| Components | | | | +| | Weakly Connected Components | __Multi-GPU__ | | +| | Strongly Connected Components | Single-GPU | | +| Core | | | | +| | K-Core | **Multi-GPU** | | +| | Core Number | **Multi-GPU** | | +| _Flow_ | | | | +| | _MaxFlow_ | --- | | +| _Influence_ | | | | +| | _Influence Maximization_ | --- | | +| Layout | | | | +| | Force Atlas 2 | Single-GPU | | +| Linear Assignment | | | | +| | Hungarian | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) | +| Link Analysis | | | | +| | Pagerank | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | +| | Personal Pagerank | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) | +| | HITS | __Multi-GPU__ | | +| Link Prediction | | | | +| | Jaccard Similarity | **Multi-GPU** | MG as of 22.12
Directed graph only | +| | Weighted Jaccard Similarity | Single-GPU | | +| | Overlap Similarity | **Multi-GPU** | MG as of 22.12 | +| | Sorensen Coefficient | **Multi-GPU** | MG as of 22.12 | +| | _Local Clustering Coefficient_ | --- | | +| Sampling | | | | +| | Uniform Random Walks (RW) | **Multi-GPU** | | +| | *Biased Random Walks (RW)* | --- | | +| | Egonet | **Multi-GPU** | | +| | Node2Vec | Single-GPU | MG planned for 23.02 | +| | Uniform Neighborhood sampling | __Multi-GPU__ | | +| Traversal | | | | +| | Breadth First Search (BFS) | __Multi-GPU__ | with cutoff support``[C++ README](cpp/src/traversal/README.md#BFS) | +| | Single Source Shortest Path (SSSP) | __Multi-GPU__ | [C++ README](cpp/src/traversal/README.md#SSSP) | +| | _ASSP / APSP_ | --- | | +| Tree | | | | +| | Minimum Spanning Tree | Single-GPU | | +| | Maximum Spanning Tree | Single-GPU | | +| Other | | | | +| | Renumbering | __Multi-GPU__ | multiple columns, any data type | +| | Symmetrize | __Multi-GPU__ | | +| | Path Extraction | | Extract paths from BFS/SSP results in parallel | +| | Two Hop Neighbors | __Multi-GPU__ | | +| Data Generator | | | | +| | RMAT | __Multi-GPU__ | | +| | _Barabasi-Albert_ | --- | | +| | | | | diff --git a/docs/cugraph/source/graph_support/compatibility.rst b/docs/cugraph/source/graph_support/compatibility.rst new file mode 100644 index 000000000..38f6df4c7 --- /dev/null +++ b/docs/cugraph/source/graph_support/compatibility.rst @@ -0,0 +1,8 @@ +Compatibility +============= + + +.. toctree:: + :maxdepth: 3 + + Compatibility \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/cugraph_service.rst b/docs/cugraph/source/graph_support/cugraph_service.rst new file mode 100644 index 000000000..620b22d1e --- /dev/null +++ b/docs/cugraph/source/graph_support/cugraph_service.rst @@ -0,0 +1,9 @@ +=============== +CuGraph Service +=============== + + +.. toctree:: + :maxdepth: 3 + +Cugraph Service for remote access to a server-based cuGraph(https://github.com/rapidsai/cugraph/blob/branch-23.04/python/cugraph-service/README.md) \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/cugraphops_support.rst b/docs/cugraph/source/graph_support/cugraphops_support.rst new file mode 100644 index 000000000..08ae3b218 --- /dev/null +++ b/docs/cugraph/source/graph_support/cugraphops_support.rst @@ -0,0 +1,10 @@ +================== +cugraphops Support +================== + +cugraph-ops aims to be a low-level, framework agnostic library providing commonly used computational primitives for GNNs and other graph operations. + +.. toctree:: + :maxdepth: 3 + + https://github.com/rapidsai/cugraph-ops/blob/branch-23.04/README.md \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/datastores.rst b/docs/cugraph/source/graph_support/datastores.rst new file mode 100644 index 000000000..82db2c0e1 --- /dev/null +++ b/docs/cugraph/source/graph_support/datastores.rst @@ -0,0 +1,11 @@ +=========== +Data Stores +=========== + + +.. toctree:: + :maxdepth: 3 + + property_graph.md + feature_stores.md + knowledge_stores.md \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/feature_stores.md b/docs/cugraph/source/graph_support/feature_stores.md new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/graph_support/gnn_support.rst b/docs/cugraph/source/graph_support/gnn_support.rst new file mode 100644 index 000000000..2acb2254a --- /dev/null +++ b/docs/cugraph/source/graph_support/gnn_support.rst @@ -0,0 +1,12 @@ +============================ +Graph Neural Network Support +============================ + + +.. toctree:: + :maxdepth: 3 + + PyG_support.md + DGL_support.md + cugraphops_support.rst + wholegraph_support.rst \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/graph_algorithms.rst b/docs/cugraph/source/graph_support/graph_algorithms.rst new file mode 100644 index 000000000..885be03bf --- /dev/null +++ b/docs/cugraph/source/graph_support/graph_algorithms.rst @@ -0,0 +1,9 @@ +========== +Algorithms +========== + + +.. toctree:: + :maxdepth: 3 + + algorithms.md \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/index.rst b/docs/cugraph/source/graph_support/index.rst new file mode 100644 index 000000000..9526fae7e --- /dev/null +++ b/docs/cugraph/source/graph_support/index.rst @@ -0,0 +1,13 @@ +============= +Graph Support +============= + + +.. toctree:: + :maxdepth: 3 + + graph_algorithms.rst + compatibility.rst + gnn_support.rst + datastores.rst + cugraph_service.rst \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/knowledge_stores.md b/docs/cugraph/source/graph_support/knowledge_stores.md new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/graph_support/pg_example.png b/docs/cugraph/source/graph_support/pg_example.png new file mode 100644 index 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b/docs/cugraph/source/graph_support/property_graph.md @@ -0,0 +1,54 @@ +

+
+ cuGraph +

+

+
+Property Graph +

+ +Part of [RAPIDS](https://rapids.ai) cuGraph, Property Graph allows all the great benefits of cuGraph to be applied to property-rich datasets stored in a graph structure. A Property Graph is really a data model rather than a type of graph. Within the cuGraph ecosystem, a Property Graph is a meta-graph that can encapsulate and instantiate all the other graph types. That view stems from property graphs being originally created for database systems. Conceptually a Property Graph can be viewed as a property rich structure that can be projected onto any graph types. The Dataversity, has a good definition of [Property Graph](https://www.dataversity.net/what-is-a-property-graph) which contains definitions from a collection of resources. + +Property Graph enables: + +* Multiple edge and node types as seen in the Property Graph API +* Subgraph extractions based on properties and/or edge and node types as seen below. +* Storage of properties either within the graph structure on gpu or using GNN-centric storage extensions on host storage. +* Adding additional properties, nodes and edges into the property graph to store derived data like analytic results. +* Client access managed by a remote server allowing shared access and remote operations using [CuGraph Service](./cugraph_service.md). + +This is an example of using the cuGraph Property Graph in a two stage analysis. + +``` +import cudf +import cugraph +from cugraph.experimental import PropertyGraph + +# Import a built-in dataset +from cugraph.experimental.datasets import karate + +# Read edgelist data into a DataFrame, load into PropertyGraph as edge data. +# Create a graph using the imported Dataset object +graph = cugraph.Graph(directed=False) +G = karate.get_graph(create_using=graph,fetch=True) + +df = G.edgelist.edgelist_df +pG = PropertyGraph() +pG. add_edge_data(df, vertex_col_names=("src", "dst")) + +# Run Louvain to get the partition number for each vertex. +# Set resolution accordingly to identify two primary partitions. +(partition_info, _) = cugraph.louvain(pG.extract_subgraph(create_using=graph), resolution=0.6) + +# Add the partition numbers back to the Property Graph as vertex properties +pG.add_vertex_data(partition_info, vertex_col_name="vertex") + +# Use the partition properties to extract a Graph for each partition. +G0 = pG.extract_subgraph(selection=pG.select_vertices("partition == 0")) +G1 = pG.extract_subgraph(selection=pG. select_vertices("partition == 1")) +# Run pagerank on each graph, print results. +pageranks0 = cugraph.pagerank(G0) +pageranks1 = cugraph.pagerank(G1) +print(pageranks0.sort_values (by="pagerank", ascending=False).head(3)) +print(pageranks1.sort_values (by="pagerank", ascending=False).head(3)) +``` \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/wholegraph_support.rst b/docs/cugraph/source/graph_support/wholegraph_support.rst new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index f1012c0fb..2dd871c4c 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -1,53 +1,24 @@ -Welcome to cugraph's documentation! +Welcome to cuGraph's documentation! =================================== RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. -.. image:: images/Stack2.png - :width: 600 - :alt: Alternative text - -Basics -###### -.. toctree:: - :maxdepth: 2 - :caption: Basic cuGraph Information: - -General CuGraph Information - - `CuGraph Intro <./basics/cugraph_intro.md>`_ - - `Blogs and Presentation <./basics/cugraph_blogs.rst>`_ - - `How-to Guides <./basics/coming_soon.md>`_ - - `Performance <./basics/coming_soon.md>`_ - - `References <./basics/cugraph_ref.rst>`_ - -CuGraph Development and Contributing - - `Getting cuGraph Packages <./basics/coming_soon.md>`_ - - `Contributing to cuGraph <./basics/coming_soon.md>`_ - - `CuGraph Development Guide <./basics/coming_soon.md>`_ - -Algorithms - - `Current list of algorithms <./basics/coming_soon.md>`_ - - - -API -### -.. toctree:: - :maxdepth: 2 - - api_docs/index.rst - - .. toctree:: :maxdepth: 2 + :caption: Contents: - api_docs/c_and_cpp.rst + installation/index + tutorials/index + graph_support/index + references/index + dev_resources/index + releases/index + api_docs/index Indices and tables ================== * :ref:`genindex` -* :ref:`modindex` * :ref:`search` diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md new file mode 100644 index 000000000..390ef5221 --- /dev/null +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -0,0 +1,58 @@ + +# Getting cuGraph Packages + +There are 4 ways to get cuGraph packages: +1. [Quick start with Docker Repo](#docker) +2. [Conda Installation](#conda) +3. [Pip Installation](#pip) +4. [Build from Source](#SOURCE) + +Or checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. + +
+ +## Docker +The RAPIDS Docker containers contain all RAPIDS packages, including all from cuGraph, as well as all required supporting packages. To download a container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph. + +
+ + +## Conda +It is easy to install cuGraph using conda. You can get a minimal conda installation with [Miniconda](https://conda.io/miniconda.html) or get the full installation with [Anaconda](https://www.anaconda.com/download). + +cuGraph Conda packages + * cugraph - this will also import: + * pylibcugraph + * libcugraph + * cugraph-service-client + * cugraph-service-server + * cugraph-dgl + * cugraph-pyg + +Replace the package name in the example below to the one you want to install. + + +Install and update cuGraph using the conda command: + +```bash +conda install -c rapidsai -c numba -c conda-forge -c nvidia cugraph cudatoolkit=11.8 +``` + +Note: This conda installation only applies to Linux and Python versions 3.8/3.10. + +
+ +## PIP +cuGraph, and all of RAPIDS, is available via pip. + +``` +pip install cugraph-cu11 --extra-index-url=https://pypi.ngc.nvidia.com +``` + +pip packages for other packages are being worked and should be available in early 2023 + +
+ +## SOURCE +cuGraph can be build directly from source. First check to make sure you have or can configure a supported environment. +Instructions for building from source is in our [source build](./SOURCEBUILD.md) page. diff --git a/docs/cugraph/source/installation/index.rst b/docs/cugraph/source/installation/index.rst new file mode 100644 index 000000000..f9e8acaa5 --- /dev/null +++ b/docs/cugraph/source/installation/index.rst @@ -0,0 +1,9 @@ +============ +Installation +============ + + +.. toctree:: + :maxdepth: 3 + + getting_cugraph \ No newline at end of file diff --git a/docs/cugraph/source/basics/cugraph_ref.rst b/docs/cugraph/source/references/cugraph_ref.rst similarity index 100% rename from docs/cugraph/source/basics/cugraph_ref.rst rename to docs/cugraph/source/references/cugraph_ref.rst diff --git a/docs/cugraph/source/references/datasets.rst b/docs/cugraph/source/references/datasets.rst new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/references/index.rst b/docs/cugraph/source/references/index.rst new file mode 100644 index 000000000..e70df627f --- /dev/null +++ b/docs/cugraph/source/references/index.rst @@ -0,0 +1,9 @@ +========== +References +========== + + +.. toctree:: + :maxdepth: 3 + + cugraph_ref \ No newline at end of file diff --git a/docs/cugraph/source/references/licenses.rst b/docs/cugraph/source/references/licenses.rst new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/releases/index.rst b/docs/cugraph/source/releases/index.rst new file mode 100644 index 000000000..7bd4f6dfa --- /dev/null +++ b/docs/cugraph/source/releases/index.rst @@ -0,0 +1,5 @@ +======== +Releases +======== + +https://github.com/rapidsai/cugraph/blob/main/CHANGELOG.md \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/blogs.rst b/docs/cugraph/source/tutorials/blogs.rst new file mode 100644 index 000000000..8f6623a98 --- /dev/null +++ b/docs/cugraph/source/tutorials/blogs.rst @@ -0,0 +1,8 @@ +============ +Blogs +============ + + +.. toctree:: + :maxdepth: 3 + cugraph_blogs.rst \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/community_resources.md b/docs/cugraph/source/tutorials/community_resources.md new file mode 100644 index 000000000..572f85a01 --- /dev/null +++ b/docs/cugraph/source/tutorials/community_resources.md @@ -0,0 +1,2 @@ +# Commmunity Resources +[Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib) \ No newline at end of file diff --git a/docs/cugraph/source/basics/cugraph_blogs.rst b/docs/cugraph/source/tutorials/cugraph_blogs.rst similarity index 100% rename from docs/cugraph/source/basics/cugraph_blogs.rst rename to docs/cugraph/source/tutorials/cugraph_blogs.rst diff --git a/docs/cugraph/source/tutorials/cugraph_notebooks.md b/docs/cugraph/source/tutorials/cugraph_notebooks.md new file mode 100644 index 000000000..1624ef10a --- /dev/null +++ b/docs/cugraph/source/tutorials/cugraph_notebooks.md @@ -0,0 +1,77 @@ +# cuGraph Notebooks + +![GraphAnalyticsFigure](img/GraphAnalyticsFigure.jpg) + +This repository contains a collection of Jupyter Notebooks that outline how to run various cuGraph analytics. The notebooks do not address a complete data science problem. The notebooks are simply examples of how to run the graph analytics. Manipulation of the data before or after the graph analytic is not covered here. Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended + +## Summary + +| Folder | Notebook | Description | +| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | +| Centrality | | | +| | [Centrality](algorithms/centrality/Centrality.ipynb) | Compute and compare multiple (currently 5) centrality scores | +| | [Katz](algorithms/centrality/Katz.ipynb) | Compute the Katz centrality for every vertex | +| | [Betweenness](algorithms/centrality/Betweenness.ipynb) | Compute both Edge and Vertex Betweenness centrality | +| | [Degree](algorithms/centrality/Degree.ipynb) | Compute Degree Centraility for each vertex | +| | [Eigenvector](algorithms/centrality/Eigenvector.ipynb) | Compute Eigenvector for every vertex | +| Community | | | +| | [Louvain](algorithms/community/Louvain.ipynb) and Leiden | Identify clusters in a graph using both the Louvain and Leiden algorithms | +| | [ECG](algorithms/community/ECG.ipynb) | Identify clusters in a graph using the Ensemble Clustering for Graph | +| | [K-Truss](algorithms/community/ktruss.ipynb) | Extracts the K-Truss cluster | +| | [Spectral-Clustering](algorithms/community/Spectral-Clustering.ipynb) | Identify clusters in a graph using Spectral Clustering with both
- Balanced Cut
- Modularity Modularity | +| | [Subgraph Extraction](algorithms/community/Subgraph-Extraction.ipynb) | Compute a subgraph of the existing graph including only the specified vertices | +| | [Triangle Counting](algorithms/community/Triangle-Counting.ipynb) | Count the number of Triangle in a graph | +| Components | | | +| | [Connected Components](algorithms/components/ConnectedComponents.ipynb) | Find weakly and strongly connected components in a graph | +| Core | | | +| | [K-Core](algorithms/cores/kcore.ipynb) | Extracts the K-core cluster | +| | [Core Number](algorithms/cores/core-number.ipynb) | Computer the Core number for each vertex in a graph | +Layout | | | +| | [Force-Atlas2](algorithms/layout/Force-Atlas2.ipynb) |A large graph visualization achieved with cuGraph. | +| Link Analysis | | | +| | [Pagerank](algorithms/link_analysis/Pagerank.ipynb) | Compute the PageRank of every vertex in a graph | +| | [HITS](algorithms/link_analysis/HITS.ipynb) | Compute the HITS' Hub and Authority scores for every vertex in a graph | +| Link Prediction | | | +| | [Jaccard Similarity](algorithms/link_prediction/Jaccard-Similarity.ipynb) | Compute vertex similarity score using both:
- Jaccard Similarity
- Weighted Jaccard | +| | [Overlap Similarity](algorithms/link_prediction/Overlap-Similarity.ipynb) | Compute vertex similarity score using the Overlap Coefficient | +| Sampling | +| | [Random Walk](algorithms/sampling/RandomWalk.ipynb) | Compute Random Walk for a various number of seeds and path lengths | +| Traversal | | | +| | [BFS](algorithms/traversal/BFS.ipynb) | Compute the Breadth First Search path from a starting vertex to every other vertex in a graph | +| | [SSSP](algorithms/traversal/SSSP.ipynb) | Single Source Shortest Path - compute the shortest path from a starting vertex to every other vertex | +| Structure | | | +| | [Renumbering](algorithms/structure/Renumber.ipynb)
[Renumbering 2](algorithms/structure/Renumber-2.ipynb) | Renumber the vertex IDs in a graph (two sample notebooks) | +| | [Symmetrize](algorithms/structure/Symmetrize.ipynb) | Symmetrize the edges in a graph | + + +## RAPIDS notebooks +Visit the main RAPIDS [notebooks](https://github.com/rapidsai/notebooks) repo for a listing of all notebooks across all RAPIDS libraries. + +## Requirements + +Running the example in these notebooks requires: + +* The latest version of RAPIDS with cuGraph. + * Download via Docker, Conda (See [__Getting Started__](https://rapids.ai/start.html)) + +* cuGraph is dependent on the latest version of cuDF. Please install all components of RAPIDS +* Python 3.8+ +* A system with an NVIDIA GPU: Pascal architecture or better +* CUDA 11.4+ +* NVIDIA driver 450.51+ + +#### Copyright + +Copyright (c) 2019-2022, NVIDIA CORPORATION. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. + + + + + +![RAPIDS](img/rapids_logo.png) \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/how_to_guides.md b/docs/cugraph/source/tutorials/how_to_guides.md new file mode 100644 index 000000000..42da6ed21 --- /dev/null +++ b/docs/cugraph/source/tutorials/how_to_guides.md @@ -0,0 +1,9 @@ +# How To Guides +- Basic use of cuGraph, on the page +- Property graph with analytic flow +- GNN – model building +- cuGraph Service – client/server setup and use (ucx) +- MNMG Graph – dask, rmm basics and examples +- Pylibcugraph – why and how +- Cugraph for C, C++ users +- Use of nvidia-smi with cugraph \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/index.rst b/docs/cugraph/source/tutorials/index.rst new file mode 100644 index 000000000..2470bced7 --- /dev/null +++ b/docs/cugraph/source/tutorials/index.rst @@ -0,0 +1,12 @@ +========= +Tutorials +========= + + +.. toctree:: + :maxdepth: 3 + + how_to_guides.md + cugraph_blogs.rst + cugraph_notebooks.md + community_resources.md \ No newline at end of file From 1b031ab264b658787ff4fb75f7d7ab4511b64b43 Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Mon, 3 Apr 2023 09:41:21 -0400 Subject: [PATCH 238/384] Generate pyproject dependencies using dfg (#3355) This PR uses dependencies.yaml to generate the dependency lists in pyproject.toml Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3355 --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a5d5a00ba..3c2f5fe2c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,7 +51,7 @@ repos: pass_filenames: false additional_dependencies: [gitpython] - repo: https://github.com/rapidsai/dependency-file-generator - rev: v1.4.0 + rev: v1.5.1 hooks: - id: rapids-dependency-file-generator args: ["--clean"] From f51f00ff7a26ea35c2ba72d0d46fa50b288b5daa Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Wed, 5 Apr 2023 08:23:16 -0500 Subject: [PATCH 239/384] Remove legacy renumbering (#2949) closes #2437 closes #1679 closes #2535 closes #2536 closes #2537 Authors: - Joseph Nke (https://github.com/jnke2016) - Chuck Hastings (https://github.com/ChuckHastings) - Alex Barghi (https://github.com/alexbarghi-nv) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) - Chuck Hastings (https://github.com/ChuckHastings) - Alex Barghi (https://github.com/alexbarghi-nv) - Vibhu Jawa (https://github.com/VibhuJawa) - Rick Ratzel (https://github.com/rlratzel) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/2949 --- docs/cugraph/source/api_docs/structure.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst index ab097156c..0d6e28792 100644 --- a/docs/cugraph/source/api_docs/structure.rst +++ b/docs/cugraph/source/api_docs/structure.rst @@ -102,4 +102,3 @@ Other :toctree: api/ cugraph.hypergraph - cugraph.structure.shuffle From b01c85923f91a05bfb2f74c8a9db639dc40f6d0a Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 5 Apr 2023 14:59:36 -0400 Subject: [PATCH 240/384] Doc Updates (#3418) Trying to fix doc build issues Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3418 --- .../{ => cugraph-dgl}/cugraph_dgl.rst | 6 +- .../{ => cugraph-pyg}/cugraph_pyg.rst | 2 +- .../api_docs/{ => cugraph}/centrality.rst | 12 +- .../api_docs/{ => cugraph}/community.rst | 18 +- .../api_docs/{ => cugraph}/components.rst | 4 +- .../source/api_docs/{ => cugraph}/cores.rst | 4 +- .../api_docs/{ => cugraph}/cugraph_top.rst | 0 .../api_docs/{ => cugraph}/dask-cugraph.rst | 0 .../api_docs/{ => cugraph}/generator.rst | 2 +- .../{ => cugraph}/graph_implementation.rst | 2 +- .../{ => cugraph}/helper_functions.rst | 2 +- .../source/api_docs/{ => cugraph}/layout.rst | 2 +- .../{ => cugraph}/linear_assignment.rst | 2 +- .../api_docs/{ => cugraph}/link_analysis.rst | 8 +- .../{ => cugraph}/link_prediction.rst | 6 +- .../api_docs/{ => cugraph}/property_graph.rst | 2 +- .../api_docs/{ => cugraph}/sampling.rst | 4 +- .../source/api_docs/cugraph/structure.rst | 104 +++++++ .../api_docs/{ => cugraph}/traversal.rst | 8 +- .../source/api_docs/{ => cugraph}/tree.rst | 4 +- .../api_docs/{ => cugraph_c}/c_and_cpp.rst | 0 docs/cugraph/source/api_docs/index.rst | 12 +- .../api_docs/{ => plc}/pylibcugraph.rst | 2 +- .../{ => service}/cugraph_service_client.rst | 5 +- .../{ => service}/cugraph_service_server.rst | 2 +- docs/cugraph/source/basics/index.rst | 3 + .../source/graph_support/compatibility.rst | 2 +- docs/cugraph/source/index.rst | 1 + .../source/installation/getting_cugraph.md | 3 - docs/cugraph/source/installation/index.rst | 3 +- .../source/installation/source_build.md | 271 ++++++++++++++++++ docs/cugraph/source/tutorials/blogs.rst | 8 - docs/cugraph/source/tutorials/index.rst | 3 +- 33 files changed, 436 insertions(+), 71 deletions(-) rename docs/cugraph/source/api_docs/{ => cugraph-dgl}/cugraph_dgl.rst (58%) rename docs/cugraph/source/api_docs/{ => cugraph-pyg}/cugraph_pyg.rst (89%) rename docs/cugraph/source/api_docs/{ => cugraph}/centrality.rst (80%) rename docs/cugraph/source/api_docs/{ => cugraph}/community.rst (78%) rename docs/cugraph/source/api_docs/{ => cugraph}/components.rst (86%) rename docs/cugraph/source/api_docs/{ => cugraph}/cores.rst (73%) rename docs/cugraph/source/api_docs/{ => cugraph}/cugraph_top.rst (100%) rename docs/cugraph/source/api_docs/{ => cugraph}/dask-cugraph.rst (100%) rename docs/cugraph/source/api_docs/{ => cugraph}/generator.rst (80%) rename docs/cugraph/source/api_docs/{ => cugraph}/graph_implementation.rst (94%) rename docs/cugraph/source/api_docs/{ => cugraph}/helper_functions.rst (95%) rename docs/cugraph/source/api_docs/{ => cugraph}/layout.rst (80%) rename docs/cugraph/source/api_docs/{ => cugraph}/linear_assignment.rst (85%) rename docs/cugraph/source/api_docs/{ => cugraph}/link_analysis.rst (75%) rename docs/cugraph/source/api_docs/{ => cugraph}/link_prediction.rst (84%) rename docs/cugraph/source/api_docs/{ => cugraph}/property_graph.rst (96%) rename docs/cugraph/source/api_docs/{ => cugraph}/sampling.rst (80%) create mode 100644 docs/cugraph/source/api_docs/cugraph/structure.rst rename docs/cugraph/source/api_docs/{ => cugraph}/traversal.rst (82%) rename docs/cugraph/source/api_docs/{ => cugraph}/tree.rst (83%) rename docs/cugraph/source/api_docs/{ => cugraph_c}/c_and_cpp.rst (100%) rename docs/cugraph/source/api_docs/{ => plc}/pylibcugraph.rst (94%) rename docs/cugraph/source/api_docs/{ => service}/cugraph_service_client.rst (95%) rename docs/cugraph/source/api_docs/{ => service}/cugraph_service_server.rst (92%) create mode 100644 docs/cugraph/source/installation/source_build.md delete mode 100644 docs/cugraph/source/tutorials/blogs.rst diff --git a/docs/cugraph/source/api_docs/cugraph_dgl.rst b/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst similarity index 58% rename from docs/cugraph/source/api_docs/cugraph_dgl.rst rename to docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst index 98e101f99..4ffecd8d0 100644 --- a/docs/cugraph/source/api_docs/cugraph_dgl.rst +++ b/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst @@ -9,7 +9,7 @@ cugraph-dgl Methods ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph-dgl/ - cugraph_dgl.cugraph_storage_from_heterograph - cugraph_dgl.cugraph_storage.CuGraphStorage + convert.cugraph_storage_from_heterograph + cugraph_storage.CuGraphStorage diff --git a/docs/cugraph/source/api_docs/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst similarity index 89% rename from docs/cugraph/source/api_docs/cugraph_pyg.rst rename to docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst index 7e25b3547..2cd8969aa 100644 --- a/docs/cugraph/source/api_docs/cugraph_pyg.rst +++ b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst @@ -7,7 +7,7 @@ cugraph-pyg .. currentmodule:: cugraph_pyg .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph-pyg/ cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/cugraph/centrality.rst similarity index 80% rename from docs/cugraph/source/api_docs/centrality.rst rename to docs/cugraph/source/api_docs/cugraph/centrality.rst index 9b634cb6d..c3b026597 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/cugraph/centrality.rst @@ -8,7 +8,7 @@ Centrality Betweenness Centrality ---------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.betweenness_centrality cugraph.edge_betweenness_centrality @@ -16,34 +16,34 @@ Betweenness Centrality Katz Centrality --------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.katz_centrality Katz Centrality (MG) -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.centrality.katz_centrality.katz_centrality Degree Centrality ----------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.degree_centrality Eigenvector Centrality ---------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.centrality.eigenvector_centrality Eigenvector Centrality (MG) --------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.centrality.eigenvector_centrality.eigenvector_centrality diff --git a/docs/cugraph/source/api_docs/community.rst b/docs/cugraph/source/api_docs/cugraph/community.rst similarity index 78% rename from docs/cugraph/source/api_docs/community.rst rename to docs/cugraph/source/api_docs/cugraph/community.rst index 94eb1a49c..acbaa086f 100644 --- a/docs/cugraph/source/api_docs/community.rst +++ b/docs/cugraph/source/api_docs/cugraph/community.rst @@ -8,7 +8,7 @@ Community EgoNet ------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.batched_ego_graphs cugraph.ego_graph @@ -16,7 +16,7 @@ EgoNet Ensemble clustering for graphs (ECG) ------------------------------------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.ecg @@ -24,7 +24,7 @@ Ensemble clustering for graphs (ECG) K-Truss ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.k_truss cugraph.ktruss_subgraph @@ -32,7 +32,7 @@ K-Truss Leiden ------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.leiden @@ -40,7 +40,7 @@ Leiden Louvain ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.louvain @@ -48,14 +48,14 @@ Louvain Louvain (MG) ------------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.community.louvain.louvain Spectral Clustering ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.analyzeClustering_edge_cut cugraph.analyzeClustering_modularity @@ -67,7 +67,7 @@ Spectral Clustering Subgraph Extraction ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.subgraph @@ -75,6 +75,6 @@ Subgraph Extraction Triangle Counting ----------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.triangle_count diff --git a/docs/cugraph/source/api_docs/components.rst b/docs/cugraph/source/api_docs/cugraph/components.rst similarity index 86% rename from docs/cugraph/source/api_docs/components.rst rename to docs/cugraph/source/api_docs/cugraph/components.rst index 8616c8742..5835972cd 100644 --- a/docs/cugraph/source/api_docs/components.rst +++ b/docs/cugraph/source/api_docs/cugraph/components.rst @@ -8,7 +8,7 @@ Components Connected Components -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.connected_components cugraph.strongly_connected_components @@ -18,7 +18,7 @@ Connected Components Connected Components (MG) ------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.components.connectivity.weakly_connected_components diff --git a/docs/cugraph/source/api_docs/cores.rst b/docs/cugraph/source/api_docs/cugraph/cores.rst similarity index 73% rename from docs/cugraph/source/api_docs/cores.rst rename to docs/cugraph/source/api_docs/cugraph/cores.rst index 0e48c584b..9d274d1c4 100644 --- a/docs/cugraph/source/api_docs/cores.rst +++ b/docs/cugraph/source/api_docs/cugraph/cores.rst @@ -8,7 +8,7 @@ Cores Core Number ----------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.core_number @@ -16,6 +16,6 @@ Core Number K-Core ------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.k_core diff --git a/docs/cugraph/source/api_docs/cugraph_top.rst b/docs/cugraph/source/api_docs/cugraph/cugraph_top.rst similarity index 100% rename from docs/cugraph/source/api_docs/cugraph_top.rst rename to docs/cugraph/source/api_docs/cugraph/cugraph_top.rst diff --git a/docs/cugraph/source/api_docs/dask-cugraph.rst b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst similarity index 100% rename from docs/cugraph/source/api_docs/dask-cugraph.rst rename to docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst diff --git a/docs/cugraph/source/api_docs/generator.rst b/docs/cugraph/source/api_docs/cugraph/generator.rst similarity index 80% rename from docs/cugraph/source/api_docs/generator.rst rename to docs/cugraph/source/api_docs/cugraph/generator.rst index e89fe25e8..9b4ebbcf7 100644 --- a/docs/cugraph/source/api_docs/generator.rst +++ b/docs/cugraph/source/api_docs/cugraph/generator.rst @@ -8,7 +8,7 @@ Generator RMAT ---- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.generators.rmat diff --git a/docs/cugraph/source/api_docs/graph_implementation.rst b/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst similarity index 94% rename from docs/cugraph/source/api_docs/graph_implementation.rst rename to docs/cugraph/source/api_docs/cugraph/graph_implementation.rst index f6dc38a27..91c16c242 100644 --- a/docs/cugraph/source/api_docs/graph_implementation.rst +++ b/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst @@ -7,7 +7,7 @@ Graph Implementation Graph Implementation -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ view_edge_list delete_edge_list diff --git a/docs/cugraph/source/api_docs/helper_functions.rst b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst similarity index 95% rename from docs/cugraph/source/api_docs/helper_functions.rst rename to docs/cugraph/source/api_docs/cugraph/helper_functions.rst index 08585d264..ec3248bfa 100644 --- a/docs/cugraph/source/api_docs/helper_functions.rst +++ b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst @@ -8,7 +8,7 @@ DASK MG Helper functions Methods ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.comms.comms.initialize cugraph.dask.comms.comms.destroy diff --git a/docs/cugraph/source/api_docs/layout.rst b/docs/cugraph/source/api_docs/cugraph/layout.rst similarity index 80% rename from docs/cugraph/source/api_docs/layout.rst rename to docs/cugraph/source/api_docs/cugraph/layout.rst index b3943e4d3..1c097346b 100644 --- a/docs/cugraph/source/api_docs/layout.rst +++ b/docs/cugraph/source/api_docs/cugraph/layout.rst @@ -8,7 +8,7 @@ Layout Force Atlas 2 ------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.force_atlas2 diff --git a/docs/cugraph/source/api_docs/linear_assignment.rst b/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst similarity index 85% rename from docs/cugraph/source/api_docs/linear_assignment.rst rename to docs/cugraph/source/api_docs/cugraph/linear_assignment.rst index 383bb4d43..dfdf6da96 100644 --- a/docs/cugraph/source/api_docs/linear_assignment.rst +++ b/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst @@ -8,7 +8,7 @@ Linear Assignment Hungarian --------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.hungarian cugraph.dense_hungarian diff --git a/docs/cugraph/source/api_docs/link_analysis.rst b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst similarity index 75% rename from docs/cugraph/source/api_docs/link_analysis.rst rename to docs/cugraph/source/api_docs/cugraph/link_analysis.rst index b84dd2ccb..5f977b477 100644 --- a/docs/cugraph/source/api_docs/link_analysis.rst +++ b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst @@ -8,14 +8,14 @@ Link Analysis HITS ---- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.hits HITS (MG) --------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.link_analysis.hits.hits @@ -23,14 +23,14 @@ HITS (MG) Pagerank -------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.pagerank Pagerank (MG) ------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.link_analysis.pagerank.pagerank diff --git a/docs/cugraph/source/api_docs/link_prediction.rst b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst similarity index 84% rename from docs/cugraph/source/api_docs/link_prediction.rst rename to docs/cugraph/source/api_docs/cugraph/link_prediction.rst index 7c7b34f2f..b2134fba9 100644 --- a/docs/cugraph/source/api_docs/link_prediction.rst +++ b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst @@ -8,7 +8,7 @@ Link Prediction Jaccard Coefficient ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.jaccard cugraph.jaccard_coefficient @@ -18,7 +18,7 @@ Jaccard Coefficient Overlap Coefficient ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.overlap cugraph.overlap_coefficient @@ -28,7 +28,7 @@ Overlap Coefficient Sorensen Coefficient -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.sorensen cugraph.sorensen_coefficient diff --git a/docs/cugraph/source/api_docs/property_graph.rst b/docs/cugraph/source/api_docs/cugraph/property_graph.rst similarity index 96% rename from docs/cugraph/source/api_docs/property_graph.rst rename to docs/cugraph/source/api_docs/cugraph/property_graph.rst index f3653c085..672aa7dae 100644 --- a/docs/cugraph/source/api_docs/property_graph.rst +++ b/docs/cugraph/source/api_docs/cugraph/property_graph.rst @@ -7,7 +7,7 @@ Property Graph Property Graph ------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ PropertySelection PropertyGraph diff --git a/docs/cugraph/source/api_docs/sampling.rst b/docs/cugraph/source/api_docs/cugraph/sampling.rst similarity index 80% rename from docs/cugraph/source/api_docs/sampling.rst rename to docs/cugraph/source/api_docs/cugraph/sampling.rst index bab8aee6b..52004a5b1 100644 --- a/docs/cugraph/source/api_docs/sampling.rst +++ b/docs/cugraph/source/api_docs/cugraph/sampling.rst @@ -8,7 +8,7 @@ Sampling Random Walks ------------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.random_walks cugraph.ego_graph @@ -17,6 +17,6 @@ Random Walks Node2Vec --------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.node2vec diff --git a/docs/cugraph/source/api_docs/cugraph/structure.rst b/docs/cugraph/source/api_docs/cugraph/structure.rst new file mode 100644 index 000000000..5114cb57b --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph/structure.rst @@ -0,0 +1,104 @@ +============= +Graph Classes +============= +.. currentmodule:: cugraph + +Constructors +------------ +.. autosummary:: + :toctree: ../api/cugraph/ + + Graph + MultiGraph + BiPartiteGraph + + + +Adding Data +----------- +.. autosummary:: + :toctree: ../api/cugraph/ + + + Graph.from_cudf_adjlist + Graph.from_cudf_edgelist + Graph.from_dask_cudf_edgelist + Graph.from_pandas_adjacency + Graph.from_pandas_edgelist + Graph.from_numpy_array + Graph.from_numpy_matrix + Graph.add_internal_vertex_id + Graph.add_nodes_from + Graph.clear + Graph.unrenumber + +Checks +------ +.. autosummary:: + :toctree: ../api/cugraph/ + + Graph.has_isolated_vertices + Graph.is_bipartite + Graph.is_directed + Graph.is_multigraph + Graph.is_multipartite + Graph.is_renumbered + Graph.is_weighted + Graph.lookup_internal_vertex_id + Graph.to_directed + Graph.to_undirected + + +Symmetrize +---------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.symmetrize + cugraph.symmetrize_ddf + cugraph.symmetrize_df + + +Conversion from Other Formats +----------------------------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.from_adjlist + cugraph.from_cudf_edgelist + cugraph.from_edgelist + cugraph.from_numpy_array + cugraph.from_numpy_matrix + cugraph.from_pandas_adjacency + cugraph.from_pandas_edgelist + cugraph.to_numpy_array + cugraph.to_numpy_matrix + cugraph.to_pandas_adjacency + cugraph.to_pandas_edgelist + +NumberMap +----------------------------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.structure.NumberMap + cugraph.structure.NumberMap.MultiGPU + cugraph.structure.NumberMap.SingleGPU + cugraph.structure.NumberMap.from_internal_vertex_id + cugraph.structure.NumberMap.to_internal_vertex_id + cugraph.structure.NumberMap.add_internal_vertex_id + cugraph.structure.NumberMap.compute_vals + cugraph.structure.NumberMap.compute_vals_types + cugraph.structure.NumberMap.generate_unused_column_name + cugraph.structure.NumberMap.renumber + cugraph.structure.NumberMap.renumber_and_segment + cugraph.structure.NumberMap.set_renumbered_col_names + cugraph.structure.NumberMap.unrenumber + cugraph.structure.NumberMap.vertex_column_size + +Other +----------------------------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.hypergraph diff --git a/docs/cugraph/source/api_docs/traversal.rst b/docs/cugraph/source/api_docs/cugraph/traversal.rst similarity index 82% rename from docs/cugraph/source/api_docs/traversal.rst rename to docs/cugraph/source/api_docs/cugraph/traversal.rst index 675bf89f3..c8fcc6b72 100644 --- a/docs/cugraph/source/api_docs/traversal.rst +++ b/docs/cugraph/source/api_docs/cugraph/traversal.rst @@ -8,7 +8,7 @@ Traversal Breadth-first-search -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.bfs cugraph.bfs_edges @@ -16,14 +16,14 @@ Breadth-first-search Breadth-first-search (MG) ------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.traversal.bfs.bfs Single-source-shortest-path --------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.filter_unreachable cugraph.shortest_path @@ -33,6 +33,6 @@ Single-source-shortest-path Single-source-shortest-path (MG) -------------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.traversal.sssp.sssp diff --git a/docs/cugraph/source/api_docs/tree.rst b/docs/cugraph/source/api_docs/cugraph/tree.rst similarity index 83% rename from docs/cugraph/source/api_docs/tree.rst rename to docs/cugraph/source/api_docs/cugraph/tree.rst index 7952c4fa3..38bfbad7d 100644 --- a/docs/cugraph/source/api_docs/tree.rst +++ b/docs/cugraph/source/api_docs/cugraph/tree.rst @@ -8,7 +8,7 @@ Tree Minimum Spanning Tree --------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.tree.minimum_spanning_tree.minimum_spanning_tree @@ -16,7 +16,7 @@ Minimum Spanning Tree Maximum Spanning Tree --------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.tree.minimum_spanning_tree.maximum_spanning_tree diff --git a/docs/cugraph/source/api_docs/c_and_cpp.rst b/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst similarity index 100% rename from docs/cugraph/source/api_docs/c_and_cpp.rst rename to docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 0e7598af0..f6307d5ac 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -8,9 +8,9 @@ This page provides a list of all publicly accessible modules, methods and classe :maxdepth: 2 :caption: Python API Documentation - cugraph_top.rst - pylibcugraph.rst - cugraph_dgl.rst - cugraph_pyg.rst - cugraph_service_client.rst - cugraph_service_server.rst + cugraph/cugraph_top.rst + plc/pylibcugraph.rst + cugraph-dgl/cugraph_dgl.rst + cugraph-pyg/cugraph_pyg.rst + service/cugraph_service_client.rst + service/cugraph_service_server.rst diff --git a/docs/cugraph/source/api_docs/pylibcugraph.rst b/docs/cugraph/source/api_docs/plc/pylibcugraph.rst similarity index 94% rename from docs/cugraph/source/api_docs/pylibcugraph.rst rename to docs/cugraph/source/api_docs/plc/pylibcugraph.rst index 7b63cd7dc..7ebdd67e9 100644 --- a/docs/cugraph/source/api_docs/pylibcugraph.rst +++ b/docs/cugraph/source/api_docs/plc/pylibcugraph.rst @@ -9,7 +9,7 @@ pylibcugraph Methods ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/plc/ pylibcugraph.eigenvector_centrality pylibcugraph.katz_centrality diff --git a/docs/cugraph/source/api_docs/cugraph_service_client.rst b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst similarity index 95% rename from docs/cugraph/source/api_docs/cugraph_service_client.rst rename to docs/cugraph/source/api_docs/service/cugraph_service_client.rst index 1ea727bd3..383b31d26 100644 --- a/docs/cugraph/source/api_docs/cugraph_service_client.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst @@ -7,7 +7,7 @@ cugraph-service .. currentmodule:: cugraph-service .. autosummary:: - :toctree: api/ + :toctree: ../api/service/ cugraph_service_client.client.RunAsyncioThread cugraph_service_client.client.run_async @@ -18,6 +18,3 @@ cugraph-service cugraph_service_client.types.UnionWrapper cugraph_service_client.types.ValueWrapper cugraph_service_client.types.GraphVertexEdgeIDWrapper - - - diff --git a/docs/cugraph/source/api_docs/cugraph_service_server.rst b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst similarity index 92% rename from docs/cugraph/source/api_docs/cugraph_service_server.rst rename to docs/cugraph/source/api_docs/service/cugraph_service_server.rst index 5f4c6aa5e..a7e8b5475 100644 --- a/docs/cugraph/source/api_docs/cugraph_service_server.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst @@ -7,7 +7,7 @@ cugraph-service .. currentmodule:: cugraph-service .. autosummary:: - :toctree: api/ + :toctree: ../api/service/ cugraph_service_server.cugraph_handler.call_algo cugraph_service_server.cugraph_handler.ExtensionServerFacade diff --git a/docs/cugraph/source/basics/index.rst b/docs/cugraph/source/basics/index.rst index 46331fc6c..1875ac22b 100644 --- a/docs/cugraph/source/basics/index.rst +++ b/docs/cugraph/source/basics/index.rst @@ -6,4 +6,7 @@ Basics .. toctree:: :maxdepth: 2 + cugraph_intro cugraph_toc.md + nx_transition + cugraph_cascading diff --git a/docs/cugraph/source/graph_support/compatibility.rst b/docs/cugraph/source/graph_support/compatibility.rst index 38f6df4c7..be0cc21c2 100644 --- a/docs/cugraph/source/graph_support/compatibility.rst +++ b/docs/cugraph/source/graph_support/compatibility.rst @@ -5,4 +5,4 @@ Compatibility .. toctree:: :maxdepth: 3 - Compatibility \ No newline at end of file +Compatibility document coming soon \ No newline at end of file diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 2dd871c4c..12bc74aea 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -9,6 +9,7 @@ NetworkX Graphs, or even CuPy or SciPy sparse Matrices. :maxdepth: 2 :caption: Contents: + basics/index installation/index tutorials/index graph_support/index diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index 390ef5221..005938f47 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -53,6 +53,3 @@ pip packages for other packages are being worked and should be available in earl
-## SOURCE -cuGraph can be build directly from source. First check to make sure you have or can configure a supported environment. -Instructions for building from source is in our [source build](./SOURCEBUILD.md) page. diff --git a/docs/cugraph/source/installation/index.rst b/docs/cugraph/source/installation/index.rst index f9e8acaa5..27858b770 100644 --- a/docs/cugraph/source/installation/index.rst +++ b/docs/cugraph/source/installation/index.rst @@ -6,4 +6,5 @@ Installation .. toctree:: :maxdepth: 3 - getting_cugraph \ No newline at end of file + getting_cugraph + source_build diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md new file mode 100644 index 000000000..cd9297abc --- /dev/null +++ b/docs/cugraph/source/installation/source_build.md @@ -0,0 +1,271 @@ +# Building from Source + +The following instructions are for users wishing to build cuGraph from source code. These instructions are tested on supported distributions of Linux, CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for list of supported environments. Other operating systems _might be_ compatible, but are not currently tested. + +The cuGraph package include both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly. + +## Prerequisites + +__Compiler__: +* `gcc` version 9.3+ +* `nvcc` version 11.0+ +* `cmake` version 3.20.1+ + +__CUDA:__ +* CUDA 11.0+ +* NVIDIA driver 450.80.02+ +* Pascal architecture or better + +You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). + + +## Building cuGraph +To install cuGraph from source, ensure the dependencies are met. + + +### Clone Repo and Configure Conda Environment +__GIT clone a version of the repository__ + + ```bash + # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME + export CUGRAPH_HOME=$(pwd)/cugraph + + # Download the cuGraph repo - if you have a folked version, use that path here instead + git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME + + cd $CUGRAPH_HOME + ``` + +__Create the conda development environment__ + +```bash +# create the conda environment (assuming in base `cugraph` directory) + +# for CUDA 11.5 +conda env create --name cugraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml + +# activate the environment +conda activate cugraph_dev + +# to deactivate an environment +conda deactivate +``` + + - The environment can be updated as development includes/changes the dependencies. To do so, run: + + +```bash + +# Where XXX is the CUDA 11 version +conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda11.XXX.yml + +conda activate cugraph_dev +``` + + +### Build and Install Using the `build.sh` Script +Using the `build.sh` script make compiling and installing cuGraph a breeze. To build and install, simply do: + +```bash +$ cd $CUGRAPH_HOME +$ ./build.sh clean +$ ./build.sh libcugraph +$ ./build.sh cugraph +``` + +There are several other options available on the build script for advanced users. +`build.sh` options: +```bash +build.sh [ ...] [ ...] + where is: + clean - remove all existing build artifacts and configuration (start over) + uninstall - uninstall libcugraph and cugraph from a prior build/install (see also -n) + libcugraph - build libcugraph.so and SG test binaries + libcugraph_etl - build libcugraph_etl.so and SG test binaries + pylibcugraph - build the pylibcugraph Python package + cugraph - build the cugraph Python package + cugraph-service - build the cugraph-service_client and cugraph-service_server Python package + cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. + cugraph-dgl - build the cugraph-dgl extensions for DGL + cugraph-pyg - build the cugraph-dgl extensions for PyG + docs - build the docs + and is: + -v - verbose build mode + -g - build for debug + -n - do not install after a successful build + --pydevelop - use setup.py develop instead of install + --allgpuarch - build for all supported GPU architectures + --skip_cpp_tests - do not build the SG test binaries as part of the libcugraph and libcugraph_etl targets + --without_cugraphops - do not build algos that require cugraph-ops + --cmake_default_generator - use the default cmake generator instead of ninja + --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) + -h - print this text + + default action (no args) is to build and install 'libcugraph' then 'libcugraph_etl' then 'pylibcugraph' then 'cugraph' then 'cugraph-service' targets + +examples: +$ ./build.sh clean # remove prior build artifacts (start over) +$ ./build.sh libcugraph -v # compile and install libcugraph with verbose output +$ ./build.sh libcugraph -g # compile and install libcugraph for debug +$ ./build.sh libcugraph -n # compile libcugraph but do not install + +# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4) +$ PARALLEL_LEVEL=4 ./build.sh libcugraph + +Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. +``` + + +## Building each section independently +#### Build and Install the C++/CUDA `libcugraph` Library +CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. + +This project uses cmake for building the C/C++ library. To configure cmake, run: + + ```bash + # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME + export CUGRAPH_HOME=$(pwd)/cugraph + + cd $CUGRAPH_HOME + cd cpp # enter cpp directory + mkdir build # create build directory + cd build # enter the build directory + cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX + + # now build the code + make -j # "-j" starts multiple threads + make install # install the libraries + ``` +The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/cugraph` respectively. + +#### Updating the RAFT branch + +`libcugraph` uses the [RAFT](https://github.com/rapidsai/raft) library and there are times when it might be desirable to build against a different RAFT branch, such as when working on new features that might span both RAFT and cuGraph. + +For local development, the `CPM_raft_SOURCE=` option can be passed to the `cmake` command to enable `libcugraph` to use the local RAFT branch. + +To have CI test a `cugraph` pull request against a different RAFT branch, modify the bottom of the `cpp/cmake/thirdparty/get_raft.cmake` file as follows: + +```cmake +# Change pinned tag and fork here to test a commit in CI +# To use a different RAFT locally, set the CMake variable +# RPM_raft_SOURCE=/path/to/local/raft +find_and_configure_raft(VERSION ${CUGRAPH_MIN_VERSION_raft} + FORK + PINNED_TAG + + # When PINNED_TAG above doesn't match cugraph, + # force local raft clone in build directory + # even if it's already installed. + CLONE_ON_PIN ON + ) +``` + +When the above change is pushed to a pull request, the continuous integration servers will use the specified RAFT branch to run the cuGraph tests. After the changes in the RAFT branch are merged to the release branch, remember to revert the `get_raft.cmake` file back to the original cuGraph branch. + +### Building and installing the Python package + +2) Install the Python packages to your Python path: + +```bash +cd $CUGRAPH_HOME +cd python +cd pylibcugraph +python setup.py build_ext --inplace +python setup.py install # install pylibcugraph +cd ../cugraph +python setup.py build_ext --inplace +python setup.py install # install cugraph python bindings + +``` + + + +## Run tests + +If you already have the datasets: + + ```bash + export RAPIDS_DATASET_ROOT_DIR= + ``` + If you do not have the datasets: + + ```bash + cd $CUGRAPH_HOME/datasets + source get_test_data.sh #This takes about 10 minutes and downloads 1GB data (>5 GB uncompressed) + ``` + +Run either the C++ or the Python tests with datasets + + - **Python tests with datasets** + + + ```bash + pip install python-louvain #some tests require this package to run + cd $CUGRAPH_HOME + cd python + pytest + ``` + - **C++ stand alone tests** + + From the build directory : + + ```bash + # Run the cugraph tests + cd $CUGRAPH_HOME + cd cpp/build + gtests/GDFGRAPH_TEST # this is an executable file + ``` + - **C++ tests with larger datasets** + + + + Run the C++ tests on large input: + + ```bash + cd $CUGRAPH_HOME/cpp/build + #test one particular analytics (eg. pagerank) + gtests/PAGERANK_TEST + #test everything + make test + ``` + +Note: This conda installation only applies to Linux and Python versions 3.8/3.10. + +### (OPTIONAL) Set environment variable on activation + +It is possible to configure the conda environment to set environmental variables on activation. Providing instructions to set PATH to include the CUDA toolkit bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be helpful. + +```bash +cd ~/anaconda3/envs/cugraph_dev + +mkdir -p ./etc/conda/activate.d +mkdir -p ./etc/conda/deactivate.d +touch ./etc/conda/activate.d/env_vars.sh +touch ./etc/conda/deactivate.d/env_vars.sh +``` + +Next the env_vars.sh file needs to be edited + +```bash +vi ./etc/conda/activate.d/env_vars.sh + +#!/bin/bash +export PATH=/usr/local/cuda-11.0/bin:$PATH # or cuda-11.1 if using CUDA 11.1 and cuda-11.2 if using CUDA 11.2, respectively +export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:$LD_LIBRARY_PATH # or cuda-11.1 if using CUDA 11.1 and cuda-11.2 if using CUDA 11.2, respectively +``` + +``` +vi ./etc/conda/deactivate.d/env_vars.sh + +#!/bin/bash +unset PATH +unset LD_LIBRARY_PATH +``` + +## Creating documentation + +Python API documentation can be generated from _./docs/cugraph directory_. Or through using "./build.sh docs" + +## Attribution +Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md diff --git a/docs/cugraph/source/tutorials/blogs.rst b/docs/cugraph/source/tutorials/blogs.rst deleted file mode 100644 index 8f6623a98..000000000 --- a/docs/cugraph/source/tutorials/blogs.rst +++ /dev/null @@ -1,8 +0,0 @@ -============ -Blogs -============ - - -.. toctree:: - :maxdepth: 3 - cugraph_blogs.rst \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/index.rst b/docs/cugraph/source/tutorials/index.rst index 2470bced7..cce352509 100644 --- a/docs/cugraph/source/tutorials/index.rst +++ b/docs/cugraph/source/tutorials/index.rst @@ -8,5 +8,4 @@ Tutorials how_to_guides.md cugraph_blogs.rst - cugraph_notebooks.md - community_resources.md \ No newline at end of file + community_resources.md From b2e29e839515029d30ff03adaa5c903870467375 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 10 Apr 2023 07:23:45 -0700 Subject: [PATCH 241/384] Branch 23.06 resolve merge conflict for forward merge (#3409) Resolves merge conflict Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Joseph Nke (https://github.com/jnke2016) - Vibhu Jawa (https://github.com/VibhuJawa) - Don Acosta (https://github.com/acostadon) - Vyas Ramasubramani (https://github.com/vyasr) - Artur (https://github.com/ArturKasymov) - Naim (https://github.com/naimnv) - Lawrence Mitchell (https://github.com/wence-) - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) - Tingyu Wang (https://github.com/tingyu66) - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) - GALI PREM SAGAR (https://github.com/galipremsagar) - Ray Douglass (https://github.com/raydouglass) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3409 --- .pre-commit-config.yaml | 2 +- .../api_docs/cugraph-dgl/cugraph_dgl.rst | 15 + .../{ => cugraph-pyg}/cugraph_pyg.rst | 2 +- .../api_docs/{ => cugraph}/centrality.rst | 12 +- .../api_docs/{ => cugraph}/community.rst | 18 +- .../api_docs/{ => cugraph}/components.rst | 4 +- .../source/api_docs/{ => cugraph}/cores.rst | 4 +- .../api_docs/{ => cugraph}/cugraph_top.rst | 0 .../api_docs/{ => cugraph}/dask-cugraph.rst | 0 .../api_docs/{ => cugraph}/generator.rst | 2 +- .../{ => cugraph}/graph_implementation.rst | 2 +- .../{ => cugraph}/helper_functions.rst | 2 +- .../source/api_docs/{ => cugraph}/layout.rst | 2 +- .../{ => cugraph}/linear_assignment.rst | 2 +- .../api_docs/{ => cugraph}/link_analysis.rst | 8 +- .../{ => cugraph}/link_prediction.rst | 6 +- .../api_docs/{ => cugraph}/property_graph.rst | 2 +- .../api_docs/{ => cugraph}/sampling.rst | 4 +- .../source/api_docs/cugraph/structure.rst | 104 +++++++ .../api_docs/{ => cugraph}/traversal.rst | 8 +- .../source/api_docs/{ => cugraph}/tree.rst | 4 +- .../api_docs/{ => cugraph_c}/c_and_cpp.rst | 0 docs/cugraph/source/api_docs/cugraph_dgl.rst | 15 - docs/cugraph/source/api_docs/index.rst | 12 +- .../api_docs/{ => plc}/pylibcugraph.rst | 2 +- .../{ => service}/cugraph_service_client.rst | 5 +- .../{ => service}/cugraph_service_server.rst | 2 +- docs/cugraph/source/api_docs/structure.rst | 1 - docs/cugraph/source/basics/index.rst | 3 + docs/cugraph/source/dev_resources/API.rst | 5 + docs/cugraph/source/dev_resources/index.rst | 10 + .../source/graph_support/DGL_support.md | 55 ++++ .../source/graph_support/PyG_support.md | 3 + .../source/graph_support/algorithms.md | 85 ++++++ .../source/graph_support/compatibility.rst | 8 + .../source/graph_support/cugraph_service.rst | 9 + .../graph_support/cugraphops_support.rst | 10 + .../source/graph_support/datastores.rst | 11 + .../source/graph_support/feature_stores.md | 0 .../source/graph_support/gnn_support.rst | 12 + .../source/graph_support/graph_algorithms.rst | 9 + docs/cugraph/source/graph_support/index.rst | 13 + .../source/graph_support/knowledge_stores.md | 0 .../source/graph_support/pg_example.png | Bin 0 -> 242398 bytes .../source/graph_support/property_graph.md | 54 ++++ .../graph_support/wholegraph_support.rst | 0 docs/cugraph/source/index.rst | 48 +--- .../source/installation/getting_cugraph.md | 55 ++++ docs/cugraph/source/installation/index.rst | 10 + .../source/installation/source_build.md | 271 ++++++++++++++++++ .../{basics => references}/cugraph_ref.rst | 0 docs/cugraph/source/references/datasets.rst | 0 docs/cugraph/source/references/index.rst | 9 + docs/cugraph/source/references/licenses.rst | 0 docs/cugraph/source/releases/index.rst | 5 + .../source/tutorials/community_resources.md | 2 + .../{basics => tutorials}/cugraph_blogs.rst | 0 .../source/tutorials/cugraph_notebooks.md | 77 +++++ .../cugraph/source/tutorials/how_to_guides.md | 9 + docs/cugraph/source/tutorials/index.rst | 11 + 60 files changed, 916 insertions(+), 108 deletions(-) create mode 100644 docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst rename docs/cugraph/source/api_docs/{ => cugraph-pyg}/cugraph_pyg.rst (89%) rename docs/cugraph/source/api_docs/{ => cugraph}/centrality.rst (80%) rename docs/cugraph/source/api_docs/{ => cugraph}/community.rst (78%) rename docs/cugraph/source/api_docs/{ => cugraph}/components.rst (86%) rename docs/cugraph/source/api_docs/{ => cugraph}/cores.rst (73%) rename docs/cugraph/source/api_docs/{ => cugraph}/cugraph_top.rst (100%) rename docs/cugraph/source/api_docs/{ => cugraph}/dask-cugraph.rst (100%) rename docs/cugraph/source/api_docs/{ => cugraph}/generator.rst (80%) rename docs/cugraph/source/api_docs/{ => cugraph}/graph_implementation.rst (94%) rename docs/cugraph/source/api_docs/{ => cugraph}/helper_functions.rst (95%) rename docs/cugraph/source/api_docs/{ => cugraph}/layout.rst (80%) rename docs/cugraph/source/api_docs/{ => cugraph}/linear_assignment.rst (85%) rename docs/cugraph/source/api_docs/{ => cugraph}/link_analysis.rst (75%) rename docs/cugraph/source/api_docs/{ => cugraph}/link_prediction.rst (84%) rename docs/cugraph/source/api_docs/{ => cugraph}/property_graph.rst (96%) rename docs/cugraph/source/api_docs/{ => cugraph}/sampling.rst (80%) create mode 100644 docs/cugraph/source/api_docs/cugraph/structure.rst rename docs/cugraph/source/api_docs/{ => cugraph}/traversal.rst (82%) rename docs/cugraph/source/api_docs/{ => cugraph}/tree.rst (83%) rename docs/cugraph/source/api_docs/{ => cugraph_c}/c_and_cpp.rst (100%) delete mode 100644 docs/cugraph/source/api_docs/cugraph_dgl.rst rename docs/cugraph/source/api_docs/{ => plc}/pylibcugraph.rst (94%) rename docs/cugraph/source/api_docs/{ => service}/cugraph_service_client.rst (95%) rename docs/cugraph/source/api_docs/{ => service}/cugraph_service_server.rst (92%) create mode 100644 docs/cugraph/source/dev_resources/API.rst create mode 100644 docs/cugraph/source/dev_resources/index.rst create mode 100644 docs/cugraph/source/graph_support/DGL_support.md create mode 100644 docs/cugraph/source/graph_support/PyG_support.md create mode 100644 docs/cugraph/source/graph_support/algorithms.md create mode 100644 docs/cugraph/source/graph_support/compatibility.rst create mode 100644 docs/cugraph/source/graph_support/cugraph_service.rst create mode 100644 docs/cugraph/source/graph_support/cugraphops_support.rst create mode 100644 docs/cugraph/source/graph_support/datastores.rst create mode 100644 docs/cugraph/source/graph_support/feature_stores.md create mode 100644 docs/cugraph/source/graph_support/gnn_support.rst create mode 100644 docs/cugraph/source/graph_support/graph_algorithms.rst create mode 100644 docs/cugraph/source/graph_support/index.rst create mode 100644 docs/cugraph/source/graph_support/knowledge_stores.md create mode 100644 docs/cugraph/source/graph_support/pg_example.png create mode 100644 docs/cugraph/source/graph_support/property_graph.md create mode 100644 docs/cugraph/source/graph_support/wholegraph_support.rst create mode 100644 docs/cugraph/source/installation/getting_cugraph.md create mode 100644 docs/cugraph/source/installation/index.rst create mode 100644 docs/cugraph/source/installation/source_build.md rename docs/cugraph/source/{basics => references}/cugraph_ref.rst (100%) create mode 100644 docs/cugraph/source/references/datasets.rst create mode 100644 docs/cugraph/source/references/index.rst create mode 100644 docs/cugraph/source/references/licenses.rst create mode 100644 docs/cugraph/source/releases/index.rst create mode 100644 docs/cugraph/source/tutorials/community_resources.md rename docs/cugraph/source/{basics => tutorials}/cugraph_blogs.rst (100%) create mode 100644 docs/cugraph/source/tutorials/cugraph_notebooks.md create mode 100644 docs/cugraph/source/tutorials/how_to_guides.md create mode 100644 docs/cugraph/source/tutorials/index.rst diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a5d5a00ba..3c2f5fe2c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,7 +51,7 @@ repos: pass_filenames: false additional_dependencies: [gitpython] - repo: https://github.com/rapidsai/dependency-file-generator - rev: v1.4.0 + rev: v1.5.1 hooks: - id: rapids-dependency-file-generator args: ["--clean"] diff --git a/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst b/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst new file mode 100644 index 000000000..4ffecd8d0 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst @@ -0,0 +1,15 @@ +~~~~~~~~~~~~~~~~~~~~~~~~~~ +cugraph-dgl API Reference +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +cugraph-dgl + +.. currentmodule:: cugraph_dgl + +Methods +------- +.. autosummary:: + :toctree: ../api/cugraph-dgl/ + + convert.cugraph_storage_from_heterograph + cugraph_storage.CuGraphStorage diff --git a/docs/cugraph/source/api_docs/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst similarity index 89% rename from docs/cugraph/source/api_docs/cugraph_pyg.rst rename to docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst index 7e25b3547..2cd8969aa 100644 --- a/docs/cugraph/source/api_docs/cugraph_pyg.rst +++ b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst @@ -7,7 +7,7 @@ cugraph-pyg .. currentmodule:: cugraph_pyg .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph-pyg/ cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler diff --git a/docs/cugraph/source/api_docs/centrality.rst b/docs/cugraph/source/api_docs/cugraph/centrality.rst similarity index 80% rename from docs/cugraph/source/api_docs/centrality.rst rename to docs/cugraph/source/api_docs/cugraph/centrality.rst index 9b634cb6d..c3b026597 100644 --- a/docs/cugraph/source/api_docs/centrality.rst +++ b/docs/cugraph/source/api_docs/cugraph/centrality.rst @@ -8,7 +8,7 @@ Centrality Betweenness Centrality ---------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.betweenness_centrality cugraph.edge_betweenness_centrality @@ -16,34 +16,34 @@ Betweenness Centrality Katz Centrality --------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.katz_centrality Katz Centrality (MG) -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.centrality.katz_centrality.katz_centrality Degree Centrality ----------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.degree_centrality Eigenvector Centrality ---------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.centrality.eigenvector_centrality Eigenvector Centrality (MG) --------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.centrality.eigenvector_centrality.eigenvector_centrality diff --git a/docs/cugraph/source/api_docs/community.rst b/docs/cugraph/source/api_docs/cugraph/community.rst similarity index 78% rename from docs/cugraph/source/api_docs/community.rst rename to docs/cugraph/source/api_docs/cugraph/community.rst index 94eb1a49c..acbaa086f 100644 --- a/docs/cugraph/source/api_docs/community.rst +++ b/docs/cugraph/source/api_docs/cugraph/community.rst @@ -8,7 +8,7 @@ Community EgoNet ------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.batched_ego_graphs cugraph.ego_graph @@ -16,7 +16,7 @@ EgoNet Ensemble clustering for graphs (ECG) ------------------------------------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.ecg @@ -24,7 +24,7 @@ Ensemble clustering for graphs (ECG) K-Truss ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.k_truss cugraph.ktruss_subgraph @@ -32,7 +32,7 @@ K-Truss Leiden ------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.leiden @@ -40,7 +40,7 @@ Leiden Louvain ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.louvain @@ -48,14 +48,14 @@ Louvain Louvain (MG) ------------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.community.louvain.louvain Spectral Clustering ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.analyzeClustering_edge_cut cugraph.analyzeClustering_modularity @@ -67,7 +67,7 @@ Spectral Clustering Subgraph Extraction ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.subgraph @@ -75,6 +75,6 @@ Subgraph Extraction Triangle Counting ----------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.triangle_count diff --git a/docs/cugraph/source/api_docs/components.rst b/docs/cugraph/source/api_docs/cugraph/components.rst similarity index 86% rename from docs/cugraph/source/api_docs/components.rst rename to docs/cugraph/source/api_docs/cugraph/components.rst index 8616c8742..5835972cd 100644 --- a/docs/cugraph/source/api_docs/components.rst +++ b/docs/cugraph/source/api_docs/cugraph/components.rst @@ -8,7 +8,7 @@ Components Connected Components -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.connected_components cugraph.strongly_connected_components @@ -18,7 +18,7 @@ Connected Components Connected Components (MG) ------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.components.connectivity.weakly_connected_components diff --git a/docs/cugraph/source/api_docs/cores.rst b/docs/cugraph/source/api_docs/cugraph/cores.rst similarity index 73% rename from docs/cugraph/source/api_docs/cores.rst rename to docs/cugraph/source/api_docs/cugraph/cores.rst index 0e48c584b..9d274d1c4 100644 --- a/docs/cugraph/source/api_docs/cores.rst +++ b/docs/cugraph/source/api_docs/cugraph/cores.rst @@ -8,7 +8,7 @@ Cores Core Number ----------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.core_number @@ -16,6 +16,6 @@ Core Number K-Core ------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.k_core diff --git a/docs/cugraph/source/api_docs/cugraph_top.rst b/docs/cugraph/source/api_docs/cugraph/cugraph_top.rst similarity index 100% rename from docs/cugraph/source/api_docs/cugraph_top.rst rename to docs/cugraph/source/api_docs/cugraph/cugraph_top.rst diff --git a/docs/cugraph/source/api_docs/dask-cugraph.rst b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst similarity index 100% rename from docs/cugraph/source/api_docs/dask-cugraph.rst rename to docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst diff --git a/docs/cugraph/source/api_docs/generator.rst b/docs/cugraph/source/api_docs/cugraph/generator.rst similarity index 80% rename from docs/cugraph/source/api_docs/generator.rst rename to docs/cugraph/source/api_docs/cugraph/generator.rst index e89fe25e8..9b4ebbcf7 100644 --- a/docs/cugraph/source/api_docs/generator.rst +++ b/docs/cugraph/source/api_docs/cugraph/generator.rst @@ -8,7 +8,7 @@ Generator RMAT ---- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.generators.rmat diff --git a/docs/cugraph/source/api_docs/graph_implementation.rst b/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst similarity index 94% rename from docs/cugraph/source/api_docs/graph_implementation.rst rename to docs/cugraph/source/api_docs/cugraph/graph_implementation.rst index f6dc38a27..91c16c242 100644 --- a/docs/cugraph/source/api_docs/graph_implementation.rst +++ b/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst @@ -7,7 +7,7 @@ Graph Implementation Graph Implementation -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ view_edge_list delete_edge_list diff --git a/docs/cugraph/source/api_docs/helper_functions.rst b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst similarity index 95% rename from docs/cugraph/source/api_docs/helper_functions.rst rename to docs/cugraph/source/api_docs/cugraph/helper_functions.rst index 08585d264..ec3248bfa 100644 --- a/docs/cugraph/source/api_docs/helper_functions.rst +++ b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst @@ -8,7 +8,7 @@ DASK MG Helper functions Methods ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.comms.comms.initialize cugraph.dask.comms.comms.destroy diff --git a/docs/cugraph/source/api_docs/layout.rst b/docs/cugraph/source/api_docs/cugraph/layout.rst similarity index 80% rename from docs/cugraph/source/api_docs/layout.rst rename to docs/cugraph/source/api_docs/cugraph/layout.rst index b3943e4d3..1c097346b 100644 --- a/docs/cugraph/source/api_docs/layout.rst +++ b/docs/cugraph/source/api_docs/cugraph/layout.rst @@ -8,7 +8,7 @@ Layout Force Atlas 2 ------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.force_atlas2 diff --git a/docs/cugraph/source/api_docs/linear_assignment.rst b/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst similarity index 85% rename from docs/cugraph/source/api_docs/linear_assignment.rst rename to docs/cugraph/source/api_docs/cugraph/linear_assignment.rst index 383bb4d43..dfdf6da96 100644 --- a/docs/cugraph/source/api_docs/linear_assignment.rst +++ b/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst @@ -8,7 +8,7 @@ Linear Assignment Hungarian --------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.hungarian cugraph.dense_hungarian diff --git a/docs/cugraph/source/api_docs/link_analysis.rst b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst similarity index 75% rename from docs/cugraph/source/api_docs/link_analysis.rst rename to docs/cugraph/source/api_docs/cugraph/link_analysis.rst index b84dd2ccb..5f977b477 100644 --- a/docs/cugraph/source/api_docs/link_analysis.rst +++ b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst @@ -8,14 +8,14 @@ Link Analysis HITS ---- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.hits HITS (MG) --------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.link_analysis.hits.hits @@ -23,14 +23,14 @@ HITS (MG) Pagerank -------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.pagerank Pagerank (MG) ------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.link_analysis.pagerank.pagerank diff --git a/docs/cugraph/source/api_docs/link_prediction.rst b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst similarity index 84% rename from docs/cugraph/source/api_docs/link_prediction.rst rename to docs/cugraph/source/api_docs/cugraph/link_prediction.rst index 7c7b34f2f..b2134fba9 100644 --- a/docs/cugraph/source/api_docs/link_prediction.rst +++ b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst @@ -8,7 +8,7 @@ Link Prediction Jaccard Coefficient ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.jaccard cugraph.jaccard_coefficient @@ -18,7 +18,7 @@ Jaccard Coefficient Overlap Coefficient ------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.overlap cugraph.overlap_coefficient @@ -28,7 +28,7 @@ Overlap Coefficient Sorensen Coefficient -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.sorensen cugraph.sorensen_coefficient diff --git a/docs/cugraph/source/api_docs/property_graph.rst b/docs/cugraph/source/api_docs/cugraph/property_graph.rst similarity index 96% rename from docs/cugraph/source/api_docs/property_graph.rst rename to docs/cugraph/source/api_docs/cugraph/property_graph.rst index f3653c085..672aa7dae 100644 --- a/docs/cugraph/source/api_docs/property_graph.rst +++ b/docs/cugraph/source/api_docs/cugraph/property_graph.rst @@ -7,7 +7,7 @@ Property Graph Property Graph ------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ PropertySelection PropertyGraph diff --git a/docs/cugraph/source/api_docs/sampling.rst b/docs/cugraph/source/api_docs/cugraph/sampling.rst similarity index 80% rename from docs/cugraph/source/api_docs/sampling.rst rename to docs/cugraph/source/api_docs/cugraph/sampling.rst index bab8aee6b..52004a5b1 100644 --- a/docs/cugraph/source/api_docs/sampling.rst +++ b/docs/cugraph/source/api_docs/cugraph/sampling.rst @@ -8,7 +8,7 @@ Sampling Random Walks ------------ .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.random_walks cugraph.ego_graph @@ -17,6 +17,6 @@ Random Walks Node2Vec --------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.node2vec diff --git a/docs/cugraph/source/api_docs/cugraph/structure.rst b/docs/cugraph/source/api_docs/cugraph/structure.rst new file mode 100644 index 000000000..5114cb57b --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph/structure.rst @@ -0,0 +1,104 @@ +============= +Graph Classes +============= +.. currentmodule:: cugraph + +Constructors +------------ +.. autosummary:: + :toctree: ../api/cugraph/ + + Graph + MultiGraph + BiPartiteGraph + + + +Adding Data +----------- +.. autosummary:: + :toctree: ../api/cugraph/ + + + Graph.from_cudf_adjlist + Graph.from_cudf_edgelist + Graph.from_dask_cudf_edgelist + Graph.from_pandas_adjacency + Graph.from_pandas_edgelist + Graph.from_numpy_array + Graph.from_numpy_matrix + Graph.add_internal_vertex_id + Graph.add_nodes_from + Graph.clear + Graph.unrenumber + +Checks +------ +.. autosummary:: + :toctree: ../api/cugraph/ + + Graph.has_isolated_vertices + Graph.is_bipartite + Graph.is_directed + Graph.is_multigraph + Graph.is_multipartite + Graph.is_renumbered + Graph.is_weighted + Graph.lookup_internal_vertex_id + Graph.to_directed + Graph.to_undirected + + +Symmetrize +---------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.symmetrize + cugraph.symmetrize_ddf + cugraph.symmetrize_df + + +Conversion from Other Formats +----------------------------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.from_adjlist + cugraph.from_cudf_edgelist + cugraph.from_edgelist + cugraph.from_numpy_array + cugraph.from_numpy_matrix + cugraph.from_pandas_adjacency + cugraph.from_pandas_edgelist + cugraph.to_numpy_array + cugraph.to_numpy_matrix + cugraph.to_pandas_adjacency + cugraph.to_pandas_edgelist + +NumberMap +----------------------------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.structure.NumberMap + cugraph.structure.NumberMap.MultiGPU + cugraph.structure.NumberMap.SingleGPU + cugraph.structure.NumberMap.from_internal_vertex_id + cugraph.structure.NumberMap.to_internal_vertex_id + cugraph.structure.NumberMap.add_internal_vertex_id + cugraph.structure.NumberMap.compute_vals + cugraph.structure.NumberMap.compute_vals_types + cugraph.structure.NumberMap.generate_unused_column_name + cugraph.structure.NumberMap.renumber + cugraph.structure.NumberMap.renumber_and_segment + cugraph.structure.NumberMap.set_renumbered_col_names + cugraph.structure.NumberMap.unrenumber + cugraph.structure.NumberMap.vertex_column_size + +Other +----------------------------- +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.hypergraph diff --git a/docs/cugraph/source/api_docs/traversal.rst b/docs/cugraph/source/api_docs/cugraph/traversal.rst similarity index 82% rename from docs/cugraph/source/api_docs/traversal.rst rename to docs/cugraph/source/api_docs/cugraph/traversal.rst index 675bf89f3..c8fcc6b72 100644 --- a/docs/cugraph/source/api_docs/traversal.rst +++ b/docs/cugraph/source/api_docs/cugraph/traversal.rst @@ -8,7 +8,7 @@ Traversal Breadth-first-search -------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.bfs cugraph.bfs_edges @@ -16,14 +16,14 @@ Breadth-first-search Breadth-first-search (MG) ------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.traversal.bfs.bfs Single-source-shortest-path --------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.filter_unreachable cugraph.shortest_path @@ -33,6 +33,6 @@ Single-source-shortest-path Single-source-shortest-path (MG) -------------------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.dask.traversal.sssp.sssp diff --git a/docs/cugraph/source/api_docs/tree.rst b/docs/cugraph/source/api_docs/cugraph/tree.rst similarity index 83% rename from docs/cugraph/source/api_docs/tree.rst rename to docs/cugraph/source/api_docs/cugraph/tree.rst index 7952c4fa3..38bfbad7d 100644 --- a/docs/cugraph/source/api_docs/tree.rst +++ b/docs/cugraph/source/api_docs/cugraph/tree.rst @@ -8,7 +8,7 @@ Tree Minimum Spanning Tree --------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.tree.minimum_spanning_tree.minimum_spanning_tree @@ -16,7 +16,7 @@ Minimum Spanning Tree Maximum Spanning Tree --------------------- .. autosummary:: - :toctree: api/ + :toctree: ../api/cugraph/ cugraph.tree.minimum_spanning_tree.maximum_spanning_tree diff --git a/docs/cugraph/source/api_docs/c_and_cpp.rst b/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst similarity index 100% rename from docs/cugraph/source/api_docs/c_and_cpp.rst rename to docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst diff --git a/docs/cugraph/source/api_docs/cugraph_dgl.rst b/docs/cugraph/source/api_docs/cugraph_dgl.rst deleted file mode 100644 index 9a8b3ba6c..000000000 --- a/docs/cugraph/source/api_docs/cugraph_dgl.rst +++ /dev/null @@ -1,15 +0,0 @@ -~~~~~~~~~~~~~~~~~~~~~~~~~~ -cugraph-dgl API Refrerence -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -cugraph-dgl - -.. currentmodule:: cugraph_dgl - -Methods -------- -.. autosummary:: - :toctree: api/ - - cugraph_dgl.cugraph_storage_from_heterograph - cugraph_dgl.cugraph_storage.CuGraphStorage diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 0e7598af0..f6307d5ac 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -8,9 +8,9 @@ This page provides a list of all publicly accessible modules, methods and classe :maxdepth: 2 :caption: Python API Documentation - cugraph_top.rst - pylibcugraph.rst - cugraph_dgl.rst - cugraph_pyg.rst - cugraph_service_client.rst - cugraph_service_server.rst + cugraph/cugraph_top.rst + plc/pylibcugraph.rst + cugraph-dgl/cugraph_dgl.rst + cugraph-pyg/cugraph_pyg.rst + service/cugraph_service_client.rst + service/cugraph_service_server.rst diff --git a/docs/cugraph/source/api_docs/pylibcugraph.rst b/docs/cugraph/source/api_docs/plc/pylibcugraph.rst similarity index 94% rename from docs/cugraph/source/api_docs/pylibcugraph.rst rename to docs/cugraph/source/api_docs/plc/pylibcugraph.rst index 7b63cd7dc..7ebdd67e9 100644 --- a/docs/cugraph/source/api_docs/pylibcugraph.rst +++ b/docs/cugraph/source/api_docs/plc/pylibcugraph.rst @@ -9,7 +9,7 @@ pylibcugraph Methods ------- .. autosummary:: - :toctree: api/ + :toctree: ../api/plc/ pylibcugraph.eigenvector_centrality pylibcugraph.katz_centrality diff --git a/docs/cugraph/source/api_docs/cugraph_service_client.rst b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst similarity index 95% rename from docs/cugraph/source/api_docs/cugraph_service_client.rst rename to docs/cugraph/source/api_docs/service/cugraph_service_client.rst index 1ea727bd3..383b31d26 100644 --- a/docs/cugraph/source/api_docs/cugraph_service_client.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst @@ -7,7 +7,7 @@ cugraph-service .. currentmodule:: cugraph-service .. autosummary:: - :toctree: api/ + :toctree: ../api/service/ cugraph_service_client.client.RunAsyncioThread cugraph_service_client.client.run_async @@ -18,6 +18,3 @@ cugraph-service cugraph_service_client.types.UnionWrapper cugraph_service_client.types.ValueWrapper cugraph_service_client.types.GraphVertexEdgeIDWrapper - - - diff --git a/docs/cugraph/source/api_docs/cugraph_service_server.rst b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst similarity index 92% rename from docs/cugraph/source/api_docs/cugraph_service_server.rst rename to docs/cugraph/source/api_docs/service/cugraph_service_server.rst index 5f4c6aa5e..a7e8b5475 100644 --- a/docs/cugraph/source/api_docs/cugraph_service_server.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst @@ -7,7 +7,7 @@ cugraph-service .. currentmodule:: cugraph-service .. autosummary:: - :toctree: api/ + :toctree: ../api/service/ cugraph_service_server.cugraph_handler.call_algo cugraph_service_server.cugraph_handler.ExtensionServerFacade diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst index ab097156c..0d6e28792 100644 --- a/docs/cugraph/source/api_docs/structure.rst +++ b/docs/cugraph/source/api_docs/structure.rst @@ -102,4 +102,3 @@ Other :toctree: api/ cugraph.hypergraph - cugraph.structure.shuffle diff --git a/docs/cugraph/source/basics/index.rst b/docs/cugraph/source/basics/index.rst index 46331fc6c..1875ac22b 100644 --- a/docs/cugraph/source/basics/index.rst +++ b/docs/cugraph/source/basics/index.rst @@ -6,4 +6,7 @@ Basics .. toctree:: :maxdepth: 2 + cugraph_intro cugraph_toc.md + nx_transition + cugraph_cascading diff --git a/docs/cugraph/source/dev_resources/API.rst b/docs/cugraph/source/dev_resources/API.rst new file mode 100644 index 000000000..409e307fd --- /dev/null +++ b/docs/cugraph/source/dev_resources/API.rst @@ -0,0 +1,5 @@ +=== +API +=== + +https://docs.rapids.ai/api/cugraph/nightly/api_docs/index.html \ No newline at end of file diff --git a/docs/cugraph/source/dev_resources/index.rst b/docs/cugraph/source/dev_resources/index.rst new file mode 100644 index 000000000..8568772b3 --- /dev/null +++ b/docs/cugraph/source/dev_resources/index.rst @@ -0,0 +1,10 @@ +=================== +Developer Resources +=================== + + +.. toctree:: + :maxdepth: 3 + + https://docs.rapids.ai/maintainers + API.rst \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/DGL_support.md b/docs/cugraph/source/graph_support/DGL_support.md new file mode 100644 index 000000000..4db2a4d77 --- /dev/null +++ b/docs/cugraph/source/graph_support/DGL_support.md @@ -0,0 +1,55 @@ +# cugraph_dgl + +## Description + +[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.DGLGraph) class, which uses cugraph for storing graph structure and node/edge feature data. Using cugraph as the backend allows DGL users to access a collection of GPU accelerated algorithms for graph analytics, such as centrality computation and community detection. + +## Conda + +Install and update cugraph-dgl and the required dependencies using the command: + +``` +conda install mamba -n base -c conda-forge +mamba install cugraph-dgl -c rapidsai-nightly -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam +``` + +## Build from Source + +### Create the conda development environment +``` +mamba env create -n cugraph_dgl_dev --file conda/cugraph_dgl_dev_11.6.yml +``` + +### Install in editable mode +``` +pip install -e . +``` + +### Run tests + +``` +pytest tests/* +``` + + +## Usage +```diff + ++from cugraph_dgl.convert import cugraph_storage_from_heterograph ++cugraph_g = cugraph_storage_from_heterograph(dgl_g) + +sampler = dgl.dataloading.NeighborSampler( + [15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label']) + +train_dataloader = dgl.dataloading.DataLoader( +- dgl_g, ++ cugraph_g, +train_idx, +sampler, +device=device, +batch_size=1024, +shuffle=True, +drop_last=False, +num_workers=0) +``` + diff --git a/docs/cugraph/source/graph_support/PyG_support.md b/docs/cugraph/source/graph_support/PyG_support.md new file mode 100644 index 000000000..42d4d1c55 --- /dev/null +++ b/docs/cugraph/source/graph_support/PyG_support.md @@ -0,0 +1,3 @@ +# cugraph_pyg + +[RAPIDS](https://rapids.ai) cugraph_pyg enables the ability to use cugraph graph storage and sampling with PyTorch Geometric (PyG). PyG users will have access to cuGraph through the PyG GraphStore, FeatureStore, and Sampler interfaces. \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md new file mode 100644 index 000000000..fa2e7cc95 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms.md @@ -0,0 +1,85 @@ +# List of Supported and Planned Algorithms + +## Supported Graph + +| Type | Description | +| ---------- | ----------------------------------------------------------- | +| Graph | A directed or undirected Graph (use directed={True, False}) | +| Multigraph | A Graph with multiple edges between a vertex pair | +| | | + +ALL Algorithms support Graphs and MultiGraph (directed and undirected) + +--- + +
+ +# Supported Algorithms + +_Italic_ algorithms are planned for future releases. + +Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called MNMG). + +| Category | Algorithm | Scale | Notes | +| ----------------- | ---------------------------------- | ------------------- | --------------------------------------------------------------- | +| Centrality | | | | +| | Katz | __Multi-GPU__ | | +| | Betweenness Centrality | Single-GPU | MG planned for 23.02 | +| | Edge Betweenness Centrality | Single-GPU | MG planned for 23.02 | +| | Eigenvector Centrality | __Multi-GPU__ | | +| | Degree Centrality | __Multi-GPU__ | Python only | +| Community | | | | +| | Leiden | Single-GPU | MG planned for 23.02 | +| | Louvain | __Multi-GPU__ | | +| | Ensemble Clustering for Graphs | Single-GPU | | +| | Spectral-Clustering - Balanced Cut | Single-GPU | | +| | Spectral-Clustering - Modularity | Single-GPU | | +| | Subgraph Extraction | Single-GPU | | +| | Triangle Counting | __Multi-GPU__ | | +| | K-Truss | Single-GPU | | +| Components | | | | +| | Weakly Connected Components | __Multi-GPU__ | | +| | Strongly Connected Components | Single-GPU | | +| Core | | | | +| | K-Core | **Multi-GPU** | | +| | Core Number | **Multi-GPU** | | +| _Flow_ | | | | +| | _MaxFlow_ | --- | | +| _Influence_ | | | | +| | _Influence Maximization_ | --- | | +| Layout | | | | +| | Force Atlas 2 | Single-GPU | | +| Linear Assignment | | | | +| | Hungarian | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) | +| Link Analysis | | | | +| | Pagerank | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | +| | Personal Pagerank | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) | +| | HITS | __Multi-GPU__ | | +| Link Prediction | | | | +| | Jaccard Similarity | **Multi-GPU** | MG as of 22.12
Directed graph only | +| | Weighted Jaccard Similarity | Single-GPU | | +| | Overlap Similarity | **Multi-GPU** | MG as of 22.12 | +| | Sorensen Coefficient | **Multi-GPU** | MG as of 22.12 | +| | _Local Clustering Coefficient_ | --- | | +| Sampling | | | | +| | Uniform Random Walks (RW) | **Multi-GPU** | | +| | *Biased Random Walks (RW)* | --- | | +| | Egonet | **Multi-GPU** | | +| | Node2Vec | Single-GPU | MG planned for 23.02 | +| | Uniform Neighborhood sampling | __Multi-GPU__ | | +| Traversal | | | | +| | Breadth First Search (BFS) | __Multi-GPU__ | with cutoff support``[C++ README](cpp/src/traversal/README.md#BFS) | +| | Single Source Shortest Path (SSSP) | __Multi-GPU__ | [C++ README](cpp/src/traversal/README.md#SSSP) | +| | _ASSP / APSP_ | --- | | +| Tree | | | | +| | Minimum Spanning Tree | Single-GPU | | +| | Maximum Spanning Tree | Single-GPU | | +| Other | | | | +| | Renumbering | __Multi-GPU__ | multiple columns, any data type | +| | Symmetrize | __Multi-GPU__ | | +| | Path Extraction | | Extract paths from BFS/SSP results in parallel | +| | Two Hop Neighbors | __Multi-GPU__ | | +| Data Generator | | | | +| | RMAT | __Multi-GPU__ | | +| | _Barabasi-Albert_ | --- | | +| | | | | diff --git a/docs/cugraph/source/graph_support/compatibility.rst b/docs/cugraph/source/graph_support/compatibility.rst new file mode 100644 index 000000000..be0cc21c2 --- /dev/null +++ b/docs/cugraph/source/graph_support/compatibility.rst @@ -0,0 +1,8 @@ +Compatibility +============= + + +.. toctree:: + :maxdepth: 3 + +Compatibility document coming soon \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/cugraph_service.rst b/docs/cugraph/source/graph_support/cugraph_service.rst new file mode 100644 index 000000000..620b22d1e --- /dev/null +++ b/docs/cugraph/source/graph_support/cugraph_service.rst @@ -0,0 +1,9 @@ +=============== +CuGraph Service +=============== + + +.. toctree:: + :maxdepth: 3 + +Cugraph Service for remote access to a server-based cuGraph(https://github.com/rapidsai/cugraph/blob/branch-23.04/python/cugraph-service/README.md) \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/cugraphops_support.rst b/docs/cugraph/source/graph_support/cugraphops_support.rst new file mode 100644 index 000000000..08ae3b218 --- /dev/null +++ b/docs/cugraph/source/graph_support/cugraphops_support.rst @@ -0,0 +1,10 @@ +================== +cugraphops Support +================== + +cugraph-ops aims to be a low-level, framework agnostic library providing commonly used computational primitives for GNNs and other graph operations. + +.. toctree:: + :maxdepth: 3 + + https://github.com/rapidsai/cugraph-ops/blob/branch-23.04/README.md \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/datastores.rst b/docs/cugraph/source/graph_support/datastores.rst new file mode 100644 index 000000000..82db2c0e1 --- /dev/null +++ b/docs/cugraph/source/graph_support/datastores.rst @@ -0,0 +1,11 @@ +=========== +Data Stores +=========== + + +.. toctree:: + :maxdepth: 3 + + property_graph.md + feature_stores.md + knowledge_stores.md \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/feature_stores.md b/docs/cugraph/source/graph_support/feature_stores.md new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/graph_support/gnn_support.rst b/docs/cugraph/source/graph_support/gnn_support.rst new file mode 100644 index 000000000..2acb2254a --- /dev/null +++ b/docs/cugraph/source/graph_support/gnn_support.rst @@ -0,0 +1,12 @@ +============================ +Graph Neural Network Support +============================ + + +.. toctree:: + :maxdepth: 3 + + PyG_support.md + DGL_support.md + cugraphops_support.rst + wholegraph_support.rst \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/graph_algorithms.rst b/docs/cugraph/source/graph_support/graph_algorithms.rst new file mode 100644 index 000000000..885be03bf --- /dev/null +++ b/docs/cugraph/source/graph_support/graph_algorithms.rst @@ -0,0 +1,9 @@ +========== +Algorithms +========== + + +.. toctree:: + :maxdepth: 3 + + algorithms.md \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/index.rst b/docs/cugraph/source/graph_support/index.rst new file mode 100644 index 000000000..9526fae7e --- /dev/null +++ b/docs/cugraph/source/graph_support/index.rst @@ -0,0 +1,13 @@ +============= +Graph Support +============= + + +.. toctree:: + :maxdepth: 3 + + graph_algorithms.rst + compatibility.rst + gnn_support.rst + datastores.rst + cugraph_service.rst \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/knowledge_stores.md b/docs/cugraph/source/graph_support/knowledge_stores.md new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/graph_support/pg_example.png b/docs/cugraph/source/graph_support/pg_example.png new file mode 100644 index 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zdw##10(K2?e8|p!mNxr#fB9Gq;j6oC;>EjW!x|6@e|;L#`*b5R5v1q@in*u0a(cq( zT)d*(j%&v*$OU!x&tRyjDc5g(RHNbcd}Dd@ryTWTV#MDR0I&J0`~#45Osoc@W)5Y& z(VYH*YYC+o-mZ*1)wD4&9jO&TDXhmH|3Tm{4TvGGHP(r8m{BN zhF)1WcTNYp@T(oPU0xze1U|xM5Y~7@wDqWSw1l-g8UQ-2#us1p5Q7JddFJ9s$NSHb zem-ucID2i89Nne%*&!K`eJI$!%GaIdcwLW>AL9Eo6NgfgcS1(9*V8@MiA zf7`wrp`~RD++Gvc913+%(;IXB(%sHG@>mR}jo*)}pcd(^$9?;(!k^1;aU%mWO+C1O zE&$8l)9uzOXYhREEREX@AEXx1hHZRNd&}-MeV-wGCZq_u#Oeq5lNF%^hZ17YZNLjy z3E-M%Zn$Yx71g55`G6qaY(a^?`H~X$?r%gURgQ=sydSQec{8Gm2Jx12I;(*;;%#y1 zZp~%WN;7_k7D5a<0a1;`S*YQ8p6t-Ch38qszuYE_CbIzv*c;@w{z;&p8)d$skHt zwTY#r4=JvU{qrVy8X75%&c;nVSh1R793#j45JdVl<2OT!00v^A@-aLo!4an!_990qwt+urwq=!t2QXxbc+(EM+Nb9PojO;1>R`YB!Yi)@AD1eE{aHk|1#n4I`e;%_ctq&xDOy$jQ{gKBBI|i{{Q__!K()m YT>1_}|7E?P7l? literal 0 HcmV?d00001 diff --git a/docs/cugraph/source/graph_support/property_graph.md b/docs/cugraph/source/graph_support/property_graph.md new file mode 100644 index 000000000..4610cb7c3 --- /dev/null +++ b/docs/cugraph/source/graph_support/property_graph.md @@ -0,0 +1,54 @@ +

+
+ cuGraph +

+

+
+Property Graph +

+ +Part of [RAPIDS](https://rapids.ai) cuGraph, Property Graph allows all the great benefits of cuGraph to be applied to property-rich datasets stored in a graph structure. A Property Graph is really a data model rather than a type of graph. Within the cuGraph ecosystem, a Property Graph is a meta-graph that can encapsulate and instantiate all the other graph types. That view stems from property graphs being originally created for database systems. Conceptually a Property Graph can be viewed as a property rich structure that can be projected onto any graph types. The Dataversity, has a good definition of [Property Graph](https://www.dataversity.net/what-is-a-property-graph) which contains definitions from a collection of resources. + +Property Graph enables: + +* Multiple edge and node types as seen in the Property Graph API +* Subgraph extractions based on properties and/or edge and node types as seen below. +* Storage of properties either within the graph structure on gpu or using GNN-centric storage extensions on host storage. +* Adding additional properties, nodes and edges into the property graph to store derived data like analytic results. +* Client access managed by a remote server allowing shared access and remote operations using [CuGraph Service](./cugraph_service.md). + +This is an example of using the cuGraph Property Graph in a two stage analysis. + +``` +import cudf +import cugraph +from cugraph.experimental import PropertyGraph + +# Import a built-in dataset +from cugraph.experimental.datasets import karate + +# Read edgelist data into a DataFrame, load into PropertyGraph as edge data. +# Create a graph using the imported Dataset object +graph = cugraph.Graph(directed=False) +G = karate.get_graph(create_using=graph,fetch=True) + +df = G.edgelist.edgelist_df +pG = PropertyGraph() +pG. add_edge_data(df, vertex_col_names=("src", "dst")) + +# Run Louvain to get the partition number for each vertex. +# Set resolution accordingly to identify two primary partitions. +(partition_info, _) = cugraph.louvain(pG.extract_subgraph(create_using=graph), resolution=0.6) + +# Add the partition numbers back to the Property Graph as vertex properties +pG.add_vertex_data(partition_info, vertex_col_name="vertex") + +# Use the partition properties to extract a Graph for each partition. +G0 = pG.extract_subgraph(selection=pG.select_vertices("partition == 0")) +G1 = pG.extract_subgraph(selection=pG. select_vertices("partition == 1")) +# Run pagerank on each graph, print results. +pageranks0 = cugraph.pagerank(G0) +pageranks1 = cugraph.pagerank(G1) +print(pageranks0.sort_values (by="pagerank", ascending=False).head(3)) +print(pageranks1.sort_values (by="pagerank", ascending=False).head(3)) +``` \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/wholegraph_support.rst b/docs/cugraph/source/graph_support/wholegraph_support.rst new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index f1012c0fb..12bc74aea 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -1,53 +1,25 @@ -Welcome to cugraph's documentation! +Welcome to cuGraph's documentation! =================================== RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. -.. image:: images/Stack2.png - :width: 600 - :alt: Alternative text - -Basics -###### -.. toctree:: - :maxdepth: 2 - :caption: Basic cuGraph Information: - -General CuGraph Information - - `CuGraph Intro <./basics/cugraph_intro.md>`_ - - `Blogs and Presentation <./basics/cugraph_blogs.rst>`_ - - `How-to Guides <./basics/coming_soon.md>`_ - - `Performance <./basics/coming_soon.md>`_ - - `References <./basics/cugraph_ref.rst>`_ - -CuGraph Development and Contributing - - `Getting cuGraph Packages <./basics/coming_soon.md>`_ - - `Contributing to cuGraph <./basics/coming_soon.md>`_ - - `CuGraph Development Guide <./basics/coming_soon.md>`_ - -Algorithms - - `Current list of algorithms <./basics/coming_soon.md>`_ - - - -API -### -.. toctree:: - :maxdepth: 2 - - api_docs/index.rst - - .. toctree:: :maxdepth: 2 + :caption: Contents: - api_docs/c_and_cpp.rst + basics/index + installation/index + tutorials/index + graph_support/index + references/index + dev_resources/index + releases/index + api_docs/index Indices and tables ================== * :ref:`genindex` -* :ref:`modindex` * :ref:`search` diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md new file mode 100644 index 000000000..005938f47 --- /dev/null +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -0,0 +1,55 @@ + +# Getting cuGraph Packages + +There are 4 ways to get cuGraph packages: +1. [Quick start with Docker Repo](#docker) +2. [Conda Installation](#conda) +3. [Pip Installation](#pip) +4. [Build from Source](#SOURCE) + +Or checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. + +
+ +## Docker +The RAPIDS Docker containers contain all RAPIDS packages, including all from cuGraph, as well as all required supporting packages. To download a container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph. + +
+ + +## Conda +It is easy to install cuGraph using conda. You can get a minimal conda installation with [Miniconda](https://conda.io/miniconda.html) or get the full installation with [Anaconda](https://www.anaconda.com/download). + +cuGraph Conda packages + * cugraph - this will also import: + * pylibcugraph + * libcugraph + * cugraph-service-client + * cugraph-service-server + * cugraph-dgl + * cugraph-pyg + +Replace the package name in the example below to the one you want to install. + + +Install and update cuGraph using the conda command: + +```bash +conda install -c rapidsai -c numba -c conda-forge -c nvidia cugraph cudatoolkit=11.8 +``` + +Note: This conda installation only applies to Linux and Python versions 3.8/3.10. + +
+ +## PIP +cuGraph, and all of RAPIDS, is available via pip. + +``` +pip install cugraph-cu11 --extra-index-url=https://pypi.ngc.nvidia.com +``` + +pip packages for other packages are being worked and should be available in early 2023 + +
+ diff --git a/docs/cugraph/source/installation/index.rst b/docs/cugraph/source/installation/index.rst new file mode 100644 index 000000000..27858b770 --- /dev/null +++ b/docs/cugraph/source/installation/index.rst @@ -0,0 +1,10 @@ +============ +Installation +============ + + +.. toctree:: + :maxdepth: 3 + + getting_cugraph + source_build diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md new file mode 100644 index 000000000..cd9297abc --- /dev/null +++ b/docs/cugraph/source/installation/source_build.md @@ -0,0 +1,271 @@ +# Building from Source + +The following instructions are for users wishing to build cuGraph from source code. These instructions are tested on supported distributions of Linux, CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for list of supported environments. Other operating systems _might be_ compatible, but are not currently tested. + +The cuGraph package include both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly. + +## Prerequisites + +__Compiler__: +* `gcc` version 9.3+ +* `nvcc` version 11.0+ +* `cmake` version 3.20.1+ + +__CUDA:__ +* CUDA 11.0+ +* NVIDIA driver 450.80.02+ +* Pascal architecture or better + +You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). + + +## Building cuGraph +To install cuGraph from source, ensure the dependencies are met. + + +### Clone Repo and Configure Conda Environment +__GIT clone a version of the repository__ + + ```bash + # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME + export CUGRAPH_HOME=$(pwd)/cugraph + + # Download the cuGraph repo - if you have a folked version, use that path here instead + git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME + + cd $CUGRAPH_HOME + ``` + +__Create the conda development environment__ + +```bash +# create the conda environment (assuming in base `cugraph` directory) + +# for CUDA 11.5 +conda env create --name cugraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml + +# activate the environment +conda activate cugraph_dev + +# to deactivate an environment +conda deactivate +``` + + - The environment can be updated as development includes/changes the dependencies. To do so, run: + + +```bash + +# Where XXX is the CUDA 11 version +conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda11.XXX.yml + +conda activate cugraph_dev +``` + + +### Build and Install Using the `build.sh` Script +Using the `build.sh` script make compiling and installing cuGraph a breeze. To build and install, simply do: + +```bash +$ cd $CUGRAPH_HOME +$ ./build.sh clean +$ ./build.sh libcugraph +$ ./build.sh cugraph +``` + +There are several other options available on the build script for advanced users. +`build.sh` options: +```bash +build.sh [ ...] [ ...] + where is: + clean - remove all existing build artifacts and configuration (start over) + uninstall - uninstall libcugraph and cugraph from a prior build/install (see also -n) + libcugraph - build libcugraph.so and SG test binaries + libcugraph_etl - build libcugraph_etl.so and SG test binaries + pylibcugraph - build the pylibcugraph Python package + cugraph - build the cugraph Python package + cugraph-service - build the cugraph-service_client and cugraph-service_server Python package + cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. + cugraph-dgl - build the cugraph-dgl extensions for DGL + cugraph-pyg - build the cugraph-dgl extensions for PyG + docs - build the docs + and is: + -v - verbose build mode + -g - build for debug + -n - do not install after a successful build + --pydevelop - use setup.py develop instead of install + --allgpuarch - build for all supported GPU architectures + --skip_cpp_tests - do not build the SG test binaries as part of the libcugraph and libcugraph_etl targets + --without_cugraphops - do not build algos that require cugraph-ops + --cmake_default_generator - use the default cmake generator instead of ninja + --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) + -h - print this text + + default action (no args) is to build and install 'libcugraph' then 'libcugraph_etl' then 'pylibcugraph' then 'cugraph' then 'cugraph-service' targets + +examples: +$ ./build.sh clean # remove prior build artifacts (start over) +$ ./build.sh libcugraph -v # compile and install libcugraph with verbose output +$ ./build.sh libcugraph -g # compile and install libcugraph for debug +$ ./build.sh libcugraph -n # compile libcugraph but do not install + +# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4) +$ PARALLEL_LEVEL=4 ./build.sh libcugraph + +Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. +``` + + +## Building each section independently +#### Build and Install the C++/CUDA `libcugraph` Library +CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. + +This project uses cmake for building the C/C++ library. To configure cmake, run: + + ```bash + # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME + export CUGRAPH_HOME=$(pwd)/cugraph + + cd $CUGRAPH_HOME + cd cpp # enter cpp directory + mkdir build # create build directory + cd build # enter the build directory + cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX + + # now build the code + make -j # "-j" starts multiple threads + make install # install the libraries + ``` +The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/cugraph` respectively. + +#### Updating the RAFT branch + +`libcugraph` uses the [RAFT](https://github.com/rapidsai/raft) library and there are times when it might be desirable to build against a different RAFT branch, such as when working on new features that might span both RAFT and cuGraph. + +For local development, the `CPM_raft_SOURCE=` option can be passed to the `cmake` command to enable `libcugraph` to use the local RAFT branch. + +To have CI test a `cugraph` pull request against a different RAFT branch, modify the bottom of the `cpp/cmake/thirdparty/get_raft.cmake` file as follows: + +```cmake +# Change pinned tag and fork here to test a commit in CI +# To use a different RAFT locally, set the CMake variable +# RPM_raft_SOURCE=/path/to/local/raft +find_and_configure_raft(VERSION ${CUGRAPH_MIN_VERSION_raft} + FORK + PINNED_TAG + + # When PINNED_TAG above doesn't match cugraph, + # force local raft clone in build directory + # even if it's already installed. + CLONE_ON_PIN ON + ) +``` + +When the above change is pushed to a pull request, the continuous integration servers will use the specified RAFT branch to run the cuGraph tests. After the changes in the RAFT branch are merged to the release branch, remember to revert the `get_raft.cmake` file back to the original cuGraph branch. + +### Building and installing the Python package + +2) Install the Python packages to your Python path: + +```bash +cd $CUGRAPH_HOME +cd python +cd pylibcugraph +python setup.py build_ext --inplace +python setup.py install # install pylibcugraph +cd ../cugraph +python setup.py build_ext --inplace +python setup.py install # install cugraph python bindings + +``` + + + +## Run tests + +If you already have the datasets: + + ```bash + export RAPIDS_DATASET_ROOT_DIR= + ``` + If you do not have the datasets: + + ```bash + cd $CUGRAPH_HOME/datasets + source get_test_data.sh #This takes about 10 minutes and downloads 1GB data (>5 GB uncompressed) + ``` + +Run either the C++ or the Python tests with datasets + + - **Python tests with datasets** + + + ```bash + pip install python-louvain #some tests require this package to run + cd $CUGRAPH_HOME + cd python + pytest + ``` + - **C++ stand alone tests** + + From the build directory : + + ```bash + # Run the cugraph tests + cd $CUGRAPH_HOME + cd cpp/build + gtests/GDFGRAPH_TEST # this is an executable file + ``` + - **C++ tests with larger datasets** + + + + Run the C++ tests on large input: + + ```bash + cd $CUGRAPH_HOME/cpp/build + #test one particular analytics (eg. pagerank) + gtests/PAGERANK_TEST + #test everything + make test + ``` + +Note: This conda installation only applies to Linux and Python versions 3.8/3.10. + +### (OPTIONAL) Set environment variable on activation + +It is possible to configure the conda environment to set environmental variables on activation. Providing instructions to set PATH to include the CUDA toolkit bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be helpful. + +```bash +cd ~/anaconda3/envs/cugraph_dev + +mkdir -p ./etc/conda/activate.d +mkdir -p ./etc/conda/deactivate.d +touch ./etc/conda/activate.d/env_vars.sh +touch ./etc/conda/deactivate.d/env_vars.sh +``` + +Next the env_vars.sh file needs to be edited + +```bash +vi ./etc/conda/activate.d/env_vars.sh + +#!/bin/bash +export PATH=/usr/local/cuda-11.0/bin:$PATH # or cuda-11.1 if using CUDA 11.1 and cuda-11.2 if using CUDA 11.2, respectively +export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:$LD_LIBRARY_PATH # or cuda-11.1 if using CUDA 11.1 and cuda-11.2 if using CUDA 11.2, respectively +``` + +``` +vi ./etc/conda/deactivate.d/env_vars.sh + +#!/bin/bash +unset PATH +unset LD_LIBRARY_PATH +``` + +## Creating documentation + +Python API documentation can be generated from _./docs/cugraph directory_. Or through using "./build.sh docs" + +## Attribution +Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md diff --git a/docs/cugraph/source/basics/cugraph_ref.rst b/docs/cugraph/source/references/cugraph_ref.rst similarity index 100% rename from docs/cugraph/source/basics/cugraph_ref.rst rename to docs/cugraph/source/references/cugraph_ref.rst diff --git a/docs/cugraph/source/references/datasets.rst b/docs/cugraph/source/references/datasets.rst new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/references/index.rst b/docs/cugraph/source/references/index.rst new file mode 100644 index 000000000..e70df627f --- /dev/null +++ b/docs/cugraph/source/references/index.rst @@ -0,0 +1,9 @@ +========== +References +========== + + +.. toctree:: + :maxdepth: 3 + + cugraph_ref \ No newline at end of file diff --git a/docs/cugraph/source/references/licenses.rst b/docs/cugraph/source/references/licenses.rst new file mode 100644 index 000000000..e69de29bb diff --git a/docs/cugraph/source/releases/index.rst b/docs/cugraph/source/releases/index.rst new file mode 100644 index 000000000..7bd4f6dfa --- /dev/null +++ b/docs/cugraph/source/releases/index.rst @@ -0,0 +1,5 @@ +======== +Releases +======== + +https://github.com/rapidsai/cugraph/blob/main/CHANGELOG.md \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/community_resources.md b/docs/cugraph/source/tutorials/community_resources.md new file mode 100644 index 000000000..572f85a01 --- /dev/null +++ b/docs/cugraph/source/tutorials/community_resources.md @@ -0,0 +1,2 @@ +# Commmunity Resources +[Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib) \ No newline at end of file diff --git a/docs/cugraph/source/basics/cugraph_blogs.rst b/docs/cugraph/source/tutorials/cugraph_blogs.rst similarity index 100% rename from docs/cugraph/source/basics/cugraph_blogs.rst rename to docs/cugraph/source/tutorials/cugraph_blogs.rst diff --git a/docs/cugraph/source/tutorials/cugraph_notebooks.md b/docs/cugraph/source/tutorials/cugraph_notebooks.md new file mode 100644 index 000000000..1624ef10a --- /dev/null +++ b/docs/cugraph/source/tutorials/cugraph_notebooks.md @@ -0,0 +1,77 @@ +# cuGraph Notebooks + +![GraphAnalyticsFigure](img/GraphAnalyticsFigure.jpg) + +This repository contains a collection of Jupyter Notebooks that outline how to run various cuGraph analytics. The notebooks do not address a complete data science problem. The notebooks are simply examples of how to run the graph analytics. Manipulation of the data before or after the graph analytic is not covered here. Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended + +## Summary + +| Folder | Notebook | Description | +| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | +| Centrality | | | +| | [Centrality](algorithms/centrality/Centrality.ipynb) | Compute and compare multiple (currently 5) centrality scores | +| | [Katz](algorithms/centrality/Katz.ipynb) | Compute the Katz centrality for every vertex | +| | [Betweenness](algorithms/centrality/Betweenness.ipynb) | Compute both Edge and Vertex Betweenness centrality | +| | [Degree](algorithms/centrality/Degree.ipynb) | Compute Degree Centraility for each vertex | +| | [Eigenvector](algorithms/centrality/Eigenvector.ipynb) | Compute Eigenvector for every vertex | +| Community | | | +| | [Louvain](algorithms/community/Louvain.ipynb) and Leiden | Identify clusters in a graph using both the Louvain and Leiden algorithms | +| | [ECG](algorithms/community/ECG.ipynb) | Identify clusters in a graph using the Ensemble Clustering for Graph | +| | [K-Truss](algorithms/community/ktruss.ipynb) | Extracts the K-Truss cluster | +| | [Spectral-Clustering](algorithms/community/Spectral-Clustering.ipynb) | Identify clusters in a graph using Spectral Clustering with both
- Balanced Cut
- Modularity Modularity | +| | [Subgraph Extraction](algorithms/community/Subgraph-Extraction.ipynb) | Compute a subgraph of the existing graph including only the specified vertices | +| | [Triangle Counting](algorithms/community/Triangle-Counting.ipynb) | Count the number of Triangle in a graph | +| Components | | | +| | [Connected Components](algorithms/components/ConnectedComponents.ipynb) | Find weakly and strongly connected components in a graph | +| Core | | | +| | [K-Core](algorithms/cores/kcore.ipynb) | Extracts the K-core cluster | +| | [Core Number](algorithms/cores/core-number.ipynb) | Computer the Core number for each vertex in a graph | +Layout | | | +| | [Force-Atlas2](algorithms/layout/Force-Atlas2.ipynb) |A large graph visualization achieved with cuGraph. | +| Link Analysis | | | +| | [Pagerank](algorithms/link_analysis/Pagerank.ipynb) | Compute the PageRank of every vertex in a graph | +| | [HITS](algorithms/link_analysis/HITS.ipynb) | Compute the HITS' Hub and Authority scores for every vertex in a graph | +| Link Prediction | | | +| | [Jaccard Similarity](algorithms/link_prediction/Jaccard-Similarity.ipynb) | Compute vertex similarity score using both:
- Jaccard Similarity
- Weighted Jaccard | +| | [Overlap Similarity](algorithms/link_prediction/Overlap-Similarity.ipynb) | Compute vertex similarity score using the Overlap Coefficient | +| Sampling | +| | [Random Walk](algorithms/sampling/RandomWalk.ipynb) | Compute Random Walk for a various number of seeds and path lengths | +| Traversal | | | +| | [BFS](algorithms/traversal/BFS.ipynb) | Compute the Breadth First Search path from a starting vertex to every other vertex in a graph | +| | [SSSP](algorithms/traversal/SSSP.ipynb) | Single Source Shortest Path - compute the shortest path from a starting vertex to every other vertex | +| Structure | | | +| | [Renumbering](algorithms/structure/Renumber.ipynb)
[Renumbering 2](algorithms/structure/Renumber-2.ipynb) | Renumber the vertex IDs in a graph (two sample notebooks) | +| | [Symmetrize](algorithms/structure/Symmetrize.ipynb) | Symmetrize the edges in a graph | + + +## RAPIDS notebooks +Visit the main RAPIDS [notebooks](https://github.com/rapidsai/notebooks) repo for a listing of all notebooks across all RAPIDS libraries. + +## Requirements + +Running the example in these notebooks requires: + +* The latest version of RAPIDS with cuGraph. + * Download via Docker, Conda (See [__Getting Started__](https://rapids.ai/start.html)) + +* cuGraph is dependent on the latest version of cuDF. Please install all components of RAPIDS +* Python 3.8+ +* A system with an NVIDIA GPU: Pascal architecture or better +* CUDA 11.4+ +* NVIDIA driver 450.51+ + +#### Copyright + +Copyright (c) 2019-2022, NVIDIA CORPORATION. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. + + + + + +![RAPIDS](img/rapids_logo.png) \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/how_to_guides.md b/docs/cugraph/source/tutorials/how_to_guides.md new file mode 100644 index 000000000..42da6ed21 --- /dev/null +++ b/docs/cugraph/source/tutorials/how_to_guides.md @@ -0,0 +1,9 @@ +# How To Guides +- Basic use of cuGraph, on the page +- Property graph with analytic flow +- GNN – model building +- cuGraph Service – client/server setup and use (ucx) +- MNMG Graph – dask, rmm basics and examples +- Pylibcugraph – why and how +- Cugraph for C, C++ users +- Use of nvidia-smi with cugraph \ No newline at end of file diff --git a/docs/cugraph/source/tutorials/index.rst b/docs/cugraph/source/tutorials/index.rst new file mode 100644 index 000000000..cce352509 --- /dev/null +++ b/docs/cugraph/source/tutorials/index.rst @@ -0,0 +1,11 @@ +========= +Tutorials +========= + + +.. toctree:: + :maxdepth: 3 + + how_to_guides.md + cugraph_blogs.rst + community_resources.md From 9dd74c9d47b651a962e101a41e2916979ef2e2e2 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Thu, 13 Apr 2023 12:03:28 -0400 Subject: [PATCH 242/384] Sphinx updates (#3468) Spinx is very picky and looks for indentations and black lines. Also during the build, if there is an error, Spinx just ignores the function that caused the error rather to terminating the build. Some docstrings also used reserved character {* and _} which are used for formating This PR: addresses Spinx Warnings and Errors addresses issues where rst is md is not added to the output any any other issues You will see a lot of additional blank lines added and some text clean-up for readability. Seems like the PR also picked up all the the 23.06 updates - so half the files are not really for this PR Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3468 --- .../source/api_docs/cugraph/centrality.rst | 38 +++++-- .../source/api_docs/cugraph/components.rst | 1 - .../api_docs/cugraph/helper_functions.rst | 1 - .../cugraph/{cugraph_top.rst => index.rst} | 0 .../source/api_docs/cugraph/layout.rst | 1 - .../api_docs/cugraph/linear_assignment.rst | 1 - .../source/api_docs/cugraph/link_analysis.rst | 1 - .../api_docs/cugraph/link_prediction.rst | 1 - .../source/api_docs/cugraph/structure.rst | 7 +- .../source/api_docs/cugraph/traversal.rst | 1 - docs/cugraph/source/api_docs/cugraph/tree.rst | 1 - docs/cugraph/source/api_docs/index.rst | 10 +- .../cugraph/source/api_docs/service/index.rst | 10 ++ docs/cugraph/source/api_docs/structure.rst | 104 ------------------ docs/cugraph/source/basics/index.rst | 1 - docs/cugraph/source/index.rst | 3 + .../source/tutorials/cugraph_notebooks.md | 4 +- 17 files changed, 51 insertions(+), 134 deletions(-) rename docs/cugraph/source/api_docs/cugraph/{cugraph_top.rst => index.rst} (100%) create mode 100644 docs/cugraph/source/api_docs/service/index.rst delete mode 100644 docs/cugraph/source/api_docs/structure.rst diff --git a/docs/cugraph/source/api_docs/cugraph/centrality.rst b/docs/cugraph/source/api_docs/cugraph/centrality.rst index c3b026597..344c95195 100644 --- a/docs/cugraph/source/api_docs/cugraph/centrality.rst +++ b/docs/cugraph/source/api_docs/cugraph/centrality.rst @@ -7,42 +7,64 @@ Centrality Betweenness Centrality ---------------------- +single-GPU +^^^^^^^^^^ .. autosummary:: :toctree: ../api/cugraph/ - cugraph.betweenness_centrality - cugraph.edge_betweenness_centrality + cugraph.centrality.betweenness_centrality + cugraph.centrality.edge_betweenness_centrality + +multi-GPU +^^^^^^^^^^ +.. autosummary:: + :toctree: ../api/cugraph/ + + cugraph.dask.centrality.betweenness_centrality + + Katz Centrality --------------- +single-GPU +^^^^^^^^^^ .. autosummary:: :toctree: ../api/cugraph/ - cugraph.katz_centrality + cugraph.centrality.katz_centrality -Katz Centrality (MG) --------------------- +multi-GPU +^^^^^^^^^^ .. autosummary:: :toctree: ../api/cugraph/ cugraph.dask.centrality.katz_centrality.katz_centrality + Degree Centrality ----------------- +single-GPU +^^^^^^^^^^ .. autosummary:: :toctree: ../api/cugraph/ - cugraph.degree_centrality + cugraph.centrality.degree_centrality + +multi-GPU +^^^^^^^^^^ + Eigenvector Centrality ---------------------- +single-GPU +^^^^^^^^^^ .. autosummary:: :toctree: ../api/cugraph/ cugraph.centrality.eigenvector_centrality -Eigenvector Centrality (MG) ---------------------------- +multi-GPU +^^^^^^^^^^ .. autosummary:: :toctree: ../api/cugraph/ diff --git a/docs/cugraph/source/api_docs/cugraph/components.rst b/docs/cugraph/source/api_docs/cugraph/components.rst index 5835972cd..560aa1f8c 100644 --- a/docs/cugraph/source/api_docs/cugraph/components.rst +++ b/docs/cugraph/source/api_docs/cugraph/components.rst @@ -4,7 +4,6 @@ Components .. currentmodule:: cugraph - Connected Components -------------------- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/cugraph/helper_functions.rst b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst index ec3248bfa..02cb599ae 100644 --- a/docs/cugraph/source/api_docs/cugraph/helper_functions.rst +++ b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst @@ -4,7 +4,6 @@ DASK MG Helper functions .. currentmodule:: cugraph - Methods ------- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/cugraph/cugraph_top.rst b/docs/cugraph/source/api_docs/cugraph/index.rst similarity index 100% rename from docs/cugraph/source/api_docs/cugraph/cugraph_top.rst rename to docs/cugraph/source/api_docs/cugraph/index.rst diff --git a/docs/cugraph/source/api_docs/cugraph/layout.rst b/docs/cugraph/source/api_docs/cugraph/layout.rst index 1c097346b..ed97caf54 100644 --- a/docs/cugraph/source/api_docs/cugraph/layout.rst +++ b/docs/cugraph/source/api_docs/cugraph/layout.rst @@ -4,7 +4,6 @@ Layout .. currentmodule:: cugraph - Force Atlas 2 ------------- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst b/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst index dfdf6da96..e0b0b4d11 100644 --- a/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst +++ b/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst @@ -4,7 +4,6 @@ Linear Assignment .. currentmodule:: cugraph - Hungarian --------- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/cugraph/link_analysis.rst b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst index 5f977b477..48b5ec117 100644 --- a/docs/cugraph/source/api_docs/cugraph/link_analysis.rst +++ b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst @@ -4,7 +4,6 @@ Link Analysis .. currentmodule:: cugraph - HITS ---- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/cugraph/link_prediction.rst b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst index b2134fba9..f05dce6f7 100644 --- a/docs/cugraph/source/api_docs/cugraph/link_prediction.rst +++ b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst @@ -4,7 +4,6 @@ Link Prediction .. currentmodule:: cugraph - Jaccard Coefficient ------------------- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/cugraph/structure.rst b/docs/cugraph/source/api_docs/cugraph/structure.rst index 5114cb57b..6369e1bb3 100644 --- a/docs/cugraph/source/api_docs/cugraph/structure.rst +++ b/docs/cugraph/source/api_docs/cugraph/structure.rst @@ -10,8 +10,6 @@ Constructors Graph MultiGraph - BiPartiteGraph - Adding Data @@ -19,7 +17,6 @@ Adding Data .. autosummary:: :toctree: ../api/cugraph/ - Graph.from_cudf_adjlist Graph.from_cudf_edgelist Graph.from_dask_cudf_edgelist @@ -35,7 +32,7 @@ Adding Data Checks ------ .. autosummary:: - :toctree: ../api/cugraph/ + :toctree: ../api/cugraph/ Graph.has_isolated_vertices Graph.is_bipartite @@ -82,8 +79,6 @@ NumberMap :toctree: ../api/cugraph/ cugraph.structure.NumberMap - cugraph.structure.NumberMap.MultiGPU - cugraph.structure.NumberMap.SingleGPU cugraph.structure.NumberMap.from_internal_vertex_id cugraph.structure.NumberMap.to_internal_vertex_id cugraph.structure.NumberMap.add_internal_vertex_id diff --git a/docs/cugraph/source/api_docs/cugraph/traversal.rst b/docs/cugraph/source/api_docs/cugraph/traversal.rst index c8fcc6b72..31296f3b8 100644 --- a/docs/cugraph/source/api_docs/cugraph/traversal.rst +++ b/docs/cugraph/source/api_docs/cugraph/traversal.rst @@ -4,7 +4,6 @@ Traversal .. currentmodule:: cugraph - Breadth-first-search -------------------- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/cugraph/tree.rst b/docs/cugraph/source/api_docs/cugraph/tree.rst index 38bfbad7d..5ba2242eb 100644 --- a/docs/cugraph/source/api_docs/cugraph/tree.rst +++ b/docs/cugraph/source/api_docs/cugraph/tree.rst @@ -4,7 +4,6 @@ Tree .. currentmodule:: cugraph - Minimum Spanning Tree --------------------- .. autosummary:: diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index f6307d5ac..229bea451 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -1,16 +1,16 @@ Python API reference ==================== -This page provides a list of all publicly accessible modules, methods and classes through -``cugraph.*`` namespace. +This page provides a list of all publicly accessible Python modules, +methods and classes through ``cugraph.*`` namespace. .. toctree:: :maxdepth: 2 :caption: Python API Documentation - cugraph/cugraph_top.rst + cugraph/index.rst plc/pylibcugraph.rst cugraph-dgl/cugraph_dgl.rst cugraph-pyg/cugraph_pyg.rst - service/cugraph_service_client.rst - service/cugraph_service_server.rst + service/index.rst + diff --git a/docs/cugraph/source/api_docs/service/index.rst b/docs/cugraph/source/api_docs/service/index.rst new file mode 100644 index 000000000..a58cf2074 --- /dev/null +++ b/docs/cugraph/source/api_docs/service/index.rst @@ -0,0 +1,10 @@ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +cugraph-service API Reference +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 2 + :caption: cugraph-service API Documentation + + cugraph_service_client + cugraph_service_server \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/structure.rst b/docs/cugraph/source/api_docs/structure.rst deleted file mode 100644 index 0d6e28792..000000000 --- a/docs/cugraph/source/api_docs/structure.rst +++ /dev/null @@ -1,104 +0,0 @@ -============= -Graph Classes -============= -.. currentmodule:: cugraph - -Constructors ------------- -.. autosummary:: - :toctree: api/ - - Graph - MultiGraph - BiPartiteGraph - - - -Adding Data ------------ -.. autosummary:: - :toctree: api/ - - - Graph.from_cudf_adjlist - Graph.from_cudf_edgelist - Graph.from_dask_cudf_edgelist - Graph.from_pandas_adjacency - Graph.from_pandas_edgelist - Graph.from_numpy_array - Graph.from_numpy_matrix - Graph.add_internal_vertex_id - Graph.add_nodes_from - Graph.clear - Graph.unrenumber - -Checks ------- -.. autosummary:: - :toctree: api/ - - Graph.has_isolated_vertices - Graph.is_bipartite - Graph.is_directed - Graph.is_multigraph - Graph.is_multipartite - Graph.is_renumbered - Graph.is_weighted - Graph.lookup_internal_vertex_id - Graph.to_directed - Graph.to_undirected - - -Symmetrize ----------- -.. autosummary:: - :toctree: api/ - - cugraph.symmetrize - cugraph.symmetrize_ddf - cugraph.symmetrize_df - - -Conversion from Other Formats ------------------------------ -.. autosummary:: - :toctree: api/ - - cugraph.from_adjlist - cugraph.from_cudf_edgelist - cugraph.from_edgelist - cugraph.from_numpy_array - cugraph.from_numpy_matrix - cugraph.from_pandas_adjacency - cugraph.from_pandas_edgelist - cugraph.to_numpy_array - cugraph.to_numpy_matrix - cugraph.to_pandas_adjacency - cugraph.to_pandas_edgelist - -NumberMap ------------------------------ -.. autosummary:: - :toctree: api/ - - cugraph.structure.NumberMap - cugraph.structure.NumberMap.MultiGPU - cugraph.structure.NumberMap.SingleGPU - cugraph.structure.NumberMap.from_internal_vertex_id - cugraph.structure.NumberMap.to_internal_vertex_id - cugraph.structure.NumberMap.add_internal_vertex_id - cugraph.structure.NumberMap.compute_vals - cugraph.structure.NumberMap.compute_vals_types - cugraph.structure.NumberMap.generate_unused_column_name - cugraph.structure.NumberMap.renumber - cugraph.structure.NumberMap.renumber_and_segment - cugraph.structure.NumberMap.set_renumbered_col_names - cugraph.structure.NumberMap.unrenumber - cugraph.structure.NumberMap.vertex_column_size - -Other ------------------------------ -.. autosummary:: - :toctree: api/ - - cugraph.hypergraph diff --git a/docs/cugraph/source/basics/index.rst b/docs/cugraph/source/basics/index.rst index 1875ac22b..7bba301b6 100644 --- a/docs/cugraph/source/basics/index.rst +++ b/docs/cugraph/source/basics/index.rst @@ -7,6 +7,5 @@ Basics :maxdepth: 2 cugraph_intro - cugraph_toc.md nx_transition cugraph_cascading diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 12bc74aea..e6731ed51 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -5,6 +5,9 @@ into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. +Note: We are redoing all of our documents, please be patient as we update +the docs and links + .. toctree:: :maxdepth: 2 :caption: Contents: diff --git a/docs/cugraph/source/tutorials/cugraph_notebooks.md b/docs/cugraph/source/tutorials/cugraph_notebooks.md index 1624ef10a..ff9d6b199 100644 --- a/docs/cugraph/source/tutorials/cugraph_notebooks.md +++ b/docs/cugraph/source/tutorials/cugraph_notebooks.md @@ -1,6 +1,6 @@ # cuGraph Notebooks -![GraphAnalyticsFigure](img/GraphAnalyticsFigure.jpg) +![GraphAnalyticsFigure](https://github.com/rapidsai/cugraph/tree/main/img/GraphAnalyticsFigure.jpg) This repository contains a collection of Jupyter Notebooks that outline how to run various cuGraph analytics. The notebooks do not address a complete data science problem. The notebooks are simply examples of how to run the graph analytics. Manipulation of the data before or after the graph analytic is not covered here. Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended @@ -74,4 +74,4 @@ Unless required by applicable law or agreed to in writing, software distributed -![RAPIDS](img/rapids_logo.png) \ No newline at end of file +![RAPIDS](https://github.com/rapidsai/cugraph/tree/main/img/rapids_logo.png) \ No newline at end of file From b360a2fc0df85aa82897c5cd839f7b77762b5c30 Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Mon, 17 Apr 2023 15:15:39 -0500 Subject: [PATCH 243/384] Update clang-format to 16.0.1. (#3485) This PR updates the clang-format version used by pre-commit. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3485 --- .pre-commit-config.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3c2f5fe2c..0f05aedf1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -2,6 +2,7 @@ # # Before first use: `pre-commit install` # To run: `pre-commit run --all-files` +exclude: '^thirdparty' repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.4.0 @@ -32,13 +33,13 @@ repos: additional_dependencies: - flake8==6.0.0 - repo: https://github.com/pre-commit/mirrors-clang-format - rev: v11.1.0 + rev: v16.0.1 hooks: - id: clang-format exclude: | (?x)^( cpp/libcugraph_etl| - cpp/tests/c_api/.* + cpp/tests/c_api ) types_or: [c, c++, cuda] args: ["-fallback-style=none", "-style=file", "-i"] From 3b1e6a93b9acc0bd24b96c19d03f25bd613857fb Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Wed, 19 Apr 2023 13:37:38 -0400 Subject: [PATCH 244/384] adding cugraph-ops (#3488) The goal is to have a unified docs area for all graph work. This PR is the first step in getting cugraph-ops included in the main graph doc area Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Tingyu Wang (https://github.com/tingyu66) - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3488 --- .../cugraph-ops/bipartite_operators.rst | 16 ++++ .../source/api_docs/cugraph-ops/dimenet.rst | 24 ++++++ .../api_docs/cugraph-ops/fg_operators.rst | 83 +++++++++++++++++++ .../api_docs/cugraph-ops/graph_types.rst | 33 ++++++++ .../source/api_docs/cugraph-ops/index.rst | 18 ++++ .../api_docs/cugraph-ops/mfg_operators.rst | 31 +++++++ .../source/api_docs/cugraph-ops/pytorch.rst | 36 ++++++++ .../api_docs/cugraph-ops/static_operators.rst | 16 ++++ docs/cugraph/source/api_docs/index.rst | 4 +- docs/cugraph/source/index.rst | 26 ++++-- 10 files changed, 279 insertions(+), 8 deletions(-) create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/index.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst diff --git a/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst new file mode 100644 index 000000000..e172309fa --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst @@ -0,0 +1,16 @@ +============================= +Operators on Bipartite Graphs +============================= + +.. currentmodule:: pylibcugraphops + +Update Edges: Concatenation or Sum of Edge and Node Features +------------------------------------------------------------ +.. autosummary:: + :toctree: ../api/ops/ + + operators.update_efeat_bipartite_e2e_concat_fwd + operators.update_efeat_bipartite_e2e_concat_bwd + + operators.update_efeat_bipartite_e2e_sum_fwd + operators.update_efeat_bipartite_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst b/docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst new file mode 100644 index 000000000..b709464c7 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst @@ -0,0 +1,24 @@ +================= +Dimenet operators +================= + +.. currentmodule:: pylibcugraphops + +Radial Basis Functions +---------------------- +.. autosummary:: + :toctree: ../api/ops/ + + dimenet.radial_basis_fwd + dimenet.radial_basis_bwd + dimenet.radial_basis_bwd_bwd + +Edge-to-Edge Aggregation +------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + dimenet.agg_edge_to_edge_fwd + dimenet.agg_edge_to_edge_bwd + dimenet.agg_edge_to_edge_bwd2_grad + dimenet.agg_edge_to_edge_bwd2_main diff --git a/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst new file mode 100644 index 000000000..387844f68 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst @@ -0,0 +1,83 @@ +======================== +Operators on Full Graphs +======================== + +.. currentmodule:: pylibcugraphops + +Simple Neighborhood Aggregator (SAGEConv) +----------------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.agg_simple_fg_n2n_fwd + operators.agg_simple_fg_n2n_bwd + operators.agg_simple_fg_e2n_fwd + operators.agg_simple_fg_e2n_bwd + operators.agg_simple_fg_n2n_e2n_fwd + operators.agg_simple_fg_n2n_e2n_bwd + + operators.agg_concat_fg_n2n_fwd + operators.agg_concat_fg_n2n_bwd + operators.agg_concat_fg_e2n_fwd + operators.agg_concat_fg_e2n_bwd + operators.agg_concat_fg_n2n_e2n_fwd + operators.agg_concat_fg_n2n_e2n_bwd + +Weighted Neighborhood Aggregation +--------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.agg_weighted_fg_n2n_fwd + operators.agg_weighted_fg_n2n_bwd + operators.agg_concat_weighted_fg_n2n_fwd + operators.agg_concat_weighted_fg_n2n_bwd + +Heterogenous Aggregator using Basis Decomposition (RGCNConv) +------------------------------------------------------------ +.. autosummary:: + :toctree: ../api/ops/ + + operators.agg_hg_basis_fg_n2n_post_fwd + operators.agg_hg_basis_fg_n2n_post_bwd + +Graph Attention (GATConv/GATv2Conv) +----------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.mha_gat_fg_n2n_fwd + operators.mha_gat_fg_n2n_bwd + operators.mha_gat_fg_n2n_efeat_fwd + operators.mha_gat_fg_n2n_efeat_bwd + + operators.mha_gat_v2_fg_n2n_fwd + operators.mha_gat_v2_fg_n2n_bwd + operators.mha_gat_v2_fg_n2n_efeat_fwd + operators.mha_gat_v2_fg_n2n_efeat_bwd + +Transformer-like Graph Attention (TransformerConv) +-------------------------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.mha_gat_v2_fg_n2n_fwd + operators.mha_gat_v2_fg_n2n_bwd + operators.mha_gat_v2_fg_n2n_efeat_fwd + operators.mha_gat_v2_fg_n2n_efeat_bwd + +Directional Message-Passing (DMPNN) +----------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.agg_dmpnn_fg_e2e_fwd + operators.agg_dmpnn_fg_e2e_bwd + +Graph Pooling +------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.pool_fg_n2s_fwd + operators.pool_fg_n2s_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst b/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst new file mode 100644 index 000000000..9289ce53e --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst @@ -0,0 +1,33 @@ +=========== +Graph types +=========== + +.. currentmodule:: pylibcugraphops + +Message-Flow Graph (MFG) +------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + make_mfg_csr + +Heterogenous MFG +---------------- +.. autosummary:: + :toctree: ../api/ops/ + + make_mfg_csr_hg + +"Full" Graph (FG) +----------------- +.. autosummary:: + :toctree: ../api/ops/ + + make_fg_csr + +Heterogenous FG +--------------- +.. autosummary:: + :toctree: ../api/ops/ + + make_fg_csr_hg diff --git a/docs/cugraph/source/api_docs/cugraph-ops/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/index.rst new file mode 100644 index 000000000..e2338dc18 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/index.rst @@ -0,0 +1,18 @@ +========================= +cugraph-ops API reference +========================= + +This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace. + +.. toctree:: + :maxdepth: 2 + :caption: API Documentation + + graph_types + pytorch + mfg_operators + bipartite_operators + static_operators + fg_operators + dimenet + pytorch diff --git a/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst new file mode 100644 index 000000000..f3dd1faa2 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst @@ -0,0 +1,31 @@ +================================ +Operators on Message-Flow Graphs +================================ + +.. currentmodule:: pylibcugraphops + +Simple Neighborhood Aggregator (SAGEConv) +----------------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.agg_simple_mfg_n2n_fwd + operators.agg_simple_mfg_n2n_bwd + operators.agg_concat_mfg_n2n_fwd + operators.agg_concat_mfg_n2n_bwd + +Graph Attention (GATConv) +------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + operators.mha_gat_mfg_n2n_fwd + operators.mha_gat_mfg_n2n_bwd + +Heterogenous Aggregator using Basis Decomposition (RGCNConv) +------------------------------------------------------------ +.. autosummary:: + :toctree: ../api/ops/ + + operators.agg_hg_basis_mfg_n2n_post_fwd + operators.agg_hg_basis_mfg_n2n_post_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst b/docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst new file mode 100644 index 000000000..83800fbc5 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst @@ -0,0 +1,36 @@ +========================== +PyTorch Autograd Wrappers +========================== + +.. currentmodule:: pylibcugraphops + +Simple Neighborhood Aggregator (SAGEConv) +----------------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + pytorch.operators.agg_concat_n2n + +Graph Attention (GATConv/GATv2Conv) +----------------------------------- +.. autosummary:: + :toctree: ../api/ops/ + + pytorch.operators.mha_gat_n2n + pytorch.operators.mha_gat_v2_n2n + +Heterogenous Aggregator using Basis Decomposition (RGCNConv) +------------------------------------------------------------ +.. autosummary:: + :toctree: ../api/ops/ + + pytorch.operators.agg_hg_basis_n2n_post + + +Update Edges: Concatenation or Sum of Edge and Node Features +------------------------------------------------------------ +.. autosummary:: + :toctree: ../api/ops/ + + pytorch.operators.update_efeat_bipartite_e2e + pytorch.operators.update_efeat_static_e2e diff --git a/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst new file mode 100644 index 000000000..f3ecc068f --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst @@ -0,0 +1,16 @@ +========================== +Operators on Static Graphs +========================== + +.. currentmodule:: pylibcugraphops + +Update Edges: Concatenation or Sum of Edge and Node Features +------------------------------------------------------------ +.. autosummary:: + :toctree: ../api/ops/ + + operators.update_efeat_static_e2e_concat_fwd + operators.update_efeat_static_e2e_concat_bwd + + operators.update_efeat_static_e2e_sum_fwd + operators.update_efeat_static_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 229bea451..45f7210f5 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -1,8 +1,7 @@ Python API reference ==================== -This page provides a list of all publicly accessible Python modules, -methods and classes through ``cugraph.*`` namespace. +This page provides a list of all publicly accessible Python modules with in the Graph collection .. toctree:: :maxdepth: 2 @@ -13,4 +12,5 @@ methods and classes through ``cugraph.*`` namespace. cugraph-dgl/cugraph_dgl.rst cugraph-pyg/cugraph_pyg.rst service/index.rst + cugraph-ops/index.rst diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index e6731ed51..f5fdadacf 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -1,13 +1,27 @@ -Welcome to cuGraph's documentation! -=================================== -RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates -into the RAPIDS data science ecosystem and allows the data scientist to -easily call graph algorithms using data stored in GPU DataFrames, -NetworkX Graphs, or even CuPy or SciPy sparse Matrices. +Welcome to RAPIDS Graph documentation +===================================== +RAPIDS Graph covers a range of libraries and packages, that includes: + +__cugraph__: focuses on GPU accelerated graph algorithms. + +__cugraph-ops__: focuses on GNN aggregators and operators. + +__cugraph-pg__: focuses on providing support for Property Graph (and future Knowledge Graph). + +__cugraph-service__: focuses on wrapping graph features as a service. + +__cugraph-pyg__: focuses on exposing features under the Graph umbrella into the PyG framework. + +__cugraph-dgl__: focuses on exposing features under the Graph umbrella into the DGL framework + +__WholeGraph__: focuses on GNN training using shared memory. + +cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. Note: We are redoing all of our documents, please be patient as we update the docs and links + .. toctree:: :maxdepth: 2 :caption: Contents: From e16ce4b251db9218c9cbd7e34eeb1bb46813d8b9 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Fri, 21 Apr 2023 09:14:34 -0400 Subject: [PATCH 245/384] REL v23.04.01 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 4d11d4ef9..11225100e 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -78,7 +78,7 @@ # The short X.Y version. version = '23.04' # The full version, including alpha/beta/rc tags. -release = '23.04.00' +release = '23.04.01' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From a5663c9eec618225ce037687ec8299ec769e0432 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Fri, 28 Apr 2023 11:07:46 -0400 Subject: [PATCH 246/384] update docs (#3510) Addressing a number of doc issues closes #2500 closes #3141 closes #3213 Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3510 --- .../cugraph/{generator.rst => generators.rst} | 6 +- .../cugraph/source/api_docs/cugraph/index.rst | 2 +- .../source/api_docs/cugraph_c/c_and_cpp.rst | 2 +- docs/cugraph/source/basics/coming_soon.md | 6 - docs/cugraph/source/basics/cugraph_toc.md | 13 -- .../source/graph_support/datastores.rst | 8 +- .../source/graph_support/feature_stores.md | 3 + .../source/graph_support/gnn_support.rst | 4 +- .../source/graph_support/graph_algorithms.rst | 1 - .../source/graph_support/knowledge_stores.md | 3 + .../source/graph_support/property_graph.md | 12 +- .../graph_support/wholegraph_support.md | 4 + .../graph_support/wholegraph_support.rst | 0 docs/cugraph/source/images/pg_example.png | Bin 0 -> 242398 bytes docs/cugraph/source/index.rst | 23 +- .../source/installation/getting_cugraph.md | 5 +- .../source/installation/source_build.md | 2 +- .../{cugraph_ref.rst => cugraph_ref.md} | 81 ++----- docs/cugraph/source/references/datasets.md | 21 ++ docs/cugraph/source/references/datasets.rst | 0 docs/cugraph/source/references/index.rst | 4 +- docs/cugraph/source/references/licenses.md | 208 ++++++++++++++++++ docs/cugraph/source/references/licenses.rst | 0 docs/cugraph/source/tutorials/index.rst | 1 + 24 files changed, 285 insertions(+), 124 deletions(-) rename docs/cugraph/source/api_docs/cugraph/{generator.rst => generators.rst} (77%) delete mode 100644 docs/cugraph/source/basics/coming_soon.md delete mode 100644 docs/cugraph/source/basics/cugraph_toc.md create mode 100644 docs/cugraph/source/graph_support/wholegraph_support.md delete mode 100644 docs/cugraph/source/graph_support/wholegraph_support.rst create mode 100644 docs/cugraph/source/images/pg_example.png rename docs/cugraph/source/references/{cugraph_ref.rst => cugraph_ref.md} (55%) create mode 100644 docs/cugraph/source/references/datasets.md delete mode 100644 docs/cugraph/source/references/datasets.rst create mode 100644 docs/cugraph/source/references/licenses.md delete mode 100644 docs/cugraph/source/references/licenses.rst diff --git a/docs/cugraph/source/api_docs/cugraph/generator.rst b/docs/cugraph/source/api_docs/cugraph/generators.rst similarity index 77% rename from docs/cugraph/source/api_docs/cugraph/generator.rst rename to docs/cugraph/source/api_docs/cugraph/generators.rst index 9b4ebbcf7..4f93d943e 100644 --- a/docs/cugraph/source/api_docs/cugraph/generator.rst +++ b/docs/cugraph/source/api_docs/cugraph/generators.rst @@ -1,6 +1,6 @@ -========= -Generator -========= +========== +Generators +========== .. currentmodule:: cugraph diff --git a/docs/cugraph/source/api_docs/cugraph/index.rst b/docs/cugraph/source/api_docs/cugraph/index.rst index 1b920a841..20b63d50a 100644 --- a/docs/cugraph/source/api_docs/cugraph/index.rst +++ b/docs/cugraph/source/api_docs/cugraph/index.rst @@ -20,6 +20,6 @@ cugraph API Reference sampling traversal tree - generator + generators helper_functions dask-cugraph.rst diff --git a/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst b/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst index a3a88e8f6..34b812785 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst @@ -1,4 +1,4 @@ CuGraph C and C++ API Links =========================== -`CuGraph C and C++ API <../basics/coming_soon.md>`_ \ No newline at end of file +coming soon - see https://docs.rapids.ai/api/libcugraph/nightly/ \ No newline at end of file diff --git a/docs/cugraph/source/basics/coming_soon.md b/docs/cugraph/source/basics/coming_soon.md deleted file mode 100644 index 6cf15a954..000000000 --- a/docs/cugraph/source/basics/coming_soon.md +++ /dev/null @@ -1,6 +0,0 @@ - - - - -This part of the cuGraph documentation is currently under development. \ No newline at end of file diff --git a/docs/cugraph/source/basics/cugraph_toc.md b/docs/cugraph/source/basics/cugraph_toc.md deleted file mode 100644 index 8b71ad378..000000000 --- a/docs/cugraph/source/basics/cugraph_toc.md +++ /dev/null @@ -1,13 +0,0 @@ - -- General - - [Basic CuGraph Information]() - - [Latest News]() - - [Blogs and Presentation]() - - [How-to Guides]() - - [Performance]() -- CuGraph Development and Contributing - - [Getting cuGraph Packages]() - - [Contributing to cuGraph]() - - [CuGraph Development Guide]() -- Algorithms - - [Current list of algorithms]() diff --git a/docs/cugraph/source/graph_support/datastores.rst b/docs/cugraph/source/graph_support/datastores.rst index 82db2c0e1..f92110077 100644 --- a/docs/cugraph/source/graph_support/datastores.rst +++ b/docs/cugraph/source/graph_support/datastores.rst @@ -1,11 +1,9 @@ -=========== Data Stores =========== - - .. toctree:: :maxdepth: 3 - + property_graph.md + knowledge_stores.md feature_stores.md - knowledge_stores.md \ No newline at end of file + diff --git a/docs/cugraph/source/graph_support/feature_stores.md b/docs/cugraph/source/graph_support/feature_stores.md index e69de29bb..f40cab72e 100644 --- a/docs/cugraph/source/graph_support/feature_stores.md +++ b/docs/cugraph/source/graph_support/feature_stores.md @@ -0,0 +1,3 @@ +# Feature Store + +Coming Soon \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/gnn_support.rst b/docs/cugraph/source/graph_support/gnn_support.rst index 2acb2254a..3c92dc360 100644 --- a/docs/cugraph/source/graph_support/gnn_support.rst +++ b/docs/cugraph/source/graph_support/gnn_support.rst @@ -4,9 +4,9 @@ Graph Neural Network Support .. toctree:: - :maxdepth: 3 + :maxdepth: 2 PyG_support.md DGL_support.md cugraphops_support.rst - wholegraph_support.rst \ No newline at end of file + wholegraph_support.md \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/graph_algorithms.rst b/docs/cugraph/source/graph_support/graph_algorithms.rst index 885be03bf..a8ba01aa9 100644 --- a/docs/cugraph/source/graph_support/graph_algorithms.rst +++ b/docs/cugraph/source/graph_support/graph_algorithms.rst @@ -2,7 +2,6 @@ Algorithms ========== - .. toctree:: :maxdepth: 3 diff --git a/docs/cugraph/source/graph_support/knowledge_stores.md b/docs/cugraph/source/graph_support/knowledge_stores.md index e69de29bb..4d6028a59 100644 --- a/docs/cugraph/source/graph_support/knowledge_stores.md +++ b/docs/cugraph/source/graph_support/knowledge_stores.md @@ -0,0 +1,3 @@ +# Knowledge Store + +Coming Soon \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/property_graph.md b/docs/cugraph/source/graph_support/property_graph.md index 4610cb7c3..614910b79 100644 --- a/docs/cugraph/source/graph_support/property_graph.md +++ b/docs/cugraph/source/graph_support/property_graph.md @@ -1,14 +1,10 @@ -

-
- cuGraph -

-

-
-Property Graph -

+# Property Graph Part of [RAPIDS](https://rapids.ai) cuGraph, Property Graph allows all the great benefits of cuGraph to be applied to property-rich datasets stored in a graph structure. A Property Graph is really a data model rather than a type of graph. Within the cuGraph ecosystem, a Property Graph is a meta-graph that can encapsulate and instantiate all the other graph types. That view stems from property graphs being originally created for database systems. Conceptually a Property Graph can be viewed as a property rich structure that can be projected onto any graph types. The Dataversity, has a good definition of [Property Graph](https://www.dataversity.net/what-is-a-property-graph) which contains definitions from a collection of resources. +![Sample Property Graph](../images/pg_example.png) + + Property Graph enables: * Multiple edge and node types as seen in the Property Graph API diff --git a/docs/cugraph/source/graph_support/wholegraph_support.md b/docs/cugraph/source/graph_support/wholegraph_support.md new file mode 100644 index 000000000..fa26700a6 --- /dev/null +++ b/docs/cugraph/source/graph_support/wholegraph_support.md @@ -0,0 +1,4 @@ +# WholeGraph + +[RAPIDS](https://rapids.ai) [WholeGraph](https://github.com/rapidsai/wholegraph) is designed to help train large-scale Graph Neural Networks(GNN). +Please see [WholeGraph Introduction](https://github.com/rapidsai/wholegraph/blob/main/README.md) for more details \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/wholegraph_support.rst 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zdw##10(K2?e8|p!mNxr#fB9Gq;j6oC;>EjW!x|6@e|;L#`*b5R5v1q@in*u0a(cq( zT)d*(j%&v*$OU!x&tRyjDc5g(RHNbcd}Dd@ryTWTV#MDR0I&J0`~#45Osoc@W)5Y& z(VYH*YYC+o-mZ*1)wD4&9jO&TDXhmH|3Tm{4TvGGHP(r8m{BN zhF)1WcTNYp@T(oPU0xze1U|xM5Y~7@wDqWSw1l-g8UQ-2#us1p5Q7JddFJ9s$NSHb zem-ucID2i89Nne%*&!K`eJI$!%GaIdcwLW>AL9Eo6NgfgcS1(9*V8@MiA zf7`wrp`~RD++Gvc913+%(;IXB(%sHG@>mR}jo*)}pcd(^$9?;(!k^1;aU%mWO+C1O zE&$8l)9uzOXYhREEREX@AEXx1hHZRNd&}-MeV-wGCZq_u#Oeq5lNF%^hZ17YZNLjy z3E-M%Zn$Yx71g55`G6qaY(a^?`H~X$?r%gURgQ=sydSQec{8Gm2Jx12I;(*;;%#y1 zZp~%WN;7_k7D5a<0a1;`S*YQ8p6t-Ch38qszuYE_CbIzv*c;@w{z;&p8)d$skHt zwTY#r4=JvU{qrVy8X75%&c;nVSh1R793#j45JdVl<2OT!00v^A@-aLo!4an!_990qwt+urwq=!t2QXxbc+(EM+Nb9PojO;1>R`YB!Yi)@AD1eE{aHk|1#n4I`e;%_ctq&xDOy$jQ{gKBBI|i{{Q__!K()m YT>1_}|7E?P7l? literal 0 HcmV?d00001 diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index f5fdadacf..469066920 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -1,20 +1,13 @@ Welcome to RAPIDS Graph documentation ===================================== -RAPIDS Graph covers a range of libraries and packages, that includes: - -__cugraph__: focuses on GPU accelerated graph algorithms. - -__cugraph-ops__: focuses on GNN aggregators and operators. - -__cugraph-pg__: focuses on providing support for Property Graph (and future Knowledge Graph). - -__cugraph-service__: focuses on wrapping graph features as a service. - -__cugraph-pyg__: focuses on exposing features under the Graph umbrella into the PyG framework. - -__cugraph-dgl__: focuses on exposing features under the Graph umbrella into the DGL framework - -__WholeGraph__: focuses on GNN training using shared memory. +RAPIDS Graph covers a range of graph libraries and packages, that includes: + +* cugraph: GPU-accelerated graph algorithms +* cugraph-ops: GPU-accelerated GNN aggregators and operators +* cugraph-service: multi-user, remote GPU-accelerated graph algorithm service +* cugraph-pyg: GPU-accelerated extensions for use with the PyG framework +* cugraph-dgl: GPU-accelerated extensions for use with the DGL framework +* wholegraph: shared memory-based GPU-accelerated GNN training cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index 005938f47..374a529d6 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -1,13 +1,16 @@ # Getting cuGraph Packages +Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) +and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. + + There are 4 ways to get cuGraph packages: 1. [Quick start with Docker Repo](#docker) 2. [Conda Installation](#conda) 3. [Pip Installation](#pip) 4. [Build from Source](#SOURCE) -Or checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options.
diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md index cd9297abc..9a93ed6c5 100644 --- a/docs/cugraph/source/installation/source_build.md +++ b/docs/cugraph/source/installation/source_build.md @@ -41,7 +41,7 @@ __Create the conda development environment__ ```bash # create the conda environment (assuming in base `cugraph` directory) -# for CUDA 11.5 +# for CUDA 11.x conda env create --name cugraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml # activate the environment diff --git a/docs/cugraph/source/references/cugraph_ref.rst b/docs/cugraph/source/references/cugraph_ref.md similarity index 55% rename from docs/cugraph/source/references/cugraph_ref.rst rename to docs/cugraph/source/references/cugraph_ref.md index f099752c5..a646d6da2 100644 --- a/docs/cugraph/source/references/cugraph_ref.rst +++ b/docs/cugraph/source/references/cugraph_ref.md @@ -1,97 +1,46 @@ -########## -References -########## +# References -************ -Architecture -************ +## Architecture 2-D Data Partitioning -- Kang, S., Fender, A., Eaton, J., & Rees, B. (2020, September) *Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters*. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE. +- Kang, S., Fender, A., Eaton, J., & Rees, B. (2020, September) *Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters*. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE. +- S. Kang, J. Nke and B. Rees, (2022 September) *Analyzing Multi-trillion Edge Graphs on Large GPU Clusters: A Case Study with PageRank*, In 2022 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2022, pp. 1-7, doi: 10.1109/HPEC55821.2022.9926341. -| +

-| - -********** -Algorithms -********** - -Betweenness Centrality +## Algorithms +### Betweenness Centrality - Brandes, U. (2001). *A faster algorithm for betweenness centrality*. Journal of mathematical sociology, 25(2), 163-177. - Brandes, U. (2008). *On variants of shortest-path betweenness centrality and their generic computation*. Social Networks, 30(2), 136-145. - McLaughlin, A., & Bader, D. A. (2018). *Accelerating GPU betweenness centrality*. Communications of the ACM, 61(8), 85-92. - -Katz - +### Katz - Katz, L. (1953). *A new status index derived from sociometric analysis*. Psychometrika, 18(1), 39-43. - Foster, K.C., Muth, S.Q., Potterat, J.J. et al. *A faster Katz status score algorithm*. Computational & Mathematical Organization Theory (2001) 7: 275. - - -K-Truss - +### K-Truss - J. Cohen, *Trusses: Cohesive subgraphs for social network analysis* National security agency technical report, 2008 - O. Green, J. Fox, E. Kim, F. Busato, et al. *Quickly Finding a Truss in a Haystack* IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038 - O. Green, P. Yalamanchili, L.M. Munguia, *Fast Triangle Counting on GPU* Irregular Applications: Architectures and Algorithms (IA3), 2014 -Hungarian Algorithm - +### Hungarian Algorithm - Date, K., & Nagi, R. (2016). GPU-accelerated Hungarian algorithms for the Linear Assignment Problem. Parallel Computing, 57, 52-72. -Leiden - +### Leiden - Traag, V. A., Waltman, L., & Van Eck, N. J. (2019). *From Louvain to Leiden: guaranteeing well-connected communities*. Scientific reports, 9(1), 1-12. -Louvain - +### Louvain - VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre. *Fast unfolding of community hierarchies in large networks*. J Stat Mech P10008 (2008) +

- - -| - -| - -************* -Other Papers -************* +## Other Papers - Hricik, T., Bader, D., & Green, O. (2020, September). *Using RAPIDS AI to Accelerate Graph Data Science Workflows*. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE. -| - -| - -********** -Data Sets -********** - -karate - - W. W. Zachary, *An information flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). -dolphins - - D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, - *The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations*, - Behavioral Ecology and Sociobiology 54, 396-405 (2003). -netscience - - M. E. J. Newman, - *Finding community structure in networks using the eigenvectors of matrices*, - Preprint physics/0605087 (2006). -email-Eu-core - - Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. - *Local Higher-order Graph Clustering.* - In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. - - J. Leskovec, J. Kleinberg and C. Faloutsos. - *Graph Evolution: Densification and Shrinking Diameters*. - ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf -polbooks - - V. Krebs, unpublished, http://www.orgnet.com/. - - +

diff --git a/docs/cugraph/source/references/datasets.md b/docs/cugraph/source/references/datasets.md new file mode 100644 index 000000000..3d45dec18 --- /dev/null +++ b/docs/cugraph/source/references/datasets.md @@ -0,0 +1,21 @@ +# Data Sets + +karate + - W. W. Zachary, *An information flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). +dolphins + - D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, + *The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations*, + Behavioral Ecology and Sociobiology 54, 396-405 (2003). +netscience + - M. E. J. Newman, + *Finding community structure in networks using the eigenvectors of matrices*, + Preprint physics/0605087 (2006). +email-Eu-core + - Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. + *Local Higher-order Graph Clustering.* + In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. + - J. Leskovec, J. Kleinberg and C. Faloutsos. + *Graph Evolution: Densification and Shrinking Diameters*. + ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf +polbooks + - V. Krebs, unpublished, http://www.orgnet.com/. \ No newline at end of file diff --git a/docs/cugraph/source/references/datasets.rst b/docs/cugraph/source/references/datasets.rst deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/cugraph/source/references/index.rst b/docs/cugraph/source/references/index.rst index e70df627f..d7a173a83 100644 --- a/docs/cugraph/source/references/index.rst +++ b/docs/cugraph/source/references/index.rst @@ -6,4 +6,6 @@ References .. toctree:: :maxdepth: 3 - cugraph_ref \ No newline at end of file + cugraph_ref.md + datasets.md + licenses.md diff --git a/docs/cugraph/source/references/licenses.md b/docs/cugraph/source/references/licenses.md new file mode 100644 index 000000000..dfc950023 --- /dev/null +++ b/docs/cugraph/source/references/licenses.md @@ -0,0 +1,208 @@ +# License + +Most of the Graph code is open-sourced and developed under the Apache 2.0 licnese. +The cugraph-ops code is closed sourced and developed under a NVIDIA copyright + + + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2018 NVIDIA CORPORATION + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/docs/cugraph/source/references/licenses.rst b/docs/cugraph/source/references/licenses.rst deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/cugraph/source/tutorials/index.rst b/docs/cugraph/source/tutorials/index.rst index cce352509..525fbe4f5 100644 --- a/docs/cugraph/source/tutorials/index.rst +++ b/docs/cugraph/source/tutorials/index.rst @@ -9,3 +9,4 @@ Tutorials how_to_guides.md cugraph_blogs.rst community_resources.md + cugraph_notebooks.md From efe27b12629f745e311c1fd3df48b2c21a3ebd72 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Thu, 4 May 2023 12:26:11 -0400 Subject: [PATCH 247/384] Fixed notebook links in algorithm and cugraph notebook pages (#3515) resolves #3426 Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3515 --- .../source/graph_support/algorithms.md | 56 +++++++++---------- .../source/tutorials/cugraph_notebooks.md | 48 ++++++++-------- 2 files changed, 52 insertions(+), 52 deletions(-) diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md index fa2e7cc95..60243fefb 100644 --- a/docs/cugraph/source/graph_support/algorithms.md +++ b/docs/cugraph/source/graph_support/algorithms.md @@ -22,47 +22,47 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | Category | Algorithm | Scale | Notes | | ----------------- | ---------------------------------- | ------------------- | --------------------------------------------------------------- | -| Centrality | | | | -| | Katz | __Multi-GPU__ | | -| | Betweenness Centrality | Single-GPU | MG planned for 23.02 | -| | Edge Betweenness Centrality | Single-GPU | MG planned for 23.02 | -| | Eigenvector Centrality | __Multi-GPU__ | | -| | Degree Centrality | __Multi-GPU__ | Python only | +| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | | | | +| | [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) | __Multi-GPU__ | | +| | [Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | Single-GPU | MG planned for 23.02 | +| | [Edge Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | Single-GPU | MG planned for 23.02 | +| | [Eigenvector Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Eigenvector.ipynb) | __Multi-GPU__ | | +| | [Degree Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Degree.ipynb) | __Multi-GPU__ | Python only | | Community | | | | -| | Leiden | Single-GPU | MG planned for 23.02 | -| | Louvain | __Multi-GPU__ | | -| | Ensemble Clustering for Graphs | Single-GPU | | -| | Spectral-Clustering - Balanced Cut | Single-GPU | | -| | Spectral-Clustering - Modularity | Single-GPU | | -| | Subgraph Extraction | Single-GPU | | -| | Triangle Counting | __Multi-GPU__ | | -| | K-Truss | Single-GPU | | +| | [Leiden](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | Single-GPU | MG planned for 23.02 | +| | [Louvain](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | | +| | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Single-GPU | | +| | [Spectral-Clustering - Balanced Cut](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | +| | [Spectral-Clustering - Modularity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | +| | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipyn) | Single-GPU | | +| | [Triangle Counting](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Triangle-Counting.ipynb) | __Multi-GPU__ | | +| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Single-GPU | | | Components | | | | -| | Weakly Connected Components | __Multi-GPU__ | | -| | Strongly Connected Components | Single-GPU | | +| | [Weakly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | __Multi-GPU__ | | +| | [Strongly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | Single-GPU | | | Core | | | | -| | K-Core | **Multi-GPU** | | -| | Core Number | **Multi-GPU** | | +| | [K-Core](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/kcore.ipynb) | **Multi-GPU** | | +| | [Core Number](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/core-number.ipynb) | **Multi-GPU** | | | _Flow_ | | | | | | _MaxFlow_ | --- | | | _Influence_ | | | | | | _Influence Maximization_ | --- | | | Layout | | | | -| | Force Atlas 2 | Single-GPU | | +| | [Force Atlas 2](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/layout/Force-Atlas2.ipynb) | Single-GPU | | | Linear Assignment | | | | -| | Hungarian | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) | +| | [Hungarian]() | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) | | Link Analysis | | | | -| | Pagerank | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | -| | Personal Pagerank | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) | -| | HITS | __Multi-GPU__ | | +| | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | +| | [Personal Pagerank]() | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) | +| | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb) | __Multi-GPU__ | | | Link Prediction | | | | -| | Jaccard Similarity | **Multi-GPU** | MG as of 22.12
Directed graph only | -| | Weighted Jaccard Similarity | Single-GPU | | -| | Overlap Similarity | **Multi-GPU** | MG as of 22.12 | -| | Sorensen Coefficient | **Multi-GPU** | MG as of 22.12 | +| | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | **Multi-GPU** | MG as of 22.12
Directed graph only | +| | [Weighted Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | Single-GPU | | +| | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) | **Multi-GPU** | MG as of 22.12 | +| | [Sorensen Coefficient](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen-Similarity.ipynb) | **Multi-GPU** | MG as of 22.12 | | | _Local Clustering Coefficient_ | --- | | | Sampling | | | | -| | Uniform Random Walks (RW) | **Multi-GPU** | | +| | [Uniform Random Walks RW](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/sampling/RandomWalk.ipynb) | **Multi-GPU** | | | | *Biased Random Walks (RW)* | --- | | | | Egonet | **Multi-GPU** | | | | Node2Vec | Single-GPU | MG planned for 23.02 | diff --git a/docs/cugraph/source/tutorials/cugraph_notebooks.md b/docs/cugraph/source/tutorials/cugraph_notebooks.md index ff9d6b199..ade6ed913 100644 --- a/docs/cugraph/source/tutorials/cugraph_notebooks.md +++ b/docs/cugraph/source/tutorials/cugraph_notebooks.md @@ -9,43 +9,43 @@ This repository contains a collection of Jupyter Notebooks that outline how to r | Folder | Notebook | Description | | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | | Centrality | | | -| | [Centrality](algorithms/centrality/Centrality.ipynb) | Compute and compare multiple (currently 5) centrality scores | -| | [Katz](algorithms/centrality/Katz.ipynb) | Compute the Katz centrality for every vertex | -| | [Betweenness](algorithms/centrality/Betweenness.ipynb) | Compute both Edge and Vertex Betweenness centrality | -| | [Degree](algorithms/centrality/Degree.ipynb) | Compute Degree Centraility for each vertex | -| | [Eigenvector](algorithms/centrality/Eigenvector.ipynb) | Compute Eigenvector for every vertex | +| | [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | Compute and compare multiple (currently 5) centrality scores | +| | [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) | Compute the Katz centrality for every vertex | +| | [Betweenness](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | Compute both Edge and Vertex Betweenness centrality | +| | [Degree](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Degree.ipynb) | Compute Degree Centraility for each vertex | +| | [Eigenvector](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Eigenvector.ipynb) | Compute Eigenvector for every vertex | | Community | | | -| | [Louvain](algorithms/community/Louvain.ipynb) and Leiden | Identify clusters in a graph using both the Louvain and Leiden algorithms | -| | [ECG](algorithms/community/ECG.ipynb) | Identify clusters in a graph using the Ensemble Clustering for Graph | -| | [K-Truss](algorithms/community/ktruss.ipynb) | Extracts the K-Truss cluster | -| | [Spectral-Clustering](algorithms/community/Spectral-Clustering.ipynb) | Identify clusters in a graph using Spectral Clustering with both
- Balanced Cut
- Modularity Modularity | -| | [Subgraph Extraction](algorithms/community/Subgraph-Extraction.ipynb) | Compute a subgraph of the existing graph including only the specified vertices | -| | [Triangle Counting](algorithms/community/Triangle-Counting.ipynb) | Count the number of Triangle in a graph | +| | [Louvain](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) and Leiden | Identify clusters in a graph using both the Louvain and Leiden algorithms | +| | [ECG](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Identify clusters in a graph using the Ensemble Clustering for Graph | +| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Extracts the K-Truss cluster | +| | [Spectral-Clustering](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Identify clusters in a graph using Spectral Clustering with both
- Balanced Cut
- Modularity Modularity | +| | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipynb) | Compute a subgraph of the existing graph including only the specified vertices | +| | [Triangle Counting](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Triangle-Counting.ipynb) | Count the number of Triangle in a graph | | Components | | | -| | [Connected Components](algorithms/components/ConnectedComponents.ipynb) | Find weakly and strongly connected components in a graph | +| | [Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | Find weakly and strongly connected components in a graph | | Core | | | -| | [K-Core](algorithms/cores/kcore.ipynb) | Extracts the K-core cluster | -| | [Core Number](algorithms/cores/core-number.ipynb) | Computer the Core number for each vertex in a graph | +| | [K-Core](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/kcore.ipynb) | Extracts the K-core cluster | +| | [Core Number](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/core-number.ipynb) | Computer the Core number for each vertex in a graph | Layout | | | -| | [Force-Atlas2](algorithms/layout/Force-Atlas2.ipynb) |A large graph visualization achieved with cuGraph. | +| | [Force-Atlas2](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/layout/Force-Atlas2.ipynb) |A large graph visualization achieved with cuGraph. | | Link Analysis | | | -| | [Pagerank](algorithms/link_analysis/Pagerank.ipynb) | Compute the PageRank of every vertex in a graph | -| | [HITS](algorithms/link_analysis/HITS.ipynb) | Compute the HITS' Hub and Authority scores for every vertex in a graph | +| | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) | Compute the PageRank of every vertex in a graph | +| | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb) | Compute the HITS' Hub and Authority scores for every vertex in a graph | | Link Prediction | | | -| | [Jaccard Similarity](algorithms/link_prediction/Jaccard-Similarity.ipynb) | Compute vertex similarity score using both:
- Jaccard Similarity
- Weighted Jaccard | -| | [Overlap Similarity](algorithms/link_prediction/Overlap-Similarity.ipynb) | Compute vertex similarity score using the Overlap Coefficient | +| | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | Compute vertex similarity score using both:
- Jaccard Similarity
- Weighted Jaccard | +| | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) | Compute vertex similarity score using the Overlap Coefficient | | Sampling | -| | [Random Walk](algorithms/sampling/RandomWalk.ipynb) | Compute Random Walk for a various number of seeds and path lengths | +| | [Random Walk](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/sampling/RandomWalk.ipynb) | Compute Random Walk for a various number of seeds and path lengths | | Traversal | | | -| | [BFS](algorithms/traversal/BFS.ipynb) | Compute the Breadth First Search path from a starting vertex to every other vertex in a graph | -| | [SSSP](algorithms/traversal/SSSP.ipynb) | Single Source Shortest Path - compute the shortest path from a starting vertex to every other vertex | +| | [BFS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/traversal/BFS.ipynb) | Compute the Breadth First Search path from a starting vertex to every other vertex in a graph | +| | [SSSP](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/traversal/SSSP.ipynb) | Single Source Shortest Path - compute the shortest path from a starting vertex to every other vertex | | Structure | | | | | [Renumbering](algorithms/structure/Renumber.ipynb)
[Renumbering 2](algorithms/structure/Renumber-2.ipynb) | Renumber the vertex IDs in a graph (two sample notebooks) | | | [Symmetrize](algorithms/structure/Symmetrize.ipynb) | Symmetrize the edges in a graph | ## RAPIDS notebooks -Visit the main RAPIDS [notebooks](https://github.com/rapidsai/notebooks) repo for a listing of all notebooks across all RAPIDS libraries. +Visit the main RAPIDS [notebooks](https://github.com/rapidsai/cugraph/blob/main/notebooks/) repo for a listing of all notebooks across all RAPIDS libraries. ## Requirements @@ -74,4 +74,4 @@ Unless required by applicable law or agreed to in writing, software distributed -![RAPIDS](https://github.com/rapidsai/cugraph/tree/main/img/rapids_logo.png) \ No newline at end of file +![RAPIDS](https://github.com/rapidsai/cugraph/blob/main/img/rapids_logo.png) \ No newline at end of file From 08d84c5e98e3383039be6d8ff0cf343622489cb8 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Thu, 4 May 2023 13:06:09 -0400 Subject: [PATCH 248/384] Creates Notebook that runs Multi-GPU versions of Jaccard, Sorenson and overlap. (#3504) resolves #2669 Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3504 --- notebooks/demo/mg_jaccard.ipynb | 384 ++++++++++++++++++++++++++++++++ 1 file changed, 384 insertions(+) create mode 100644 notebooks/demo/mg_jaccard.ipynb diff --git a/notebooks/demo/mg_jaccard.ipynb b/notebooks/demo/mg_jaccard.ipynb new file mode 100644 index 000000000..028deb72c --- /dev/null +++ b/notebooks/demo/mg_jaccard.ipynb @@ -0,0 +1,384 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Running Jaccard, Sorensen, and Overlap on Multiple GPUs\n", + "\n", + "This is a Multi-GPU notebook that loads data into a dask_cudf dataframe, creates a Graph, and then runs Jaccard, Sorensen, and Overlap.\n", + "\n", + "\n", + "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "|---------------|------------|------------------|-----------------|-----------------------|\n", + "| Don Acosta | 04/21/2023 | created | 23.06 nightly | 2xA6000 CUDA 11.7 |\n", + "| Brad Rees | 04/24/2023 | Added RMAT | 23.06 nightly | 2xA6000 CUDA 11.7 |\n", + "\n", + "\n", + "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Basic setup" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Refer to https://docs.rapids.ai/install to learn how to create an environment for running cuGraph notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import needed libraries. We recommend using a [conda environment](https://github.com/rapidsai/cugraph/tree/HEAD/conda/environments) provided in the cugraph repo.\n", + "from dask.distributed import Client, wait\n", + "from dask_cuda import LocalCUDACluster\n", + "from cugraph.dask.comms import comms as Comms\n", + "\n", + "import cugraph.dask as dask_cugraph\n", + "import cugraph\n", + "from cugraph.generators import rmat\n", + "\n", + "import dask_cudf\n", + "import time\n", + "import urllib.request\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize multi-GPU environment\n", + "Before we get started, we need to set up a Dask local cluster of workers to execute our work, and a client to coordinate and schedule work for that cluster. As we see below, we can initiate a cluster and client using only 3 lines of code.\n", + "\n", + "The enable_spilling feature allows the graph stored in GPU memory to spill to host memory if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def enable_spilling():\n", + " import cudf\n", + " cudf.set_option(\"spill\", True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "enable_spilling()\n", + "cluster = LocalCUDACluster()\n", + "client = Client(cluster)\n", + "client.run(enable_spilling)\n", + "Comms.initialize(p2p=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "This notebook will use RMAT to generate a synthetic dataset. The size of the dataset will be determined by the number of GPUs present.\n", + "This appraoch removes the need to find test data of various sizes.\n", + "\n", + "The notebook will call the cugraph.rmat function and have a dask_cudf DataFrame returned. The rmat function could simply return a Graph object, which is more memory effecient, but the goal is to show the process starting with a dataframe\n", + "\n", + "| Number of GPUs | Scale | Edge Factor | Est Number of Nodes | Est Number of Edges |\n", + "|----------------|-------|-------------|---------------------|---------------------|\n", + "| 1 | 24 | 16 | 16,000,000 | 256,000,000 |\n", + "| 2 | 25 | 16 | 32,000,000 | 512,000,000 |\n", + "| 3 | 25 | 24 | 32,000,000 | 768,000,000 |\n", + "| 4 | 26 | 16 | 64,000,000 | 1,024,000,000 |\n", + "| 5 | 26 | 20 | 64,000,000 | 1,280,000,000 |\n", + "| 6 | 26 | 24 | 64,000,000 | 1,536,000,000 |\n", + "| 7 | 26 | 28 | 64,000,000 | 1,792,000,000 |\n", + "| 8 | 27 | 16 | 128,000,000 | 2,048,000,000 |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rmat_settings = (\n", + " [24 , 16],\n", + " [25 , 16],\n", + " [25 , 24],\n", + " [26 , 16],\n", + " [26 , 20],\n", + " [26 , 24],\n", + " [26 , 28],\n", + " [27 , 16],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "def get_gpu_memory_size():\n", + " result = subprocess.check_output(\n", + " [\n", + " 'nvidia-smi', '--query-gpu=memory.total'\n", + " , '--format=csv,nounits,noheader'\n", + " ]\n", + " ) \n", + " return result.decode('utf-8').strip().split('\\n')#).strip().split('\\n')\n", + "\n", + "gpu_info = get_gpu_memory_size()\n", + "number_of_gpus = len(gpu_info)\n", + "gpu_memory = int(gpu_info[0])\n", + "print(f\"the cluster has {number_of_gpus} GPUs where each GPU has {gpu_memory} GB of memory\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scale, factor = rmat_settings[number_of_gpus - 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if gpu_memory < 3600:\n", + " factor = (int)(factor * 0.75)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the RMAT dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "numedges = (2**scale)* factor\n", + "ddf = rmat(\n", + " scale=scale,\n", + " num_edges=numedges,\n", + " a=0.57,\n", + " b=0.19,\n", + " c=0.19,\n", + " seed=42,\n", + " clip_and_flip=False,\n", + " scramble_vertex_ids=True,\n", + " create_using=None,\n", + " mg=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's see how many edges were created" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(ddf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ddf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Graph\n", + "yes, the rmat generator could have returned a Graph, but the goal for for this code to also be used as if data was loaded into cuDF via \n", + "read_csv, read_parquet, or similar data loading process. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G = cugraph.Graph(directed=False)\n", + "G.from_dask_cudf_edgelist(ddf, renumber=False, source='src', destination='dst')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we are using 1-hop pairs for demonstration\n", + "vertex_pairs = ddf.loc[0:1000]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vertex_pairs.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Multi-GPU jaccard\n", + "\n", + "Additional Reading\n", + "- [Wikipedia: Jaccard](https://en.wikipedia.org/wiki/Jaccard_index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jdf = dask_cugraph.jaccard(G,vertex_pairs)\n", + "jdf.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Multi-GPU Sorensen\n", + "\n", + "Additional Reading\n", + "- [Wikipedia: Sorensen Coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sdf = jdf = dask_cugraph.sorensen(G,vertex_pairs)\n", + "sdf.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Multi-GPU overlap\n", + "\n", + "Additional Reading\n", + "- [Wikipedia: Sorensen Coefficient](https://en.wikipedia.org/wiki/Overlap_coefficient)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "odf = jdf = dask_cugraph.overlap(G,vertex_pairs)\n", + "odf.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean up and Shut down the multi-GPU Environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "del(ddf)\n", + "del(G)\n", + "\n", + "Comms.destroy()\n", + "client.close()\n", + "cluster.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2023, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_0411", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From b3dfacc630c0bba5f318a49ac08906fe4b9d26ec Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Thu, 18 May 2023 13:16:18 -0400 Subject: [PATCH 249/384] removed deprecated calls and modified demo notebooks to run with 23.06 (#3561) resolves #3555 resolves #3556 resolves #3557 Is part of epic [196](https://github.com/rapidsai/graph_dl/issues/196) Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3561 --- .../cugraph_benchmarks/bfs_benchmark.ipynb | 54 +++++---- .../louvain_benchmark.ipynb | 56 +++++---- .../pagerank_benchmark.ipynb | 106 +++++------------- .../cugraph_benchmarks/sssp_benchmark.ipynb | 59 +++++----- 4 files changed, 124 insertions(+), 151 deletions(-) diff --git a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb index 6ae695e20..5a3d502c9 100644 --- a/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/bfs_benchmark.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -9,22 +10,15 @@ "\n", "This notebook benchmarks performance of running BFS within cuGraph against NetworkX. \n", "\n", - "Notebook Credits\n", "\n", - " Original Authors: Bradley Rees\n", - " Last Edit: 08/16/2020\n", - " \n", - "RAPIDS Versions: 0.15\n", - "\n", - "Test Hardware\n", - "\n", - " GV100 32G, CUDA 10.2\n", - " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", - " 32GB system memory\n", - " \n" + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|------------------|-----------------|----------------|\n", + "| Brad Rees | 08/16/2020 | created | 0.15 | GV100 32G, CUDA 10.2\n", + "| Don Acosta | 05/15/2023 | update and test | 23.06 | A6000 32G, CUDA 11.7" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -45,6 +39,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -54,6 +49,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -64,6 +60,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -81,7 +78,8 @@ "import time\n", "import rmm\n", "import cugraph\n", - "import cudf" + "import cudf\n", + "import os" ] }, { @@ -118,10 +116,24 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Define the test data" + "### Define the test data\n", + "\n", + "README.md contains instructions on importing data using the dataPrep.sh script\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "|:---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| caidaRouterLevel | 192,244 | 1,218,132 |\n", + "| coAuthorsDBLP | 299,067 | 1,955,352 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| citationCiteseer | 268,495 | 2,313,294 |\n", + "| coPapersDBLP | 540,486 | 30,491,458 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |" ] }, { @@ -144,6 +156,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -183,7 +196,7 @@ " t1 = time.time()\n", " \n", " # cugraph Pagerank Call\n", - " G = cugraph.DiGraph()\n", + " G = cugraph.Graph(directed=True)\n", " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", " \n", " df = cugraph.bfs(G, 1)\n", @@ -236,6 +249,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -316,6 +330,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -353,11 +368,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -368,9 +384,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "cugraph_0510", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -382,7 +398,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb index 00e99a286..dc1cfebe3 100644 --- a/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/louvain_benchmark.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -11,22 +12,10 @@ "

\n", "\n", "\n", - "#### Notebook Credits\n", - "\n", - " Original Authors: Bradley Rees\n", - " Last Edit: 06/10/2020\n", - "\n", - "\n", - "#### Test Environment\n", - "\n", - " RAPIDS Versions: 0.15\n", - "\n", - " Test Hardware:\n", - " GV100 32G, CUDA 10,0\n", - " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", - " 32GB system memory\n", - "\n", - "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|------------------|-----------------|----------------|\n", + "| Brad Rees | 08/16/2020 | created | 0.15 | GV100 32G, CUDA 10.0\n", + "| Don Acosta | 05/15/2023 | update and test | 23.06 | A6000 32G, CUDA 11.7\n", "\n", "#### Updates\n", "- moved loading ploting libraries to front so that dependencies can be checked before running algorithms\n", @@ -36,7 +25,6 @@ "\n", "\n", "#### Dependencies\n", - "- RAPIDS cuDF and cuGraph version 0.6.0 \n", "- NetworkX \n", "- Matplotlib \n", "- Scipy \n", @@ -47,14 +35,15 @@ "#### Note: Comparison against published results\n", "\n", "\n", - "The cuGraph blog post included performance numbers that were collected over a year ago. For the test graphs, int32 values are now used. That improves GPUs performance. Additionally, the initial benchamrks were measured on a P100 GPU. \n", + "The cuGraph blog post included performance numbers that were collected over a year ago. For the test graphs, int32 values are now used. That improves GPUs performance.\n", "\n", - "This test only comparse the modularity scores and a success is if the scores are within 15% of each other. That comparison is done by adjusting the NetworkX modularity score and then verifying that the cuGraph score is higher.\n", + "This test only compares the modularity scores and a success if the scores are within 15% of each other. That comparison is done by adjusting the NetworkX modularity score and then verifying that the cuGraph score is higher.\n", "\n", "cuGraph did a full validation of NetworkX results against cuGraph results. That included cross-validation of every cluster. That test is very slow and not included here" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -123,10 +112,24 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Define the test data" + "### Define the test data\n", + "\n", + "README.md contains instructions on downloading the data using the dataPrep.sh script\n", + "\n", + "| File Name | Num of Vertices | Num of Edges |\n", + "|:---------------------- | --------------: | -----------: |\n", + "| preferentialAttachment | 100,000 | 999,970 |\n", + "| caidaRouterLevel | 192,244 | 1,218,132 |\n", + "| coAuthorsDBLP | 299,067 | 1,955,352 |\n", + "| dblp-2010 | 326,186 | 1,615,400 |\n", + "| citationCiteseer | 268,495 | 2,313,294 |\n", + "| coPapersDBLP | 540,486 | 30,491,458 |\n", + "| coPapersCiteseer | 434,102 | 32,073,440 |\n", + "| as-Skitter | 1,696,415 | 22,190,596 |" ] }, { @@ -149,6 +152,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -235,6 +239,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -278,6 +283,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -303,6 +309,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -337,11 +344,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -352,9 +360,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "cugraph_0510", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -366,7 +374,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb index d0416efdd..b4c328cb2 100644 --- a/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb @@ -1,35 +1,30 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# PageRank Performance Benchmarking\n", "# Skip notebook test\n", "\n", - "This notebook benchmarks performance of running PageRank within cuGraph against NetworkX. NetworkX contains several implementations of PageRank. This benchmark will compare cuGraph versus the defaukt Nx implementation as well as the SciPy version\n", + "This notebook benchmarks performance of running PageRank within cuGraph against NetworkX. \n", "\n", - "Notebook Credits\n", - "\n", - " Original Authors: Bradley Rees\n", - " Last Edit: 08/16/2020\n", - " \n", - "RAPIDS Versions: 0.15\n", - "\n", - "Test Hardware\n", - "\n", - " GV100 32G, CUDA 10,0\n", - " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", - " 32GB system memory\n", - " \n" + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|------------------|-----------------|----------------|\n", + "| Brad Rees | 08/16/2020 | created | 0.15 | GV100 32G, CUDA 10.0\n", + "| Don Acosta | 05/17/2023 | removed outdated pagerank_scipy and tested | 23.06 | A6000 32G, CUDA 11.7\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Test Data\n", "\n", + "README.md contains instructions to download data using the dataPrep.sh script\n", + "\n", "| File Name | Num of Vertices | Num of Edges |\n", "|:---------------------- | --------------: | -----------: |\n", "| preferentialAttachment | 100,000 | 999,970 |\n", @@ -39,12 +34,11 @@ "| citationCiteseer | 268,495 | 2,313,294 |\n", "| coPapersDBLP | 540,486 | 30,491,458 |\n", "| coPapersCiteseer | 434,102 | 32,073,440 |\n", - "| as-Skitter | 1,696,415 | 22,190,596 |\n", - "\n", - "\n" + "| as-Skitter | 1,696,415 | 22,190,596 |\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -57,6 +51,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -67,6 +62,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -84,7 +80,8 @@ "import time\n", "import rmm\n", "import cugraph\n", - "import cudf" + "import cudf\n", + "import os" ] }, { @@ -121,6 +118,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -147,6 +145,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -186,8 +185,8 @@ " t1 = time.time()\n", " \n", " # cugraph Pagerank Call\n", - " G = cugraph.DiGraph()\n", - " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", + " G = cugraph.Graph(directed=True)\n", + " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False, store_transposed=True)\n", " \n", " df = cugraph.pagerank(G, alpha=alpha, max_iter=max_iter, tol=tol)\n", " t2 = time.time() - t1\n", @@ -239,46 +238,7 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# SciPy PageRank\n", - "\n", - "def networkx_scipy_call(M, max_iter, tol, alpha):\n", - " nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n", - " for nnz in range(M.getnnz()):\n", - " nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n", - " for nnz in range(M.getnnz()):\n", - " M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n", - "\n", - " M = M.tocsr()\n", - " if M is None:\n", - " raise TypeError('Could not read the input graph')\n", - " if M.shape[0] != M.shape[1]:\n", - " raise TypeError('Shape is not square')\n", - "\n", - " # should be autosorted, but check just to make sure\n", - " if not M.has_sorted_indices:\n", - " print('sort_indices ... ')\n", - " M.sort_indices()\n", - "\n", - " z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}\n", - "\n", - " # SciPy Pagerank Call\n", - " print('\\tSciPy Solving... ')\n", - " t1 = time.time()\n", - " \n", - " Gnx = nx.DiGraph(M) \n", - " \n", - " pr = nx.pagerank_scipy(Gnx, alpha, z, max_iter, tol)\n", - " t2 = time.time() - t1\n", - "\n", - " return t2" - ] - }, - { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -324,18 +284,13 @@ " speedUp = (tn / trapids)\n", " perf_nx.append(speedUp)\n", " time_nx.append(tn)\n", - " \n", - " # Now call SciPy\n", - " tsp = networkx_scipy_call(M, 100, 0.00001, 0.85)\n", - " speedUp = (tsp / trapids)\n", - " perf_sp.append(speedUp) \n", - " time_sp.append(tsp)\n", - " \n", - " print(\"cuGraph (\" + str(trapids) + \") Nx (\" + str(tn) + \") SciPy (\" + str(tsp) + \")\" )\n", + " \n", + " print(\"cuGraph (\" + str(trapids) + \") Nx (\" + str(tn) + \")\" )\n", " del M" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -356,7 +311,6 @@ "index = np.arange(len(names))\n", "\n", "_ = plt.bar(index, perf_nx, bar_width, color='g', label='vs Nx')\n", - "_ = plt.bar(index + bar_width, perf_sp, bar_width, color='b', label='vs SciPy')\n", "\n", "plt.xlabel('Datasets')\n", "plt.ylabel('Speedup')\n", @@ -368,15 +322,12 @@ "for i in range(len(perf_nx)):\n", " plt.text(x = (i - 0.55) + bar_width, y = perf_nx[i] + 25, s = round(perf_nx[i], 1), size = 12)\n", "\n", - "for i in range(len(perf_sp)):\n", - " plt.text(x = (i - 0.1) + bar_width, y = perf_sp[i] + 25, s = round(perf_sp[i], 1), size = 12)\n", - "\n", - "\n", "plt.legend()\n", "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -429,11 +380,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -444,9 +396,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "cugraph_0510", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -458,7 +410,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb index 32b562e7a..f6c239f28 100644 --- a/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb +++ b/notebooks/cugraph_benchmarks/sssp_benchmark.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -9,27 +10,22 @@ "\n", "This notebook benchmarks performance of running SSSP within cuGraph against NetworkX. \n", "\n", - "Notebook Credits\n", "\n", - " Original Authors: Bradley Rees\n", - " Last Edit: 06/10/2020\n", - " \n", - "RAPIDS Versions: 0.15\n", - "\n", - "Test Hardware\n", - "\n", - " GV100 32G, CUDA 10,0\n", - " Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n", - " 32GB system memory\n", - " \n" + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|------------------|-----------------|----------------|\n", + "| Brad Rees | 06/10/2020 | created | 0.15 | GV100 32G, CUDA 10.0\n", + "| Don Acosta | 05/17/2023 | update and test | 23.06 | A6000 48G, CUDA 11.7\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Test Data\n", "\n", + "README.md contains instructions to download data using the dataPrep.sh script\n", + "\n", "| File Name | Num of Vertices | Num of Edges |\n", "|:---------------------- | --------------: | -----------: |\n", "| preferentialAttachment | 100,000 | 999,970 |\n", @@ -45,6 +41,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -57,6 +54,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -67,6 +65,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -84,17 +83,7 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# since this is a shared machine - let's pick a GPU that no one else is using\n", - "import os\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"" - ] - }, - { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -112,7 +101,8 @@ "import time\n", "import rmm\n", "import cugraph\n", - "import cudf" + "import cudf\n", + "import os" ] }, { @@ -132,7 +122,7 @@ "metadata": {}, "outputs": [], "source": [ - "try: \n", + "try:\n", " import matplotlib\n", "except ModuleNotFoundError:\n", " os.system('pip install matplotlib')" @@ -149,6 +139,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -175,6 +166,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -208,14 +200,16 @@ " gdf = cudf.DataFrame()\n", " gdf['src'] = M.row\n", " gdf['dst'] = M.col\n", + " # added this since SSSP now requires a weight\n", + " gdf['weight'] = 1\n", " \n", " print('\\tcuGraph Solving... ')\n", " \n", " t1 = time.time()\n", " \n", " # cugraph SSSP Call\n", - " G = cugraph.DiGraph()\n", - " G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n", + " G = cugraph.Graph(directed=True)\n", + " G.from_cudf_edgelist(gdf, source='src', destination='dst', weight = 'weight', renumber=False)\n", " \n", " df = cugraph.sssp(G, 1)\n", " t2 = time.time() - t1\n", @@ -267,6 +261,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -345,6 +340,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -379,11 +375,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -394,9 +391,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "cugraph_0510", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -408,7 +405,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.10.10" } }, "nbformat": 4, From a298b8cce8120f19af3069363efeefda5fb5eb00 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Fri, 19 May 2023 08:59:03 -0400 Subject: [PATCH 250/384] updated cugraph Demo notebooks for 23.06 (#3558) This is done as part of epic [#196](https://github.com/rapidsai/graph_dl/issues/196) Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3558 --- notebooks/demo/batch_betweenness.ipynb | 397 ------------------------- notebooks/demo/mg_louvain.ipynb | 196 ++++++++++-- notebooks/demo/mg_pagerank.ipynb | 384 +++++++++++++++++++++++- notebooks/demo/mg_property_graph.ipynb | 18 +- 4 files changed, 558 insertions(+), 437 deletions(-) delete mode 100644 notebooks/demo/batch_betweenness.ipynb diff --git a/notebooks/demo/batch_betweenness.ipynb b/notebooks/demo/batch_betweenness.ipynb deleted file mode 100644 index 854621eb3..000000000 --- a/notebooks/demo/batch_betweenness.ipynb +++ /dev/null @@ -1,397 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multi-GPU Batch Betweenness Centrality\n", - "#### Author : Xavier Cadet\n", - "In this notebook, we will compute Betweenness Centrality for vertices using cuGraph and will see how to **use Multiple GPUs to compute Betweenness Centrality scores**.\n", - "\n", - "This notebook was tested using 4 NVIDIA Tesla V100-DGX 32G GPUs, using RAPIDS 0.15, and CUDA 10.1. Please be aware that your system may be different and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "Betweenness Centrality can be slow to compute on large graphs, in order to speed up the process we can leverage multiple GPUs.\n", - "In this notebook we will showcase how it would have been done with a Single GPU approach, then we will show how it can be done using multiple GPUs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "The soc-LiveJournal1 dataset which can be obtained on [SNAP](https://snap.stanford.edu/data/soc-LiveJournal1.html). This graph contains roughly 5 million nodes, and 70 million edges and was extracted from the LiveJournal online social network, further information can be found in:\n", - "\n", - "*Group Formation in Large Social Networks: Membership, Growth, and Evolution., L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan., KDD, 2006.*\n", - "\n", - "and:\n", - "\n", - "*Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters., J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney., Internet Mathematics 6(1) 29--123, 2009.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Betweenness Centrality with cuGraph" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The imports:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cugraph\n", - "import cudf\n", - "\n", - "import dask\n", - "import dask_cuda\n", - "import cugraph.comms as Comms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import cupy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get the data\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "import os\n", - "\n", - "data_dir = '../data/'\n", - "if not os.path.exists(data_dir):\n", - " print('creating data directory')\n", - " os.system('mkdir ../data')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# download the soc-LiveJournal1 dataset\n", - "base_url = 'https://snap.stanford.edu/data/'\n", - "fn = 'soc-LiveJournal1.txt'\n", - "comp = '.gz'\n", - "if not os.path.isfile(data_dir + fn):\n", - " if not os.path.isfile(data_dir + fn + comp):\n", - " print(f'Downloading {base_url + fn + comp} to {data_dir + fn + comp}')\n", - " urllib.request.urlretrieve(base_url + fn + comp, data_dir + fn + comp)\n", - " print(f'Decompressing {data_dir + fn + comp}...')\n", - " os.system('gunzip ' + data_dir + fn + comp)\n", - " print(f'{data_dir + fn + comp} decompressed!')\n", - "else:\n", - " print(f'Your data file, {data_dir + fn}, already exists')\n", - "input_data_path = data_dir + fn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single GPU" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading the Data - Single GPU\n", - "The following shows how we would read the csv file using a single GPU as it is commonly done when using a single GPU with CuGraph." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t_start_read_sg = time.perf_counter()\n", - "e_list = cudf.read_csv(input_data_path, delimiter='\\t', names=['src', 'dst'], dtype=['int32', 'int32'], comment='#')\n", - "t_stop_read_sg = time.perf_counter()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"SG Read time: {}s\".format(t_stop_read_sg - t_start_read_sg))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Building the Graph - Single GPU\n", - "Once we read the file, we need to build the Graph, we will use a DiGraph, and use the content extracted from the .csv file as an edge list." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t_start_build_sg = time.perf_counter()\n", - "G = cugraph.DiGraph()\n", - "G.from_cudf_edgelist(e_list, source='src', destination='dst')\n", - "t_stop_build_sg = time.perf_counter()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"SG Build time: {}s\".format(t_stop_build_sg - t_start_build_sg))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calling the Algorithm - Single GPU\n", - "Now that our graph is built, we can get its betweenness centrality score. Here we will use a sub-sample of 1024 sources in order to have a better approximation of the overall betweenness centrality. We set the seed for comparability with the multi GPU version that comes next." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t_start_sg = time.perf_counter()\n", - "sg_df = cugraph.betweenness_centrality(G, k=1024, seed=123)\n", - "t_stop_sg = time.perf_counter()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"SG Time elapsed: {}s\".format(t_stop_sg - t_start_sg))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Now let's use multiple GPUs!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a Dask Cluster\n", - "In order to use multiple GPU, we need to ensure that we have Dask Cluster and Client running, further more we need to initialize the CuGraph Communicator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cluster = dask_cuda.LocalCUDACluster()\n", - "client = dask.distributed.Client(cluster)\n", - "Comms.initialize(p2p=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Enabling Multi GPU Batch Processing\n", - "The good thing is that with a simple `enable_mg_batch` call you can harness the power of Multiple GPUs to operate Batch Processing.\n", - "This step might take a few seconds, indeed we need to get the graph available to all GPUS, do not worry, this is only required once or when adding new representations to the graph (adjacency list for example)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t_start_mg = time.perf_counter()\n", - "G.enable_batch()\n", - "print(\"MG Batch Enabling Time elapsed: {}s\".format(time.perf_counter() - t_start_mg))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calling the algorithm\n", - "We call the algorithm the same way as we used to, but this time it is much faster as we leverage multiple GPUs to compute the Betweenness Centrality scores." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t_start_mg = time.perf_counter()\n", - "batch_df = cugraph.betweenness_centrality(G, k=1024, seed=123)\n", - "t_stop_mg = time.perf_counter()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"MG Time elapsed: {}s\".format(t_stop_mg - t_start_mg))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verification\n", - "Order in the DataFrame might vary, but scores for each vertices match, in order to display them side by side we will first sort the resluts based on the `vertex` key, and renew the DataFramee index." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sorted_sg_df = sg_df.sort_values(\"vertex\").reset_index(drop=True)\n", - "sorted_batch_df = batch_df.sort_values(\"vertex\").reset_index(drop=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now compare score for each of the vertices:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cupy.allclose(sorted_sg_df[\"betweenness_centrality\"], sorted_batch_df[\"betweenness_centrality\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And just to visually compare the results we can display the DataFrames:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(sorted_sg_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(sorted_batch_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do not forget to clear the Communicator / client /cluster if required." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Comms.destroy()\n", - "client.close()\n", - "cluster.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", - "\n", - "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", - "___" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cugraph_dev", - "language": "python", - "name": "cugraph_dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/demo/mg_louvain.ipynb b/notebooks/demo/mg_louvain.ipynb index a23c8eadb..8e8c5b041 100644 --- a/notebooks/demo/mg_louvain.ipynb +++ b/notebooks/demo/mg_louvain.ipynb @@ -7,11 +7,11 @@ "source": [ "# Multiple GPU Louvain in cuGraph\n", "\n", - "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", - "|---------------|------------|------------------|-----------------|-----------------------|\n", - "| Chuck Hastings| 11/08/2021 | created | 21.10 nightly | |\n", - "| Don Acosta | 01/30/2023 | updated | 23.02 nightly | 2xA6000 CUDA 11.7 |\n", - "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "|---------------|------------|--------------------|-----------------|-----------------------|\n", + "| Chuck Hastings| 11/08/2021 | created | 21.10 nightly | |\n", + "| Don Acosta | 01/30/2023 | updated | 23.02 nightly | 2xA6000 CUDA 11.7 |\n", + "| Don Acosta | 05/12/2023 | updated and tested | 23.06 nightly | 2xA6000 CUDA 11.7 |\n", "\n", "In this notebook, we will show how to use multiple GPUs in cuGraph to compute the Louvain partitions and global modularity score for a dataset.\n", "\n", @@ -22,6 +22,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -31,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,6 +47,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -53,6 +55,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -64,11 +67,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your data file, ../data/hollywood.csv, already exists\n" + ] + } + ], "source": [ "import urllib.request\n", "import os\n", @@ -98,6 +109,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -107,9 +119,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-12 08:59:04,694 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-t6i5b0t1', purging\n", + "2023-05-12 08:59:04,694 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-pn3z4erj', purging\n", + "2023-05-12 08:59:04,695 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", + "2023-05-12 08:59:04,695 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n", + "2023-05-12 08:59:04,718 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", + "2023-05-12 08:59:04,718 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n" + ] + } + ], "source": [ "cluster = LocalCUDACluster()\n", "client = Client(cluster)\n", @@ -117,6 +142,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -126,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "tags": [] }, @@ -136,13 +162,14 @@ "t_start = time.time()\n", "\n", "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", - "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", + "blocksize = dask_cugraph.get_chunksize(input_data_path)\n", "\n", "# Multi-GPU CSV reader\n", - "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])" + "e_list = dask_cudf.read_csv(input_data_path, blocksize = blocksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -151,11 +178,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read, load and renumber: 8.843012809753418 s\n" + ] + } + ], "source": [ "# Create an undirected graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", "G = cugraph.Graph(directed=False)\n", @@ -166,6 +201,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -174,12 +210,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Louvain: 5.56727409362793 s\n" + ] + } + ], "source": [ "# Start Pagerank timer\n", "t_start = time.time()\n", @@ -192,6 +236,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -199,6 +244,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -209,19 +255,127 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + " vertex partition\n", + "0 601225 2\n", + "1 599864 2\n", + "2 601226 2\n", + "3 791343 0\n", + "4 600687 2\n", + "... ... ...\n", + "570285 1139894 20139\n", + "570286 1139896 20140\n", + "570287 1139897 3451\n", + "570288 1139898 40215\n", + "570289 1139903 31869\n", + "\n", + "[1139905 rows x 2 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "louvain_df.compute()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -261,7 +415,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.10" }, "vscode": { "interpreter": { diff --git a/notebooks/demo/mg_pagerank.ipynb b/notebooks/demo/mg_pagerank.ipynb index 5e3f05210..bb3330484 100644 --- a/notebooks/demo/mg_pagerank.ipynb +++ b/notebooks/demo/mg_pagerank.ipynb @@ -1,21 +1,37 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Multiple GPU in cuGraph\n", - "#### Author : Alex Fender\n", + "\n", "\n", "In this notebook, we will show how to use multiple GPUs in cuGraph to compute the PageRank of each user in Twitter's dataset.\n", "\n", - "This notebook was tested using RAPIDS 0.15 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", + " If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", + "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "|---------------|------------|------------------|-----------------|----------------------------------------------------------|\n", + "|Alex Fender | 11/08/2021 | created | 0.15 | Tesla V100-DGX 32G GPU CUDA 10.2 |\n", + "| Don Acosta | 05/11/2023 | updated/tested | 23.06 nightly | 2xA6000 CUDA 11.7\n", "\n", "\n", "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." ] }, { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note:\n", + "This notebook runs only in releases 23.06 and later. It requires memory optimizations delivered in 23.06" + ] + }, + { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -27,6 +43,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -36,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -51,6 +68,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -58,6 +76,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -69,11 +88,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your data file, ../data/twitter-2010.csv, already exists\n" + ] + } + ], "source": [ "import urllib.request\n", "import os\n", @@ -102,6 +129,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -111,16 +139,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], + "source": [ + "def enable_spilling():\n", + " import cudf\n", + " cudf.set_option(\"spill\", True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-12 09:22:17,684 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", + "2023-05-12 09:22:17,684 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n", + "2023-05-12 09:22:17,688 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", + "2023-05-12 09:22:17,688 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n" + ] + } + ], "source": [ "cluster = LocalCUDACluster()\n", + "enable_spilling()\n", "client = Client(cluster)\n", "Comms.initialize(p2p=True)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -130,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "tags": [] }, @@ -140,13 +192,14 @@ "t_start = time.time()\n", "\n", "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", - "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", + "blocksize = dask_cugraph.get_chunksize(input_data_path)\n", "\n", "# Multi-GPU CSV reader\n", - "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n" + "e_list = dask_cudf.read_csv(input_data_path, blocksize = blocksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -155,11 +208,305 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-12 09:25:01,974 - distributed.sizeof - WARNING - Sizeof calculation failed. Defaulting to 0.95 MiB\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/sizeof.py\", line 17, in safe_sizeof\n", + " return sizeof(obj)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/utils.py\", line 642, in __call__\n", + " return meth(arg, *args, **kwargs)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask_cudf/backends.py\", line 430, in sizeof_cudf_dataframe\n", + " + df._index.memory_usage()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 1594, in memory_usage\n", + " if self.levels:\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 605, in levels\n", + " self._compute_levels_and_codes()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 748, in _compute_levels_and_codes\n", + " code, cats = cudf.Series._from_data({None: col}).factorize()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/single_column_frame.py\", line 311, in factorize\n", + " return cudf.core.algorithms.factorize(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/algorithms.py\", line 138, in factorize\n", + " labels = values._column._label_encoding(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1385, in _label_encoding\n", + " order = order.take(left_gather_map, check_bounds=False).argsort()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1101, in argsort\n", + " return self.as_frame()._get_sorted_inds(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/frame.py\", line 1572, in _get_sorted_inds\n", + " return libcudf.sort.order_by(to_sort, ascending, na_position)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"sort.pyx\", line 141, in cudf._lib.sort.order_by\n", + "MemoryError: std::bad_alloc: out_of_memory: CUDA error at: /home/dacosta/miniconda3/envs/cugraph_0411/include/rmm/mr/device/cuda_memory_resource.hpp\n", + "2023-05-12 09:25:01,976 - distributed.sizeof - WARNING - Sizeof calculation failed. Defaulting to 0.95 MiB\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/sizeof.py\", line 17, in safe_sizeof\n", + " return sizeof(obj)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/utils.py\", line 642, in __call__\n", + " return meth(arg, *args, **kwargs)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask_cudf/backends.py\", line 430, in sizeof_cudf_dataframe\n", + " + df._index.memory_usage()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 1594, in memory_usage\n", + " if self.levels:\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 605, in levels\n", + " self._compute_levels_and_codes()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 748, in _compute_levels_and_codes\n", + " code, cats = cudf.Series._from_data({None: col}).factorize()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/single_column_frame.py\", line 311, in factorize\n", + " return cudf.core.algorithms.factorize(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/algorithms.py\", line 138, in factorize\n", + " labels = values._column._label_encoding(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1385, in _label_encoding\n", + " order = order.take(left_gather_map, check_bounds=False).argsort()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1101, in argsort\n", + " return self.as_frame()._get_sorted_inds(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/frame.py\", line 1572, in _get_sorted_inds\n", + " return libcudf.sort.order_by(to_sort, ascending, na_position)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"sort.pyx\", line 141, in cudf._lib.sort.order_by\n", + "MemoryError: std::bad_alloc: out_of_memory: CUDA error at: /home/dacosta/miniconda3/envs/cugraph_0411/include/rmm/mr/device/cuda_memory_resource.hpp\n", + "2023-05-12 09:25:03,767 - distributed.sizeof - WARNING - Sizeof calculation failed. Defaulting to 0.95 MiB\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/sizeof.py\", line 17, in safe_sizeof\n", + " return sizeof(obj)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/utils.py\", line 642, in __call__\n", + " return meth(arg, *args, **kwargs)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask_cudf/backends.py\", line 430, in sizeof_cudf_dataframe\n", + " + df._index.memory_usage()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 1594, in memory_usage\n", + " if self.levels:\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 605, in levels\n", + " self._compute_levels_and_codes()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 748, in _compute_levels_and_codes\n", + " code, cats = cudf.Series._from_data({None: col}).factorize()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/single_column_frame.py\", line 311, in factorize\n", + " return cudf.core.algorithms.factorize(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/algorithms.py\", line 138, in factorize\n", + " labels = values._column._label_encoding(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1385, in _label_encoding\n", + " order = order.take(left_gather_map, check_bounds=False).argsort()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1101, in argsort\n", + " return self.as_frame()._get_sorted_inds(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/frame.py\", line 1572, in _get_sorted_inds\n", + " return libcudf.sort.order_by(to_sort, ascending, na_position)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"sort.pyx\", line 141, in cudf._lib.sort.order_by\n", + "MemoryError: std::bad_alloc: out_of_memory: CUDA error at: /home/dacosta/miniconda3/envs/cugraph_0411/include/rmm/mr/device/cuda_memory_resource.hpp\n", + "2023-05-12 09:25:03,768 - distributed.sizeof - WARNING - Sizeof calculation failed. Defaulting to 0.95 MiB\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/sizeof.py\", line 17, in safe_sizeof\n", + " return sizeof(obj)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/utils.py\", line 642, in __call__\n", + " return meth(arg, *args, **kwargs)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask_cudf/backends.py\", line 430, in sizeof_cudf_dataframe\n", + " + df._index.memory_usage()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 1594, in memory_usage\n", + " if self.levels:\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 605, in levels\n", + " self._compute_levels_and_codes()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/multiindex.py\", line 748, in _compute_levels_and_codes\n", + " code, cats = cudf.Series._from_data({None: col}).factorize()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/single_column_frame.py\", line 311, in factorize\n", + " return cudf.core.algorithms.factorize(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/algorithms.py\", line 138, in factorize\n", + " labels = values._column._label_encoding(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1385, in _label_encoding\n", + " order = order.take(left_gather_map, check_bounds=False).argsort()\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1101, in argsort\n", + " return self.as_frame()._get_sorted_inds(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/frame.py\", line 1572, in _get_sorted_inds\n", + " return libcudf.sort.order_by(to_sort, ascending, na_position)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"sort.pyx\", line 141, in cudf._lib.sort.order_by\n", + "MemoryError: std::bad_alloc: out_of_memory: CUDA error at: /home/dacosta/miniconda3/envs/cugraph_0411/include/rmm/mr/device/cuda_memory_resource.hpp\n", + "2023-05-12 09:25:03,820 - distributed.worker - ERROR - Could not deserialize task ('len-chunk-319fe46af5510615b2fae86c6e732896-841a12bf4568ebb80eb2030cc4d9651d', 1)\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2923, in loads_function\n", + " result = cache_loads[bytes_object]\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/collections.py\", line 24, in __getitem__\n", + " value = super().__getitem__(key)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/collections/__init__.py\", line 1106, in __getitem__\n", + " raise KeyError(key)\n", + "KeyError: 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+ "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2244, in execute\n", + " function, args, kwargs = await self._maybe_deserialize_task(ts)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2216, in _maybe_deserialize_task\n", + " function, args, kwargs = _deserialize(*ts.run_spec)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2937, in _deserialize\n", + " function = loads_function(function)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2925, in loads_function\n", + " result = pickle.loads(bytes_object)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/protocol/pickle.py\", line 96, in loads\n", + " return pickle.loads(x)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/abc.py\", line 176, in host_deserialize\n", + " obj = cls.device_deserialize(header, frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/abc.py\", line 130, in device_deserialize\n", + " return typ.deserialize(header, frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/dataframe.py\", line 1019, in deserialize\n", + " obj = super().deserialize(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/frame.py\", line 106, in deserialize\n", + " columns = deserialize_columns(header[\"columns\"], frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 2450, in deserialize_columns\n", + " colobj = col_typ.deserialize(meta, frames[:col_frame_count])\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1216, in deserialize\n", + " data, frames = unpack(header[\"data\"], frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1204, in unpack\n", + " obj = klass.deserialize(header, frames[:count])\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py\", line 574, in deserialize\n", + " return SpillableBuffer.deserialize(header, frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/buffer.py\", line 335, in deserialize\n", + " return cls._from_device_memory(frame)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py\", line 235, in _from_device_memory\n", + " ret._finalize_init(ptr_desc={\"type\": \"gpu\"}, exposed=exposed)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py\", line 206, in _finalize_init\n", + " raise ValueError(\n", + "ValueError: cannot create without a global spill manager\n", + "2023-05-12 09:25:03,817 - distributed.worker - ERROR - Could not deserialize task ('len-chunk-319fe46af5510615b2fae86c6e732896-841a12bf4568ebb80eb2030cc4d9651d', 0)\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2923, in loads_function\n", + " result = cache_loads[bytes_object]\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/collections.py\", line 24, in __getitem__\n", + " value = super().__getitem__(key)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/collections/__init__.py\", line 1106, in __getitem__\n", + " raise KeyError(key)\n", + "KeyError: 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+ "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2244, in execute\n", + " function, args, kwargs = await self._maybe_deserialize_task(ts)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2216, in _maybe_deserialize_task\n", + " function, args, kwargs = _deserialize(*ts.run_spec)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py\", line 79, in inner\n", + " return func(*args, **kwds)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2937, in _deserialize\n", + " function = loads_function(function)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py\", line 2925, in loads_function\n", + " result = pickle.loads(bytes_object)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/protocol/pickle.py\", line 96, in loads\n", + " return pickle.loads(x)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/abc.py\", line 176, in host_deserialize\n", + " obj = cls.device_deserialize(header, frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/abc.py\", line 130, in device_deserialize\n", + " return typ.deserialize(header, frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/dataframe.py\", line 1019, in deserialize\n", + " obj = super().deserialize(\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/frame.py\", line 106, in deserialize\n", + " columns = deserialize_columns(header[\"columns\"], frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 2450, in deserialize_columns\n", + " colobj = col_typ.deserialize(meta, frames[:col_frame_count])\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1216, in deserialize\n", + " data, frames = unpack(header[\"data\"], frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py\", line 1204, in unpack\n", + " obj = klass.deserialize(header, frames[:count])\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py\", line 574, in deserialize\n", + " return SpillableBuffer.deserialize(header, frames)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/buffer.py\", line 335, in deserialize\n", + " return cls._from_device_memory(frame)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py\", line 235, in _from_device_memory\n", + " ret._finalize_init(ptr_desc={\"type\": \"gpu\"}, exposed=exposed)\n", + " File \"/home/dacosta/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py\", line 206, in _finalize_init\n", + " raise ValueError(\n", + "ValueError: cannot create without a global spill manager\n" + ] + }, + { + "ename": "ValueError", + "evalue": "cannot create without a global spill manager", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \u001b[39;00m\n\u001b[1;32m 2\u001b[0m G \u001b[39m=\u001b[39m cugraph\u001b[39m.\u001b[39mGraph(directed\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m----> 3\u001b[0m G\u001b[39m.\u001b[39;49mfrom_dask_cudf_edgelist(e_list, source\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39msrc\u001b[39;49m\u001b[39m'\u001b[39;49m, destination\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mdst\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m 5\u001b[0m \u001b[39m# Print time\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mRead, load and renumber: \u001b[39m\u001b[39m\"\u001b[39m, time\u001b[39m.\u001b[39mtime()\u001b[39m-\u001b[39mt_start, \u001b[39m\"\u001b[39m\u001b[39ms\u001b[39m\u001b[39m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cugraph/structure/graph_classes.py:309\u001b[0m, in \u001b[0;36mGraph.from_dask_cudf_edgelist\u001b[0;34m(self, input_ddf, source, destination, edge_attr, renumber, store_transposed, legacy_renum_only)\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_Impl\u001b[39m.\u001b[39medgelist \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 308\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mGraph already has values\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 309\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_Impl\u001b[39m.\u001b[39;49m_simpleDistributedGraphImpl__from_edgelist(\n\u001b[1;32m 310\u001b[0m input_ddf,\n\u001b[1;32m 311\u001b[0m source,\n\u001b[1;32m 312\u001b[0m destination,\n\u001b[1;32m 313\u001b[0m edge_attr,\n\u001b[1;32m 314\u001b[0m renumber,\n\u001b[1;32m 315\u001b[0m store_transposed,\n\u001b[1;32m 316\u001b[0m legacy_renum_only,\n\u001b[1;32m 317\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cugraph/structure/graph_implementation/simpleDistributedGraph.py:272\u001b[0m, in \u001b[0;36msimpleDistributedGraphImpl.__from_edgelist\u001b[0;34m(self, input_ddf, source, destination, edge_attr, renumber, store_transposed, legacy_renum_only)\u001b[0m\n\u001b[1;32m 268\u001b[0m dst_col_name \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrenumber_map\u001b[39m.\u001b[39mrenumbered_dst_col_name\n\u001b[1;32m 270\u001b[0m ddf \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39medgelist\u001b[39m.\u001b[39medgelist_df\n\u001b[0;32m--> 272\u001b[0m num_edges \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39;49m(ddf)\n\u001b[1;32m 273\u001b[0m edge_data \u001b[39m=\u001b[39m get_distributed_data(ddf)\n\u001b[1;32m 275\u001b[0m graph_props \u001b[39m=\u001b[39m GraphProperties(\n\u001b[1;32m 276\u001b[0m is_multigraph\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproperties\u001b[39m.\u001b[39mmulti_edge,\n\u001b[1;32m 277\u001b[0m is_symmetric\u001b[39m=\u001b[39m\u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproperties\u001b[39m.\u001b[39mdirected,\n\u001b[1;32m 278\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/dataframe/core.py:4775\u001b[0m, in \u001b[0;36mDataFrame.__len__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 4773\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__len__\u001b[39m()\n\u001b[1;32m 4774\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 4775\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mlen\u001b[39;49m(s)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/dataframe/core.py:843\u001b[0m, in \u001b[0;36m_Frame.__len__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 840\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__len__\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 841\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mreduction(\n\u001b[1;32m 842\u001b[0m \u001b[39mlen\u001b[39;49m, np\u001b[39m.\u001b[39;49msum, token\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mlen\u001b[39;49m\u001b[39m\"\u001b[39;49m, meta\u001b[39m=\u001b[39;49m\u001b[39mint\u001b[39;49m, split_every\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[0;32m--> 843\u001b[0m )\u001b[39m.\u001b[39;49mcompute()\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/base.py:314\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcompute\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 291\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[1;32m 292\u001b[0m \n\u001b[1;32m 293\u001b[0m \u001b[39m This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[39m dask.base.compute\u001b[39;00m\n\u001b[1;32m 313\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 314\u001b[0m (result,) \u001b[39m=\u001b[39m compute(\u001b[39mself\u001b[39;49m, traverse\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 315\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/dask/base.py:599\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m 596\u001b[0m keys\u001b[39m.\u001b[39mappend(x\u001b[39m.\u001b[39m__dask_keys__())\n\u001b[1;32m 597\u001b[0m postcomputes\u001b[39m.\u001b[39mappend(x\u001b[39m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m--> 599\u001b[0m results \u001b[39m=\u001b[39m schedule(dsk, keys, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 600\u001b[0m \u001b[39mreturn\u001b[39;00m repack([f(r, \u001b[39m*\u001b[39ma) \u001b[39mfor\u001b[39;00m r, (f, a) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(results, postcomputes)])\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/client.py:3186\u001b[0m, in \u001b[0;36mClient.get\u001b[0;34m(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m 3184\u001b[0m should_rejoin \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 3185\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3186\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgather(packed, asynchronous\u001b[39m=\u001b[39;49masynchronous, direct\u001b[39m=\u001b[39;49mdirect)\n\u001b[1;32m 3187\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 3188\u001b[0m \u001b[39mfor\u001b[39;00m f \u001b[39min\u001b[39;00m futures\u001b[39m.\u001b[39mvalues():\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/client.py:2345\u001b[0m, in \u001b[0;36mClient.gather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m 2343\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 2344\u001b[0m local_worker \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 2345\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msync(\n\u001b[1;32m 2346\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_gather,\n\u001b[1;32m 2347\u001b[0m futures,\n\u001b[1;32m 2348\u001b[0m errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m 2349\u001b[0m direct\u001b[39m=\u001b[39;49mdirect,\n\u001b[1;32m 2350\u001b[0m local_worker\u001b[39m=\u001b[39;49mlocal_worker,\n\u001b[1;32m 2351\u001b[0m asynchronous\u001b[39m=\u001b[39;49masynchronous,\n\u001b[1;32m 2352\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/utils.py:349\u001b[0m, in \u001b[0;36mSyncMethodMixin.sync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[39mreturn\u001b[39;00m future\n\u001b[1;32m 348\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 349\u001b[0m \u001b[39mreturn\u001b[39;00m sync(\n\u001b[1;32m 350\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mloop, func, \u001b[39m*\u001b[39;49margs, callback_timeout\u001b[39m=\u001b[39;49mcallback_timeout, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 351\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/utils.py:416\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[39mif\u001b[39;00m error:\n\u001b[1;32m 415\u001b[0m typ, exc, tb \u001b[39m=\u001b[39m error\n\u001b[0;32m--> 416\u001b[0m \u001b[39mraise\u001b[39;00m exc\u001b[39m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m 417\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 418\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/utils.py:389\u001b[0m, in \u001b[0;36msync..f\u001b[0;34m()\u001b[0m\n\u001b[1;32m 387\u001b[0m future \u001b[39m=\u001b[39m wait_for(future, callback_timeout)\n\u001b[1;32m 388\u001b[0m future \u001b[39m=\u001b[39m asyncio\u001b[39m.\u001b[39mensure_future(future)\n\u001b[0;32m--> 389\u001b[0m result \u001b[39m=\u001b[39m \u001b[39myield\u001b[39;00m future\n\u001b[1;32m 390\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m error \u001b[39m=\u001b[39m sys\u001b[39m.\u001b[39mexc_info()\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/tornado/gen.py:769\u001b[0m, in \u001b[0;36mRunner.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 766\u001b[0m exc_info \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 768\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 769\u001b[0m value \u001b[39m=\u001b[39m future\u001b[39m.\u001b[39;49mresult()\n\u001b[1;32m 770\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m 771\u001b[0m exc_info \u001b[39m=\u001b[39m sys\u001b[39m.\u001b[39mexc_info()\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/client.py:2208\u001b[0m, in \u001b[0;36mClient._gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m 2206\u001b[0m exc \u001b[39m=\u001b[39m CancelledError(key)\n\u001b[1;32m 2207\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 2208\u001b[0m \u001b[39mraise\u001b[39;00m exception\u001b[39m.\u001b[39mwith_traceback(traceback)\n\u001b[1;32m 2209\u001b[0m \u001b[39mraise\u001b[39;00m exc\n\u001b[1;32m 2210\u001b[0m \u001b[39mif\u001b[39;00m errors \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mskip\u001b[39m\u001b[39m\"\u001b[39m:\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/contextlib.py:79\u001b[0m, in \u001b[0;36minner\u001b[0;34m()\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[39m@wraps\u001b[39m(func)\n\u001b[1;32m 77\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds):\n\u001b[1;32m 78\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_recreate_cm():\n\u001b[0;32m---> 79\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py:2937\u001b[0m, in \u001b[0;36m_deserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2934\u001b[0m \u001b[39m# Some objects require threadlocal state during deserialization, e.g. to\u001b[39;00m\n\u001b[1;32m 2935\u001b[0m \u001b[39m# detect the current worker\u001b[39;00m\n\u001b[1;32m 2936\u001b[0m \u001b[39mif\u001b[39;00m function \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 2937\u001b[0m function \u001b[39m=\u001b[39m loads_function(function)\n\u001b[1;32m 2938\u001b[0m \u001b[39mif\u001b[39;00m args \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(args, \u001b[39mbytes\u001b[39m):\n\u001b[1;32m 2939\u001b[0m args \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(args)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/worker.py:2925\u001b[0m, in \u001b[0;36mloads_function\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2923\u001b[0m result \u001b[39m=\u001b[39m cache_loads[bytes_object]\n\u001b[1;32m 2924\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m-> 2925\u001b[0m result \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(bytes_object)\n\u001b[1;32m 2926\u001b[0m cache_loads[bytes_object] \u001b[39m=\u001b[39m result\n\u001b[1;32m 2927\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/distributed/protocol/pickle.py:96\u001b[0m, in \u001b[0;36mloads\u001b[0;34m()\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[39mreturn\u001b[39;00m pickle\u001b[39m.\u001b[39mloads(x, buffers\u001b[39m=\u001b[39mbuffers)\n\u001b[1;32m 95\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 96\u001b[0m \u001b[39mreturn\u001b[39;00m pickle\u001b[39m.\u001b[39mloads(x)\n\u001b[1;32m 97\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mFailed to deserialize \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, x[:\u001b[39m10000\u001b[39m], exc_info\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/abc.py:176\u001b[0m, in \u001b[0;36mhost_deserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Perform device-side deserialization tasks.\u001b[39;00m\n\u001b[1;32m 155\u001b[0m \n\u001b[1;32m 156\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[39m:meta private:\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 172\u001b[0m frames \u001b[39m=\u001b[39m [\n\u001b[1;32m 173\u001b[0m cudf\u001b[39m.\u001b[39mcore\u001b[39m.\u001b[39mbuffer\u001b[39m.\u001b[39mas_buffer(f) \u001b[39mif\u001b[39;00m c \u001b[39melse\u001b[39;00m f\n\u001b[1;32m 174\u001b[0m \u001b[39mfor\u001b[39;00m c, f \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(header[\u001b[39m\"\u001b[39m\u001b[39mis-cuda\u001b[39m\u001b[39m\"\u001b[39m], \u001b[39mmap\u001b[39m(\u001b[39mmemoryview\u001b[39m, frames))\n\u001b[1;32m 175\u001b[0m ]\n\u001b[0;32m--> 176\u001b[0m obj \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39mdevice_deserialize(header, frames)\n\u001b[1;32m 177\u001b[0m \u001b[39mreturn\u001b[39;00m obj\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/abc.py:130\u001b[0m, in \u001b[0;36mdevice_deserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 125\u001b[0m typ \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(header[\u001b[39m\"\u001b[39m\u001b[39mtype-serialized\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m 126\u001b[0m frames \u001b[39m=\u001b[39m [\n\u001b[1;32m 127\u001b[0m cudf\u001b[39m.\u001b[39mcore\u001b[39m.\u001b[39mbuffer\u001b[39m.\u001b[39mas_buffer(f) \u001b[39mif\u001b[39;00m c \u001b[39melse\u001b[39;00m \u001b[39mmemoryview\u001b[39m(f)\n\u001b[1;32m 128\u001b[0m \u001b[39mfor\u001b[39;00m c, f \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(header[\u001b[39m\"\u001b[39m\u001b[39mis-cuda\u001b[39m\u001b[39m\"\u001b[39m], frames)\n\u001b[1;32m 129\u001b[0m ]\n\u001b[0;32m--> 130\u001b[0m \u001b[39mreturn\u001b[39;00m typ\u001b[39m.\u001b[39mdeserialize(header, frames)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/dataframe.py:1019\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1016\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m 1017\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdeserialize\u001b[39m(\u001b[39mcls\u001b[39m, header, frames):\n\u001b[1;32m 1018\u001b[0m index_nframes \u001b[39m=\u001b[39m header[\u001b[39m\"\u001b[39m\u001b[39mindex_frame_count\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m-> 1019\u001b[0m obj \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mdeserialize(\n\u001b[1;32m 1020\u001b[0m header, frames[header[\u001b[39m\"\u001b[39m\u001b[39mindex_frame_count\u001b[39m\u001b[39m\"\u001b[39m] :]\n\u001b[1;32m 1021\u001b[0m )\n\u001b[1;32m 1023\u001b[0m idx_typ \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(header[\u001b[39m\"\u001b[39m\u001b[39mindex\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mtype-serialized\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m 1024\u001b[0m index \u001b[39m=\u001b[39m idx_typ\u001b[39m.\u001b[39mdeserialize(header[\u001b[39m\"\u001b[39m\u001b[39mindex\u001b[39m\u001b[39m\"\u001b[39m], frames[:index_nframes])\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/frame.py:106\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 104\u001b[0m cls_deserialize \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(header[\u001b[39m\"\u001b[39m\u001b[39mtype-serialized\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m 105\u001b[0m column_names \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(header[\u001b[39m\"\u001b[39m\u001b[39mcolumn_names\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m--> 106\u001b[0m columns \u001b[39m=\u001b[39m deserialize_columns(header[\u001b[39m\"\u001b[39m\u001b[39mcolumns\u001b[39m\u001b[39m\"\u001b[39m], frames)\n\u001b[1;32m 107\u001b[0m \u001b[39mreturn\u001b[39;00m cls_deserialize\u001b[39m.\u001b[39m_from_data(\u001b[39mdict\u001b[39m(\u001b[39mzip\u001b[39m(column_names, columns)))\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py:2450\u001b[0m, in \u001b[0;36mdeserialize_columns\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2448\u001b[0m col_frame_count \u001b[39m=\u001b[39m meta[\u001b[39m\"\u001b[39m\u001b[39mframe_count\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 2449\u001b[0m col_typ \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(meta[\u001b[39m\"\u001b[39m\u001b[39mtype-serialized\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m-> 2450\u001b[0m colobj \u001b[39m=\u001b[39m col_typ\u001b[39m.\u001b[39mdeserialize(meta, frames[:col_frame_count])\n\u001b[1;32m 2451\u001b[0m columns\u001b[39m.\u001b[39mappend(colobj)\n\u001b[1;32m 2452\u001b[0m \u001b[39m# Advance frames\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py:1216\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1214\u001b[0m dtype \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(header[\u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m 1215\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m header:\n\u001b[0;32m-> 1216\u001b[0m data, frames \u001b[39m=\u001b[39m unpack(header[\u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m], frames)\n\u001b[1;32m 1217\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1218\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/column/column.py:1204\u001b[0m, in \u001b[0;36munpack\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1202\u001b[0m count \u001b[39m=\u001b[39m header[\u001b[39m\"\u001b[39m\u001b[39mframe_count\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 1203\u001b[0m klass \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mloads(header[\u001b[39m\"\u001b[39m\u001b[39mtype-serialized\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m-> 1204\u001b[0m obj \u001b[39m=\u001b[39m klass\u001b[39m.\u001b[39mdeserialize(header, frames[:count])\n\u001b[1;32m 1205\u001b[0m \u001b[39mreturn\u001b[39;00m obj, frames[count:]\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py:574\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m 568\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdeserialize\u001b[39m(\u001b[39mcls\u001b[39m, header: \u001b[39mdict\u001b[39m, frames: \u001b[39mlist\u001b[39m):\n\u001b[1;32m 569\u001b[0m \u001b[39m# TODO: because of the hack in `SpillableBuffer.serialize()` where\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[39m# deserialize into `SpillableBufferSlice` when the frames hasn't been\u001b[39;00m\n\u001b[1;32m 573\u001b[0m \u001b[39m# copied.\u001b[39;00m\n\u001b[0;32m--> 574\u001b[0m \u001b[39mreturn\u001b[39;00m SpillableBuffer\u001b[39m.\u001b[39mdeserialize(header, frames)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/buffer.py:335\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m()\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[39mreturn\u001b[39;00m frame \u001b[39m# The frame is already deserialized\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(frame, \u001b[39m\"\u001b[39m\u001b[39m__cuda_array_interface__\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 335\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_from_device_memory(frame)\n\u001b[1;32m 336\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_from_host_memory(frame)\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py:235\u001b[0m, in \u001b[0;36m_from_device_memory\u001b[0;34m()\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Create a spillabe buffer from device memory.\u001b[39;00m\n\u001b[1;32m 219\u001b[0m \n\u001b[1;32m 220\u001b[0m \u001b[39mNo data is being copied.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[39m Buffer representing the same device memory as `data`\u001b[39;00m\n\u001b[1;32m 233\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 234\u001b[0m ret \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m_from_device_memory(data)\n\u001b[0;32m--> 235\u001b[0m ret\u001b[39m.\u001b[39m_finalize_init(ptr_desc\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mtype\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mgpu\u001b[39m\u001b[39m\"\u001b[39m}, exposed\u001b[39m=\u001b[39mexposed)\n\u001b[1;32m 236\u001b[0m \u001b[39mreturn\u001b[39;00m ret\n", + "File \u001b[0;32m~/miniconda3/envs/cugraph_0411/lib/python3.10/site-packages/cudf/core/buffer/spillable_buffer.py:206\u001b[0m, in \u001b[0;36m_finalize_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m 204\u001b[0m manager \u001b[39m=\u001b[39m get_global_manager()\n\u001b[1;32m 205\u001b[0m \u001b[39mif\u001b[39;00m manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 206\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 207\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcannot create \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m without \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 208\u001b[0m \u001b[39m\"\u001b[39m\u001b[39ma global spill manager\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 209\u001b[0m )\n\u001b[1;32m 211\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_manager \u001b[39m=\u001b[39m manager\n\u001b[1;32m 212\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_manager\u001b[39m.\u001b[39madd(\u001b[39mself\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: cannot create without a global spill manager" + ] + } + ], "source": [ "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", "G = cugraph.Graph(directed=True)\n", @@ -170,6 +517,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -196,6 +544,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -204,6 +553,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -236,6 +586,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -251,6 +602,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -269,6 +621,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -280,11 +633,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -295,9 +649,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "cugraph_0411", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -309,7 +663,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/demo/mg_property_graph.ipynb b/notebooks/demo/mg_property_graph.ipynb index 0a5aa5df8..d2f0f456c 100644 --- a/notebooks/demo/mg_property_graph.ipynb +++ b/notebooks/demo/mg_property_graph.ipynb @@ -14,7 +14,7 @@ "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", "|---------------|------------|------------------|-----------------|-----------------------|\n", "| Don Acosta | 01/30/2023 | created | 23.02 nightly | 2xA6000 CUDA 11.7 |\n", - "\n", + "| Don Acosta | 05/15/2023 | update/test | 23.06 nightly | 2xA6000 CUDA 11.7 |\n", "\n", "CuGraph's multi-GPU features leverage Dask. RAPIDS has other projects based on Dask such as dask-cudf and dask-cuda. These products will also be used in this example. Check out [RAPIDS.ai](https://rapids.ai/) to learn more about these technologies." ] @@ -89,6 +89,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -108,6 +109,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -126,10 +128,10 @@ "\n", "# Helper function to set the reader chunk size to automatically get one partition per GPU \n", "input_data_path = get_data_file()\n", - "chunksize = dask_cugraph.get_chunksize(input_data_path)\n", + "blocksize = dask_cugraph.get_chunksize(input_data_path)\n", "\n", "# Multi-GPU CSV reader\n", - "e_list = dask_cudf.read_csv(input_data_path, chunksize = chunksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])" + "e_list = dask_cudf.read_csv(input_data_path, blocksize = blocksize, delimiter=' ', names=['src', 'dst'], dtype=['int32', 'int32'])" ] }, { @@ -154,6 +156,14 @@ "pG.get_num_edges()" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run louvain and print out the partition data" + ] + }, { "cell_type": "code", "execution_count": null, @@ -215,7 +225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.10.10" }, "orig_nbformat": 4, "vscode": { From 62ef0f5eba8b8f67bdab009dadfe697896ca25d1 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Fri, 19 May 2023 09:50:01 -0400 Subject: [PATCH 251/384] DOC --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 983584839..394acf0e9 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -76,9 +76,9 @@ # built documents. # # The short X.Y version. -version = '23.06' +version = '23.08' # The full version, including alpha/beta/rc tags. -release = '23.06.00' +release = '23.08.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 69a4ee674a49876fb74495cc0f2c14a8492b5416 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Fri, 19 May 2023 21:56:15 -0500 Subject: [PATCH 252/384] Updates pytest benchmarks to use synthetic data and multi-GPUs (#3540) closes #2810 closes #3282 * Adds the ability to use datasets read from files on disk and/or RMAT-generated synthetic datasets. * Adds markers for "file_data" and "rmat_data" for use by benchmark scripts, based on cluster size. * Adds CLI options for specifying the RMAT scale and edgefactor in order to generate datasets large enough for MNMG runs. * Adds fixtures for use by `bench_algos.py` benchmarks which will instantiate graph objs based on dataset type and SG or MG markers. * Updated `Dataset` class to allow instances to be used as test params and properly provide human-readable/deterministic test IDs. * Added ability for `Dataset` ctor to take a .csf file as input, useful when a metadata.yaml file for a dataset has not been created yet. * Added options to `get_test_data.sh` in the CI scripts to download a subset of datasets for C++ (to save time/space since most datasets aren't needed), and to only download the benchmark data for python (for use when running benchmarks as tests). Authors: - Rick Ratzel (https://github.com/rlratzel) - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Vibhu Jawa (https://github.com/VibhuJawa) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3540 --- benchmarks/pytest.ini | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index b61fa92d4..6af3aab27 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -14,7 +14,6 @@ markers = managedmem_off: RMM managed memory disabled poolallocator_on: RMM pool allocator enabled poolallocator_off: RMM pool allocator disabled - ETL: benchmarks for ETL steps small: small datasets tiny: tiny datasets directed: directed datasets @@ -50,6 +49,8 @@ markers = num_clients_32: start 32 cugraph-service clients fanout_10_25: fanout [10, 25] for sampling algos fanout_5_10_15: fanout [5, 10, 15] for sampling algos + rmat_data: RMAT-generated synthetic datasets + file_data: datasets from $RAPIDS_DATASET_ROOT_DIR python_classes = Bench* From 911768caefdc12eee9f1fe22bb4677b38e0a4831 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 23 May 2023 08:27:17 -0400 Subject: [PATCH 253/384] UVM notebook update and add tracker for notebooks to readme (#3595) resolves [graph_dl issue 192](https://github.com/rapidsai/graph_dl/issues/192) fixed deprecated item in uvm notebook Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3595 --- notebooks/demo/uvm.ipynb | 37 +++++++++++++++++++++++++++++-------- 1 file changed, 29 insertions(+), 8 deletions(-) diff --git a/notebooks/demo/uvm.ipynb b/notebooks/demo/uvm.ipynb index d4d9caeba..96dcb425f 100644 --- a/notebooks/demo/uvm.ipynb +++ b/notebooks/demo/uvm.ipynb @@ -1,22 +1,31 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Oversubscribing GPU memory in cuGraph\n", - "#### Author : Alex Fender\n", "# Skip notebook test\n", "\n", + "\n", "In this notebook, we will show how to **scale to 4x larger graphs than before** without incurring a performance drop using managed memory features in cuGraph. We will compute the PageRank of each user in Twitter's dataset on a single GPU as an example. This technique applies to all features.\n", "\n", "Unified Memory is a single memory address space accessible from any processor in a system. If a kernel tries to access any absent pages,the Page Migration Engine migrates the pages. When the GPU memory is full, the least recently used pages are evicted. In other words, Unified Memory transparently enables oversubscribing GPU memory, enabling out-of-core computations.\n", "\n", "\n", - "This notebook was tested on an NVIDIA 48GB RTX8000 GPU using RAPIDS 0.14 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)" + "This notebook was tested on an NVIDIA 48GB RTX8000 GPU using RAPIDS 0.14 and CUDA 10.2. Please be aware that your system may be different, and you may need to modify the code or install packages to run the below examples. If you think you have found a bug or an error, please file an issue in [cuGraph](https://github.com/rapidsai/cugraph/issues)\n", + "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "|-----------------|------------|-----------------------|-----------------|--------------------------------|\n", + "|Alex Fender | 05/15/2020 | created | 0.14 | RTX8000 48G GPU CUDA 10.2 |\n", + "|Chuck Hastings | 02/21/2021 | incorporate dendrogram| 0.18 | |\n", + "|Jordan Jacobelli | 11/08/2021 | update dataset URL | 23.04 | |\n", + "| Don Acosta | 05/21/2023 | updated/tested | 23.06 nightly | 2xA6000 CUDA 11.7 |\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -30,6 +39,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -58,6 +68,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -78,6 +89,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -85,6 +97,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -135,6 +148,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -159,6 +173,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -174,8 +189,8 @@ "t_start = time.time()\n", "\n", "# Create a directed graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n", - "G = cugraph.DiGraph()\n", - "G.from_cudf_edgelist(e_list, source='src', destination='dst', renumber=False)\n", + "G = cugraph.Graph(directed=True)\n", + "G.from_cudf_edgelist(e_list, source='src', destination='dst', renumber=False, store_transposed=True)\n", "\n", "# (optional) request the transposed here so that we can analyse pagerank solver time alone\n", "G.view_transposed_adj_list()\n", @@ -185,6 +200,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -210,6 +226,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -218,6 +235,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -249,6 +267,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -264,6 +283,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -275,11 +295,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "___\n", - "Copyright (c) 2020, NVIDIA CORPORATION.\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", "\n", @@ -290,9 +311,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cugraph_dev", + "display_name": "cugraph_0411", "language": "python", - "name": "cugraph_dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -304,7 +325,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.10" } }, "nbformat": 4, From 575fb3eca1a6df135f67e74b318cf5381b0b4fc8 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Thu, 25 May 2023 22:16:11 -0400 Subject: [PATCH 254/384] [BUG] Fix Issue in cuGraph-PyG Tests Blocking CI (#3607) One of the fixtures for the cuGraph-PyG tests is not properly partitioning the edgelist, causing some tests to fail. This PR updates that fixture to correctly partition the edgelist. Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Bradley Dice (https://github.com/bdice) - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3607 --- docs/cugraph/source/installation/getting_cugraph.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index 374a529d6..509508c52 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -38,7 +38,7 @@ Replace the package name in the example below to the one you want to install. Install and update cuGraph using the conda command: ```bash -conda install -c rapidsai -c numba -c conda-forge -c nvidia cugraph cudatoolkit=11.8 +conda install -c rapidsai -c conda-forge -c nvidia cugraph cudatoolkit=11.8 ``` Note: This conda installation only applies to Linux and Python versions 3.8/3.10. From 4c32c5c1d739b69f38bfc383b78f33c5df5eed23 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Wed, 7 Jun 2023 17:03:58 -0400 Subject: [PATCH 255/384] REL v23.06.00 release From f9161930dd26487c74491231e40471a78f1c09d3 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Fri, 9 Jun 2023 09:46:40 -0400 Subject: [PATCH 256/384] REL v23.06.01 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 983584839..96d813c6e 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -78,7 +78,7 @@ # The short X.Y version. version = '23.06' # The full version, including alpha/beta/rc tags. -release = '23.06.00' +release = '23.06.01' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From d94466f32bcc48247bf85a7e6417ca589b9bd2f6 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Tue, 13 Jun 2023 09:43:36 -0400 Subject: [PATCH 257/384] REL v23.06.02 release --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 96d813c6e..5b7ba8d1b 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -78,7 +78,7 @@ # The short X.Y version. version = '23.06' # The full version, including alpha/beta/rc tags. -release = '23.06.01' +release = '23.06.02' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From 4dfe0707014a88839a830b938eb09e1912b2a92b Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 27 Jun 2023 12:23:39 -0400 Subject: [PATCH 258/384] this fixes github links in cugraph, cugraph-dgl and cugraph-pyg (#3650) resolves #3635 AJ Schmidt came up with this fix. It does correct all the links in the cugraph projects. Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3650 --- docs/cugraph/source/conf.py | 5 +- docs/cugraph/source/sphinxext/github_link.py | 72 ++++++++------------ 2 files changed, 29 insertions(+), 48 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 394acf0e9..b64901772 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -204,6 +204,5 @@ def setup(app): # The following is used by sphinx.ext.linkcode to provide links to github linkcode_resolve = make_linkcode_resolve( - 'cugraph', 'https://github.com/rapidsai/' - 'cugraph/blob/{revision}/python/' - '{package}/{path}#L{lineno}') + "https://github.com/rapidsai/cugraph/blob/{revision}/python/{path}#L{lineno}" +) \ No newline at end of file diff --git a/docs/cugraph/source/sphinxext/github_link.py b/docs/cugraph/source/sphinxext/github_link.py index fa8fe3f5f..cc28dc6e8 100644 --- a/docs/cugraph/source/sphinxext/github_link.py +++ b/docs/cugraph/source/sphinxext/github_link.py @@ -1,4 +1,4 @@ -# Copyright (c) 2019-2021, NVIDIA CORPORATION. +# Copyright (c) 2019-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,10 +16,8 @@ # license in /thirdparty/LICENSES/LICENSE.scikit_learn import inspect -import os import re import subprocess -import sys from functools import partial from operator import attrgetter @@ -56,7 +54,7 @@ def _get_git_revision(): return revision.decode('utf-8') -def _linkcode_resolve(domain, info, package, url_fmt, revision): +def _linkcode_resolve(domain, info, url_fmt, revision): """Determine a link to online source for a class/method/function This is called by sphinx.ext.linkcode @@ -73,7 +71,7 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision): if revision is None: return - if domain not in ('py', 'pyx'): + if domain != 'py': return if not info.get('module') or not info.get('fullname'): return @@ -89,41 +87,29 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision): fn: str = None lineno: str = None - try: - fn = inspect.getsourcefile(obj) - except Exception: - fn = None - if not fn: - try: - fn = inspect.getsourcefile(sys.modules[obj.__module__]) - except Exception: - fn = None - - if not fn: - # Possibly Cython code. Search docstring for source - m = source_regex.search(obj.__doc__) - - if (m is not None): - source_file = m.group(1) - lineno = m.group(2) - - # fn is expected to be the absolute path. - fn = os.path.relpath(source_file, start=package) - print("{}:{}".format( - os.path.abspath(os.path.join("..", "python", "cuml", fn)), - lineno)) - else: - return - else: - # Test if we are absolute or not (pyx are relative) - if (not os.path.isabs(fn)): - # Should be relative to docs right now - fn = os.path.abspath(os.path.join("..", "python", fn)) - - # Convert to relative from module root - fn = os.path.relpath(fn, - start=os.path.dirname( - __import__(package).__file__)) + obj_module = inspect.getmodule(obj) + if not obj_module: + print(f"could not infer source code link for: {info}") + return + module_name = obj_module.__name__.split('.')[0] + + module_dir_dict = { + "cugraph_dgl": "cugraph-dgl", + "cugraph_pyg": "cugraph-pyg", + "cugraph_service_client": "cugraph-service/client", + "cugraph_service_server": "cugraph-service/server", + "cugraph": "cugraph", + "pylibcugraph": "pylibcugraph", + } + module_dir = module_dir_dict.get(module_name) + if not module_dir: + print(f"no source path directory set for {module_name}") + return + + obj_path = "/".join(obj_module.__name__.split(".")[1:]) + obj_file_ext = obj_module.__file__.split('.')[-1] + source_ext = "pyx" if obj_file_ext == "so" else "py" + fn = f"{module_dir}/{module_name}/{obj_path}.{source_ext}" # Get the line number if we need it. (Can work without it) if (lineno is None): @@ -137,18 +123,15 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision): else: lineno = '' return url_fmt.format(revision=revision, - package=package, path=fn, lineno=lineno) -def make_linkcode_resolve(package, url_fmt): +def make_linkcode_resolve(url_fmt): """Returns a linkcode_resolve function for the given URL format revision is a git commit reference (hash or name) - package is the name of the root module of the package - url_fmt is along the lines of ('https://github.com/USER/PROJECT/' 'blob/{revision}/{package}/' '{path}#L{lineno}') @@ -156,5 +139,4 @@ def make_linkcode_resolve(package, url_fmt): revision = _get_git_revision() return partial(_linkcode_resolve, revision=revision, - package=package, url_fmt=url_fmt) From b1e7a4df8bd4d8fce9f296dfdd24f0e0070f7619 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Fri, 21 Jul 2023 09:55:43 -0400 Subject: [PATCH 259/384] v23.10 Updates [skip ci] --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index b64901772..a96f0fc1e 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -76,9 +76,9 @@ # built documents. # # The short X.Y version. -version = '23.08' +version = '23.10' # The full version, including alpha/beta/rc tags. -release = '23.08.00' +release = '23.10.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From b0721e9323623cceb66cc7bc1973dd53a3cb7d3b Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 25 Jul 2023 09:28:13 -0400 Subject: [PATCH 260/384] Created landing spot for centrality and similarity algorithms (#3620) resolves #3030 resolves #3023 Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3620 --- .../source/graph_support/DGL_support.md | 14 ++- .../source/graph_support/algorithms.md | 84 ++++++++++-------- .../graph_support/algorithms/Centrality.md | 38 ++++++++ .../graph_support/algorithms/Similarity.md | 38 ++++++++ .../algorithms/betweenness_centrality.md | 42 +++++++++ .../algorithms/degree_centrality.md | 32 +++++++ .../algorithms/eigenvector_centrality.md | 42 +++++++++ .../algorithms/jaccard_similarity.md | 34 +++++++ .../algorithms/katz_centrality.md | 28 ++++++ .../algorithms/overlap_similarity.md | 32 +++++++ .../algorithms/sorensen_coefficient.md | 34 +++++++ .../images/zachary_graph_centrality.png | Bin 0 -> 141188 bytes .../source/tutorials/cugraph_notebooks.md | 22 ++--- 13 files changed, 386 insertions(+), 54 deletions(-) create mode 100644 docs/cugraph/source/graph_support/algorithms/Centrality.md create mode 100644 docs/cugraph/source/graph_support/algorithms/Similarity.md create mode 100644 docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md create mode 100644 docs/cugraph/source/graph_support/algorithms/degree_centrality.md create mode 100644 docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md create mode 100644 docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md create mode 100644 docs/cugraph/source/graph_support/algorithms/katz_centrality.md create mode 100644 docs/cugraph/source/graph_support/algorithms/overlap_similarity.md create mode 100644 docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md create mode 100644 docs/cugraph/source/images/zachary_graph_centrality.png diff --git a/docs/cugraph/source/graph_support/DGL_support.md b/docs/cugraph/source/graph_support/DGL_support.md index 4db2a4d77..fc1f1b45b 100644 --- a/docs/cugraph/source/graph_support/DGL_support.md +++ b/docs/cugraph/source/graph_support/DGL_support.md @@ -35,15 +35,14 @@ pytest tests/* ## Usage ```diff -+from cugraph_dgl.convert import cugraph_storage_from_heterograph -+cugraph_g = cugraph_storage_from_heterograph(dgl_g) +from cugraph_dgl.convert import cugraph_storage_from_heterograph +cugraph_g = cugraph_storage_from_heterograph(dgl_g) sampler = dgl.dataloading.NeighborSampler( [15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label']) train_dataloader = dgl.dataloading.DataLoader( -- dgl_g, -+ cugraph_g, +cugraph_g, train_idx, sampler, device=device, @@ -53,3 +52,10 @@ drop_last=False, num_workers=0) ``` +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md index 60243fefb..f6cb7c0d8 100644 --- a/docs/cugraph/source/graph_support/algorithms.md +++ b/docs/cugraph/source/graph_support/algorithms.md @@ -20,55 +20,55 @@ _Italic_ algorithms are planned for future releases. Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called MNMG). -| Category | Algorithm | Scale | Notes | +| Category | Notebooks | Scale | Notes | | ----------------- | ---------------------------------- | ------------------- | --------------------------------------------------------------- | -| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | | | | -| | [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) | __Multi-GPU__ | | -| | [Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | Single-GPU | MG planned for 23.02 | -| | [Edge Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | Single-GPU | MG planned for 23.02 | -| | [Eigenvector Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Eigenvector.ipynb) | __Multi-GPU__ | | -| | [Degree Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Degree.ipynb) | __Multi-GPU__ | Python only | +| [Centrality](./algorithms/Centrality.md) | [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | | | +| | [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) | __Multi-GPU__ | | +| | [Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | __Multi-GPU__ | MG as of 23.06 | +| | [Edge Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | __Multi-GPU__ | MG as of 23.08 | +| | [Eigenvector Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Eigenvector.ipynb) | __Multi-GPU__ | | +| | [Degree Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Degree.ipynb) | __Multi-GPU__ | Python only | | Community | | | | -| | [Leiden](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | Single-GPU | MG planned for 23.02 | -| | [Louvain](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | | -| | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Single-GPU | | -| | [Spectral-Clustering - Balanced Cut](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | -| | [Spectral-Clustering - Modularity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | -| | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipyn) | Single-GPU | | -| | [Triangle Counting](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Triangle-Counting.ipynb) | __Multi-GPU__ | | -| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Single-GPU | | -| Components | | | | -| | [Weakly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | __Multi-GPU__ | | -| | [Strongly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | Single-GPU | | -| Core | | | | -| | [K-Core](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/kcore.ipynb) | **Multi-GPU** | | -| | [Core Number](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/core-number.ipynb) | **Multi-GPU** | | -| _Flow_ | | | | -| | _MaxFlow_ | --- | | -| _Influence_ | | | | -| | _Influence Maximization_ | --- | | +| | [Leiden](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | MG as of 23.06 | +| | [Louvain](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | | +| | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Single-GPU | MG planned for 23.10 | +| | [Spectral-Clustering - Balanced Cut](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | +| | [Spectral-Clustering - Modularity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | +| | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipyn) | Single-GPU | | +| | [Triangle Counting](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Triangle-Counting.ipynb) | __Multi-GPU__ | | +| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Single-GPU | MG planned for 23.10 | +| Components | | | | +| | [Weakly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | __Multi-GPU__ | | +| | [Strongly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | Single-GPU | | +| Core | | | | +| | [K-Core](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/kcore.ipynb) | __Multi-GPU__ | | +| | [Core Number](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/core-number.ipynb) | __Multi-GPU__ | | +| _Flow_ | | | | +| | _MaxFlow_ | --- | | +| _Influence_ | | | | +| | _Influence Maximization_ | --- | | | Layout | | | | -| | [Force Atlas 2](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/layout/Force-Atlas2.ipynb) | Single-GPU | | +| | [Force Atlas 2](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/layout/Force-Atlas2.ipynb) | Single-GPU | | | Linear Assignment | | | | -| | [Hungarian]() | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) | +| | [Hungarian]() | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) | | Link Analysis | | | | -| | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | +| | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | | | [Personal Pagerank]() | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) | -| | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb) | __Multi-GPU__ | | -| Link Prediction | | | | -| | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | **Multi-GPU** | MG as of 22.12
Directed graph only | +| | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb) | __Multi-GPU__ | | +| [Link Prediction](./algorithms/Similarity.md) | | | | +| | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | __Multi-GPU__ | Directed graph only | | | [Weighted Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | Single-GPU | | -| | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) | **Multi-GPU** | MG as of 22.12 | -| | [Sorensen Coefficient](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen-Similarity.ipynb) | **Multi-GPU** | MG as of 22.12 | +| | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) | **Multi-GPU** | | +| | [Sorensen Coefficient](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb) | __Multi-GPU__ | MG is unweighted only | | | _Local Clustering Coefficient_ | --- | | | Sampling | | | | -| | [Uniform Random Walks RW](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/sampling/RandomWalk.ipynb) | **Multi-GPU** | | +| | [Uniform Random Walks RW](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/sampling/RandomWalk.ipynb) | __Multi-GPU__ | | | | *Biased Random Walks (RW)* | --- | | -| | Egonet | **Multi-GPU** | | -| | Node2Vec | Single-GPU | MG planned for 23.02 | +| | Egonet | __Multi-GPU__ | | +| | Node2Vec | Single-GPU | | | | Uniform Neighborhood sampling | __Multi-GPU__ | | | Traversal | | | | -| | Breadth First Search (BFS) | __Multi-GPU__ | with cutoff support``[C++ README](cpp/src/traversal/README.md#BFS) | +| | Breadth First Search (BFS) | __Multi-GPU__ | with cutoff support [C++ README](cpp/src/traversal/README.md#BFS) | | | Single Source Shortest Path (SSSP) | __Multi-GPU__ | [C++ README](cpp/src/traversal/README.md#SSSP) | | | _ASSP / APSP_ | --- | | | Tree | | | | @@ -83,3 +83,13 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | | RMAT | __Multi-GPU__ | | | | _Barabasi-Albert_ | --- | | | | | | | + +

+ +___ +Copyright (c) 2019 - 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms/Centrality.md b/docs/cugraph/source/graph_support/algorithms/Centrality.md new file mode 100644 index 000000000..e42bbe238 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/Centrality.md @@ -0,0 +1,38 @@ + +# cuGraph Centrality Notebooks + + + +The RAPIDS cuGraph Centrality folder contains a collection of Jupyter Notebooks that demonstrate algorithms to identify and quantify the importance of vertices to the structure of the graph. In the diagram above, the highlighted vertices are highly important and are likely answers to questions like: + +* Which vertices have the highest degree (most direct links) ? +* Which vertices are on the most efficient paths through the graph? +* Which vertices connect the most important vertices to each other? + +But which vertices are most important? The answer depends on which measure/algorithm is run. Manipulation of the data before or after the graph analytic is not covered here. Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended + +## Summary + +|Algorithm |Notebooks Containing |Description | +| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | +|[Degree Centrality](./degree_centrality.md)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb) |Measure based on counting direct connections for each vertex| +|[Betweenness Centrality](./betweenness_centrality.md)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb) |Number of shortest paths through the vertex| +|[Eigenvector Centrality](./eigenvector_centrality.md)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| +|[Katz Centrality](./katz_centrality.md)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | +|Pagerank|[Centrality](./Centrality.ipynb), [Pagerank](../../link_analysis/Pagerank.ipynb) |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices. | + +[System Requirements](../../README.md#requirements) + +| Author Credit | Date | Update | cuGraph Version | Test Hardware | +| --------------|------------|------------------|-----------------|----------------| +| Brad Rees | 04/19/2021 | created | 0.19 | GV100, CUDA 11.0 +| Don Acosta | 07/05/2022 | tested / updated | 22.08 nightly | DGX Tesla V100 CUDA 11.5 + +## Copyright + +Copyright (c) 2019 - 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ diff --git a/docs/cugraph/source/graph_support/algorithms/Similarity.md b/docs/cugraph/source/graph_support/algorithms/Similarity.md new file mode 100644 index 000000000..450beb373 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/Similarity.md @@ -0,0 +1,38 @@ + +# cuGraph Similarity Notebooks + +The RAPIDS cuGraph Similarity folder contain a collection of Jupyter Notebooks that demonstrate algorithms to quantify the similarity between pairs of vertices in the graph. +Results of Similarity algorithms are often used to answer questions like: +* Could two vertices be duplicates or aliases of the same actor? +* Can we predict missing edges based of the similarity between two nodes? +* Are multiple similar communities within the graph? +* Can I create recommendations based on the similarity between vertices in the graph. + + +Manipulation of the data before or after the graph analytic is not covered here. Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended + +## Summary + +|Algorithm |Notebooks Containing |Description | +| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | +|[Jaccard Smiliarity](./jaccard_similarity.md)| [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) || +|[Overlap Similarity](./overlap_similarity.md)| [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) || +|[Sorensen](./sorensen_coefficient.md)|[Sorensen Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb)|| +|Personal Pagerank|[Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) || + + +[System Requirements](../../README.md#requirements) + +| Author Credit | Date | Update | cuGraph Version | Test Hardware | +| --------------|------------|------------------|-----------------|----------------| +| Brad Rees | 04/19/2021 | created | 0.19 | GV100, CUDA 11.0 +| Don Acosta | 07/05/2022 | tested / updated | 22.08 nightly | DGX Tesla V100 CUDA 11.5 + +## Copyright + +Copyright (c) 2019 - 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md b/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md new file mode 100644 index 000000000..7dc692684 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md @@ -0,0 +1,42 @@ +# Betweenness Centrality (BC) + +Betweenness centrality is a measure of the relative importance based on measuring the number of shortest paths that pass through each vertex or over each edge. High betweenness centrality vertices have a greater number of path cross through the vertex. Likewise, high centrality edges have more shortest paths that pass over the edge. + +See [Betweenness on Wikipedia](https://en.wikipedia.org/wiki/Betweenness_centrality) for more details on the algorithm. + +Betweenness centrality of a node 𝑣 is the sum of the fraction of all-pairs shortest paths that pass through 𝑣 + +$c_B(v) =\sum_{s,t \in V} \frac{\sigma(s, t|v)}{\sigma(s, t)}$ + + +## When to use BC +Betweenness centrality is often used to answer questions like: +* Which vertices are most influential in the network? +* What are the bridge vertices in a network? +* How robust/redundant is the network? +* In a social network analysis, betweenness centrality can be used to identify roles in an organization. + +## When not to use BC +Betweenness Centrality is less efficient in certain circumstances: +* Large graphs may require approximationing betweenness centrality as the computational cost increases. +* Disconnected networks or networks with many isolated components limit the value of betweenness centrality +* Betweenness centality is more costly and less useful in weighted graphs. +* In networks with hierarchical structure, BC might not accurately reflect true influence +* Networks with multiple edge types often require a seperate method of measuring influence for each edge type. + + +## How computationally expensive is BC? +While cuGraph's parallelism migigates run time, [Big O notation](https://en.wikipedia.org/wiki/Big_O_notation) is still the standard to compare algorithm costs. +* The cost is O(V(E+V)) for a non-weighted graph and O(V(E+V)log(V)) for a weighted graph. +* A breadth-first search is done to determine shortest paths betweeb all nodes prior to calculating BC. + +## Sample benchmarks +Coming Soon + +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms/degree_centrality.md b/docs/cugraph/source/graph_support/algorithms/degree_centrality.md new file mode 100644 index 000000000..64fe694e4 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/degree_centrality.md @@ -0,0 +1,32 @@ +# Degree Centrality +Degree centrality is the simplest measure of the relative importance based on counting the connections with each vertex. Vertices with the most connections are the most central by this measure. + +See [Degree Centrality on Wikipedia](https://en.wikipedia.org/wiki/Degree_centrality) for more details on the algorithm. + +Degree centrality of a vertex 𝑣 is the sum of the edges incident on that node. + + + +## When to use Degree Centrality +* When you need a really quick identifcation of important nodes on very simply structured data. +* In cases like collaboration networks where all links have equal importance. +* In many biologic and transportation networks, shear number of connections is important to itentify critical nodes whether they be proteins or airports. +* In huge graphs, Degree centrality is a the quickest + +## When not to use Degree Centrality +* When weights, edge direction or edge types matter +* Graphs with self loops +* Multi-graphs ( graphs with multiple edges between the same two nodes) +* In general Degree Centrality falls short in most cases where the data is complex or nuanced. + +## How computationally expensive is it? +While cuGraph's parallelism migigates run time, [Big O notation](https://en.wikipedia.org/wiki/Big_O_notation) is still the standard to compare algorithm costs. + +The cost of Degree Centrality is O(n) where n is the number of nodes. +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md b/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md new file mode 100644 index 000000000..5217dc497 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md @@ -0,0 +1,42 @@ +# Eigenvector Centrality + +Eigenvector centrality computes the centrality for a vertex based on the +centrality of its neighbors. The Eigenvector of a node measures influence within a graph by taking into account a vertex's connections to other highly connected vertices. + + +See [Eigenvector Centrality on Wikipedia](https://en.wikipedia.org/wiki/Eigenvector_centrality) for more details on the algorithm. + +The eigenvector centrality for node i is the +i-th element of the vector x defined by the eigenvector equation. + + + + +Where M(v) is the adjacency list for the set of vertices(v) and λ is a constant. + +[Learn more about EigenVector Centrality](https://www.sci.unich.it/~francesc/teaching/network/eigenvector.html) + +## When to use Eigenvector Centrality +* When the quality and quantity of edges matters, in other words, connections to other high-degree nodes is important +* To calculate influence in nuanced networks like social and financial networks. + +## When not to use Eigenvector Centrality +* in graphs with many disconnected groups +* in graphs containing many distinct and different communities +* in networks with negative weights +* in huge networks eigenvector centrality can become computationally infeasible in single threaded systems. + + +## How computationally expensive is it? +While cuGraph's parallelism migigates run time, [Big O notation](https://en.wikipedia.org/wiki/Big_O_notation) is still the standard to compare algorithm costs. + +O(VE) where V is the number of vertices(nodes) and Eis the number of edges. + +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ + diff --git a/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md b/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md new file mode 100644 index 000000000..c9ffc76d1 --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md @@ -0,0 +1,34 @@ +# Jaccard Similarity + +The Jaccard similarity between two sets is defined as the ratio of the volume of their intersection divided by the volume of their union. + +The Jaccard Similarity can then be defined as + +Jaccard similarity coefficient = $\frac{|A \cap B|}{|A \cup B|}$ + +In graphs, the sets refer to the set of connected nodes or neighborhood of nodes A and B. + +[Learn more about Jaccard Similarity](https://en.wikipedia.org/wiki/Jaccard_index) + +## When to use Jaccard Similarity +* You want to find whether two nodes in a graph are in similar communities. +* You want to compare the structure of two graphs. +* You have a set of graphs and want to classify them as particular types + +## When not to use Jaccard Similarity +* In directed graphs +* in very large sparse graphs +* Graphs with large disparities in node degrees + +## How computationally expensive is it? +While cuGraph's parallelism mitigates run cost, [Big O notation](https://en.wikipedia.org/wiki/Big_O_notation) is still the standard to compare algorithm costs. + +The cost of calculating the Jaccard Similarity for a graph is O(d * n) where d is the average degree of the nodes and n is the number of nodes. + +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms/katz_centrality.md b/docs/cugraph/source/graph_support/algorithms/katz_centrality.md new file mode 100644 index 000000000..3bfe4d40c --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/katz_centrality.md @@ -0,0 +1,28 @@ +# Katz Centrality + +Katz centrality is a measure of the relative importance of a vertex within the graph based on measuring the influence across the total number of walks between vertex pairs. Katz is similar to Eigenvector centrality. The main difference is that Katz also takes into account indirect relationships. The Katz calculation includes a user-controlled attenuation variable that controls the weight of indirect relationships. Otherwise it shares many of the advantages and disadvantages of Eigenvector centrality. + +$C_{katz}(i) = \sum_{k=1}^{\infty} \sum_{j=1}^{n} \alpha ^k(A^k)_{ji}$ + +See [Katz on Wikipedia](https://en.wikipedia.org/wiki/Katz_centrality) for more details on the algorithm. + +## When to use Katz Centrality +* in disconnected graphs +* in sparse graphs +* in graphs with multi-hop propogation like innovation + +## When not to use Katz Centrality +* in graphs with heavy cyclical dependency (feedback loops), Katz Centrality might not converge preventing usable results. +* when a graph contains multiple distinct influence factors Katz can blur them. +* Katz is very expensive so use in large graphs depends on cuGraph parallelism to be viable. + +## How computationally expensive is it? +Katz centraility has several stages with costs that add up as the graph gets larger. The overall cost is often O(n2) to O(n3) where n is the number of nodes. + +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md b/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md new file mode 100644 index 000000000..026d713fb --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md @@ -0,0 +1,32 @@ +# Overlap Similarity + +The Overlap Coefficient, also known as th Szymkiewicz–Simpson coefficient, between two sets is defined as the ratio of the volume of their intersection divided by the volume of the smaller set. +The Overlap Coefficient can be defined as + +$overlap(A,B) = \frac{|A \cap B|}{min(|A|,|B|)}$ + +[Learn more about Overlap Similarity](https://en.wikipedia.org/wiki/Overlap_coefficient) + +## When to use Overlap Similarity +* You want to find similarty based on shared neighbors instead of the sets as a whole. +* You want to partition a graph into non-overlapping clusters. +* You want to compare subgraphs within a graph + +## When not to use Overlap Similarity +* You are trying to compare graphs of extremely different sizes +* In overly sparse or dense graph can overlap similarity can miss relationships or give fals positives respectively. +* In directed graphs, there are better algorithms to use. + + +## How computationally expensive is it? +While cuGraph's parallelism migigates run time, [Big O notation](https://en.wikipedia.org/wiki/Big_O_notation) is still the standard to compare algorithm costs. + +The cost to compute overlap similarity is O(n*d) where n is the number of nodes and d is the average degree of the nodes. + +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. +___ \ No newline at end of file diff --git a/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md b/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md new file mode 100644 index 000000000..01b6709de --- /dev/null +++ b/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md @@ -0,0 +1,34 @@ +# Sørensen Coefficient + +The Sørensen Coefficient, also called the Sørensen-Dice similarity coefficient, quantifies the similarity and overlap between two samples. + +It is defined as two times the size of the set intersection divided by the sum of the size of the two sets. The value ranges from 0 to 1. + +Sørensen coefficient = $\left(2 * |A \cap B| \right) \over \left(|A| + |B| \right)$ + + +In graphs, the sets refer to the set of connected nodes or neighborhood of nodes A and B. + +[Learn more about Sørensen Coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) + +## When to use Sørensen Coefficient +* When you want to compare nodes with vastly different sized neighborhoods. +* When the intersection of the node neigborhoods is more important than the overall similarity + + +## When not to use Sørensen Coefficient +* In directed graphs +* Comparing graphs with different underlying data relationships. +* In weighted graphs, while cuGraph does have a weighted Sørensen implementation, the algorithm did not originally use weights. + +## How computationally expensive is it? +While cuGraph's parallelism mitigates run cost, [Big O notation](https://en.wikipedia.org/wiki/Big_O_notation) is still the standard to compare algorithm execution time. +The cost to run O(n * m) where n is the number of nodes in the graph and m is the number of groups to test. + +___ +Copyright (c) 2023, NVIDIA CORPORATION. + +Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. 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- Modularity Modularity | @@ -28,11 +28,11 @@ This repository contains a collection of Jupyter Notebooks that outline how to r | | [Core Number](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/cores/core-number.ipynb) | Computer the Core number for each vertex in a graph | Layout | | | | | [Force-Atlas2](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/layout/Force-Atlas2.ipynb) |A large graph visualization achieved with cuGraph. | -| Link Analysis | | | +| Link Analysis | | | | | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) | Compute the PageRank of every vertex in a graph | | | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb) | Compute the HITS' Hub and Authority scores for every vertex in a graph | -| Link Prediction | | | -| | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | Compute vertex similarity score using both:
- Jaccard Similarity
- Weighted Jaccard | +| Link Prediction | | | +| | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | Compute vertex similarity score using both:
- Jaccard Similarity
- Weighted Jaccard | | | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) | Compute vertex similarity score using the Overlap Coefficient | | Sampling | | | [Random Walk](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/sampling/RandomWalk.ipynb) | Compute Random Walk for a various number of seeds and path lengths | @@ -60,9 +60,9 @@ Running the example in these notebooks requires: * CUDA 11.4+ * NVIDIA driver 450.51+ -#### Copyright +## Copyright -Copyright (c) 2019-2022, NVIDIA CORPORATION. All rights reserved. +Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -70,8 +70,4 @@ http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. - - - - -![RAPIDS](https://github.com/rapidsai/cugraph/blob/main/img/rapids_logo.png) \ No newline at end of file +--- From 4cb904a24ce016bebc92823b8631ce6a5594772e Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Wed, 9 Aug 2023 17:12:39 -0400 Subject: [PATCH 261/384] REL v23.08.00 release From ff240bb142522d69df029cb765d1fa25387eee5c Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 16 Aug 2023 01:03:33 -0500 Subject: [PATCH 262/384] Add new cugraph-nx package (networkx backend using pylibcugraph) (#3614) `cugraph-nx` is working with two algorithm (`betweenness_centrality`, `edge_betweenness_centrality`). It can efficiently (i.e., faster than `cugraph` in my benchmarks) convert from (and to) networkx graphs, and runs (and passes) networkx tests using networkx dispatching machinery. It renumbers vertex labels, which we handle with python dicts. It only depends on `networkx`, `cupy`, and `pylibcugraph`. `cupy` is a transitive dependency from `pylibcugraph`, so we don't actually need to list it as a dependency. I think it's nice the way we depend on `networkx`, which allows us to use its exceptions and utilities. There is still plenty to do (and I have a few questions), but I think it's a good start and gets cugraph in on the NetworkX dispatching party. I was planning on including `cugraph-pg` with this PR, but now I think it's probably better to have that in a new PR. Authors: - Erik Welch (https://github.com/eriknw) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3614 --- docs/cugraph/source/installation/source_build.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md index 9a93ed6c5..453149d6c 100644 --- a/docs/cugraph/source/installation/source_build.md +++ b/docs/cugraph/source/installation/source_build.md @@ -84,6 +84,7 @@ build.sh [ ...] [ ...] libcugraph_etl - build libcugraph_etl.so and SG test binaries pylibcugraph - build the pylibcugraph Python package cugraph - build the cugraph Python package + cugraph-nx - build the cugraph-nx Python package cugraph-service - build the cugraph-service_client and cugraph-service_server Python package cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. cugraph-dgl - build the cugraph-dgl extensions for DGL From 47a9c66fe2e26904407b106189604899927ff735 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 6 Sep 2023 13:04:23 -0500 Subject: [PATCH 263/384] Rename `cugraph-nx` to `nx-cugraph` (#3840) We decided to rename `cugraph-nx` to `nx-cugraph` to follow (and help establish) conventions for names of networkx backends. See: https://github.com/networkx/networkx/discussions/6883 This PR was created from the following commands: ```sh mv notebooks/ ../notebooks-bak find * -type f -print0 | xargs -0 sed -i 's/cugraph_nx/nx_cugraph/g' find * -type f -print0 | xargs -0 sed -i 's/cugraph-nx/nx-cugraph/g' git mv ./conda/recipes/cugraph-nx ./conda/recipes/nx-cugraph git mv ./python/cugraph-nx ./python/nx-cugraph git mv ./python/nx-cugraph/cugraph_nx ./python/nx-cugraph/nx_cugraph mv ../notebooks-bak/ notebooks ``` (a more reliable bash script would ensure the destination of `git mv` does not exist yet, b/c if the destination is a directory, it will happily--and incorrectly--move the target _into_ the directory) ```sh # Make sure everything got renamed correctly git grep -i 'cugraph.nx' find . -iname '*cugraph*nx*' -print ``` Should we remove `cugraph-nx` nightlies once this is merged? Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3840 --- .../source/installation/source_build.md | 2 +- python/nx-cugraph/.flake8 | 13 + python/nx-cugraph/LICENSE | 1 + python/nx-cugraph/Makefile | 7 + python/nx-cugraph/README.md | 32 + python/nx-cugraph/conftest.py | 28 + python/nx-cugraph/lint.yaml | 86 +++ python/nx-cugraph/nx_cugraph/__init__.py | 20 + .../nx_cugraph/algorithms/__init__.py | 14 + .../algorithms/centrality/__init__.py | 13 + .../algorithms/centrality/betweenness.py | 73 +++ .../algorithms/community/__init__.py | 13 + .../algorithms/community/louvain.py | 62 ++ .../nx-cugraph/nx_cugraph/classes/__init__.py | 15 + .../nx-cugraph/nx_cugraph/classes/digraph.py | 61 ++ python/nx-cugraph/nx_cugraph/classes/graph.py | 600 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/convert.py | 528 +++++++++++++++ python/nx-cugraph/nx_cugraph/interface.py | 201 ++++++ .../nx-cugraph/nx_cugraph/tests/__init__.py | 13 + .../nx_cugraph/tests/bench_convert.py | 179 ++++++ .../nx-cugraph/nx_cugraph/tests/conftest.py | 31 + .../nx_cugraph/tests/test_convert.py | 203 ++++++ .../nx_cugraph/tests/test_match_api.py | 106 ++++ python/nx-cugraph/nx_cugraph/typing.py | 25 + .../nx-cugraph/nx_cugraph/utils/__init__.py | 14 + .../nx-cugraph/nx_cugraph/utils/decorators.py | 69 ++ python/nx-cugraph/nx_cugraph/utils/misc.py | 60 ++ python/nx-cugraph/pyproject.toml | 218 +++++++ python/nx-cugraph/run_nx_tests.sh | 36 ++ python/nx-cugraph/setup.py | 15 + 30 files changed, 2737 insertions(+), 1 deletion(-) create mode 100644 python/nx-cugraph/.flake8 create mode 120000 python/nx-cugraph/LICENSE create mode 100644 python/nx-cugraph/Makefile create mode 100644 python/nx-cugraph/README.md create mode 100644 python/nx-cugraph/conftest.py create mode 100644 python/nx-cugraph/lint.yaml create mode 100644 python/nx-cugraph/nx_cugraph/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/community/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py create mode 100644 python/nx-cugraph/nx_cugraph/classes/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/classes/digraph.py create mode 100644 python/nx-cugraph/nx_cugraph/classes/graph.py create mode 100644 python/nx-cugraph/nx_cugraph/convert.py create mode 100644 python/nx-cugraph/nx_cugraph/interface.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/bench_convert.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/conftest.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_convert.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_match_api.py create mode 100644 python/nx-cugraph/nx_cugraph/typing.py create mode 100644 python/nx-cugraph/nx_cugraph/utils/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/utils/decorators.py create mode 100644 python/nx-cugraph/nx_cugraph/utils/misc.py create mode 100644 python/nx-cugraph/pyproject.toml create mode 100755 python/nx-cugraph/run_nx_tests.sh create mode 100644 python/nx-cugraph/setup.py diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md index 453149d6c..7782591f1 100644 --- a/docs/cugraph/source/installation/source_build.md +++ b/docs/cugraph/source/installation/source_build.md @@ -84,7 +84,7 @@ build.sh [ ...] [ ...] libcugraph_etl - build libcugraph_etl.so and SG test binaries pylibcugraph - build the pylibcugraph Python package cugraph - build the cugraph Python package - cugraph-nx - build the cugraph-nx Python package + nx-cugraph - build the nx-cugraph Python package cugraph-service - build the cugraph-service_client and cugraph-service_server Python package cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. cugraph-dgl - build the cugraph-dgl extensions for DGL diff --git a/python/nx-cugraph/.flake8 b/python/nx-cugraph/.flake8 new file mode 100644 index 000000000..3a2e3fb86 --- /dev/null +++ b/python/nx-cugraph/.flake8 @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. + +[flake8] +max-line-length = 88 +inline-quotes = " +extend-ignore = + E203, + SIM105, + SIM401, +# E203 whitespace before ':' (to be compatible with black) +per-file-ignores = + nx_cugraph/tests/*.py:T201, + __init__.py:F401,F403, diff --git a/python/nx-cugraph/LICENSE b/python/nx-cugraph/LICENSE new file mode 120000 index 000000000..30cff7403 --- /dev/null +++ b/python/nx-cugraph/LICENSE @@ -0,0 +1 @@ +../../LICENSE \ No newline at end of file diff --git a/python/nx-cugraph/Makefile b/python/nx-cugraph/Makefile new file mode 100644 index 000000000..c9caf147d --- /dev/null +++ b/python/nx-cugraph/Makefile @@ -0,0 +1,7 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +SHELL= /bin/bash + +lint: + git ls-files | xargs pre-commit run --config lint.yaml --files +lint-update: + pre-commit autoupdate --config lint.yaml diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md new file mode 100644 index 000000000..e7cd26218 --- /dev/null +++ b/python/nx-cugraph/README.md @@ -0,0 +1,32 @@ +# nx-cugraph + +## Description +[RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends) +with minimal dependencies (`networkx`, `cupy`, and `pylibcugraph`) to run graph algorithms on the GPU. + +### Contribute + +Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.10/readme_pages/CONTRIBUTING.md) +and [building from source](https://docs.rapids.ai/api/cugraph/stable/installation/source_build/), then build nx-cugraph in develop (i.e., editable) mode: +``` +$ ./build.sh nx-cugraph --pydevelop +``` + +### Run tests + +Run nx-cugraph tests from `cugraph/python/nx-cugraph` directory: +``` +$ pytest +``` +Run nx-cugraph benchmarks: +``` +$ pytest --bench +``` +Run networkx tests (requires networkx version 3.2): +``` +$ ./run_nx_tests.sh +``` +Additional arguments may be passed to pytest such as: +``` +$ ./run_nx_tests.sh -x --sw -k betweenness +``` diff --git a/python/nx-cugraph/conftest.py b/python/nx-cugraph/conftest.py new file mode 100644 index 000000000..e329b28d8 --- /dev/null +++ b/python/nx-cugraph/conftest.py @@ -0,0 +1,28 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def pytest_addoption(parser): + parser.addoption( + "--bench", + action="store_true", + default=False, + help="Run benchmarks (sugar for --benchmark-enable) and skip other tests" + " (to run both benchmarks AND tests, use --all)", + ) + parser.addoption( + "--all", + action="store_true", + default=False, + help="Run benchmarks AND tests (unlike --bench, which only runs benchmarks)", + ) diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml new file mode 100644 index 000000000..dba061bd6 --- /dev/null +++ b/python/nx-cugraph/lint.yaml @@ -0,0 +1,86 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# https://pre-commit.com/ +# +# Before first use: `pre-commit install` +# To run: `make lint` +# To update: `make lint-update` +# - &flake8_dependencies below needs updated manually +fail_fast: false +default_language_version: + python: python3 +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-merge-conflict + - id: check-symlinks + - id: check-ast + - id: check-toml + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + exclude_types: [svg] + - id: mixed-line-ending + - id: trailing-whitespace + - repo: https://github.com/abravalheri/validate-pyproject + rev: v0.14 + hooks: + - id: validate-pyproject + name: Validate pyproject.toml + - repo: https://github.com/PyCQA/autoflake + rev: v2.2.0 + hooks: + - id: autoflake + args: [--in-place] + - repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + - repo: https://github.com/asottile/pyupgrade + rev: v3.10.1 + hooks: + - id: pyupgrade + args: [--py39-plus] + - repo: https://github.com/psf/black + rev: 23.7.0 + hooks: + - id: black + # - id: black-jupyter + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.0.286 + hooks: + - id: ruff + args: [--fix-only, --show-fixes] + - repo: https://github.com/PyCQA/flake8 + rev: 6.1.0 + hooks: + - id: flake8 + additional_dependencies: &flake8_dependencies + # These versions need updated manually + - flake8==6.1.0 + - flake8-bugbear==23.7.10 + - flake8-simplify==0.20.0 + - repo: https://github.com/asottile/yesqa + rev: v1.5.0 + hooks: + - id: yesqa + additional_dependencies: *flake8_dependencies + - repo: https://github.com/codespell-project/codespell + rev: v2.2.5 + hooks: + - id: codespell + types_or: [python, rst, markdown] + additional_dependencies: [tomli] + files: ^(nx_cugraph|docs)/ + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.0.286 + hooks: + - id: ruff + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: no-commit-to-branch + args: [-p, "^branch-2....$"] diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py new file mode 100644 index 000000000..28066fe2b --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from networkx.exception import * + +from . import algorithms, classes, convert, utils +from .algorithms import * +from .classes import * +from .convert import * + +__version__ = "23.10.00" diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py new file mode 100644 index 000000000..3a585452d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from . import centrality, community +from .centrality import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py new file mode 100644 index 000000000..2ac6242e8 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .betweenness import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py new file mode 100644 index 000000000..b777919f8 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py @@ -0,0 +1,73 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import _handle_seed, networkx_algorithm + +__all__ = ["betweenness_centrality", "edge_betweenness_centrality"] + + +@networkx_algorithm +def betweenness_centrality( + G, k=None, normalized=True, weight=None, endpoints=False, seed=None +): + if weight is not None: + raise NotImplementedError( + "Weighted implementation of betweenness centrality not currently supported" + ) + seed = _handle_seed(seed) + G = _to_graph(G, weight) + node_ids, values = plc.betweenness_centrality( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + k=k, + random_state=seed, + normalized=normalized, + include_endpoints=endpoints, + do_expensive_check=False, + ) + return G._nodearrays_to_dict(node_ids, values) + + +@betweenness_centrality._can_run +def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None): + return weight is None + + +@networkx_algorithm +def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None): + if weight is not None: + raise NotImplementedError( + "Weighted implementation of betweenness centrality not currently supported" + ) + G = _to_graph(G, weight) + src_ids, dst_ids, values, _edge_ids = plc.edge_betweenness_centrality( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + k=k, + random_state=seed, + normalized=normalized, + do_expensive_check=False, + ) + if not G.is_directed(): + mask = src_ids <= dst_ids + src_ids = src_ids[mask] + dst_ids = dst_ids[mask] + values = 2 * values[mask] + return G._edgearrays_to_dict(src_ids, dst_ids, values) + + +@edge_betweenness_centrality._can_run +def _(G, k=None, normalized=True, weight=None, seed=None): + return weight is None diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/community/__init__.py new file mode 100644 index 000000000..51a4f5c19 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .louvain import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py new file mode 100644 index 000000000..ca5f05c20 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -0,0 +1,62 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import sys + +import pylibcugraph as plc + +from nx_cugraph.convert import _to_undirected_graph +from nx_cugraph.utils import ( + _groupby, + _handle_seed, + networkx_algorithm, + not_implemented_for, +) + +__all__ = ["louvain_communities"] + + +@not_implemented_for("directed") +@networkx_algorithm(extra_params="max_level") +def louvain_communities( + G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None +): + """`threshold` and `seed` parameters are currently ignored. + + Extra parameter: `max_level` controls the maximum number of levels of the algorithm. + """ + # NetworkX allows both directed and undirected, but cugraph only allows undirected. + seed = _handle_seed(seed) # Unused, but ensure it's valid for future compatibility + G = _to_undirected_graph(G, weight) + if G.row_indices.size == 0: + # TODO: PLC doesn't handle empty graphs gracefully! + return [{key} for key in G._nodeiter_to_iter(range(len(G)))] + if max_level is None: + max_level = sys.maxsize + vertices, clusters, modularity = plc.louvain( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + max_level=max_level, # TODO: add this parameter to NetworkX + resolution=resolution, + # threshold=threshold, # TODO: add this parameter to PLC + do_expensive_check=False, + ) + groups = _groupby(clusters, vertices) + return [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] + + +@louvain_communities._can_run +def _( + G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None +): + # NetworkX allows both directed and undirected, but cugraph only allows undirected. + return not G.is_directed() diff --git a/python/nx-cugraph/nx_cugraph/classes/__init__.py b/python/nx-cugraph/nx_cugraph/classes/__init__.py new file mode 100644 index 000000000..e47641ae8 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .graph import Graph + +from .digraph import DiGraph # isort:skip diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py new file mode 100644 index 000000000..0aaf88fd7 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -0,0 +1,61 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from typing import TYPE_CHECKING + +import networkx as nx + +import nx_cugraph as nxcg + +from .graph import Graph + +if TYPE_CHECKING: + from nx_cugraph.typing import NodeKey + +__all__ = ["DiGraph"] + +networkx_api = nxcg.utils.decorators.networkx_class(nx.DiGraph) + + +class DiGraph(Graph): + ################# + # Class methods # + ################# + + @classmethod + @networkx_api + def is_directed(cls) -> bool: + return True + + @classmethod + def to_networkx_class(cls) -> type[nx.DiGraph]: + return nx.DiGraph + + @networkx_api + def number_of_edges( + self, u: NodeKey | None = None, v: NodeKey | None = None + ) -> int: + if u is not None or v is not None: + raise NotImplementedError + return self.row_indices.size + + ########################## + # NetworkX graph methods # + ########################## + + @networkx_api + def reverse(self, copy: bool = True) -> DiGraph: + return self._copy(not copy, self.__class__, reverse=True) + + # Many more methods to implement... diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py new file mode 100644 index 000000000..1432f68c7 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -0,0 +1,600 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import operator as op +from copy import deepcopy +from typing import TYPE_CHECKING, ClassVar + +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +import nx_cugraph as nxcg + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + + from nx_cugraph.typing import ( + AttrKey, + Dtype, + EdgeTuple, + EdgeValue, + IndexValue, + NodeKey, + NodeValue, + ) + +__all__ = ["Graph"] + +networkx_api = nxcg.utils.decorators.networkx_class(nx.Graph) + + +class Graph: + # Tell networkx to dispatch calls with this object to nx-cugraph + __networkx_plugin__: ClassVar[str] = "cugraph" + + # networkx properties + graph: dict + graph_attr_dict_factory: ClassVar[type] = dict + + # Not networkx properties + # We store edge data in COO format with {row,col}_indices and edge_values. + row_indices: cp.ndarray[IndexValue] + col_indices: cp.ndarray[IndexValue] + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] + edge_masks: dict[AttrKey, cp.ndarray[bool]] + node_values: dict[AttrKey, cp.ndarray[NodeValue]] + node_masks: dict[AttrKey, cp.ndarray[bool]] + key_to_id: dict[NodeKey, IndexValue] | None + _id_to_key: dict[IndexValue, NodeKey] | None + _N: int + + #################### + # Creation methods # + #################### + + @classmethod + def from_coo( + cls, + N: int, + row_indices: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: dict[IndexValue, NodeKey] | None = None, + **attr, + ) -> Graph: + new_graph = object.__new__(cls) + new_graph.row_indices = row_indices + new_graph.col_indices = col_indices + new_graph.edge_values = {} if edge_values is None else dict(edge_values) + new_graph.edge_masks = {} if edge_masks is None else dict(edge_masks) + new_graph.node_values = {} if node_values is None else dict(node_values) + new_graph.node_masks = {} if node_masks is None else dict(node_masks) + new_graph.key_to_id = None if key_to_id is None else dict(key_to_id) + new_graph._id_to_key = None if id_to_key is None else dict(id_to_key) + new_graph._N = op.index(N) # Ensure N is integral + new_graph.graph = new_graph.graph_attr_dict_factory() + new_graph.graph.update(attr) + size = new_graph.row_indices.size + # Easy and fast sanity checks + if size != new_graph.col_indices.size: + raise ValueError + for attr in ["edge_values", "edge_masks"]: + if datadict := getattr(new_graph, attr): + for key, val in datadict.items(): + if val.shape[0] != size: + raise ValueError(key) + for attr in ["node_values", "node_masks"]: + if datadict := getattr(new_graph, attr): + for key, val in datadict.items(): + if val.shape[0] != N: + raise ValueError(key) + if new_graph.key_to_id is not None and len(new_graph.key_to_id) != N: + raise ValueError + if new_graph._id_to_key is not None and len(new_graph._id_to_key) != N: + raise ValueError + return new_graph + + @classmethod + def from_csr( + cls, + indptr: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: dict[IndexValue, NodeKey] | None = None, + **attr, + ) -> Graph: + N = indptr.size - 1 + row_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + **attr, + ) + + @classmethod + def from_csc( + cls, + indptr: cp.ndarray[IndexValue], + row_indices: cp.ndarray[IndexValue], + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: dict[IndexValue, NodeKey] | None = None, + **attr, + ) -> Graph: + N = indptr.size - 1 + col_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + **attr, + ) + + @classmethod + def from_dcsr( + cls, + N: int, + compressed_rows: cp.ndarray[IndexValue], + indptr: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: dict[IndexValue, NodeKey] | None = None, + **attr, + ) -> Graph: + row_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(compressed_rows.get(), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + **attr, + ) + + @classmethod + def from_dcsc( + cls, + N: int, + compressed_cols: cp.ndarray[IndexValue], + indptr: cp.ndarray[IndexValue], + row_indices: cp.ndarray[IndexValue], + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: dict[IndexValue, NodeKey] | None = None, + **attr, + ) -> Graph: + col_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(compressed_cols.get(), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + **attr, + ) + + def __new__(cls, incoming_graph_data=None, **attr) -> Graph: + if incoming_graph_data is None: + new_graph = cls.from_coo(0, cp.empty(0, np.int32), cp.empty(0, np.int32)) + elif incoming_graph_data.__class__ is new_graph.__class__: + new_graph = incoming_graph_data.copy() + elif incoming_graph_data.__class__ is new_graph.to_networkx_class(): + new_graph = nxcg.from_networkx(incoming_graph_data, preserve_all_attrs=True) + else: + raise NotImplementedError + new_graph.graph.update(attr) + return new_graph + + ################# + # Class methods # + ################# + + @classmethod + @networkx_api + def is_directed(cls) -> bool: + return False + + @classmethod + @networkx_api + def is_multigraph(cls) -> bool: + return False + + @classmethod + @networkx_api + def to_directed_class(cls) -> type[nxcg.DiGraph]: + return nxcg.DiGraph + + @classmethod + def to_networkx_class(cls) -> type[nx.Graph]: + return nx.Graph + + @classmethod + @networkx_api + def to_undirected_class(cls) -> type[Graph]: + return Graph + + ############## + # Properties # + ############## + + @property + def edge_dtypes(self) -> dict[AttrKey, Dtype]: + return {key: val.dtype for key, val in self.edge_values.items()} + + @property + def node_dtypes(self) -> dict[AttrKey, Dtype]: + return {key: val.dtype for key, val in self.node_values.items()} + + @property + def id_to_key(self) -> dict[IndexValue, NodeKey] | None: + if self.key_to_id is None: + return None + if self._id_to_key is None: + self._id_to_key = {val: key for key, val in self.key_to_id.items()} + return self._id_to_key + + name = nx.Graph.name + + ################## + # Dunder methods # + ################## + + @networkx_api + def __contains__(self, n: NodeKey) -> bool: + if self.key_to_id is not None: + container = self.key_to_id + else: + container = range(self._N) + try: + return n in container + except TypeError: + return False + + @networkx_api + def __iter__(self) -> Iterator[NodeKey]: + if self.key_to_id is not None: + return iter(self.key_to_id) + return iter(range(self._N)) + + @networkx_api + def __len__(self) -> int: + return self._N + + __str__ = nx.Graph.__str__ + + ########################## + # NetworkX graph methods # + ########################## + + @networkx_api + def clear(self) -> None: + self.edge_values.clear() + self.edge_masks.clear() + self.node_values.clear() + self.node_masks.clear() + self.graph.clear() + self.row_indices = cp.empty(0, self.row_indices.dtype) + self.col_indices = cp.empty(0, self.col_indices.dtype) + self._N = 0 + self.key_to_id = None + self._id_to_key = None + + @networkx_api + def clear_edges(self) -> None: + self.edge_values.clear() + self.edge_masks.clear() + self.row_indices = cp.empty(0, self.row_indices.dtype) + self.col_indices = cp.empty(0, self.col_indices.dtype) + + @networkx_api + def copy(self, as_view: bool = False) -> Graph: + # Does shallow copy in networkx + return self._copy(as_view, self.__class__) + + @networkx_api + def get_edge_data( + self, u: NodeKey, v: NodeKey, default: EdgeValue | None = None + ) -> dict[AttrKey, EdgeValue]: + if self.key_to_id is not None: + try: + u = self.key_to_id[u] + v = self.key_to_id[v] + except KeyError: + return default + index = cp.nonzero((self.row_indices == u) & (self.col_indices == v))[0] + if index.size == 0: + return default + [index] = index.tolist() + if not self.edge_values: + return {} + return { + key: val[index].tolist() + for key, val in self.edge_values.items() + if key not in self.edge_masks or self.edge_masks[key][index] + } + + @networkx_api + def has_edge(self, u: NodeKey, v: NodeKey) -> bool: + if self.key_to_id is not None: + try: + u = self.key_to_id[u] + v = self.key_to_id[v] + except KeyError: + return False + return bool(((self.row_indices == u) & (self.col_indices == v)).any()) + + @networkx_api + def has_node(self, n: NodeKey) -> bool: + return n in self + + @networkx_api + def nbunch_iter(self, nbunch=None) -> Iterator[NodeKey]: + if nbunch is None: + return iter(self) + if nbunch in self: + return iter([nbunch]) + return (node for node in nbunch if node in self) + + @networkx_api + def number_of_edges( + self, u: NodeKey | None = None, v: NodeKey | None = None + ) -> int: + if u is not None or v is not None: + raise NotImplementedError + return self.size() + + @networkx_api + def number_of_nodes(self) -> int: + return self._N + + @networkx_api + def order(self) -> int: + return self._N + + @networkx_api + def size(self, weight: AttrKey | None = None) -> int: + if weight is not None: + raise NotImplementedError + # If no self-edges, then `self.row_indices.size // 2` + return int((self.row_indices <= self.col_indices).sum()) + + @networkx_api + def to_directed(self, as_view: bool = False) -> nxcg.DiGraph: + return self._copy(as_view, self.to_directed_class()) + + @networkx_api + def to_undirected(self, as_view: bool = False) -> Graph: + # Does deep copy in networkx + return self.copy(as_view) + + # Not implemented... + # adj, adjacency, add_edge, add_edges_from, add_node, + # add_nodes_from, add_weighted_edges_from, degree, + # edge_subgraph, edges, neighbors, nodes, remove_edge, + # remove_edges_from, remove_node, remove_nodes_from, subgraph, update + + ################### + # Private methods # + ################### + + def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): + indptr = self.indptr + row_indices = self.row_indices + col_indices = self.col_indices + edge_values = self.edge_values + edge_masks = self.edge_masks + node_values = self.node_values + node_masks = self.node_masks + key_to_id = self.key_to_id + id_to_key = None if key_to_id is None else self._id_to_key + if not as_view: + indptr = indptr.copy() + row_indices = row_indices.copy() + col_indices = col_indices.copy() + edge_values = {key: val.copy() for key, val in edge_values.items()} + edge_masks = {key: val.copy() for key, val in edge_masks.items()} + node_values = {key: val.copy() for key, val in node_values.items()} + node_masks = {key: val.copy() for key, val in node_masks.items()} + if key_to_id is not None: + key_to_id = key_to_id.copy() + if id_to_key is not None: + id_to_key = id_to_key.copy() + if reverse: + row_indices, col_indices = col_indices, row_indices + rv = cls.from_coo( + indptr, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + ) + if as_view: + rv.graph = self.graph + else: + rv.graph.update(deepcopy(self.graph)) + return rv + + def _get_plc_graph( + self, + edge_attr: AttrKey | None = None, + edge_default: EdgeValue | None = None, + edge_dtype: Dtype | None = None, + *, + store_transposed: bool = False, + ): + if edge_attr is None: + edge_array = None + elif edge_attr not in self.edge_values: + raise KeyError("Graph has no edge attribute {edge_attr!r}") + elif edge_attr not in self.edge_masks: + edge_array = self.edge_values[edge_attr] + elif not self.edge_masks[edge_attr].all(): + if edge_default is None: + raise NotImplementedError( + "Missing edge attributes is not yet implemented" + ) + edge_array = cp.where( + self.edge_masks[edge_attr], self.edge_values[edge_attr], edge_default + ) + else: + # Mask is all True; don't need anymore + del self.edge_masks[edge_attr] + edge_array = self.edge_values[edge_attr] + # Should we cache PLC graph? + if edge_dtype is not None: + edge_dtype = np.dtype(edge_dtype) + if edge_array.dtype != edge_dtype: + edge_array = edge_array.astype(edge_dtype) + return plc.SGGraph( + resource_handle=plc.ResourceHandle(), + graph_properties=plc.GraphProperties( + is_multigraph=self.is_multigraph(), + is_symmetric=not self.is_directed(), + ), + src_or_offset_array=self.row_indices, + dst_or_index_array=self.col_indices, + weight_array=edge_array, + store_transposed=store_transposed, + renumber=False, + do_expensive_check=False, + ) + + def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: + """Convert an iterable of node IDs to an iterable of node keys.""" + if (id_to_key := self.id_to_key) is not None: + return map(id_to_key.__getitem__, node_ids) + return node_ids + + def _nodearray_to_list(self, node_ids: cp.ndarray[IndexValue]) -> list[NodeKey]: + if self.key_to_id is None: + return node_ids.tolist() + return list(self._nodeiter_to_iter(node_ids.tolist())) + + def _nodearrays_to_dict( + self, node_ids: cp.ndarray[IndexValue], values: cp.ndarray[NodeValue] + ) -> dict[NodeKey, NodeValue]: + it = zip(node_ids.tolist(), values.tolist()) + if (id_to_key := self.id_to_key) is not None: + return {id_to_key[key]: val for key, val in it} + return dict(it) + + def _edgearrays_to_dict( + self, + src_ids: cp.ndarray[IndexValue], + dst_ids: cp.ndarray[IndexValue], + values: cp.ndarray[EdgeValue], + ) -> dict[EdgeTuple, EdgeValue]: + it = zip(zip(src_ids.tolist(), dst_ids.tolist()), values.tolist()) + if (id_to_key := self.id_to_key) is not None: + return { + (id_to_key[src_id], id_to_key[dst_id]): val + for (src_id, dst_id), val in it + } + return dict(it) + + def _dict_to_nodearrays( + self, + d: dict[NodeKey, NodeValue], + dtype: Dtype | None = None, + ) -> tuple[cp.ndarray[IndexValue], cp.ndarray[NodeValue]]: + if self.key_to_id is None: + indices_iter = d + else: + indices_iter = map(self.key_to_id.__getitem__, d) + node_ids = cp.fromiter(indices_iter, np.int32) + if dtype is None: + values = cp.array(list(d.values())) + else: + values = cp.fromiter(d.values(), dtype) + return node_ids, values + + # def _dict_to_nodearray( + # self, + # d: dict[NodeKey, NodeValue] | cp.ndarray[NodeValue], + # default: NodeValue | None = None, + # dtype: Dtype | None = None, + # ) -> cp.ndarray[NodeValue]: + # if isinstance(d, cp.ndarray): + # if d.shape[0] != len(self): + # raise ValueError + # return d + # if default is None: + # val_iter = map(d.__getitem__, self) + # else: + # val_iter = (d.get(node, default) for node in self) + # if dtype is None: + # return cp.array(list(val_iter)) + # return cp.fromiter(val_iter, dtype) diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py new file mode 100644 index 000000000..9be8cac78 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -0,0 +1,528 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import itertools +import operator as op +from collections import Counter +from collections.abc import Mapping +from typing import TYPE_CHECKING + +import cupy as cp +import networkx as nx +import numpy as np + +import nx_cugraph as nxcg + +if TYPE_CHECKING: + from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue + +__all__ = [ + "from_networkx", + "to_networkx", +] + +concat = itertools.chain.from_iterable +# A "required" attribute is one that all edges or nodes must have or KeyError is raised +REQUIRED = ... + + +def from_networkx( + graph: nx.Graph, + edge_attrs: AttrKey | dict[AttrKey, EdgeValue | None] | None = None, + edge_dtypes: Dtype | dict[AttrKey, Dtype | None] | None = None, + *, + node_attrs: AttrKey | dict[AttrKey, NodeValue | None] | None = None, + node_dtypes: Dtype | dict[AttrKey, Dtype | None] | None = None, + preserve_all_attrs: bool = False, + preserve_edge_attrs: bool = False, + preserve_node_attrs: bool = False, + preserve_graph_attrs: bool = False, + as_directed: bool = False, + name: str | None = None, + graph_name: str | None = None, +) -> nxcg.Graph: + """Convert a networkx graph to nx_cugraph graph; can convert all attributes. + + Parameters + ---------- + G : networkx.Graph + edge_attrs : str or dict, optional + Dict that maps edge attributes to default values if missing in ``G``. + If None, then no edge attributes will be converted. + If default value is None, then missing values are handled with a mask. + A default value of ``nxcg.convert.REQUIRED`` or ``...`` indicates that + all edges have data for this attribute, and raise `KeyError` if not. + For convenience, `edge_attrs` may be a single attribute with default 1; + for example ``edge_attrs="weight"``. + edge_dtypes : dtype or dict, optional + node_attrs : str or dict, optional + Dict that maps node attributes to default values if missing in ``G``. + If None, then no node attributes will be converted. + If default value is None, then missing values are handled with a mask. + A default value of ``nxcg.convert.REQUIRED`` or ``...`` indicates that + all edges have data for this attribute, and raise `KeyError` if not. + For convenience, `node_attrs` may be a single attribute with no default; + for example ``node_attrs="weight"``. + node_dtypes : dtype or dict, optional + preserve_all_attrs : bool, default False + If True, then equivalent to setting preserve_edge_attrs, preserve_node_attrs, + and preserve_graph_attrs to True. + preserve_edge_attrs : bool, default False + Whether to preserve all edge attributes. + preserve_node_attrs : bool, default False + Whether to preserve all node attributes. + preserve_graph_attrs : bool, default False + Whether to preserve all graph attributes. + as_directed : bool, default False + If True, then the returned graph will be directed regardless of input. + If False, then the returned graph type is determined by input graph. + name : str, optional + The name of the algorithm when dispatched from networkx. + graph_name : str, optional + The name of the graph argument geing converted when dispatched from networkx. + + Returns + ------- + nx_cugraph.Graph + + Notes + ----- + For optimal performance, be as specific as possible about what is being converted: + + 1. Do you need edge values? Creating a graph with just the structure is the fastest. + 2. Do you know the edge attribute(s) you need? Specify with `edge_attrs`. + 3. Do you know the default values? Specify with ``edge_attrs={weight: default}``. + 4. Do you know if all edges have values? Specify with ``edge_attrs={weight: ...}``. + 5. Do you know the dtype of attributes? Specify with `edge_dtypes`. + + Conversely, using ``preserve_edge_attrs=True`` or ``preserve_all_attrs=True`` are + the slowest, but are also the most flexible and generic. + + See Also + -------- + to_networkx : The opposite; convert nx_cugraph graph to networkx graph + """ + # This uses `graph._adj` and `graph._node`, which are private attributes in NetworkX + if not isinstance(graph, nx.Graph): + if isinstance(graph, nx.classes.reportviews.NodeView): + # Convert to a Graph with only nodes (no edges) + G = nx.Graph() + G.add_nodes_from(graph.items()) + graph = G + else: + raise TypeError(f"Expected networkx.Graph; got {type(graph)}") + elif graph.is_multigraph(): + raise NotImplementedError("MultiGraph support is not yet implemented") + + if preserve_all_attrs: + preserve_edge_attrs = True + preserve_node_attrs = True + preserve_graph_attrs = True + + if edge_attrs is not None: + if isinstance(edge_attrs, Mapping): + # Copy so we don't mutate the original + edge_attrs = dict(edge_attrs) + else: + edge_attrs = {edge_attrs: 1} + + if node_attrs is not None: + if isinstance(node_attrs, Mapping): + # Copy so we don't mutate the original + node_attrs = dict(node_attrs) + else: + node_attrs = {node_attrs: None} + + if graph.__class__ in {nx.Graph, nx.DiGraph}: + # This is a NetworkX private attribute, but is much faster to use + adj = graph._adj + else: + adj = graph.adj + if isinstance(adj, nx.classes.coreviews.FilterAdjacency): + adj = {k: dict(v) for k, v in adj.items()} + + N = len(adj) + if ( + not preserve_edge_attrs + and not edge_attrs + # Faster than graph.number_of_edges() == 0 + or next(concat(rowdata.values() for rowdata in adj.values()), None) is None + ): + # Either we weren't asked to preserve edge attributes, or there are no edges + edge_attrs = None + elif preserve_edge_attrs: + # Using comprehensions should be just as fast starting in Python 3.11 + attr_sets = set(map(frozenset, concat(map(dict.values, adj.values())))) + attrs = frozenset.union(*attr_sets) + edge_attrs = dict.fromkeys(attrs, REQUIRED) + if len(attr_sets) > 1: + # Determine which edges have missing data + for attr, count in Counter(concat(attr_sets)).items(): + if count != len(attr_sets): + edge_attrs[attr] = None + elif None in edge_attrs.values(): + # Required edge attributes have a default of None in `edge_attrs` + # Verify all edge attributes are present! + required = frozenset( + attr for attr, default in edge_attrs.items() if default is None + ) + if len(required) == 1: + # Fast path for the common case of a single attribute with no default + [attr] = required + it = ( + attr in edgedata + for rowdata in adj.values() + for edgedata in rowdata.values() + ) + if next(it): + if all(it): + # All edges have data + edge_attrs[attr] = REQUIRED + # Else some edges have attribute (default already None) + elif not any(it): + # No edges have attribute + del edge_attrs[attr] + # Else some edges have attribute (default already None) + else: + attr_sets = set( + map(required.intersection, concat(map(dict.values, adj.values()))) + ) + for attr in required - frozenset.union(*attr_sets): + # No edges have these attributes + del edge_attrs[attr] + for attr in frozenset.intersection(*attr_sets): + # All edges have these attributes + edge_attrs[attr] = REQUIRED + + if N == 0: + node_attrs = None + elif preserve_node_attrs: + attr_sets = set(map(frozenset, graph._node.values())) + attrs = frozenset.union(*attr_sets) + node_attrs = dict.fromkeys(attrs, REQUIRED) + if len(attr_sets) > 1: + # Determine which nodes have missing data + for attr, count in Counter(concat(attr_sets)).items(): + if count != len(attr_sets): + node_attrs[attr] = None + elif node_attrs and None in node_attrs.values(): + # Required node attributes have a default of None in `node_attrs` + # Verify all node attributes are present! + required = frozenset( + attr for attr, default in node_attrs.items() if default is None + ) + if len(required) == 1: + # Fast path for the common case of a single attribute with no default + [attr] = required + it = (attr in nodedata for nodedata in graph._node.values()) + if next(it): + if all(it): + # All nodes have data + node_attrs[attr] = REQUIRED + # Else some nodes have attribute (default already None) + elif not any(it): + # No nodes have attribute + del node_attrs[attr] + # Else some nodes have attribute (default already None) + else: + attr_sets = set(map(required.intersection, graph._node.values())) + for attr in required - frozenset.union(*attr_sets): + # No nodes have these attributes + del node_attrs[attr] + for attr in frozenset.intersection(*attr_sets): + # All nodes have these attributes + node_attrs[attr] = REQUIRED + + key_to_id = dict(zip(adj, range(N))) + col_iter = concat(adj.values()) + try: + no_renumber = all(k == v for k, v in key_to_id.items()) + except Exception: + no_renumber = False + if no_renumber: + key_to_id = None + else: + col_iter = map(key_to_id.__getitem__, col_iter) + col_indices = cp.fromiter(col_iter, np.int32) + + edge_values = {} + edge_masks = {} + if edge_attrs: + if edge_dtypes is None: + edge_dtypes = {} + elif not isinstance(edge_dtypes, Mapping): + edge_dtypes = dict.fromkeys(edge_attrs, edge_dtypes) + for edge_attr, edge_default in edge_attrs.items(): + dtype = edge_dtypes.get(edge_attr) + if edge_default is None: + vals = [] + append = vals.append + iter_mask = ( + append( + edgedata[edge_attr] + if (present := edge_attr in edgedata) + else False + ) + or present + for rowdata in adj.values() + for edgedata in rowdata.values() + ) + edge_masks[edge_attr] = cp.fromiter(iter_mask, bool) + edge_values[edge_attr] = cp.array(vals, dtype) + # if vals.ndim > 1: ... + else: + if edge_default is REQUIRED: + # Using comprehensions should be fast starting in Python 3.11 + # iter_values = ( + # edgedata[edge_attr] + # for rowdata in adj.values() + # for edgedata in rowdata.values() + # ) + iter_values = map( + op.itemgetter(edge_attr), concat(map(dict.values, adj.values())) + ) + else: + iter_values = ( + edgedata.get(edge_attr, edge_default) + for rowdata in adj.values() + for edgedata in rowdata.values() + ) + if dtype is None: + edge_values[edge_attr] = cp.array(list(iter_values)) + else: + edge_values[edge_attr] = cp.fromiter(iter_values, dtype) + # if vals.ndim > 1: ... + + row_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat( + np.arange(N, dtype=np.int32), + np.fromiter(map(len, adj.values()), np.int32), + ) + ) + + node_values = {} + node_masks = {} + if node_attrs: + nodes = graph._node + if node_dtypes is None: + node_dtypes = {} + elif not isinstance(node_dtypes, Mapping): + node_dtypes = dict.fromkeys(node_attrs, node_dtypes) + for node_attr, node_default in node_attrs.items(): + # Iterate over `adj` to ensure consistent order + dtype = node_dtypes.get(node_attr) + if node_default is None: + vals = [] + append = vals.append + iter_mask = ( + append( + nodedata[node_attr] + if (present := node_attr in (nodedata := nodes[node_id])) + else False + ) + or present + for node_id in adj + ) + node_masks[node_attr] = cp.fromiter(iter_mask, bool) + node_values[node_attr] = cp.array(vals, dtype) + # if vals.ndim > 1: ... + else: + if node_default is REQUIRED: + iter_values = (nodes[node_id][node_attr] for node_id in adj) + else: + iter_values = ( + nodes[node_id].get(node_attr, node_default) for node_id in adj + ) + if dtype is None: + node_values[node_attr] = cp.array(list(iter_values)) + else: + node_values[node_attr] = cp.fromiter(iter_values, dtype) + # if vals.ndim > 1: ... + + if graph.is_directed() or as_directed: + klass = nxcg.DiGraph + else: + klass = nxcg.Graph + rv = klass.from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + ) + if preserve_graph_attrs: + rv.graph.update(graph.graph) # deepcopy? + return rv + + +def _iter_attr_dicts( + values: dict[AttrKey, cp.ndarray[EdgeValue | NodeValue]], + masks: dict[AttrKey, cp.ndarray[bool]], +): + full_attrs = list(values.keys() - masks.keys()) + if full_attrs: + full_dicts = ( + dict(zip(full_attrs, vals)) + for vals in zip(*(values[attr].tolist() for attr in full_attrs)) + ) + partial_attrs = list(values.keys() & masks.keys()) + if partial_attrs: + partial_dicts = ( + {k: v for k, (v, m) in zip(partial_attrs, vals_masks) if m} + for vals_masks in zip( + *( + zip(values[attr].tolist(), masks[attr].tolist()) + for attr in partial_attrs + ) + ) + ) + if full_attrs and partial_attrs: + full_dicts = (d1.update(d2) or d1 for d1, d2 in zip(full_dicts, partial_dicts)) + elif partial_attrs: + full_dicts = partial_dicts + return full_dicts + + +def to_networkx(G: nxcg.Graph) -> nx.Graph: + """Convert a nx_cugraph graph to networkx graph. + + All edge and node attributes and ``G.graph`` properties are converted. + + Parameters + ---------- + G : nx_cugraph.Graph + + Returns + ------- + networkx.Graph + + See Also + -------- + from_networkx : The opposite; convert networkx graph to nx_cugraph graph + """ + rv = G.to_networkx_class()() + id_to_key = G.id_to_key + + node_values = G.node_values + node_masks = G.node_masks + if node_values: + node_iter = range(len(G)) + if id_to_key is not None: + node_iter = map(id_to_key.__getitem__, node_iter) + full_node_dicts = _iter_attr_dicts(node_values, node_masks) + rv.add_nodes_from(zip(node_iter, full_node_dicts)) + elif id_to_key is not None: + rv.add_nodes_from(id_to_key.values()) + else: + rv.add_nodes_from(range(len(G))) + + row_indices = G.row_indices + col_indices = G.col_indices + edge_values = G.edge_values + edge_masks = G.edge_masks + if edge_values and not G.is_directed(): + # Only add upper triangle of the adjacency matrix so we don't double-add edges + mask = row_indices <= col_indices + row_indices = row_indices[mask] + col_indices = col_indices[mask] + edge_values = {k: v[mask] for k, v in edge_values.items()} + if edge_masks: + edge_masks = {k: v[mask] for k, v in edge_masks.items()} + row_indices = row_iter = row_indices.tolist() + col_indices = col_iter = col_indices.tolist() + if id_to_key is not None: + row_iter = map(id_to_key.__getitem__, row_indices) + col_iter = map(id_to_key.__getitem__, col_indices) + if edge_values: + full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) + rv.add_edges_from(zip(row_iter, col_iter, full_edge_dicts)) + else: + rv.add_edges_from(zip(row_iter, col_iter)) + + rv.graph.update(G.graph) + return rv + + +def _to_graph( + G, + edge_attr: AttrKey | None = None, + edge_default: EdgeValue | None = 1, + edge_dtype: Dtype | None = None, +) -> nxcg.Graph | nxcg.DiGraph: + """Ensure that input type is a nx_cugraph graph, and convert if necessary. + + Directed and undirected graphs are both allowed. + This is an internal utility function and may change or be removed. + """ + if isinstance(G, nxcg.Graph): + return G + if isinstance(G, nx.Graph): + return from_networkx( + G, {edge_attr: edge_default} if edge_attr is not None else None, edge_dtype + ) + # TODO: handle cugraph.Graph + raise TypeError + + +def _to_directed_graph( + G, + edge_attr: AttrKey | None = None, + edge_default: EdgeValue | None = 1, + edge_dtype: Dtype | None = None, +) -> nxcg.DiGraph: + """Ensure that input type is a nx_cugraph DiGraph, and convert if necessary. + + Undirected graphs will be converted to directed. + This is an internal utility function and may change or be removed. + """ + if isinstance(G, nxcg.DiGraph): + return G + if isinstance(G, nxcg.Graph): + return G.to_directed() + if isinstance(G, nx.Graph): + return from_networkx( + G, + {edge_attr: edge_default} if edge_attr is not None else None, + edge_dtype, + as_directed=True, + ) + # TODO: handle cugraph.Graph + raise TypeError + + +def _to_undirected_graph( + G, + edge_attr: AttrKey | None = None, + edge_default: EdgeValue | None = 1, + edge_dtype: Dtype | None = None, +) -> nxcg.Graph: + """Ensure that input type is a nx_cugraph Graph, and convert if necessary. + + Only undirected graphs are allowed. Directed graphs will raise ValueError. + This is an internal utility function and may change or be removed. + """ + if isinstance(G, nxcg.Graph): + if G.is_directed(): + raise ValueError("Only undirected graphs supported; got a directed graph") + return G + if isinstance(G, nx.Graph): + return from_networkx( + G, {edge_attr: edge_default} if edge_attr is not None else None, edge_dtype + ) + # TODO: handle cugraph.Graph + raise TypeError diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py new file mode 100644 index 000000000..cc750cd2d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -0,0 +1,201 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import networkx as nx + +import nx_cugraph as nxcg + + +class BackendInterface: + # Required conversions + @staticmethod + def convert_from_nx(graph, *args, edge_attrs=None, weight=None, **kwargs): + if weight is not None: + # MAINT: networkx 3.0, 3.1 + # For networkx 3.0 and 3.1 compatibility + if edge_attrs is not None: + raise TypeError( + "edge_attrs and weight arguments should not both be given" + ) + edge_attrs = {weight: 1} + return nxcg.from_networkx(graph, *args, edge_attrs=edge_attrs, **kwargs) + + @staticmethod + def convert_to_nx(obj, *, name: str | None = None): + if isinstance(obj, nxcg.Graph): + return nxcg.to_networkx(obj) + return obj + + @staticmethod + def on_start_tests(items): + """Modify pytest items after tests have been collected. + + This is called during ``pytest_collection_modifyitems`` phase of pytest. + We use this to set `xfail` on tests we expect to fail. See: + + https://docs.pytest.org/en/stable/reference/reference.html#std-hook-pytest_collection_modifyitems + """ + try: + import pytest + except ModuleNotFoundError: + return + + def key(testpath): + filename, path = testpath.split(":") + *names, testname = path.split(".") + if names: + [classname] = names + return (testname, frozenset({classname, filename})) + return (testname, frozenset({filename})) + + # Reasons for xfailing + no_weights = "weighted implementation not currently supported" + no_multigraph = "multigraphs not currently supported" + louvain_different = ( + "Louvain may be different due to RNG or unsupported threshold parameter" + ) + + xfail = {} + + from packaging.version import parse + + nxver = parse(nx.__version__) + if nxver.major == 3 and nxver.minor in {0, 1}: + # MAINT: networkx 3.0, 3.1 + # NetworkX 3.2 added the ability to "fallback to nx" if backend algorithms + # raise NotImplementedError or `can_run` returns False. The tests below + # exercise behavior we have not implemented yet, so we mark them as xfail + # for previous versions of NetworkX. + xfail.update( + { + key( + "test_agraph.py:TestAGraph.test_no_warnings_raised" + ): "pytest.warn(None) deprecated", + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_K5" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_P3_normalized" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_P3" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_krackhardt_kite_graph" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality." + "test_krackhardt_kite_graph_normalized" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality." + "test_florentine_families_graph" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_les_miserables_graph" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_ladder_graph" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_G" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_G2" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_G3" + ): no_multigraph, + key( + "test_betweenness_centrality.py:" + "TestWeightedBetweennessCentrality.test_G4" + ): no_multigraph, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality.test_K5" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality.test_C4" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality.test_P4" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality.test_balanced_tree" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality.test_weighted_graph" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality." + "test_normalized_weighted_graph" + ): no_weights, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality.test_weighted_multigraph" + ): no_multigraph, + key( + "test_betweenness_centrality.py:" + "TestWeightedEdgeBetweennessCentrality." + "test_normalized_weighted_multigraph" + ): no_multigraph, + } + ) + else: + xfail.update( + { + key( + "test_louvain.py:test_karate_club_partition" + ): louvain_different, + key("test_louvain.py:test_none_weight_param"): louvain_different, + key("test_louvain.py:test_multigraph"): louvain_different, + key("test_louvain.py:test_threshold"): louvain_different, + } + ) + + for item in items: + kset = set(item.keywords) + for (test_name, keywords), reason in xfail.items(): + if item.name == test_name and keywords.issubset(kset): + item.add_marker(pytest.mark.xfail(reason=reason)) + + @classmethod + def can_run(cls, name, args, kwargs): + """Can this backend run the specified algorithms with the given arguments? + + This is a proposed API to add to networkx dispatching machinery and may change. + """ + return ( + hasattr(cls, name) + and getattr(cls, name).can_run(*args, **kwargs) + # We don't support MultiGraphs yet + and not any(isinstance(x, nx.MultiGraph) for x in args) + and not any(isinstance(x, nx.MultiGraph) for x in kwargs.values()) + ) diff --git a/python/nx-cugraph/nx_cugraph/tests/__init__.py b/python/nx-cugraph/nx_cugraph/tests/__init__.py new file mode 100644 index 000000000..ce94db52f --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/python/nx-cugraph/nx_cugraph/tests/bench_convert.py b/python/nx-cugraph/nx_cugraph/tests/bench_convert.py new file mode 100644 index 000000000..7e6278661 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/bench_convert.py @@ -0,0 +1,179 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import random + +import networkx as nx +import numpy as np +import pytest + +import nx_cugraph as nxcg + +try: + import cugraph +except ModuleNotFoundError: + cugraph = None +try: + import scipy +except ModuleNotFoundError: + scipy = None + +# If the rapids-pytest-benchmark plugin is installed, the "gpubenchmark" +# fixture will be available automatically. Check that this fixture is available +# by trying to import rapids_pytest_benchmark, and if that fails, set +# "gpubenchmark" to the standard "benchmark" fixture provided by +# pytest-benchmark. +try: + import rapids_pytest_benchmark # noqa: F401 +except ModuleNotFoundError: + import pytest_benchmark + + gpubenchmark = pytest_benchmark.plugin.benchmark + + +def _bench_helper(gpubenchmark, N, attr_kind, create_using, method): + G = method(N, create_using=create_using) + if attr_kind: + skip = True + for *_ids, edgedict in G.edges(data=True): + skip = not skip + if skip and attr_kind not in {"full", "required", "required_dtype"}: + continue + edgedict["x"] = random.randint(0, 100000) + if attr_kind == "preserve": + gpubenchmark(nxcg.from_networkx, G, preserve_edge_attrs=True) + elif attr_kind == "half_missing": + gpubenchmark(nxcg.from_networkx, G, edge_attrs={"x": None}) + elif attr_kind == "required": + gpubenchmark(nxcg.from_networkx, G, edge_attrs={"x": ...}) + elif attr_kind == "required_dtype": + gpubenchmark( + nxcg.from_networkx, + G, + edge_attrs={"x": ...}, + edge_dtypes={"x": np.int32}, + ) + else: # full, half_default + gpubenchmark(nxcg.from_networkx, G, edge_attrs={"x": 0}) + else: + gpubenchmark(nxcg.from_networkx, G) + + +def _bench_helper_cugraph( + gpubenchmark, N, attr_kind, create_using, method, do_renumber +): + G = method(N, create_using=create_using) + if attr_kind: + for *_ids, edgedict in G.edges(data=True): + edgedict["x"] = random.randint(0, 100000) + gpubenchmark(cugraph.utilities.convert_from_nx, G, "x", do_renumber=do_renumber) + else: + gpubenchmark(cugraph.utilities.convert_from_nx, G, do_renumber=do_renumber) + + +def _bench_helper_scipy(gpubenchmark, N, attr_kind, create_using, method, fmt): + G = method(N, create_using=create_using) + if attr_kind: + for *_ids, edgedict in G.edges(data=True): + edgedict["x"] = random.randint(0, 100000) + gpubenchmark(nx.to_scipy_sparse_array, G, weight="x", format=fmt) + else: + gpubenchmark(nx.to_scipy_sparse_array, G, weight=None, format=fmt) + + +@pytest.mark.parametrize("N", [1, 10**6]) +@pytest.mark.parametrize( + "attr_kind", + [ + "required_dtype", + "required", + "full", + "half_missing", + "half_default", + "preserve", + None, + ], +) +@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +def bench_cycle_graph(gpubenchmark, N, attr_kind, create_using): + _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.cycle_graph) + + +@pytest.mark.skipif("not cugraph") +@pytest.mark.parametrize("N", [1, 10**6]) +@pytest.mark.parametrize("attr_kind", ["full", None]) +@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("do_renumber", [True, False]) +def bench_cycle_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renumber): + if N == 1 and not do_renumber: + do_renumber = True + _bench_helper_cugraph( + gpubenchmark, N, attr_kind, create_using, nx.cycle_graph, do_renumber + ) + + +@pytest.mark.skipif("not scipy") +@pytest.mark.parametrize("N", [1, 10**6]) +@pytest.mark.parametrize("attr_kind", ["full", None]) +@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("fmt", ["coo", "csr"]) +def bench_cycle_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): + _bench_helper_scipy(gpubenchmark, N, attr_kind, create_using, nx.cycle_graph, fmt) + + +@pytest.mark.parametrize("N", [1, 1500]) +@pytest.mark.parametrize( + "attr_kind", + [ + "required_dtype", + "required", + "full", + "half_missing", + "half_default", + "preserve", + None, + ], +) +@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +def bench_complete_graph_edgedata(gpubenchmark, N, attr_kind, create_using): + _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.complete_graph) + + +@pytest.mark.parametrize("N", [3000]) +@pytest.mark.parametrize("attr_kind", [None]) +@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +def bench_complete_graph_noedgedata(gpubenchmark, N, attr_kind, create_using): + _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.complete_graph) + + +@pytest.mark.skipif("not cugraph") +@pytest.mark.parametrize("N", [1, 1500]) +@pytest.mark.parametrize("attr_kind", ["full", None]) +@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("do_renumber", [True, False]) +def bench_complete_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renumber): + if N == 1 and not do_renumber: + do_renumber = True + _bench_helper_cugraph( + gpubenchmark, N, attr_kind, create_using, nx.complete_graph, do_renumber + ) + + +@pytest.mark.skipif("not scipy") +@pytest.mark.parametrize("N", [1, 1500]) +@pytest.mark.parametrize("attr_kind", ["full", None]) +@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("fmt", ["coo", "csr"]) +def bench_complete_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): + _bench_helper_scipy( + gpubenchmark, N, attr_kind, create_using, nx.complete_graph, fmt + ) diff --git a/python/nx-cugraph/nx_cugraph/tests/conftest.py b/python/nx-cugraph/nx_cugraph/tests/conftest.py new file mode 100644 index 000000000..e5a250784 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/conftest.py @@ -0,0 +1,31 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +def pytest_configure(config): + if config.getoption("--all", False): + # Run benchmarks AND tests + config.option.benchmark_skip = False + config.option.benchmark_enable = True + elif config.getoption("--bench", False) or config.getoption( + "--benchmark-enable", False + ): + # Run benchmarks (and only benchmarks) with `--bench` argument + config.option.benchmark_skip = False + config.option.benchmark_enable = True + if not config.option.keyword: + config.option.keyword = "bench_" + else: + # Run only tests + config.option.benchmark_skip = True + config.option.benchmark_enable = False + if not config.option.keyword: + config.option.keyword = "test_" diff --git a/python/nx-cugraph/nx_cugraph/tests/test_convert.py b/python/nx-cugraph/nx_cugraph/tests/test_convert.py new file mode 100644 index 000000000..ba3cd7aae --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_convert.py @@ -0,0 +1,203 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import pytest + +import nx_cugraph as nxcg +from nx_cugraph import interface + + +@pytest.mark.parametrize("graph_class", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize( + "kwargs", + [ + {}, + {"preserve_edge_attrs": True}, + {"preserve_node_attrs": True}, + {"preserve_all_attrs": True}, + {"edge_attrs": {"x": 0}}, + {"edge_attrs": {"x": None}}, + {"edge_attrs": {"x": nxcg.convert.REQUIRED}}, + {"edge_attrs": {"x": ...}}, # sugar for REQUIRED + {"edge_attrs": "x"}, + {"node_attrs": {"x": 0}}, + {"node_attrs": {"x": None}}, + {"node_attrs": {"x": nxcg.convert.REQUIRED}}, + {"node_attrs": {"x": ...}}, # sugar for REQUIRED + {"node_attrs": "x"}, + ], +) +def test_convert_empty(graph_class, kwargs): + G = graph_class() + Gcg = nxcg.from_networkx(G, **kwargs) + H = nxcg.to_networkx(Gcg) + assert G.number_of_nodes() == Gcg.number_of_nodes() == H.number_of_nodes() == 0 + assert G.number_of_edges() == Gcg.number_of_edges() == H.number_of_edges() == 0 + assert Gcg.edge_values == Gcg.edge_masks == Gcg.node_values == Gcg.node_masks == {} + assert G.graph == Gcg.graph == H.graph == {} + + +def test_convert(): + # FIXME: can we break this into smaller tests? + G = nx.Graph() + G.add_edge(0, 1, x=2) + G.add_node(0, foo=10) + G.add_node(1, foo=20, bar=100) + for kwargs in [ + {"preserve_edge_attrs": True}, + {"preserve_all_attrs": True}, + {"edge_attrs": {"x": 0}}, + {"edge_attrs": {"x": None}, "node_attrs": {"bar": None}}, + {"edge_attrs": "x", "edge_dtypes": int}, + { + "edge_attrs": {"x": nxcg.convert.REQUIRED}, + "node_attrs": {"foo": nxcg.convert.REQUIRED}, + }, + {"edge_attrs": {"x": ...}, "node_attrs": {"foo": ...}}, # sugar for REQUIRED + ]: + # All edges have "x" attribute, so all kwargs are equivalent + Gcg = nxcg.from_networkx(G, **kwargs) + cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.edge_values["x"], [2, 2]) + assert len(Gcg.edge_values) == 1 + assert Gcg.edge_masks == {} + H = nxcg.to_networkx(Gcg) + assert G.number_of_nodes() == Gcg.number_of_nodes() == H.number_of_nodes() == 2 + assert G.number_of_edges() == Gcg.number_of_edges() == H.number_of_edges() == 1 + assert G.adj == H.adj + + with pytest.raises(KeyError, match="bar"): + nxcg.from_networkx(G, node_attrs={"bar": ...}) + + # Structure-only graph (no edge attributes) + Gcg = nxcg.from_networkx(G, preserve_node_attrs=True) + cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.node_values["foo"], [10, 20]) + assert Gcg.edge_values == Gcg.edge_masks == {} + H = nxcg.to_networkx(Gcg) + assert set(G.edges) == set(H.edges) == {(0, 1)} + assert G.nodes == H.nodes + + # Fill completely missing attribute with default value + Gcg = nxcg.from_networkx(G, edge_attrs={"y": 0}) + cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.edge_values["y"], [0, 0]) + assert len(Gcg.edge_values) == 1 + assert Gcg.edge_masks == Gcg.node_values == Gcg.node_masks == {} + H = nxcg.to_networkx(Gcg) + assert list(H.edges(data=True)) == [(0, 1, {"y": 0})] + + # If attribute is completely missing (and no default), then just ignore it + Gcg = nxcg.from_networkx(G, edge_attrs={"y": None}) + cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == [] + H = nxcg.to_networkx(Gcg) + assert list(H.edges(data=True)) == [(0, 1, {})] + + G.add_edge(0, 2) + # Some edges are missing 'x' attribute; need to use a mask + for kwargs in [{"preserve_edge_attrs": True}, {"edge_attrs": {"x": None}}]: + Gcg = nxcg.from_networkx(G, **kwargs) + cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == ["x"] + cp.testing.assert_array_equal(Gcg.edge_masks["x"], [True, False, True, False]) + cp.testing.assert_array_equal(Gcg.edge_values["x"][Gcg.edge_masks["x"]], [2, 2]) + H = nxcg.to_networkx(Gcg) + assert list(H.edges(data=True)) == [(0, 1, {"x": 2}), (0, 2, {})] + + with pytest.raises(KeyError, match="x"): + nxcg.from_networkx(G, edge_attrs={"x": nxcg.convert.REQUIRED}) + with pytest.raises(KeyError, match="x"): + nxcg.from_networkx(G, edge_attrs={"x": ...}) + with pytest.raises(KeyError, match="bar"): + nxcg.from_networkx(G, node_attrs={"bar": nxcg.convert.REQUIRED}) + with pytest.raises(KeyError, match="bar"): + nxcg.from_networkx(G, node_attrs={"bar": ...}) + + # Now for something more complicated... + G = nx.Graph() + G.add_edge(10, 20, x=1) + G.add_edge(10, 30, x=2, y=1.5) + G.add_node(10, foo=100) + G.add_node(20, foo=200, bar=1000) + G.add_node(30, foo=300) + # Some edges have masks, some don't + for kwargs in [ + {"preserve_edge_attrs": True}, + {"preserve_all_attrs": True}, + {"edge_attrs": {"x": None, "y": None}}, + {"edge_attrs": {"x": 0, "y": None}}, + {"edge_attrs": {"x": 0, "y": None}}, + {"edge_attrs": {"x": 0, "y": None}, "edge_dtypes": {"x": int, "y": float}}, + ]: + Gcg = nxcg.from_networkx(G, **kwargs) + assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.edge_values["x"], [1, 2, 1, 2]) + assert sorted(Gcg.edge_masks) == ["y"] + cp.testing.assert_array_equal(Gcg.edge_masks["y"], [False, True, False, True]) + cp.testing.assert_array_equal( + Gcg.edge_values["y"][Gcg.edge_masks["y"]], [1.5, 1.5] + ) + H = nxcg.to_networkx(Gcg) + assert G.adj == H.adj + + # Some nodes have masks, some don't + for kwargs in [ + {"preserve_node_attrs": True}, + {"preserve_all_attrs": True}, + {"node_attrs": {"foo": None, "bar": None}}, + {"node_attrs": {"foo": None, "bar": None}}, + {"node_attrs": {"foo": 0, "bar": None, "missing": None}}, + ]: + Gcg = nxcg.from_networkx(G, **kwargs) + assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.node_values["foo"], [100, 200, 300]) + assert sorted(Gcg.node_masks) == ["bar"] + cp.testing.assert_array_equal(Gcg.node_masks["bar"], [False, True, False]) + cp.testing.assert_array_equal( + Gcg.node_values["bar"][Gcg.node_masks["bar"]], [1000] + ) + H = nxcg.to_networkx(Gcg) + assert G.nodes == H.nodes + + # Check default values for nodes + for kwargs in [ + {"node_attrs": {"foo": None, "bar": 0}}, + {"node_attrs": {"foo": None, "bar": 0, "missing": None}}, + {"node_attrs": {"bar": 0}}, + {"node_attrs": {"bar": 0}, "node_dtypes": {"bar": int}}, + {"node_attrs": {"bar": 0, "foo": None}, "node_dtypes": int}, + ]: + Gcg = nxcg.from_networkx(G, **kwargs) + assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.node_values["bar"], [0, 1000, 0]) + assert Gcg.node_masks == {} + + with pytest.raises( + TypeError, match="edge_attrs and weight arguments should not both be given" + ): + interface.BackendInterface.convert_from_nx(G, edge_attrs={"x": 1}, weight="x") + with pytest.raises(TypeError, match="Expected networkx.Graph"): + nxcg.from_networkx({}) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py new file mode 100644 index 000000000..64d3704dd --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py @@ -0,0 +1,106 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib +import inspect + +import networkx as nx + +import nx_cugraph as nxcg +from nx_cugraph.utils import networkx_algorithm + + +def test_match_signature_and_names(): + """Simple test to ensure our signatures and basic module layout match networkx.""" + for name, func in vars(nxcg.interface.BackendInterface).items(): + if not isinstance(func, networkx_algorithm): + continue + + # nx version >=3.2 uses utils.backends, version >=3.0,<3.2 uses classes.backends + is_nx_30_or_31 = hasattr(nx.classes, "backends") + nx_backends = nx.classes.backends if is_nx_30_or_31 else nx.utils.backends + + if is_nx_30_or_31 and name in {"louvain_communities"}: + continue + dispatchable_func = nx_backends._registered_algorithms[name] + # nx version >=3.2 uses orig_func, version >=3.0,<3.2 uses _orig_func + if is_nx_30_or_31: + orig_func = dispatchable_func._orig_func + else: + orig_func = dispatchable_func.orig_func + + # Matching signatures? + orig_sig = inspect.signature(orig_func) + func_sig = inspect.signature(func) + if not func.extra_params: + assert orig_sig == func_sig + else: + # Ignore extra parameters added to nx-cugraph algorithm + assert orig_sig == func_sig.replace( + parameters=[ + p + for name, p in func_sig.parameters.items() + if name not in func.extra_params + ] + ) + if func.can_run is not nxcg.utils.decorators._default_can_run: + assert func_sig == inspect.signature(func.can_run) + + # Matching function names? + assert func.__name__ == dispatchable_func.__name__ == orig_func.__name__ + + # Matching dispatch names? + # nx version >=3.2 uses name, version >=3.0,<3.2 uses dispatchname + if is_nx_30_or_31: + dispatchname = dispatchable_func.dispatchname + else: + dispatchname = dispatchable_func.name + assert func.name == dispatchname + + # Matching modules (i.e., where function defined)? + assert ( + "networkx." + func.__module__.split(".", 1)[1] + == dispatchable_func.__module__ + == orig_func.__module__ + ) + + # Matching package layout (i.e., which modules have the function)? + nxcg_path = func.__module__ + name = func.__name__ + while "." in nxcg_path: + # This only walks up the module tree and does not check sibling modules + nxcg_path, mod_name = nxcg_path.rsplit(".", 1) + nx_path = nxcg_path.replace("nx_cugraph", "networkx") + nxcg_mod = importlib.import_module(nxcg_path) + nx_mod = importlib.import_module(nx_path) + # Is the function present in the current module? + present_in_nxcg = hasattr(nxcg_mod, name) + present_in_nx = hasattr(nx_mod, name) + if present_in_nxcg is not present_in_nx: # pragma: no cover (debug) + if present_in_nxcg: + raise AssertionError( + f"{name} exists in {nxcg_path}, but not in {nx_path}" + ) + raise AssertionError( + f"{name} exists in {nx_path}, but not in {nxcg_path}" + ) + # Is the nested module present in the current module? + present_in_nxcg = hasattr(nxcg_mod, mod_name) + present_in_nx = hasattr(nx_mod, mod_name) + if present_in_nxcg is not present_in_nx: # pragma: no cover (debug) + if present_in_nxcg: + raise AssertionError( + f"{mod_name} exists in {nxcg_path}, but not in {nx_path}" + ) + raise AssertionError( + f"{mod_name} exists in {nx_path}, but not in {nxcg_path}" + ) diff --git a/python/nx-cugraph/nx_cugraph/typing.py b/python/nx-cugraph/nx_cugraph/typing.py new file mode 100644 index 000000000..d3045ab46 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/typing.py @@ -0,0 +1,25 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from collections.abc import Hashable +from typing import TypeVar + +AttrKey = TypeVar("AttrKey", bound=Hashable) +EdgeKey = TypeVar("EdgeKey", bound=Hashable) +NodeKey = TypeVar("NodeKey", bound=Hashable) +EdgeTuple = tuple[NodeKey, NodeKey] +EdgeValue = TypeVar("EdgeValue") +NodeValue = TypeVar("NodeValue") +IndexValue = TypeVar("IndexValue") +Dtype = TypeVar("Dtype") diff --git a/python/nx-cugraph/nx_cugraph/utils/__init__.py b/python/nx-cugraph/nx_cugraph/utils/__init__.py new file mode 100644 index 000000000..6df5fb609 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/utils/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .decorators import * +from .misc import * diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py new file mode 100644 index 000000000..3dbdb07e8 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -0,0 +1,69 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import partial, update_wrapper + +from networkx.utils.decorators import not_implemented_for + +from nx_cugraph.interface import BackendInterface + +__all__ = ["not_implemented_for", "networkx_algorithm"] + + +def networkx_class(api): + def inner(func): + func.__doc__ = getattr(api, func.__name__).__doc__ + return func + + return inner + + +class networkx_algorithm: + def __new__(cls, func=None, *, name=None, extra_params=None): + if func is None: + return partial(networkx_algorithm, name=name, extra_params=extra_params) + instance = object.__new__(cls) + # update_wrapper sets __wrapped__, which will be used for the signature + update_wrapper(instance, func) + instance.__defaults__ = func.__defaults__ + instance.__kwdefaults__ = func.__kwdefaults__ + instance.name = func.__name__ if name is None else name + # TODO: should extra_params be a dict[str, str] that describes the parameters? + if extra_params is None: + instance.extra_params = None + elif isinstance(extra_params, str): + instance.extra_params = {extra_params} + else: + instance.extra_params = set(extra_params) + instance.can_run = _default_can_run + setattr(BackendInterface, instance.name, instance) + # Set methods so they are in __dict__ + instance._can_run = instance._can_run + return instance + + def _can_run(self, func): + """Set the `can_run` attribute to the decorated function.""" + self.can_run = func + + def __call__(self, /, *args, **kwargs): + return self.__wrapped__(*args, **kwargs) + + def __reduce__(self): + return _restore_networkx_dispatched, (self.name,) + + +def _default_can_run(*args, **kwargs): + return True + + +def _restore_networkx_dispatched(name): + return getattr(BackendInterface, name) diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py new file mode 100644 index 000000000..64c0be066 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -0,0 +1,60 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import operator as op +import sys +from random import Random + +import cupy as cp + +__all__ = ["_groupby", "_handle_seed"] + + +def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: + """Perform a groupby operation given an array of group IDs and array of values. + + Parameters + ---------- + groups : cp.ndarray + Array that holds the group IDs. + Group IDs are assumed to be consecutive integers from 0. + values : cp.ndarray + Array of values to be grouped according to groups. + Must be the same size as groups array. + + Returns + ------- + dict with group IDs as keys and cp.ndarray as values. + """ + # It would actually be easy to support groups that aren't consecutive integers, + # but let's wait until we need it to implement it. + sorted_groups = cp.argsort(groups) + sorted_values = values[sorted_groups] + rv = {} + start = 0 + for i, end in enumerate( + [*(cp.nonzero(cp.diff(groups[sorted_groups]))[0] + 1).tolist(), groups.size] + ): + rv[i] = sorted_values[start:end] + start = end + return rv + + +def _handle_seed(seed: int | Random | None) -> int: + """Handle seed argument and ensure it is what pylibcugraph needs: an int.""" + if seed is None: + return + if isinstance(seed, Random): + return seed.randint(0, sys.maxsize) + return op.index(seed) # Ensure seed is integral diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml new file mode 100644 index 000000000..95e9c256e --- /dev/null +++ b/python/nx-cugraph/pyproject.toml @@ -0,0 +1,218 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. + +[build-system] + +requires = [ + "setuptools>=61.0.0", + "wheel", +] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. +build-backend = "setuptools.build_meta" + +[project] +name = "nx-cugraph" +version = "23.10.00" +description = "cugraph backend for NetworkX" +readme = { file = "README.md", content-type = "text/markdown" } +authors = [ + { name = "NVIDIA Corporation" }, +] +license = { text = "Apache 2.0" } +requires-python = ">=3.9" +classifiers = [ + "Development Status :: 3 - Alpha", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3 :: Only", + "Intended Audience :: Developers", + "Topic :: Software Development :: Libraries :: Python Modules", +] +dependencies = [ + "cupy-cuda11x>=12.0.0", + "networkx>=3.0", + "pylibcugraph==23.10.*", +] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. + +[project.optional-dependencies] +test = [ + "packaging>=21", + "pandas", + "pytest", + "pytest-benchmark", + "pytest-cov", + "pytest-mpl", + "pytest-xdist", + "scipy", +] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. + +[project.urls] +Homepage = "https://github.com/rapidsai/cugraph" +Documentation = "https://docs.rapids.ai/api/cugraph/stable/" + +[project.entry-points."networkx.plugins"] +cugraph = "nx_cugraph.interface:BackendInterface" + +[tool.setuptools] +license-files = ["LICENSE"] + +[tool.setuptools.packages.find] +include = [ + "nx_cugraph*", + "nx_cugraph.*", +] + +[tool.black] +line-length = 88 +target-version = ["py39", "py310", "py311"] + +[tool.isort] +sections = ["FUTURE", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"] +profile = "black" +skip_gitignore = true +float_to_top = true +default_section = "THIRDPARTY" +known_first_party = "nx_cugraph" +line_length = 88 + +[tool.pytest.ini_options] +minversion = "6.0" +testpaths = "nx_cugraph/tests" +xfail_strict = true +markers = [ + "slow: Skipped unless --runslow passed", +] +log_cli_level = "info" +filterwarnings = [ + # See: https://docs.python.org/3/library/warnings.html#describing-warning-filters + # and: https://docs.pytest.org/en/7.2.x/how-to/capture-warnings.html#controlling-warnings + # "error", +] +python_files = [ + "bench_*.py", + "test_*.py", +] +python_functions = [ + "bench_*", + "test_*", +] +addopts = [ + "--strict-config", # Force error if config is mispelled + "--strict-markers", # Force error if marker is mispelled (must be defined in config) + # "-ra", # Print summary of all fails/errors + "--benchmark-warmup=off", + "--benchmark-max-time=0", + "--benchmark-min-rounds=3", + "--benchmark-columns=min,median,max", +] + +[tool.coverage.run] +branch = true +source = ["nx_cugraph"] +omit = [] + +[tool.coverage.report] +ignore_errors = false +precision = 1 +fail_under = 0 +skip_covered = false # Nice to see fully covered files when running `run_nx_tests.sh` +skip_empty = true +exclude_lines = [ + "pragma: no cover", + "raise AssertionError", + "raise NotImplementedError", +] + +[tool.ruff] +# https://github.com/charliermarsh/ruff/ +line-length = 88 +target-version = "py39" +select = [ + "ALL", +] +external = [ + # noqa codes that ruff doesn't know about: https://github.com/charliermarsh/ruff#external +] +ignore = [ + # Would be nice to fix these + "D100", # Missing docstring in public module + "D101", # Missing docstring in public class + "D102", # Missing docstring in public method + "D103", # Missing docstring in public function + "D104", # Missing docstring in public package + "D105", # Missing docstring in magic method + + # Maybe consider + # "SIM300", # Yoda conditions are discouraged, use ... instead (Note: we're not this picky) + # "SIM401", # Use dict.get ... instead of if-else-block (Note: if-else better for coverage and sometimes clearer) + # "TRY004", # Prefer `TypeError` exception for invalid type (Note: good advice, but not worth the nuisance) + # "TRY200", # Use `raise from` to specify exception cause (Note: sometimes okay to raise original exception) + + # Intentionally ignored + "A003", # Class attribute ... is shadowing a python builtin + "ANN101", # Missing type annotation for `self` in method + "ARG004", # Unused static method argument: `...` + "COM812", # Trailing comma missing + "D203", # 1 blank line required before class docstring (Note: conflicts with D211, which is preferred) + "D400", # First line should end with a period (Note: prefer D415, which also allows "?" and "!") + "F403", # `from .classes import *` used; unable to detect undefined names (Note: used to match networkx) + "N801", # Class name ... should use CapWords convention (Note:we have a few exceptions to this) + "N802", # Function name ... should be lowercase + "N803", # Argument name ... should be lowercase (Maybe okay--except in tests) + "N806", # Variable ... in function should be lowercase + "N807", # Function name should not start and end with `__` + "N818", # Exception name ... should be named with an Error suffix (Note: good advice) + "PLR0911", # Too many return statements + "PLR0912", # Too many branches + "PLR0913", # Too many arguments to function call + "PLR0915", # Too many statements + "PLR2004", # Magic number used in comparison, consider replacing magic with a constant variable + "PLW2901", # Outer for loop variable ... overwritten by inner assignment target (Note: good advice, but too strict) + "RET502", # Do not implicitly `return None` in function able to return non-`None` value + "RET503", # Missing explicit `return` at the end of function able to return non-`None` value + "RET504", # Unnecessary variable assignment before `return` statement + "S110", # `try`-`except`-`pass` detected, consider logging the exception (Note: good advice, but we don't log) + "S112", # `try`-`except`-`continue` detected, consider logging the exception (Note: good advice, but we don't log) + "SIM102", # Use a single `if` statement instead of nested `if` statements (Note: often necessary) + "SIM105", # Use contextlib.suppress(...) instead of try-except-pass (Note: try-except-pass is much faster) + "SIM108", # Use ternary operator ... instead of if-else-block (Note: if-else better for coverage and sometimes clearer) + "TRY003", # Avoid specifying long messages outside the exception class (Note: why?) + + # Ignored categories + "C90", # mccabe (Too strict, but maybe we should make things less complex) + "I", # isort (Should we replace `isort` with this?) + "ANN", # flake8-annotations + "BLE", # flake8-blind-except (Maybe consider) + "FBT", # flake8-boolean-trap (Why?) + "DJ", # flake8-django (We don't use django) + "EM", # flake8-errmsg (Perhaps nicer, but too much work) + # "ICN", # flake8-import-conventions (Doesn't allow "_" prefix such as `_np`) + "PYI", # flake8-pyi (We don't have stub files yet) + "SLF", # flake8-self (We can use our own private variables--sheesh!) + "TID", # flake8-tidy-imports (Rely on isort and our own judgement) + # "TCH", # flake8-type-checking + "ARG", # flake8-unused-arguments (Sometimes helpful, but too strict) + "TD", # flake8-todos (Maybe okay to add some of these) + "FIX", # flake8-fixme (like flake8-todos) + "ERA", # eradicate (We like code in comments!) + "PD", # pandas-vet (Intended for scripts that use pandas, not libraries) +] + +[tool.ruff.per-file-ignores] +"__init__.py" = ["F401"] # Allow unused imports (w/o defining `__all__`) +# Allow assert, print, RNG, and no docstring +"nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"] + +[tool.ruff.flake8-annotations] +mypy-init-return = true + +[tool.ruff.flake8-builtins] +builtins-ignorelist = ["copyright"] + +[tool.ruff.flake8-pytest-style] +fixture-parentheses = false +mark-parentheses = false + +[tool.ruff.pydocstyle] +convention = "numpy" diff --git a/python/nx-cugraph/run_nx_tests.sh b/python/nx-cugraph/run_nx_tests.sh new file mode 100755 index 000000000..07c97cdf9 --- /dev/null +++ b/python/nx-cugraph/run_nx_tests.sh @@ -0,0 +1,36 @@ +#!/usr/bin/env bash +# +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# NETWORKX_GRAPH_CONVERT=cugraph +# Used by networkx versions 3.0 and 3.1 +# Must be set to "cugraph" to test the nx-cugraph backend. +# +# NETWORKX_TEST_BACKEND=cugraph +# Replaces NETWORKX_GRAPH_CONVERT for networkx versions >=3.2 +# Must be set to "cugraph" to test the nx-cugraph backend. +# +# NETWORKX_FALLBACK_TO_NX=True (optional) +# Used by networkx versions >=3.2. With this set, input graphs will not be +# converted to nx-cugraph and the networkx algorithm will be called for +# algorithms that we don't implement or if we raise NotImplementedError. +# This is sometimes helpful to get increased testing and coverage, but +# testing takes longer. Without it, tests will xfail when encountering a +# function that we don't implement. +# +# Coverage of `nx_cugraph.algorithms` is reported and is a good sanity check +# that algorithms run. + +# Warning: cugraph has a .coveragerc file in the /python directory, +# so be mindful of its contents and the CWD when running. +# FIXME: should something be added to detect/prevent the above? + +NETWORKX_GRAPH_CONVERT=cugraph \ +NETWORKX_TEST_BACKEND=cugraph \ +NETWORKX_FALLBACK_TO_NX=True \ + pytest \ + --pyargs networkx \ + --cov=nx_cugraph.algorithms \ + --cov-report term-missing \ + --no-cov-on-fail \ + "$@" diff --git a/python/nx-cugraph/setup.py b/python/nx-cugraph/setup.py new file mode 100644 index 000000000..87c0e1064 --- /dev/null +++ b/python/nx-cugraph/setup.py @@ -0,0 +1,15 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from setuptools import setup + +setup() From a69ae14537bd4ebad83d3ec813229cd013d5b324 Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Tue, 19 Sep 2023 14:20:15 -0500 Subject: [PATCH 264/384] Update to clang 16.0.6. (#3859) This PR updates cugraph to use clang 16.0.6. The previous version 16.0.1 has some minor formatting issues affecting several RAPIDS repos. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Brad Rees (https://github.com/BradReesWork) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3859 --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0f05aedf1..865d06b20 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -33,7 +33,7 @@ repos: additional_dependencies: - flake8==6.0.0 - repo: https://github.com/pre-commit/mirrors-clang-format - rev: v16.0.1 + rev: v16.0.6 hooks: - id: clang-format exclude: | From 4ab0770bf735eed474bc3d781102619c9978b389 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Fri, 22 Sep 2023 10:58:08 -0400 Subject: [PATCH 265/384] v23.12 Updates [skip ci] --- docs/cugraph/source/conf.py | 4 ++-- python/nx-cugraph/README.md | 2 +- python/nx-cugraph/nx_cugraph/__init__.py | 2 +- python/nx-cugraph/pyproject.toml | 4 ++-- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index a96f0fc1e..58ecca271 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -76,9 +76,9 @@ # built documents. # # The short X.Y version. -version = '23.10' +version = '23.12' # The full version, including alpha/beta/rc tags. -release = '23.10.00' +release = '23.12.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index e7cd26218..ab267e5a7 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -6,7 +6,7 @@ with minimal dependencies (`networkx`, `cupy`, and `pylibcugraph`) to run graph ### Contribute -Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.10/readme_pages/CONTRIBUTING.md) +Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.12 and [building from source](https://docs.rapids.ai/api/cugraph/stable/installation/source_build/), then build nx-cugraph in develop (i.e., editable) mode: ``` $ ./build.sh nx-cugraph --pydevelop diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 28066fe2b..c23542842 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -17,4 +17,4 @@ from .classes import * from .convert import * -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 95e9c256e..532b52256 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "nx-cugraph" -version = "23.10.00" +version = "23.12.00" description = "cugraph backend for NetworkX" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -32,7 +32,7 @@ classifiers = [ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", - "pylibcugraph==23.10.*", + "pylibcugraph==23.12.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. [project.optional-dependencies] From 0a3df82a6448bf5fcf1092a1bc4a901b49e29ad5 Mon Sep 17 00:00:00 2001 From: Paul Taylor <178183+trxcllnt@users.noreply.github.com> Date: Wed, 27 Sep 2023 12:08:12 -0700 Subject: [PATCH 266/384] Add cuGraph devcontainers (#3838) This PR adds some [devcontainers](https://containers.dev/) to help simplify building the cuGraph C++ and Python libraries. It also adds an optional job to the `pr.yaml` to [build the cuGraph libs in each devcontainer](https://github.com/trxcllnt/cugraph/blob/fea/devcontainers/.github/workflows/pr.yaml#L113-L119), so the build caches are populated for devs by CI. A devcontainer can be launched by clicking the "Reopen in Container" button that VSCode shows when opening the repo (or by using the "Rebuild and Reopen in Container" command from the command palette): ![image](https://user-images.githubusercontent.com/178183/221771999-97ab29d5-e718-4e5f-b32f-2cdd51bba25c.png) Clicking this button will cause VSCode to prompt the user to select one of these devcontainer variants: ![image](https://github.com/rapidsai/rmm/assets/178183/68d4b264-4fc2-4008-92b6-cb4bdd19b29f) On startup, the devcontainer creates or updates the conda/pip environment using `cugraph/dependencies.yaml`. The envs/package caches are cached on the host via volume mounts, which are described in more detail in [`.devcontainer/README.md`](https://github.com/trxcllnt/cugraph/blob/fea/devcontainers/.devcontainer/README.md). The container includes convenience functions to clean, configure, and build the various cuGraph components: ```shell $ clean-cugraph-cpp # only cleans the C++ build dir $ clean-cugraph-python # only cleans the Python build dir $ clean-cugraph # cleans both C++ and Python build dirs $ configure-cugraph-cpp # only configures cugraph C++ lib $ build-cugraph-cpp # only builds cugraph C++ lib $ build-cugraph-python # only builds cugraph Python lib $ build-cugraph # builds both C++ and Python libs ``` * The C++ build script is a small wrapper around `cmake -S ~/cugraph/cpp -B ~/cugraph/cpp/build` and `cmake --build ~/cugraph/cpp/build` * The Python build script is a small wrapper around `pip install --editable ~/cugraph/cpp` Unlike `build.sh`, these convenience scripts *don't* install the libraries after building them. Instead, they automatically inject the correct arguments to build the C++ libraries from source and use their build dirs as package roots: ```shell $ cmake -S ~/cugraph/cpp -B ~/cugraph/cpp/build $ CMAKE_ARGS="-Dcugraph_ROOT=~/cugraph/cpp/build" \ # <-- this argument is automatic pip install -e ~/cugraph/cpp ``` Authors: - Paul Taylor (https://github.com/trxcllnt) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Jake Awe (https://github.com/AyodeAwe) URL: https://github.com/rapidsai/cugraph/pull/3838 --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 3fda9f8a0..c6bcf6965 100644 --- a/.gitignore +++ b/.gitignore @@ -97,3 +97,7 @@ python/cugraph/cugraph/tests/dask-worker-space docs/cugraph/source/api_docs/api/* _html _text + +# clang tooling +compile_commands.json +.clangd/ From b192ae6ddcf3b5e5cb9e91aefb457a69ba51ddb4 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Thu, 28 Sep 2023 13:09:17 -0500 Subject: [PATCH 267/384] Add entry point to tell NetworkX about nx-cugraph without importing it. (#3848) This allows NetworkX docstrings to be updated (among other things). This will have a companion PR in NetworkX. We still need to determine (and agree) on the dict returned by this entry point, and NetworkX doesn't need to use everything I have here. We should probably add a string for `"description"` that gives a very short description of the backend, and maybe `"url"` or `"homepage"` or whatever so online docs can have links. Here's how to use the entry point (Python >= 3.10) after installing it: ```python In [1]: from importlib.metadata import entry_points In [2]: items = entry_points(group="networkx.plugin_info") In [3]: [plugin] = items In [4]: plugin.load()() Out[4]: {'backend_name': 'cugraph', 'project': 'nx-cugraph', 'package': 'nx_cugraph', 'functions': {'betweenness_centrality', 'edge_betweenness_centrality', 'louvain_communities'}, 'extra_docstrings': {'betweenness_centrality': '`weight` parameter is not yet supported.', 'edge_betweenness_centrality': '`weight` parameter is not yet supported.', 'louvain_communities': '`threshold` and `seed` parameters are currently ignored.'}, 'extra_parameters': {'louvain_communities': {'max_level': 'Upper limit of the number of macro-iterations.'}}} ``` CC @rlratzel @betochimas Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3848 --- python/nx-cugraph/.flake8 | 1 + python/nx-cugraph/Makefile | 10 +++ python/nx-cugraph/_nx_cugraph/__init__.py | 88 ++++++++++++++++++ python/nx-cugraph/_nx_cugraph/core.py | 90 +++++++++++++++++++ python/nx-cugraph/lint.yaml | 19 ++-- python/nx-cugraph/nx_cugraph/__init__.py | 16 +++- .../algorithms/centrality/betweenness.py | 6 +- .../algorithms/community/louvain.py | 17 ++-- python/nx-cugraph/nx_cugraph/interface.py | 5 +- .../nx_cugraph/tests/test_match_api.py | 5 +- .../nx-cugraph/nx_cugraph/utils/decorators.py | 41 +++++++-- python/nx-cugraph/nx_cugraph/utils/misc.py | 6 +- python/nx-cugraph/pyproject.toml | 10 +++ 13 files changed, 277 insertions(+), 37 deletions(-) create mode 100644 python/nx-cugraph/_nx_cugraph/__init__.py create mode 100644 python/nx-cugraph/_nx_cugraph/core.py diff --git a/python/nx-cugraph/.flake8 b/python/nx-cugraph/.flake8 index 3a2e3fb86..c5874e54f 100644 --- a/python/nx-cugraph/.flake8 +++ b/python/nx-cugraph/.flake8 @@ -11,3 +11,4 @@ extend-ignore = per-file-ignores = nx_cugraph/tests/*.py:T201, __init__.py:F401,F403, + _nx_cugraph/__init__.py:E501, diff --git a/python/nx-cugraph/Makefile b/python/nx-cugraph/Makefile index c9caf147d..6e1b98ee6 100644 --- a/python/nx-cugraph/Makefile +++ b/python/nx-cugraph/Makefile @@ -1,7 +1,17 @@ # Copyright (c) 2023, NVIDIA CORPORATION. SHELL= /bin/bash +.PHONY: all +all: plugin-info lint + +.PHONY: lint lint: git ls-files | xargs pre-commit run --config lint.yaml --files + +.PHONY: lint-update lint-update: pre-commit autoupdate --config lint.yaml + +.PHONY: plugin-info +plugin-info: + python _nx_cugraph/__init__.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py new file mode 100644 index 000000000..9b3332106 --- /dev/null +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -0,0 +1,88 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tell NetworkX about the cugraph backend. This file can update itself: + +$ make plugin-info # Recommended method for development + +or + +$ python _nx_cugraph/__init__.py +""" + +# Entries between BEGIN and END are automatically generated +_info = { + "backend_name": "cugraph", + "project": "nx-cugraph", + "package": "nx_cugraph", + "url": "https://github.com/rapidsai/cugraph/tree/branch-23.10/python/nx-cugraph", + "short_summary": "GPU-accelerated backend.", + # "description": "TODO", + "functions": { + # BEGIN: functions + "betweenness_centrality", + "edge_betweenness_centrality", + "louvain_communities", + # END: functions + }, + "extra_docstrings": { + # BEGIN: extra_docstrings + "betweenness_centrality": "`weight` parameter is not yet supported.", + "edge_betweenness_centrality": "`weight` parameter is not yet supported.", + "louvain_communities": "`threshold` and `seed` parameters are currently ignored.", + # END: extra_docstrings + }, + "extra_parameters": { + # BEGIN: extra_parameters + "louvain_communities": { + "max_level : int, optional": "Upper limit of the number of macro-iterations.", + }, + # END: extra_parameters + }, +} + + +def get_info(): + """Target of ``networkx.plugin_info`` entry point. + + This tells NetworkX about the cugraph backend without importing nx_cugraph. + """ + # Convert to e.g. `{"functions": {"myfunc": {"extra_docstring": ...}}}` + d = _info.copy() + info_keys = { + "extra_docstrings": "extra_docstring", + "extra_parameters": "extra_parameters", + } + d["functions"] = { + func: { + new_key: vals[func] + for old_key, new_key in info_keys.items() + if func in (vals := d[old_key]) + } + for func in d["functions"] + } + for key in info_keys: + del d[key] + return d + + +__version__ = "23.10.00" + +if __name__ == "__main__": + from pathlib import Path + + from _nx_cugraph.core import main + + filepath = Path(__file__) + text = main(filepath) + with filepath.open("w") as f: + f.write(text) diff --git a/python/nx-cugraph/_nx_cugraph/core.py b/python/nx-cugraph/_nx_cugraph/core.py new file mode 100644 index 000000000..72f920389 --- /dev/null +++ b/python/nx-cugraph/_nx_cugraph/core.py @@ -0,0 +1,90 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Utilities to help keep _nx_cugraph up to date.""" + + +def get_functions(): + from nx_cugraph.interface import BackendInterface + from nx_cugraph.utils import networkx_algorithm + + return { + key: val + for key, val in vars(BackendInterface).items() + if isinstance(val, networkx_algorithm) + } + + +def get_extra_docstrings(functions=None): + if functions is None: + functions = get_functions() + return {key: val.extra_doc for key, val in functions.items() if val.extra_doc} + + +def get_extra_parameters(functions=None): + if functions is None: + functions = get_functions() + return {key: val.extra_params for key, val in functions.items() if val.extra_params} + + +def update_text(text, lines_to_add, target, indent=" " * 8): + begin = f"# BEGIN: {target}\n" + end = f"# END: {target}\n" + start = text.index(begin) + stop = text.index(end) + to_add = "\n".join([f"{indent}{line}" for line in lines_to_add]) + return f"{text[:start]}{begin}{to_add}\n{indent}{text[stop:]}" + + +def dict_to_lines(d, *, indent=""): + for key in sorted(d): + val = d[key] + if "\n" not in val: + yield f"{indent}{key!r}: {val!r}," + else: + yield f"{indent}{key!r}: (" + *lines, last_line = val.split("\n") + for line in lines: + line += "\n" + yield f" {indent}{line!r}" + yield f" {indent}{last_line!r}" + yield f"{indent})," + + +def main(filepath): + from pathlib import Path + + filepath = Path(filepath) + with filepath.open() as f: + orig_text = f.read() + text = orig_text + + # Update functions + functions = get_functions() + to_add = [f'"{name}",' for name in sorted(functions)] + text = update_text(text, to_add, "functions") + + # Update extra_docstrings + extra_docstrings = get_extra_docstrings(functions) + to_add = list(dict_to_lines(extra_docstrings)) + text = update_text(text, to_add, "extra_docstrings") + + # Update extra_parameters + extra_parameters = get_extra_parameters(functions) + to_add = [] + for name in sorted(extra_parameters): + params = extra_parameters[name] + to_add.append(f"{name!r}: {{") + to_add.extend(dict_to_lines(params, indent=" " * 4)) + to_add.append("},") + text = update_text(text, to_add, "extra_parameters") + return text diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index dba061bd6..6a462a6af 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -31,7 +31,7 @@ repos: - id: validate-pyproject name: Validate pyproject.toml - repo: https://github.com/PyCQA/autoflake - rev: v2.2.0 + rev: v2.2.1 hooks: - id: autoflake args: [--in-place] @@ -40,17 +40,17 @@ repos: hooks: - id: isort - repo: https://github.com/asottile/pyupgrade - rev: v3.10.1 + rev: v3.13.0 hooks: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.7.0 + rev: 23.9.1 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.286 + rev: v0.0.291 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -58,11 +58,12 @@ repos: rev: 6.1.0 hooks: - id: flake8 + args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501'] # Why is this necessary? additional_dependencies: &flake8_dependencies - # These versions need updated manually - - flake8==6.1.0 - - flake8-bugbear==23.7.10 - - flake8-simplify==0.20.0 + # These versions need updated manually + - flake8==6.1.0 + - flake8-bugbear==23.9.16 + - flake8-simplify==0.20.0 - repo: https://github.com/asottile/yesqa rev: v1.5.0 hooks: @@ -76,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.286 + rev: v0.0.291 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 28066fe2b..4a0e95a10 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -12,9 +12,21 @@ # limitations under the License. from networkx.exception import * -from . import algorithms, classes, convert, utils -from .algorithms import * +from . import utils + +from . import classes from .classes import * + +from . import convert from .convert import * +# from . import convert_matrix +# from .convert_matrix import * + +# from . import generators +# from .generators import * + +from . import algorithms +from .algorithms import * + __version__ = "23.10.00" diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py index b777919f8..104ac8741 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py @@ -13,7 +13,7 @@ import pylibcugraph as plc from nx_cugraph.convert import _to_graph -from nx_cugraph.utils import _handle_seed, networkx_algorithm +from nx_cugraph.utils import _seed_to_int, networkx_algorithm __all__ = ["betweenness_centrality", "edge_betweenness_centrality"] @@ -22,11 +22,12 @@ def betweenness_centrality( G, k=None, normalized=True, weight=None, endpoints=False, seed=None ): + """`weight` parameter is not yet supported.""" if weight is not None: raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" ) - seed = _handle_seed(seed) + seed = _seed_to_int(seed) G = _to_graph(G, weight) node_ids, values = plc.betweenness_centrality( resource_handle=plc.ResourceHandle(), @@ -47,6 +48,7 @@ def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None): @networkx_algorithm def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None): + """`weight` parameter is not yet supported.""" if weight is not None: raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index ca5f05c20..a183b59fe 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -17,7 +17,7 @@ from nx_cugraph.convert import _to_undirected_graph from nx_cugraph.utils import ( _groupby, - _handle_seed, + _seed_to_int, networkx_algorithm, not_implemented_for, ) @@ -26,16 +26,17 @@ @not_implemented_for("directed") -@networkx_algorithm(extra_params="max_level") +@networkx_algorithm( + extra_params={ + "max_level : int, optional": "Upper limit of the number of macro-iterations." + } +) def louvain_communities( G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None ): - """`threshold` and `seed` parameters are currently ignored. - - Extra parameter: `max_level` controls the maximum number of levels of the algorithm. - """ + """`threshold` and `seed` parameters are currently ignored.""" # NetworkX allows both directed and undirected, but cugraph only allows undirected. - seed = _handle_seed(seed) # Unused, but ensure it's valid for future compatibility + seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) if G.row_indices.size == 0: # TODO: PLC doesn't handle empty graphs gracefully! @@ -46,8 +47,8 @@ def louvain_communities( resource_handle=plc.ResourceHandle(), graph=G._get_plc_graph(), max_level=max_level, # TODO: add this parameter to NetworkX + threshold=threshold, resolution=resolution, - # threshold=threshold, # TODO: add this parameter to PLC do_expensive_check=False, ) groups = _groupby(clusters, vertices) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index cc750cd2d..2ad23acd9 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -62,9 +62,7 @@ def key(testpath): # Reasons for xfailing no_weights = "weighted implementation not currently supported" no_multigraph = "multigraphs not currently supported" - louvain_different = ( - "Louvain may be different due to RNG or unsupported threshold parameter" - ) + louvain_different = "Louvain may be different due to RNG" xfail = {} @@ -176,7 +174,6 @@ def key(testpath): ): louvain_different, key("test_louvain.py:test_none_weight_param"): louvain_different, key("test_louvain.py:test_multigraph"): louvain_different, - key("test_louvain.py:test_threshold"): louvain_different, } ) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py index 64d3704dd..ecfda1397 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py @@ -45,11 +45,14 @@ def test_match_signature_and_names(): assert orig_sig == func_sig else: # Ignore extra parameters added to nx-cugraph algorithm + # The key of func.extra_params may be like "max_level : int, optional", + # but we only want "max_level" here. + extra_params = {name.split(" ")[0] for name in func.extra_params} assert orig_sig == func_sig.replace( parameters=[ p for name, p in func_sig.parameters.items() - if name not in func.extra_params + if name not in extra_params ] ) if func.can_run is not nxcg.utils.decorators._default_can_run: diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py index 3dbdb07e8..0f15d236e 100644 --- a/python/nx-cugraph/nx_cugraph/utils/decorators.py +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -10,13 +10,21 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + from functools import partial, update_wrapper -from networkx.utils.decorators import not_implemented_for +from networkx.utils.decorators import nodes_or_number, not_implemented_for from nx_cugraph.interface import BackendInterface -__all__ = ["not_implemented_for", "networkx_algorithm"] +try: + from networkx.utils.backends import _registered_algorithms +except ModuleNotFoundError: + from networkx.classes.backends import _registered_algorithms + + +__all__ = ["not_implemented_for", "nodes_or_number", "networkx_algorithm"] def networkx_class(api): @@ -28,7 +36,17 @@ def inner(func): class networkx_algorithm: - def __new__(cls, func=None, *, name=None, extra_params=None): + name: str + extra_doc: str | None + extra_params: dict[str, str] | None + + def __new__( + cls, + func=None, + *, + name: str | None = None, + extra_params: dict[str, str] | str | None = None, + ): if func is None: return partial(networkx_algorithm, name=name, extra_params=extra_params) instance = object.__new__(cls) @@ -37,13 +55,20 @@ def __new__(cls, func=None, *, name=None, extra_params=None): instance.__defaults__ = func.__defaults__ instance.__kwdefaults__ = func.__kwdefaults__ instance.name = func.__name__ if name is None else name - # TODO: should extra_params be a dict[str, str] that describes the parameters? if extra_params is None: - instance.extra_params = None + pass elif isinstance(extra_params, str): - instance.extra_params = {extra_params} - else: - instance.extra_params = set(extra_params) + extra_params = {extra_params: ""} + elif not isinstance(extra_params, dict): + raise TypeError( + f"extra_params must be dict, str, or None; got {type(extra_params)}" + ) + instance.extra_params = extra_params + # The docstring on our function is added to the NetworkX docstring. + instance.extra_doc = func.__doc__ + # Copy __doc__ from NetworkX + if instance.name in _registered_algorithms: + instance.__doc__ = _registered_algorithms[instance.name].__doc__ instance.can_run = _default_can_run setattr(BackendInterface, instance.name, instance) # Set methods so they are in __dict__ diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 64c0be066..72e4094b8 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -18,7 +18,7 @@ import cupy as cp -__all__ = ["_groupby", "_handle_seed"] +__all__ = ["_groupby", "_seed_to_int"] def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: @@ -51,8 +51,8 @@ def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: return rv -def _handle_seed(seed: int | Random | None) -> int: - """Handle seed argument and ensure it is what pylibcugraph needs: an int.""" +def _seed_to_int(seed: int | Random | None) -> int: + """Handle any valid seed argument and convert it to an int if necessary.""" if seed is None: return if isinstance(seed, Random): diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 95e9c256e..db3b3a225 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -54,6 +54,9 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [project.entry-points."networkx.plugins"] cugraph = "nx_cugraph.interface:BackendInterface" +[project.entry-points."networkx.plugin_info"] +cugraph = "_nx_cugraph:get_info" + [tool.setuptools] license-files = ["LICENSE"] @@ -61,6 +64,8 @@ license-files = ["LICENSE"] include = [ "nx_cugraph*", "nx_cugraph.*", + "_nx_cugraph*", + "_nx_cugraph.*", ] [tool.black] @@ -75,6 +80,7 @@ float_to_top = true default_section = "THIRDPARTY" known_first_party = "nx_cugraph" line_length = 88 +extend_skip_glob = ["nx_cugraph/__init__.py"] [tool.pytest.ini_options] minversion = "6.0" @@ -128,6 +134,9 @@ exclude_lines = [ # https://github.com/charliermarsh/ruff/ line-length = 88 target-version = "py39" +unfixable = [ + "F841", # unused-variable (Note: can leave useless expression) +] select = [ "ALL", ] @@ -203,6 +212,7 @@ ignore = [ "__init__.py" = ["F401"] # Allow unused imports (w/o defining `__all__`) # Allow assert, print, RNG, and no docstring "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"] +"_nx_cugraph/__init__.py" = ["E501"] [tool.ruff.flake8-annotations] mypy-init-return = true From 77c75d1d8874efb1d6b3f294b431bdf3c71e8db0 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Thu, 28 Sep 2023 14:03:26 -0500 Subject: [PATCH 268/384] Updates the source build docs to include libcugraphops as a build prerequisite (#3893) closes #3722 Updates the source build docs to include `libcugraphops` as a build prerequisite. Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3893 --- docs/cugraph/source/installation/source_build.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md index 7782591f1..f5ee0741d 100644 --- a/docs/cugraph/source/installation/source_build.md +++ b/docs/cugraph/source/installation/source_build.md @@ -6,10 +6,10 @@ The cuGraph package include both a C/C++ CUDA portion and a python portion. Bot ## Prerequisites -__Compiler__: -* `gcc` version 9.3+ -* `nvcc` version 11.0+ -* `cmake` version 3.20.1+ +__Compiler:__ +* `gcc` version 9.3+ +* `nvcc` version 11.0+ +* `cmake` version 3.20.1+ __CUDA:__ * CUDA 11.0+ @@ -18,6 +18,11 @@ __CUDA:__ You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). +__Packages:__ +* `cmake` version 3.20.1+ +* `libcugraphops` (version matching source branch version, eg. `23.10`) + +You can obtain `libcugraphops` using `conda`/`mamba` from the `nvidia` channel, or using `pip` with the `--extra-index-url=https://pypi.nvidia.com` option. See the [RAPIDS docs](https://docs.rapids.ai/install#environment) for more details. ## Building cuGraph To install cuGraph from source, ensure the dependencies are met. From 439739ecee3fdad581be65c137c050dc0c8ecb34 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 3 Oct 2023 16:50:10 -0500 Subject: [PATCH 269/384] nx-cugraph: handle louvain with isolated nodes (#3897) This handles isolated nodes in `louvain_communities` similar to what is done in #3886. This is expected to be a temporary fix until pylibcugraph can handle isolated nodes. As a bonus, I added `isolates` algorithm :tada: CC @naimnv @rlratzel Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3897 --- python/nx-cugraph/_nx_cugraph/__init__.py | 7 ++- python/nx-cugraph/lint.yaml | 2 +- .../nx_cugraph/algorithms/__init__.py | 1 + .../algorithms/community/louvain.py | 28 +++++++-- .../nx_cugraph/algorithms/isolate.py | 63 +++++++++++++++++++ .../nx-cugraph/nx_cugraph/classes/digraph.py | 2 +- python/nx-cugraph/nx_cugraph/classes/graph.py | 6 +- python/nx-cugraph/nx_cugraph/convert.py | 2 +- .../nx_cugraph/tests/test_community.py | 53 ++++++++++++++++ 9 files changed, 151 insertions(+), 13 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/isolate.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_community.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 9b3332106..ebd13dade 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -31,20 +31,23 @@ # BEGIN: functions "betweenness_centrality", "edge_betweenness_centrality", + "is_isolate", + "isolates", "louvain_communities", + "number_of_isolates", # END: functions }, "extra_docstrings": { # BEGIN: extra_docstrings "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", - "louvain_communities": "`threshold` and `seed` parameters are currently ignored.", + "louvain_communities": "`seed` parameter is currently ignored.", # END: extra_docstrings }, "extra_parameters": { # BEGIN: extra_parameters "louvain_communities": { - "max_level : int, optional": "Upper limit of the number of macro-iterations.", + "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).", }, # END: extra_parameters }, diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 6a462a6af..338ca7b23 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -63,7 +63,7 @@ repos: # These versions need updated manually - flake8==6.1.0 - flake8-bugbear==23.9.16 - - flake8-simplify==0.20.0 + - flake8-simplify==0.21.0 - repo: https://github.com/asottile/yesqa rev: v1.5.0 hooks: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 3a585452d..dfd9adfc6 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -12,3 +12,4 @@ # limitations under the License. from . import centrality, community from .centrality import * +from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index a183b59fe..dc209870c 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -10,7 +10,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import sys +import warnings import pylibcugraph as plc @@ -22,19 +22,23 @@ not_implemented_for, ) +from ..isolate import _isolates + __all__ = ["louvain_communities"] @not_implemented_for("directed") @networkx_algorithm( extra_params={ - "max_level : int, optional": "Upper limit of the number of macro-iterations." + "max_level : int, optional": ( + "Upper limit of the number of macro-iterations (max: 500)." + ) } ) def louvain_communities( G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None ): - """`threshold` and `seed` parameters are currently ignored.""" + """`seed` parameter is currently ignored.""" # NetworkX allows both directed and undirected, but cugraph only allows undirected. seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) @@ -42,7 +46,14 @@ def louvain_communities( # TODO: PLC doesn't handle empty graphs gracefully! return [{key} for key in G._nodeiter_to_iter(range(len(G)))] if max_level is None: - max_level = sys.maxsize + max_level = 500 + elif max_level > 500: + warnings.warn( + f"max_level is set too high (={max_level}), setting it to 500.", + UserWarning, + stacklevel=2, + ) + max_level = 500 vertices, clusters, modularity = plc.louvain( resource_handle=plc.ResourceHandle(), graph=G._get_plc_graph(), @@ -52,7 +63,14 @@ def louvain_communities( do_expensive_check=False, ) groups = _groupby(clusters, vertices) - return [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] + rv = [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] + # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix + isolates = _isolates(G) + if isolates.size > 0: + isolates = isolates[isolates > vertices.max()] + if isolates.size > 0: + rv.extend({node} for node in G._nodearray_to_list(isolates)) + return rv @louvain_communities._can_run diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py new file mode 100644 index 000000000..774627e84 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py @@ -0,0 +1,63 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from typing import TYPE_CHECKING + +import cupy as cp + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import networkx_algorithm + +if TYPE_CHECKING: # pragma: no cover + from nx_cugraph.typing import IndexValue + +__all__ = ["is_isolate", "isolates", "number_of_isolates"] + + +@networkx_algorithm +def is_isolate(G, n): + G = _to_graph(G) + index = n if G.key_to_id is None else G.key_to_id[n] + return not ( + (G.row_indices == index).any().tolist() + or G.is_directed() + and (G.col_indices == index).any().tolist() + ) + + +def _mark_isolates(G) -> cp.ndarray[bool]: + """Return a boolean mask array indicating indices of isolated nodes.""" + mark_isolates = cp.ones(len(G), bool) + mark_isolates[G.row_indices] = False + if G.is_directed(): + mark_isolates[G.col_indices] = False + return mark_isolates + + +def _isolates(G) -> cp.ndarray[IndexValue]: + """Like isolates, but return an array of indices instead of an iterator of nodes.""" + G = _to_graph(G) + return cp.nonzero(_mark_isolates(G))[0] + + +@networkx_algorithm +def isolates(G): + G = _to_graph(G) + return G._nodeiter_to_iter(iter(_isolates(G).tolist())) + + +@networkx_algorithm +def number_of_isolates(G): + G = _to_graph(G) + return _mark_isolates(G).sum().tolist() diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index 0aaf88fd7..72a1bff21 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -20,7 +20,7 @@ from .graph import Graph -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from nx_cugraph.typing import NodeKey __all__ = ["DiGraph"] diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 1432f68c7..ded4cc394 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -23,7 +23,7 @@ import nx_cugraph as nxcg -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from collections.abc import Iterable, Iterator from nx_cugraph.typing import ( @@ -245,9 +245,9 @@ def from_dcsc( def __new__(cls, incoming_graph_data=None, **attr) -> Graph: if incoming_graph_data is None: new_graph = cls.from_coo(0, cp.empty(0, np.int32), cp.empty(0, np.int32)) - elif incoming_graph_data.__class__ is new_graph.__class__: + elif incoming_graph_data.__class__ is cls: new_graph = incoming_graph_data.copy() - elif incoming_graph_data.__class__ is new_graph.to_networkx_class(): + elif incoming_graph_data.__class__ is cls.to_networkx_class(): new_graph = nxcg.from_networkx(incoming_graph_data, preserve_all_attrs=True) else: raise NotImplementedError diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index 9be8cac78..1240ea71d 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -24,7 +24,7 @@ import nx_cugraph as nxcg -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue __all__ = [ diff --git a/python/nx-cugraph/nx_cugraph/tests/test_community.py b/python/nx-cugraph/nx_cugraph/tests/test_community.py new file mode 100644 index 000000000..126f45c14 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_community.py @@ -0,0 +1,53 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest + +import nx_cugraph as nxcg + + +def test_louvain_isolated_nodes(): + is_nx_30_or_31 = hasattr(nx.classes, "backends") + + def check(left, right): + assert len(left) == len(right) + assert set(map(frozenset, left)) == set(map(frozenset, right)) + + # Empty graph (no nodes) + G = nx.Graph() + if is_nx_30_or_31: + with pytest.raises(ZeroDivisionError): + nx.community.louvain_communities(G) + else: + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) + # Graph with no edges + G.add_nodes_from(range(5)) + if is_nx_30_or_31: + with pytest.raises(ZeroDivisionError): + nx.community.louvain_communities(G) + else: + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) + # Graph with isolated nodes + G.add_edge(1, 2) + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) + # Another one + G.add_edge(4, 4) + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) From c3b9d9712ea9ee6741a63dcc1462fc058db38375 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Fri, 6 Oct 2023 12:18:24 -0500 Subject: [PATCH 270/384] Adds benchmarks for `nx-cugraph` (#3854) closes rapidsai/graph_dl#299 This PR adds new benchmarks for `nx-cugraph` which can be used to compare performance for NetworkX with and without the `cugraph` backend. These benchmarks depend on `pytest`, `pytest-benchmark`, `networkx>=3.0`, `cugraph` (only for the Dataset APIs), and `nx-cugraph` Some results can be seen here: ![image](https://github.com/rapidsai/cugraph/assets/3039903/d595eb3f-2ae6-4b2e-a0b1-a0f0bcb050a8) ![image](https://github.com/rapidsai/cugraph/assets/3039903/3ca367b4-14b8-4673-91d2-f83a947163e7) ### Other changes: * `black` is now run on all python sources under `benchmarks`, which resulted in several format-only changes. * Several other updates to versions, etc. were applied to various .yaml files in order to resolve CI errors. * Merged in changes from [jnke2016/branch-23.10_increase-timeout](https://github.com/rapidsai/cugraph/pull/3854/commits/cbdbd8a6d22903f251c64b1b0321ba41ec03fcb3) to get changes needed to fix dask CI failure. Authors: - Rick Ratzel (https://github.com/rlratzel) - Joseph Nke (https://github.com/jnke2016) - AJ Schmidt (https://github.com/ajschmidt8) - Naim (https://github.com/naimnv) - Seunghwa Kang (https://github.com/seunghwak) - https://github.com/lmeyerov - Erik Welch (https://github.com/eriknw) - Alex Barghi (https://github.com/alexbarghi-nv) - Vyas Ramasubramani (https://github.com/vyasr) - Divye Gala (https://github.com/divyegala) - Tingyu Wang (https://github.com/tingyu66) - Chuck Hastings (https://github.com/ChuckHastings) - ralph (https://github.com/nv-rliu) - Paul Taylor (https://github.com/trxcllnt) Approvers: - Erik Welch (https://github.com/eriknw) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3854 --- .pre-commit-config.yaml | 2 +- .../nx-cugraph/pytest-based/bench_algos.py | 212 ++++++++++++++++++ benchmarks/pytest.ini | 4 +- python/nx-cugraph/_nx_cugraph/__init__.py | 2 +- 4 files changed, 217 insertions(+), 3 deletions(-) create mode 100644 benchmarks/nx-cugraph/pytest-based/bench_algos.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 865d06b20..bab39557c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: - id: black language_version: python3 args: [--target-version=py38] - files: ^python/ + files: ^(python/.*|benchmarks/.*)$ - repo: https://github.com/PyCQA/flake8 rev: 6.0.0 hooks: diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py new file mode 100644 index 000000000..971c3ff10 --- /dev/null +++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py @@ -0,0 +1,212 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import networkx as nx +import pandas as pd +import pytest +from cugraph import datasets + +# FIXME: promote these to cugraph.datasets so the following steps aren't +# necessary +# +# These datasets can be downloaded using the script in the 'datasets' dir: +# +# cd /datasets +# ./get_test_data.sh --benchmark +# +# Then set the following env var so the dataset utils can find their location: +# +# export RAPIDS_DATASET_ROOT_DIR=/datasets +# +from cugraph_benchmarking.params import ( + hollywood, + europe_osm, + cit_patents, + soc_livejournal, +) + +# Attempt to import the NetworkX dispatching module, which is only needed when +# testing with NX <3.2 in order to dynamically switch backends. NX >=3.2 allows +# the backend to be specified directly in the API call. +try: + from networkx.classes import backends # NX <3.2 +except ImportError: + backends = None + + +################################################################################ +# Fixtures and helpers +backend_params = ["cugraph", None] + +dataset_params = [ + pytest.param(datasets.karate, marks=[pytest.mark.small, pytest.mark.undirected]), + pytest.param(datasets.netscience, marks=[pytest.mark.small, pytest.mark.directed]), + pytest.param( + datasets.email_Eu_core, marks=[pytest.mark.small, pytest.mark.directed] + ), + pytest.param(cit_patents, marks=[pytest.mark.medium, pytest.mark.directed]), + pytest.param(hollywood, marks=[pytest.mark.medium, pytest.mark.undirected]), + pytest.param(europe_osm, marks=[pytest.mark.medium, pytest.mark.undirected]), + pytest.param(soc_livejournal, marks=[pytest.mark.large, pytest.mark.directed]), +] + + +def nx_graph_from_dataset(dataset_obj): + """ + Read the dataset specified by the dataset_obj and create and return a + nx.Graph or nx.DiGraph instance based on the dataset is_directed metadata. + """ + create_using = nx.DiGraph if dataset_obj.metadata["is_directed"] else nx.Graph + names = dataset_obj.metadata["col_names"] + dtypes = dataset_obj.metadata["col_types"] + if isinstance(dataset_obj.metadata["header"], int): + header = dataset_obj.metadata["header"] + else: + header = None + + pandas_edgelist = pd.read_csv( + dataset_obj.get_path(), + delimiter=dataset_obj.metadata["delim"], + names=names, + dtype=dict(zip(names, dtypes)), + header=header, + ) + G = nx.from_pandas_edgelist( + pandas_edgelist, source=names[0], target=names[1], create_using=create_using + ) + return G + + +# Test IDs are generated using the lambda assigned to the ids arg to provide an +# easier-to-read name from the Dataset obj string repr. +# See: https://docs.pytest.org/en/stable/reference/reference.html#pytest-fixture +@pytest.fixture(scope="module", params=dataset_params, ids=lambda ds: f"ds={str(ds)}") +def graph_obj(request): + """ + Returns a NX Graph or DiGraph obj from the dataset instance parameter. + """ + dataset = request.param + return nx_graph_from_dataset(dataset) + + +def get_legacy_backend_selector(backend_name): + """ + Returns a callable that wraps an algo function with either the default + dispatch decorator, or the "testing" decorator which unconditionally + dispatches. + This is only supported for NetworkX <3.2 + """ + backends.plugin_name = "cugraph" + orig_dispatch = backends._dispatch + testing_dispatch = backends.test_override_dispatch + + # Testing with the networkx <3.2 dispatch mechanism is based on decorating + # networkx APIs. The decorator is either one that only uses a backend if + # the input graph type is for that backend (the default decorator), or the + # "testing" decorator, which unconditionally converts a graph type to the + # type needed by the backend then calls the backend. If the cugraph backend + # is specified, create a callable that decorates the benchmarked function + # with the testing decorator. + # + # Because both the default and testing decorators assume they are only + # applied once and do bookkeeping to ensure algos are not registered + # multiple times, the callable also clears bookkeeping so the decorators + # can be reapplied multiple times. This is obviously a hack and networkx + # >=3.2 makes this use case properly supported. + if backend_name == "cugraph": + + def wrapper(*args, **kwargs): + backends._registered_algorithms = {} + return testing_dispatch(*args, **kwargs) + + else: + + def wrapper(*args, **kwargs): + backends._registered_algorithms = {} + return orig_dispatch(*args, **kwargs) + + return wrapper + + +def get_backend_selector(backend_name): + """ + Returns a callable that wraps an algo function in order to set the + "backend" kwarg on it. + This is only supported for NetworkX >= 3.2 + """ + + def get_callable_for_func(func): + def wrapper(*args, **kwargs): + kwargs["backend"] = backend_name + return func(*args, **kwargs) + + return wrapper + + return get_callable_for_func + + +@pytest.fixture( + scope="module", params=backend_params, ids=lambda backend: f"backend={backend}" +) +def backend_selector(request): + """ + Returns a callable that takes a function algo and wraps it in another + function that calls the algo using the appropriate backend. + """ + backend_name = request.param + if backends is not None: + return get_legacy_backend_selector(backend_name) + else: + return get_backend_selector(backend_name) + + +################################################################################ +# Benchmarks +normalized_params = [True, False] +k_params = [10, 100] + + +@pytest.mark.parametrize("normalized", normalized_params, ids=lambda norm: f"{norm=}") +@pytest.mark.parametrize("k", k_params, ids=lambda k: f"{k=}") +def bench_betweenness_centrality(benchmark, graph_obj, backend_selector, normalized, k): + result = benchmark( + backend_selector(nx.betweenness_centrality), + graph_obj, + weight=None, + normalized=normalized, + k=k, + ) + assert type(result) is dict + + +@pytest.mark.parametrize("normalized", normalized_params, ids=lambda norm: f"{norm=}") +def bench_edge_betweenness_centrality( + benchmark, graph_obj, backend_selector, normalized +): + result = benchmark( + backend_selector(nx.edge_betweenness_centrality), + graph_obj, + weight=None, + normalized=normalized, + ) + assert type(result) is dict + + +def bench_louvain_communities(benchmark, graph_obj, backend_selector): + # The cugraph backend for louvain_communities only supports undirected graphs + if isinstance(graph_obj, nx.DiGraph): + G = graph_obj.to_undirected() + else: + G = graph_obj + result = benchmark(backend_selector(nx.community.louvain_communities), G) + assert type(result) is list diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index 6af3aab27..b3d8a8bb3 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -14,8 +14,10 @@ markers = managedmem_off: RMM managed memory disabled poolallocator_on: RMM pool allocator enabled poolallocator_off: RMM pool allocator disabled - small: small datasets tiny: tiny datasets + small: small datasets + medium: medium datasets + large: large datasets directed: directed datasets undirected: undirected datasets matrix_types: inputs are matrices diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 9b3332106..ba7fe5e91 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -75,7 +75,7 @@ def get_info(): return d -__version__ = "23.10.00" +__version__ = "23.12.00" if __name__ == "__main__": from pathlib import Path From ea39d0fc51647861f2d065d40eda3f004fdac18b Mon Sep 17 00:00:00 2001 From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com> Date: Wed, 11 Oct 2023 08:16:36 -0500 Subject: [PATCH 271/384] Link wholegrah and cugraphops XML docs (#3906) Configures `breathe` and links the uploaded `wholegraph` and `cugraph-ops` XML docs artifacts. Authors: - Jake Awe (https://github.com/AyodeAwe) Approvers: - Brad Rees (https://github.com/BradReesWork) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3906 --- docs/cugraph/source/conf.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 58ecca271..470086b4f 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -37,6 +37,7 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ + "breathe", "sphinx.ext.intersphinx", "sphinx.ext.autodoc", "sphinx.ext.autosummary", @@ -205,4 +206,10 @@ def setup(app): # The following is used by sphinx.ext.linkcode to provide links to github linkcode_resolve = make_linkcode_resolve( "https://github.com/rapidsai/cugraph/blob/{revision}/python/{path}#L{lineno}" -) \ No newline at end of file +) + +breathe_projects = { + 'libcugraphops': os.environ['XML_DIR_LIBCUGRAPHOPS'], + 'libwholegraph': os.environ['XML_DIR_LIBWHOLEGRAPH'] +} +breathe_default_project = "libcugraph" From d525a79e9113c37ad77f676abf65bea9fcac4e3d Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Wed, 11 Oct 2023 14:08:24 -0400 Subject: [PATCH 272/384] REL v23.10.00 release From eb6a3b496bd3327e46ed43994fe0181eea409fb1 Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Thu, 12 Oct 2023 12:57:24 -0700 Subject: [PATCH 273/384] TODO: revert this commit for re-running ci --- print_env.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/print_env.sh b/print_env.sh index 6f2d33b0e..f117efb3b 100644 --- a/print_env.sh +++ b/print_env.sh @@ -84,4 +84,4 @@ echo " " print_env | while read -r line; do echo " $line" done -echo "" +echo "" From 6aaa6ad94c0b6592a4d6c41a9bdedfed9793dbdf Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Thu, 12 Oct 2023 12:57:46 -0700 Subject: [PATCH 274/384] DONE: reverted to re-run ci --- print_env.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/print_env.sh b/print_env.sh index f117efb3b..6f2d33b0e 100644 --- a/print_env.sh +++ b/print_env.sh @@ -84,4 +84,4 @@ echo " " print_env | while read -r line; do echo " $line" done -echo "" +echo "" From f50791842f0d3e39f91c1ed4e10826c461bd323b Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Mon, 16 Oct 2023 09:44:21 -0400 Subject: [PATCH 275/384] Updating Docs (#3923) starting with all the packages in a table Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3923 --- docs/cugraph/source/images/cugraph_logo_2.png | Bin 0 -> 4952011 bytes docs/cugraph/source/index.rst | 49 +++++++++++++----- 2 files changed, 37 insertions(+), 12 deletions(-) create mode 100644 docs/cugraph/source/images/cugraph_logo_2.png diff --git 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zch&TY20n!G>uy1~#G*d%unD1PWQ%ppl{q8sbCeaazhKa0pvs{Tr$Ki#-{Y1g?M+KM|vRyy{{>XFqhLQr}dUFUWp_B=G_ zPpz!vfG5W(18_N+NFnvRB#&nsqvsIFlNKhucOR94lVIV$4ii0=! zRHKE*dqQZ@S2kdlJ0Omhc{&k>@L+u?hLI{1i083ERZwM(B5hoN1dex0f&?wcS4x0a zYi-74<^gI4ngt|@IH_psK-rLjIXEI;k2 ziRlt#^t-^I)4%cniGa0NGm}NOp|YmN)$aFwdz+mm?)pJ&Qf9QSpTDH;YLQ?$IO|m+ zrL!g(1`WIKdA>HXZ^5%(3~?FI_H3m52?!;77K9DkmU*hfYZddwwA)zh&h+0}HdEPy zkuUkSs|Ci>$k+X+1H6Bk=67;tdZ+Q-d1(!n7r}H|fnqw;n|TQV+%qSx2S}x7b@Q2- zng&1hvO=9H8QcNXpKI%6Of07PC6m&dh+_`yg+7i#GQ~O_UDdq%c7jZkX6!_DxALe@ zJqNnv_i@128K-f5GYA8{evrTE4ulV8elhNxawQoJ@Kw&hY{m34u*sJZCCSf9pI2?@JBXskK`jEWPX)V_rW|p{@5KwUmQ{qu0#X z&`yUF@X;Bj2+2NQUw(ah2l=YlQtb*6Zcdl=9e5C@XCPDT(wL(P$VMmzJ;| Date: Tue, 17 Oct 2023 06:28:14 -0400 Subject: [PATCH 276/384] Updates to 23.12 (#3905) A couple PRs were merged after `branch-23.12` was created and contained RAPIDS versions that need to be updated in `branch-23.12`. Ref: - https://github.com/rapidsai/cugraph/pull/3838 - https://github.com/rapidsai/cugraph/pull/3852 Authors: - Ray Douglass (https://github.com/raydouglass) - Rick Ratzel (https://github.com/rlratzel) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3905 --- python/nx-cugraph/_nx_cugraph/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 227caea29..886d7a19f 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -24,7 +24,7 @@ "backend_name": "cugraph", "project": "nx-cugraph", "package": "nx_cugraph", - "url": "https://github.com/rapidsai/cugraph/tree/branch-23.10/python/nx-cugraph", + "url": "https://github.com/rapidsai/cugraph/tree/branch-23.12/python/nx-cugraph", "short_summary": "GPU-accelerated backend.", # "description": "TODO", "functions": { From a92d2f28db1db771277a2ae94d62e20a1ecb6578 Mon Sep 17 00:00:00 2001 From: Dylan Chima-Sanchez <97180625+betochimas@users.noreply.github.com> Date: Tue, 17 Oct 2023 07:22:59 -0700 Subject: [PATCH 277/384] Add nx-cugraph notebook for showing accelerated networkX APIs (#3830) This PR adds a notebook showing how to use the `nx-cugraph` package to accelerate your NetworkX workflow. closes rapidsai/graph_dl#277 Authors: - Dylan Chima-Sanchez (https://github.com/betochimas) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/3830 --- notebooks/demo/nx_cugraph_demo.ipynb | 672 +++++++++++++++++++++++++++ 1 file changed, 672 insertions(+) create mode 100644 notebooks/demo/nx_cugraph_demo.ipynb diff --git a/notebooks/demo/nx_cugraph_demo.ipynb b/notebooks/demo/nx_cugraph_demo.ipynb new file mode 100644 index 000000000..6e50370ed --- /dev/null +++ b/notebooks/demo/nx_cugraph_demo.ipynb @@ -0,0 +1,672 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `nx-cugraph`: a NetworkX backend that provides GPU acceleration with RAPIDS cuGraph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook will demonstrate the `nx-cugraph` NetworkX backend using the NetworkX betweenness_centrality algorithm.\n", + "\n", + "## Background\n", + "Networkx version 3.0 introduced a dispatching mechanism that allows users to configure NetworkX to dispatch various algorithms to third-party backends. Backends can provide different implementations of graph algorithms, allowing users to take advantage of capabilities not available in NetworkX. `nx-cugraph` is a NetworkX backend provided by the [RAPIDS](https://rapids.ai) cuGraph project that adds GPU acceleration to greatly improve performance.\n", + "\n", + "## System Requirements\n", + "Using `nx-cugraph` with this notebook requires the following: \n", + "- NVIDIA GPU, Pascal architecture or later\n", + "- CUDA 11.2, 11.4, 11.5, 11.8, or 12.0\n", + "- Python versions 3.9, 3.10, or 3.11\n", + "- NetworkX >= version 3.2\n", + " - _NetworkX 3.0 supports dispatching and is compatible with `nx-cugraph`, but this notebook will demonstrate features added in 3.2_\n", + " - At the time of this writing, NetworkX 3.2 is only available from source and can be installed by following the [development version install instructions](https://github.com/networkx/networkx/blob/main/INSTALL.rst#install-the-development-version).\n", + "- Pandas\n", + "\n", + "More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Installation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuming NetworkX >= 3.2 has been installed using the [development version install instructions](https://github.com/networkx/networkx/blob/main/INSTALL.rst#install-the-development-version), `nx-cugraph` can be installed using either `conda` or `pip`. \n", + "\n", + "#### conda\n", + "```\n", + "conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph\n", + "```\n", + "#### pip\n", + "```\n", + "python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com\n", + "```\n", + "#### _Notes:_\n", + " * nightly wheel builds will not be available until the 23.12 release, therefore the index URL for the stable release version is being used in the pip install command above.\n", + " * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start).\n", + " * If you installed any of the packages described here since running this notebook, you may need to restart the kernel to have them visible to this notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Notebook Helper Functions\n", + "\n", + "A few helper functions will be defined here that will be used in order to help keep this notebook easy to read." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "def reimport_networkx():\n", + " \"\"\"\n", + " Re-imports networkx for demonstrating different backend configuration\n", + " options applied at import-time. This is only needed for demonstration\n", + " purposes since other mechanisms are available for runtime configuration.\n", + " \"\"\"\n", + " # Using importlib.reload(networkx) has several caveats (described here:\n", + " # https://docs.python.org/3/library/imp.html?highlight=reload#imp.reload)\n", + " # which result in backend configuration not being re-applied correctly.\n", + " # Instead, manually remove all modules and re-import\n", + " nx_mods = [m for m in sys.modules.keys()\n", + " if (m.startswith(\"networkx\") or m.startswith(\"nx_cugraph\"))]\n", + " for m in nx_mods:\n", + " sys.modules.pop(m)\n", + " import networkx\n", + " return networkx\n", + "\n", + "\n", + "from pathlib import Path\n", + "import requests\n", + "import gzip\n", + "import pandas as pd\n", + "def create_cit_patents_graph(verbose=True):\n", + " \"\"\"\n", + " Downloads the cit-Patents dataset (if not previously downloaded), reads\n", + " it, and creates a nx.DiGraph from it and returns it.\n", + " cit-Patents is described here:\n", + " https://snap.stanford.edu/data/cit-Patents.html\n", + " \"\"\"\n", + " url = \"https://snap.stanford.edu/data/cit-Patents.txt.gz\"\n", + " gz_file_name = Path(url.split(\"/\")[-1])\n", + " csv_file_name = Path(gz_file_name.stem)\n", + " if csv_file_name.exists():\n", + " if verbose: print(f\"{csv_file_name} already exists, not downloading.\")\n", + " else:\n", + " if verbose: print(f\"downloading {url}...\", end=\"\", flush=True)\n", + " req = requests.get(url)\n", + " open(gz_file_name, \"wb\").write(req.content)\n", + " if verbose: print(\"done\")\n", + " if verbose: print(f\"unzipping {gz_file_name}...\", end=\"\", flush=True)\n", + " with gzip.open(gz_file_name, \"rb\") as gz_in:\n", + " with open(csv_file_name, \"wb\") as txt_out:\n", + " txt_out.write(gz_in.read())\n", + " if verbose: print(\"done\")\n", + "\n", + " if verbose: print(\"reading csv to dataframe...\", end=\"\", flush=True)\n", + " pandas_edgelist = pd.read_csv(\n", + " csv_file_name.name,\n", + " skiprows=4,\n", + " delimiter=\"\\t\",\n", + " names=[\"src\", \"dst\"],\n", + " dtype={\"src\":\"int32\", \"dst\":\"int32\"},\n", + " )\n", + " if verbose: print(\"done\")\n", + " if verbose: print(\"creating NX graph from dataframe...\", end=\"\", flush=True)\n", + " G = nx.from_pandas_edgelist(\n", + " pandas_edgelist, source=\"src\", target=\"dst\", create_using=nx.DiGraph\n", + " )\n", + " if verbose: print(\"done\")\n", + " return G" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Running `betweenness_centrality`\n", + "Let's start by running `betweenness_centrality` on the Karate Club graph using the default NetworkX implementation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Zachary's Karate Club\n", + "\n", + "Zachary's Karate Club is a small dataset consisting of 34 nodes and 78 edges which represent the friendships between members of a karate club. This dataset is small enough to make comparing results between NetworkX and `nx-cugraph` easy." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "karate_club_graph = nx.karate_club_graph()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Having NetworkX compute the `betweenness_centrality` values for each node on this graph is quick and easy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.51 ms ± 1.02 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit global karate_nx_bc_results\n", + "karate_nx_bc_results = nx.betweenness_centrality(karate_club_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Automatic GPU acceleration\n", + "When `nx-cugraph` is installed, NetworkX will detect it on import and make it available as a backend for APIs supported by that backend. However, NetworkX does not assume the user always wants to use a particular backend, and instead looks at various configuration mechanisms in place for users to specify how NetworkX should use installed backends. Since NetworkX was not configured to use a backend for the above `betweenness_centrality` call, it used the default implementation provided by NetworkX.\n", + "\n", + "The first configuration mechanism to be demonstrated below is the `NETWORKX_AUTOMATIC_BACKENDS` environment variable. This environment variable directs NetworkX to use the backend specified everywhere it's supported and does not require the user to modify any of their existing NetworkX code.\n", + "\n", + "To use it, a user sets `NETWORKX_AUTOMATIC_BACKENDS` in their shell to the backend they'd like to use. If a user has more than one backend installed, the environment variable can also accept a comma-separated list of backends, ordered by priority in which NetworkX should use them, where the first backend that supports a particular API call will be used. For example:\n", + "```\n", + "bash> export NETWORKX_AUTOMATIC_BACKENDS=cugraph\n", + "bash> python my_nx_app.py # uses nx-cugraph wherever possible, then falls back to default implementation where it's not.\n", + "```\n", + "or in the case of multiple backends installed\n", + "```\n", + "bash> export NETWORKX_AUTOMATIC_BACKENDS=cugraph,graphblas\n", + "bash> python my_nx_app.py # uses nx-cugraph if possible, then nx-graphblas if possible, then default implementation.\n", + "```\n", + "\n", + "NetworkX looks at the environment variable and the installed backends at import time, and will not re-examine the environment after that. Because `networkx` was already imported in this notebook, the `reimport_nx()` utility will be called after the `os.environ` dictionary is updated to simulate an environment variable being set in the shell.\n", + "\n", + "**Please note, this is only needed for demonstration purposes to compare runs both with and without fully-automatic backend use enabled.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"NETWORKX_AUTOMATIC_BACKENDS\"] = \"cugraph\"\n", + "nx = reimport_networkx()\n", + "# reimporting nx requires reinstantiating Graphs since python considers\n", + "# types from the prior nx import != types from the reimported nx\n", + "karate_club_graph = nx.karate_club_graph()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Once the environment is updated, re-running the same `betweenness_centrality` call on the same graph requires no code changes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "43.9 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit global karate_cg_bc_results\n", + "karate_cg_bc_results = nx.betweenness_centrality(karate_club_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "We may see that the same computation actually took *longer* using `nx-cugraph`. This is not too surprising given how small the graph is, since there's a small amount of overhead to copy data to and from the GPU which becomes more obvious on very small graphs. We'll see with a larger graph how this overhead becomes negligible." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Results Comparison\n", + "\n", + "Let's examine the results of each run to see how they compare. \n", + "The `betweenness_centrality` results are a dictionary mapping vertex IDs to betweenness_centrality scores. The score itself is usually not as important as the relative rank of each vertex ID (e.g. vertex A is ranked higher than vertex B in both sets of results)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NX: (0, 0.437635), CG: (0, 0.437635)\n", + "NX: (33, 0.304075), CG: (33, 0.304075)\n", + "NX: (32, 0.145247), CG: (32, 0.145247)\n", + "NX: (2, 0.143657), CG: (2, 0.143657)\n", + "NX: (31, 0.138276), CG: (31, 0.138276)\n", + "NX: (8, 0.055927), CG: (8, 0.055927)\n", + "NX: (1, 0.053937), CG: (1, 0.053937)\n", + "NX: (13, 0.045863), CG: (13, 0.045863)\n", + "NX: (19, 0.032475), CG: (19, 0.032475)\n", + "NX: (5, 0.029987), CG: (5, 0.029987)\n", + "NX: (6, 0.029987), CG: (6, 0.029987)\n", + "NX: (27, 0.022333), CG: (27, 0.022333)\n", + "NX: (23, 0.017614), CG: (23, 0.017614)\n", + "NX: (30, 0.014412), CG: (30, 0.014412)\n", + "NX: (3, 0.011909), CG: (3, 0.011909)\n", + "NX: (25, 0.003840), CG: (25, 0.003840)\n", + "NX: (29, 0.002922), CG: (29, 0.002922)\n", + "NX: (24, 0.002210), CG: (24, 0.002210)\n", + "NX: (28, 0.001795), CG: (28, 0.001795)\n", + "NX: (9, 0.000848), CG: (9, 0.000848)\n", + "NX: (4, 0.000631), CG: (4, 0.000631)\n", + "NX: (10, 0.000631), CG: (10, 0.000631)\n", + "NX: (7, 0.000000), CG: (7, 0.000000)\n", + "NX: (11, 0.000000), CG: (11, 0.000000)\n", + "NX: (12, 0.000000), CG: (12, 0.000000)\n", + "NX: (14, 0.000000), CG: (14, 0.000000)\n", + "NX: (15, 0.000000), CG: (15, 0.000000)\n", + "NX: (16, 0.000000), CG: (16, 0.000000)\n", + "NX: (17, 0.000000), CG: (17, 0.000000)\n", + "NX: (18, 0.000000), CG: (18, 0.000000)\n", + "NX: (20, 0.000000), CG: (20, 0.000000)\n", + "NX: (21, 0.000000), CG: (21, 0.000000)\n", + "NX: (22, 0.000000), CG: (22, 0.000000)\n", + "NX: (26, 0.000000), CG: (26, 0.000000)\n" + ] + } + ], + "source": [ + "# The lists contain tuples of (vertex ID, betweenness_centrality score),\n", + "# sorted based on the score.\n", + "nx_sorted = sorted(karate_nx_bc_results.items(), key=lambda t:t[1], reverse=True)\n", + "cg_sorted = sorted(karate_cg_bc_results.items(), key=lambda t:t[1], reverse=True)\n", + "\n", + "for i in range(len(nx_sorted)):\n", + " print(\"NX: (%d, %.6f), CG: (%d, %.6f)\" % (nx_sorted[i] + cg_sorted[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Here we can see that the results match exactly as expected. \n", + "\n", + "For larger graphs, results are harder to compare given that `betweenness_centrality` is an approximation algorithm influenced by the random selection of paths used to compute the betweenness_centrality score of each vertex. The argument `k` is used for limiting the number of paths used in the computation, since using every path for every vertex would be prohibitively expensive for large graphs. For small graphs, `k` need not be specified, which allows `betweenness_centrality` to use all paths for all vertices and makes for an easier comparison." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### `betweenness_centrality` on larger graphs - The U.S. Patent Citation Network1\n", + "\n", + "The U.S. Patent Citation Network dataset is much larger with over 3.7M nodes and over 16.5M edges and demonstrates how `nx-cugraph` enables NetworkX to run `betweenness_centrality` on graphs this large (and larger) in seconds instead of minutes.\n", + "\n", + "#### NetworkX default implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading https://snap.stanford.edu/data/cit-Patents.txt.gz...done\n", + "unzipping cit-Patents.txt.gz...done\n", + "reading csv to dataframe...done\n", + "creating NX graph from dataframe...done\n" + ] + } + ], + "source": [ + "import os\n", + "# Unset NETWORKX_AUTOMATIC_BACKENDS so the default NetworkX implementation is used\n", + "os.environ.pop(\"NETWORKX_AUTOMATIC_BACKENDS\", None)\n", + "nx = reimport_networkx()\n", + "# Create the cit-Patents graph - this will also download the dataset if not previously downloaded\n", + "cit_patents_graph = create_cit_patents_graph()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Since this is a large graph, a k value must be set so the computation returns in a reasonable time\n", + "k = 40" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Because this run will take time, `%%timeit` is restricted to a single pass.\n", + "\n", + "*NOTE: this run may take approximately 1 minute*" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1min 4s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1\n", + "results = nx.betweenness_centrality(cit_patents_graph, k=k)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Something to note is that `%%timeit` disables garbage collection by default, which may not be something a user is able to do. To see a more realistic real-world run time, `gc` can be enabled." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# import and run the garbage collector upfront prior to using it in the benchmark\n", + "import gc\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "*NOTE: this run may take approximately 7 minutes!*" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6min 50s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(cit_patents_graph, k=k)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `nx-cugraph`\n", + "\n", + "Running on a GPU using `nx-cugraph` can result in a tremendous speedup, especially when graphs reach sizes larger than a few thousand nodes or `k` values become larger to increase accuracy.\n", + "\n", + "Rather than setting the `NETWORKX_AUTOMATIC_BACKENDS` environment variable and re-importing again, this example will demonstrate the `backend=` keyword argument to explicitly direct the NetworkX dispatcher to use the `cugraph` backend." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(cit_patents_graph, k=k, backend=\"cugraph\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "k = 150" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.6 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(cit_patents_graph, k=k, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "For the same graph and the same `k` value, the `\"cugraph\"` backend returns results in seconds instead of minutes. Increasing the `k` value has very little relative impact to runtime due to the high parallel processing ability of the GPU, allowing the user to get improved accuracy for virtually no additional cost." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Type-based dispatching\n", + "\n", + "NetworkX also supports automatically dispatching to backends associated with specific graph types. This requires the user to write code for a specific backend, and therefore requires the backend to be installed, but has the advantage of ensuring a particular behavior without the potential for runtime conversions.\n", + "\n", + "To use type-based dispatching with `nx-cugraph`, the user must import the backend directly in their code to access the utilities provided to create a Graph instance specifically for the `nx-cugraph` backend." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import nx_cugraph as nxcg" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "The `from_networkx()` API will copy the data from the NetworkX graph instance to the GPU and return a new `nx-cugraph` graph instance. By passing an explicit `nx-cugraph` graph, the NetworkX dispatcher will automatically call the `\"cugraph\"` backend (and only the `\"cugraph\"` backend) without requiring future conversions to copy data to the GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.92 s ± 2.85 ms per loop (mean ± std. dev. of 2 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 2 global nxcg_cit_patents_graph\n", + "nxcg_cit_patents_graph = nxcg.from_networkx(cit_patents_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.14 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(nxcg_cit_patents_graph, k=k)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrated `nx-cugraph`'s support for `betweenness_centrality`. At the time of this writing, `nx-cugraph` also provides support for `edge_netweenness_centrality` and `louvain_communities`. Other algorithms are scheduled to be supported based on their availability in the cuGraph [pylibcugraph](https://github.com/rapidsai/cugraph/tree/branch-23.10/python/pylibcugraph/pylibcugraph) package and demand by the NetworkX community.\n", + "\n", + "#### Benchmark Results\n", + "The results included in this notebook were generated on a workstation with the following hardware:\n", + "\n", + "\n", + " \n", + " \n", + "
CPU:Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz, 45GB
GPU:Quatro RTX 8000, 50GB
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1 Information on the U.S. Patent Citation Network dataset used in this notebook is as follows:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
Authors:Jure Leskovec and Andrej Krevl
Title:SNAP Datasets, Stanford Large Network Dataset Collection
URL:http://snap.stanford.edu/data
Date:June 2014
\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 565be2f11e667db5c4bfd952af1c1a0937940c26 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 17 Oct 2023 23:06:00 -0500 Subject: [PATCH 278/384] Add multigraph support to nx-cugraph (#3934) Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3934 --- python/nx-cugraph/lint.yaml | 14 +- .../nx-cugraph/nx_cugraph/classes/__init__.py | 2 + python/nx-cugraph/nx_cugraph/classes/graph.py | 28 +- .../nx_cugraph/classes/multidigraph.py | 30 ++ .../nx_cugraph/classes/multigraph.py | 453 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/convert.py | 167 +++++-- python/nx-cugraph/nx_cugraph/interface.py | 12 +- .../nx_cugraph/tests/bench_convert.py | 16 +- .../nx_cugraph/tests/test_convert.py | 39 +- .../nx_cugraph/tests/test_multigraph.py | 72 +++ python/nx-cugraph/pyproject.toml | 3 +- 11 files changed, 750 insertions(+), 86 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/classes/multidigraph.py create mode 100644 python/nx-cugraph/nx_cugraph/classes/multigraph.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_multigraph.py diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 338ca7b23..4f604fbeb 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -11,7 +11,7 @@ default_language_version: python: python3 repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: check-added-large-files - id: check-case-conflict @@ -26,7 +26,7 @@ repos: - id: mixed-line-ending - id: trailing-whitespace - repo: https://github.com/abravalheri/validate-pyproject - rev: v0.14 + rev: v0.15 hooks: - id: validate-pyproject name: Validate pyproject.toml @@ -40,7 +40,7 @@ repos: hooks: - id: isort - repo: https://github.com/asottile/pyupgrade - rev: v3.13.0 + rev: v3.15.0 hooks: - id: pyupgrade args: [--py39-plus] @@ -50,7 +50,7 @@ repos: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.291 + rev: v0.0.292 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -70,18 +70,18 @@ repos: - id: yesqa additional_dependencies: *flake8_dependencies - repo: https://github.com/codespell-project/codespell - rev: v2.2.5 + rev: v2.2.6 hooks: - id: codespell types_or: [python, rst, markdown] additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.291 + rev: v0.0.292 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: no-commit-to-branch args: [-p, "^branch-2....$"] diff --git a/python/nx-cugraph/nx_cugraph/classes/__init__.py b/python/nx-cugraph/nx_cugraph/classes/__init__.py index e47641ae8..9916bcbe2 100644 --- a/python/nx-cugraph/nx_cugraph/classes/__init__.py +++ b/python/nx-cugraph/nx_cugraph/classes/__init__.py @@ -11,5 +11,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from .graph import Graph +from .multigraph import MultiGraph from .digraph import DiGraph # isort:skip +from .multidigraph import MultiDiGraph # isort:skip diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index ded4cc394..f1e85c836 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -43,7 +43,8 @@ class Graph: # Tell networkx to dispatch calls with this object to nx-cugraph - __networkx_plugin__: ClassVar[str] = "cugraph" + __networkx_backend__: ClassVar[str] = "cugraph" # nx >=3.2 + __networkx_plugin__: ClassVar[str] = "cugraph" # nx <3.2 # networkx properties graph: dict @@ -58,7 +59,7 @@ class Graph: node_values: dict[AttrKey, cp.ndarray[NodeValue]] node_masks: dict[AttrKey, cp.ndarray[bool]] key_to_id: dict[NodeKey, IndexValue] | None - _id_to_key: dict[IndexValue, NodeKey] | None + _id_to_key: list[NodeKey] | None _N: int #################### @@ -77,7 +78,7 @@ def from_coo( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: new_graph = object.__new__(cls) @@ -88,7 +89,7 @@ def from_coo( new_graph.node_values = {} if node_values is None else dict(node_values) new_graph.node_masks = {} if node_masks is None else dict(node_masks) new_graph.key_to_id = None if key_to_id is None else dict(key_to_id) - new_graph._id_to_key = None if id_to_key is None else dict(id_to_key) + new_graph._id_to_key = None if id_to_key is None else list(id_to_key) new_graph._N = op.index(N) # Ensure N is integral new_graph.graph = new_graph.graph_attr_dict_factory() new_graph.graph.update(attr) @@ -123,7 +124,7 @@ def from_csr( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: N = indptr.size - 1 @@ -155,7 +156,7 @@ def from_csc( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: N = indptr.size - 1 @@ -189,7 +190,7 @@ def from_dcsr( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: row_indices = cp.array( @@ -222,7 +223,7 @@ def from_dcsc( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: col_indices = cp.array( @@ -295,11 +296,11 @@ def node_dtypes(self) -> dict[AttrKey, Dtype]: return {key: val.dtype for key, val in self.node_values.items()} @property - def id_to_key(self) -> dict[IndexValue, NodeKey] | None: + def id_to_key(self) -> [NodeKey] | None: if self.key_to_id is None: return None if self._id_to_key is None: - self._id_to_key = {val: key for key, val in self.key_to_id.items()} + self._id_to_key = sorted(self.key_to_id, key=self.key_to_id.__getitem__) return self._id_to_key name = nx.Graph.name @@ -370,6 +371,12 @@ def get_edge_data( v = self.key_to_id[v] except KeyError: return default + else: + try: + if u < 0 or v < 0 or u >= self._N or v >= self._N: + return default + except TypeError: + return default index = cp.nonzero((self.row_indices == u) & (self.col_indices == v))[0] if index.size == 0: return default @@ -447,6 +454,7 @@ def to_undirected(self, as_view: bool = False) -> Graph: ################### def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): + # DRY warning: see also MultiGraph._copy indptr = self.indptr row_indices = self.row_indices col_indices = self.col_indices diff --git a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py new file mode 100644 index 000000000..5629e2c9c --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py @@ -0,0 +1,30 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import networkx as nx + +import nx_cugraph as nxcg + +from .digraph import DiGraph +from .multigraph import MultiGraph + +__all__ = ["MultiDiGraph"] + +networkx_api = nxcg.utils.decorators.networkx_class(nx.MultiDiGraph) + + +class MultiDiGraph(MultiGraph, DiGraph): + @classmethod + def to_networkx_class(cls) -> type[nx.MultiDiGraph]: + return nx.MultiDiGraph diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py new file mode 100644 index 000000000..499ca7ce2 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -0,0 +1,453 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from copy import deepcopy +from typing import TYPE_CHECKING, ClassVar + +import cupy as cp +import networkx as nx +import numpy as np + +import nx_cugraph as nxcg + +from .graph import Graph + +if TYPE_CHECKING: + from nx_cugraph.typing import ( + AttrKey, + EdgeKey, + EdgeValue, + IndexValue, + NodeKey, + NodeValue, + ) +__all__ = ["MultiGraph"] + +networkx_api = nxcg.utils.decorators.networkx_class(nx.MultiGraph) + + +class MultiGraph(Graph): + # networkx properties + edge_key_dict_factory: ClassVar[type] = dict + + # Not networkx properties + + # In a MultiGraph, each edge has a unique `(row, col, key)` key. + # By default, `key` is 0 if possible, else 1, else 2, etc. + # This key can be any hashable Python object in NetworkX. + # We don't use a dict for our data structure here, because + # that would require a `(row, col, key)` key. + # Instead, we keep `edge_keys` and/or `edge_indices`. + # `edge_keys` is the list of Python objects for each edge. + # `edge_indices` is for the common case of default multiedge keys, + # in which case we can store it as a cupy array. + # `edge_indices` is generally preferred. It is possible to provide + # both where edge_indices is the default and edge_keys is anything. + # It is also possible for them both to be None, which means the + # default edge indices has not yet been calculated. + edge_indices: cp.ndarray[IndexValue] | None + edge_keys: list[EdgeKey] | None + + #################### + # Creation methods # + #################### + + @classmethod + def from_coo( + cls, + N: int, + row_indices: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + new_graph = super().from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + **attr, + ) + new_graph.edge_indices = edge_indices + new_graph.edge_keys = edge_keys + # Easy and fast sanity checks + if ( + new_graph.edge_keys is not None + and len(new_graph.edge_keys) != row_indices.size + ): + raise ValueError + return new_graph + + @classmethod + def from_csr( + cls, + indptr: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + N = indptr.size - 1 + row_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + @classmethod + def from_csc( + cls, + indptr: cp.ndarray[IndexValue], + row_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + N = indptr.size - 1 + col_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + @classmethod + def from_dcsr( + cls, + N: int, + compressed_rows: cp.ndarray[IndexValue], + indptr: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + row_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(compressed_rows.get(), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + @classmethod + def from_dcsc( + cls, + N: int, + compressed_cols: cp.ndarray[IndexValue], + indptr: cp.ndarray[IndexValue], + row_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> Graph: + col_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(compressed_cols.get(), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + def __new__( + cls, incoming_graph_data=None, multigraph_input=None, **attr + ) -> MultiGraph: + if isinstance(incoming_graph_data, dict) and multigraph_input is not False: + new_graph = nxcg.from_networkx( + nx.MultiGraph(incoming_graph_data, multigraph_input=multigraph_input), + preserve_all_attrs=True, + ) + else: + new_graph = super().__new__(cls, incoming_graph_data) + new_graph.graph.update(attr) + return new_graph + + ################# + # Class methods # + ################# + + @classmethod + @networkx_api + def is_directed(cls) -> bool: + return False + + @classmethod + @networkx_api + def is_multigraph(cls) -> bool: + return True + + @classmethod + @networkx_api + def to_directed_class(cls) -> type[nxcg.MultiDiGraph]: + return nxcg.MultiDiGraph + + @classmethod + def to_networkx_class(cls) -> type[nx.MultiGraph]: + return nx.MultiGraph + + @classmethod + @networkx_api + def to_undirected_class(cls) -> type[MultiGraph]: + return MultiGraph + + ########################## + # NetworkX graph methods # + ########################## + + @networkx_api + def clear(self) -> None: + super().clear() + self.edge_indices = None + self.edge_keys = None + + @networkx_api + def clear_edges(self) -> None: + super().clear_edges() + self.edge_indices = None + self.edge_keys = None + + @networkx_api + def copy(self, as_view: bool = False) -> MultiGraph: + # Does shallow copy in networkx + return self._copy(as_view, self.__class__) + + @networkx_api + def get_edge_data( + self, + u: NodeKey, + v: NodeKey, + key: EdgeKey | None = None, + default: EdgeValue | None = None, + ): + if self.key_to_id is not None: + try: + u = self.key_to_id[u] + v = self.key_to_id[v] + except KeyError: + return default + else: + try: + if u < 0 or v < 0 or u >= self._N or v >= self._N: + return default + except TypeError: + return default + mask = (self.row_indices == u) & (self.col_indices == v) + if not mask.any(): + return default + if self.edge_keys is None: + if self.edge_indices is None: + self._calculate_edge_indices() + if key is not None: + try: + mask = mask & (self.edge_indices == key) + except TypeError: + return default + indices = cp.nonzero(mask)[0] + if indices.size == 0: + return default + edge_keys = self.edge_keys + if key is not None and edge_keys is not None: + mask[[i for i in indices.tolist() if edge_keys[i] != key]] = False + indices = cp.nonzero(mask)[0] + if indices.size == 0: + return default + if key is not None: + [index] = indices.tolist() + return { + k: v[index].tolist() + for k, v in self.edge_values.items() + if k not in self.edge_masks or self.edge_masks[k][index] + } + return { + edge_keys[index] + if edge_keys is not None + else index: { + k: v[index].tolist() + for k, v in self.edge_values.items() + if k not in self.edge_masks or self.edge_masks[k][index] + } + for index in indices.tolist() + } + + @networkx_api + def has_edge(self, u: NodeKey, v: NodeKey, key: EdgeKey | None = None) -> bool: + if self.key_to_id is not None: + try: + u = self.key_to_id[u] + v = self.key_to_id[v] + except KeyError: + return False + mask = (self.row_indices == u) & (self.col_indices == v) + if key is None or (self.edge_indices is None and self.edge_keys is None): + return bool(mask.any()) + if self.edge_keys is None: + try: + return bool((mask & (self.edge_indices == key)).any()) + except TypeError: + return False + indices = cp.nonzero(mask)[0] + if indices.size == 0: + return False + edge_keys = self.edge_keys + return any(edge_keys[i] == key for i in indices.tolist()) + + @networkx_api + def to_directed(self, as_view: bool = False) -> nxcg.MultiDiGraph: + return self._copy(as_view, self.to_directed_class()) + + @networkx_api + def to_undirected(self, as_view: bool = False) -> MultiGraph: + # Does deep copy in networkx + return self.copy(as_view) + + ################### + # Private methods # + ################### + + def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): + # DRY warning: see also Graph._copy + indptr = self.indptr + row_indices = self.row_indices + col_indices = self.col_indices + edge_indices = self.edge_indices + edge_values = self.edge_values + edge_masks = self.edge_masks + node_values = self.node_values + node_masks = self.node_masks + key_to_id = self.key_to_id + id_to_key = None if key_to_id is None else self._id_to_key + edge_keys = self.edge_keys + if not as_view: + indptr = indptr.copy() + row_indices = row_indices.copy() + col_indices = col_indices.copy() + edge_indices = edge_indices.copy() + edge_values = {key: val.copy() for key, val in edge_values.items()} + edge_masks = {key: val.copy() for key, val in edge_masks.items()} + node_values = {key: val.copy() for key, val in node_values.items()} + node_masks = {key: val.copy() for key, val in node_masks.items()} + if key_to_id is not None: + key_to_id = key_to_id.copy() + if id_to_key is not None: + id_to_key = id_to_key.copy() + if edge_keys is not None: + edge_keys = edge_keys.copy() + if reverse: + row_indices, col_indices = col_indices, row_indices + rv = cls.from_coo( + indptr, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + ) + if as_view: + rv.graph = self.graph + else: + rv.graph.update(deepcopy(self.graph)) + return rv diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index 1240ea71d..e82286e5e 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -122,8 +122,6 @@ def from_networkx( graph = G else: raise TypeError(f"Expected networkx.Graph; got {type(graph)}") - elif graph.is_multigraph(): - raise NotImplementedError("MultiGraph support is not yet implemented") if preserve_all_attrs: preserve_edge_attrs = True @@ -144,7 +142,7 @@ def from_networkx( else: node_attrs = {node_attrs: None} - if graph.__class__ in {nx.Graph, nx.DiGraph}: + if graph.__class__ in {nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph}: # This is a NetworkX private attribute, but is much faster to use adj = graph._adj else: @@ -163,7 +161,11 @@ def from_networkx( edge_attrs = None elif preserve_edge_attrs: # Using comprehensions should be just as fast starting in Python 3.11 - attr_sets = set(map(frozenset, concat(map(dict.values, adj.values())))) + it = concat(map(dict.values, adj.values())) + if graph.is_multigraph(): + it = concat(map(dict.values, it)) + # PERF: should we add `filter(None, ...)` to remove empty data dicts? + attr_sets = set(map(frozenset, it)) attrs = frozenset.union(*attr_sets) edge_attrs = dict.fromkeys(attrs, REQUIRED) if len(attr_sets) > 1: @@ -180,11 +182,19 @@ def from_networkx( if len(required) == 1: # Fast path for the common case of a single attribute with no default [attr] = required - it = ( - attr in edgedata - for rowdata in adj.values() - for edgedata in rowdata.values() - ) + if graph.is_multigraph(): + it = ( + attr in edgedata + for rowdata in adj.values() + for multiedges in rowdata.values() + for edgedata in multiedges.values() + ) + else: + it = ( + attr in edgedata + for rowdata in adj.values() + for edgedata in rowdata.values() + ) if next(it): if all(it): # All edges have data @@ -195,9 +205,10 @@ def from_networkx( del edge_attrs[attr] # Else some edges have attribute (default already None) else: - attr_sets = set( - map(required.intersection, concat(map(dict.values, adj.values()))) - ) + it = concat(map(dict.values, adj.values())) + if graph.is_multigraph(): + it = concat(map(dict.values, it)) + attr_sets = set(map(required.intersection, it)) for attr in required - frozenset.union(*attr_sets): # No edges have these attributes del edge_attrs[attr] @@ -254,7 +265,23 @@ def from_networkx( key_to_id = None else: col_iter = map(key_to_id.__getitem__, col_iter) - col_indices = cp.fromiter(col_iter, np.int32) + if graph.is_multigraph(): + col_indices = np.fromiter(col_iter, np.int32) + num_multiedges = np.fromiter( + map(len, concat(map(dict.values, adj.values()))), np.int32 + ) + # cp.repeat is slow to use here, so use numpy instead + col_indices = cp.array(np.repeat(col_indices, num_multiedges)) + # Determine edge keys and edge ids for multigraphs + edge_keys = list(concat(concat(map(dict.values, adj.values())))) + edge_indices = cp.fromiter( + concat(map(range, map(len, concat(map(dict.values, adj.values()))))), + np.int32, + ) + if edge_keys == edge_indices.tolist(): + edge_keys = None # Prefer edge_indices + else: + col_indices = cp.fromiter(col_iter, np.int32) edge_values = {} edge_masks = {} @@ -268,16 +295,29 @@ def from_networkx( if edge_default is None: vals = [] append = vals.append - iter_mask = ( - append( - edgedata[edge_attr] - if (present := edge_attr in edgedata) - else False + if graph.is_multigraph(): + iter_mask = ( + append( + edgedata[edge_attr] + if (present := edge_attr in edgedata) + else False + ) + or present + for rowdata in adj.values() + for multiedges in rowdata.values() + for edgedata in multiedges.values() + ) + else: + iter_mask = ( + append( + edgedata[edge_attr] + if (present := edge_attr in edgedata) + else False + ) + or present + for rowdata in adj.values() + for edgedata in rowdata.values() ) - or present - for rowdata in adj.values() - for edgedata in rowdata.values() - ) edge_masks[edge_attr] = cp.fromiter(iter_mask, bool) edge_values[edge_attr] = cp.array(vals, dtype) # if vals.ndim > 1: ... @@ -289,8 +329,16 @@ def from_networkx( # for rowdata in adj.values() # for edgedata in rowdata.values() # ) - iter_values = map( - op.itemgetter(edge_attr), concat(map(dict.values, adj.values())) + it = concat(map(dict.values, adj.values())) + if graph.is_multigraph(): + it = concat(map(dict.values, it)) + iter_values = map(op.itemgetter(edge_attr), it) + elif graph.is_multigraph(): + iter_values = ( + edgedata.get(edge_attr, edge_default) + for rowdata in adj.values() + for multiedges in rowdata.values() + for edgedata in multiedges.values() ) else: iter_values = ( @@ -304,13 +352,13 @@ def from_networkx( edge_values[edge_attr] = cp.fromiter(iter_values, dtype) # if vals.ndim > 1: ... - row_indices = cp.array( - # cp.repeat is slow to use here, so use numpy instead - np.repeat( - np.arange(N, dtype=np.int32), - np.fromiter(map(len, adj.values()), np.int32), - ) + # cp.repeat is slow to use here, so use numpy instead + row_indices = np.repeat( + np.arange(N, dtype=np.int32), np.fromiter(map(len, adj.values()), np.int32) ) + if graph.is_multigraph(): + row_indices = np.repeat(row_indices, num_multiedges) + row_indices = cp.array(row_indices) node_values = {} node_masks = {} @@ -350,21 +398,38 @@ def from_networkx( else: node_values[node_attr] = cp.fromiter(iter_values, dtype) # if vals.ndim > 1: ... - - if graph.is_directed() or as_directed: - klass = nxcg.DiGraph + if graph.is_multigraph(): + if graph.is_directed() or as_directed: + klass = nxcg.MultiDiGraph + else: + klass = nxcg.MultiGraph + rv = klass.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + edge_keys=edge_keys, + ) else: - klass = nxcg.Graph - rv = klass.from_coo( - N, - row_indices, - col_indices, - edge_values, - edge_masks, - node_values, - node_masks, - key_to_id=key_to_id, - ) + if graph.is_directed() or as_directed: + klass = nxcg.DiGraph + else: + klass = nxcg.Graph + rv = klass.from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + ) if preserve_graph_attrs: rv.graph.update(graph.graph) # deepcopy? return rv @@ -427,7 +492,7 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: full_node_dicts = _iter_attr_dicts(node_values, node_masks) rv.add_nodes_from(zip(node_iter, full_node_dicts)) elif id_to_key is not None: - rv.add_nodes_from(id_to_key.values()) + rv.add_nodes_from(id_to_key) else: rv.add_nodes_from(range(len(G))) @@ -448,7 +513,17 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: if id_to_key is not None: row_iter = map(id_to_key.__getitem__, row_indices) col_iter = map(id_to_key.__getitem__, col_indices) - if edge_values: + if G.is_multigraph() and (G.edge_keys is not None or G.edge_indices is not None): + if G.edge_keys is not None: + edge_keys = G.edge_keys + else: + edge_keys = G.edge_indices.tolist() + if edge_values: + full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) + rv.add_edges_from(zip(row_iter, col_iter, edge_keys, full_edge_dicts)) + else: + rv.add_edges_from(zip(row_iter, col_iter, edge_keys)) + elif edge_values: full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) rv.add_edges_from(zip(row_iter, col_iter, full_edge_dicts)) else: diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 2ad23acd9..124b86a4a 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -174,6 +174,10 @@ def key(testpath): ): louvain_different, key("test_louvain.py:test_none_weight_param"): louvain_different, key("test_louvain.py:test_multigraph"): louvain_different, + # See networkx#6630 + key( + "test_louvain.py:test_undirected_selfloops" + ): "self-loops not handled in Louvain", } ) @@ -189,10 +193,4 @@ def can_run(cls, name, args, kwargs): This is a proposed API to add to networkx dispatching machinery and may change. """ - return ( - hasattr(cls, name) - and getattr(cls, name).can_run(*args, **kwargs) - # We don't support MultiGraphs yet - and not any(isinstance(x, nx.MultiGraph) for x in args) - and not any(isinstance(x, nx.MultiGraph) for x in kwargs.values()) - ) + return hasattr(cls, name) and getattr(cls, name).can_run(*args, **kwargs) diff --git a/python/nx-cugraph/nx_cugraph/tests/bench_convert.py b/python/nx-cugraph/nx_cugraph/tests/bench_convert.py index 7e6278661..2eb432230 100644 --- a/python/nx-cugraph/nx_cugraph/tests/bench_convert.py +++ b/python/nx-cugraph/nx_cugraph/tests/bench_convert.py @@ -39,6 +39,8 @@ gpubenchmark = pytest_benchmark.plugin.benchmark +CREATE_USING = [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] + def _bench_helper(gpubenchmark, N, attr_kind, create_using, method): G = method(N, create_using=create_using) @@ -103,7 +105,7 @@ def _bench_helper_scipy(gpubenchmark, N, attr_kind, create_using, method, fmt): None, ], ) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) def bench_cycle_graph(gpubenchmark, N, attr_kind, create_using): _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.cycle_graph) @@ -111,7 +113,7 @@ def bench_cycle_graph(gpubenchmark, N, attr_kind, create_using): @pytest.mark.skipif("not cugraph") @pytest.mark.parametrize("N", [1, 10**6]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("do_renumber", [True, False]) def bench_cycle_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renumber): if N == 1 and not do_renumber: @@ -124,7 +126,7 @@ def bench_cycle_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renum @pytest.mark.skipif("not scipy") @pytest.mark.parametrize("N", [1, 10**6]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("fmt", ["coo", "csr"]) def bench_cycle_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): _bench_helper_scipy(gpubenchmark, N, attr_kind, create_using, nx.cycle_graph, fmt) @@ -143,14 +145,14 @@ def bench_cycle_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): None, ], ) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) def bench_complete_graph_edgedata(gpubenchmark, N, attr_kind, create_using): _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.complete_graph) @pytest.mark.parametrize("N", [3000]) @pytest.mark.parametrize("attr_kind", [None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) def bench_complete_graph_noedgedata(gpubenchmark, N, attr_kind, create_using): _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.complete_graph) @@ -158,7 +160,7 @@ def bench_complete_graph_noedgedata(gpubenchmark, N, attr_kind, create_using): @pytest.mark.skipif("not cugraph") @pytest.mark.parametrize("N", [1, 1500]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("do_renumber", [True, False]) def bench_complete_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renumber): if N == 1 and not do_renumber: @@ -171,7 +173,7 @@ def bench_complete_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_re @pytest.mark.skipif("not scipy") @pytest.mark.parametrize("N", [1, 1500]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("fmt", ["coo", "csr"]) def bench_complete_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): _bench_helper_scipy( diff --git a/python/nx-cugraph/nx_cugraph/tests/test_convert.py b/python/nx-cugraph/nx_cugraph/tests/test_convert.py index ba3cd7aae..83820f762 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_convert.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_convert.py @@ -18,7 +18,9 @@ from nx_cugraph import interface -@pytest.mark.parametrize("graph_class", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize( + "graph_class", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] +) @pytest.mark.parametrize( "kwargs", [ @@ -48,9 +50,10 @@ def test_convert_empty(graph_class, kwargs): assert G.graph == Gcg.graph == H.graph == {} -def test_convert(): +@pytest.mark.parametrize("graph_class", [nx.Graph, nx.MultiGraph]) +def test_convert(graph_class): # FIXME: can we break this into smaller tests? - G = nx.Graph() + G = graph_class() G.add_edge(0, 1, x=2) G.add_node(0, foo=10) G.add_node(1, foo=20, bar=100) @@ -88,7 +91,10 @@ def test_convert(): cp.testing.assert_array_equal(Gcg.node_values["foo"], [10, 20]) assert Gcg.edge_values == Gcg.edge_masks == {} H = nxcg.to_networkx(Gcg) - assert set(G.edges) == set(H.edges) == {(0, 1)} + if G.is_multigraph(): + assert set(G.edges) == set(H.edges) == {(0, 1, 0)} + else: + assert set(G.edges) == set(H.edges) == {(0, 1)} assert G.nodes == H.nodes # Fill completely missing attribute with default value @@ -100,6 +106,8 @@ def test_convert(): assert Gcg.edge_masks == Gcg.node_values == Gcg.node_masks == {} H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {"y": 0})] + if Gcg.is_multigraph(): + assert set(H.edges) == {(0, 1, 0)} # If attribute is completely missing (and no default), then just ignore it Gcg = nxcg.from_networkx(G, edge_attrs={"y": None}) @@ -108,6 +116,8 @@ def test_convert(): assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == [] H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {})] + if Gcg.is_multigraph(): + assert set(H.edges) == {(0, 1, 0)} G.add_edge(0, 2) # Some edges are missing 'x' attribute; need to use a mask @@ -120,6 +130,8 @@ def test_convert(): cp.testing.assert_array_equal(Gcg.edge_values["x"][Gcg.edge_masks["x"]], [2, 2]) H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {"x": 2}), (0, 2, {})] + if Gcg.is_multigraph(): + assert set(H.edges) == {(0, 1, 0), (0, 2, 0)} with pytest.raises(KeyError, match="x"): nxcg.from_networkx(G, edge_attrs={"x": nxcg.convert.REQUIRED}) @@ -131,7 +143,7 @@ def test_convert(): nxcg.from_networkx(G, node_attrs={"bar": ...}) # Now for something more complicated... - G = nx.Graph() + G = graph_class() G.add_edge(10, 20, x=1) G.add_edge(10, 30, x=2, y=1.5) G.add_node(10, foo=100) @@ -147,7 +159,7 @@ def test_convert(): {"edge_attrs": {"x": 0, "y": None}, "edge_dtypes": {"x": int, "y": float}}, ]: Gcg = nxcg.from_networkx(G, **kwargs) - assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.edge_values["x"], [1, 2, 1, 2]) @@ -168,7 +180,7 @@ def test_convert(): {"node_attrs": {"foo": 0, "bar": None, "missing": None}}, ]: Gcg = nxcg.from_networkx(G, **kwargs) - assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["foo"], [100, 200, 300]) @@ -189,7 +201,7 @@ def test_convert(): {"node_attrs": {"bar": 0, "foo": None}, "node_dtypes": int}, ]: Gcg = nxcg.from_networkx(G, **kwargs) - assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["bar"], [0, 1000, 0]) @@ -201,3 +213,14 @@ def test_convert(): interface.BackendInterface.convert_from_nx(G, edge_attrs={"x": 1}, weight="x") with pytest.raises(TypeError, match="Expected networkx.Graph"): nxcg.from_networkx({}) + + +@pytest.mark.parametrize("graph_class", [nx.MultiGraph, nx.MultiDiGraph]) +def test_multigraph(graph_class): + G = graph_class() + G.add_edge(0, 1, "key1", x=10) + G.add_edge(0, 1, "key2", y=20) + Gcg = nxcg.from_networkx(G, preserve_edge_attrs=True) + H = nxcg.to_networkx(Gcg) + assert type(G) is type(H) + assert nx.utils.graphs_equal(G, H) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_multigraph.py b/python/nx-cugraph/nx_cugraph/tests/test_multigraph.py new file mode 100644 index 000000000..a8f189a47 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_multigraph.py @@ -0,0 +1,72 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest + +import nx_cugraph as nxcg + + +@pytest.mark.parametrize("test_nxcugraph", [False, True]) +def test_get_edge_data(test_nxcugraph): + G = nx.MultiGraph() + G.add_edge(0, 1, 0, x=10) + G.add_edge(0, 1, 1, y=20) + G.add_edge(0, 2, "a", x=100) + G.add_edge(0, 2, "b", y=200) + G.add_edge(0, 3) + G.add_edge(0, 3) + if test_nxcugraph: + G = nxcg.MultiGraph(G) + default = object() + assert G.get_edge_data(0, 0, default=default) is default + assert G.get_edge_data("a", "b", default=default) is default + assert G.get_edge_data(0, 1, 2, default=default) is default + assert G.get_edge_data(-1, 1, default=default) is default + assert G.get_edge_data(0, 1, 0, default=default) == {"x": 10} + assert G.get_edge_data(0, 1, 1, default=default) == {"y": 20} + assert G.get_edge_data(0, 1, default=default) == {0: {"x": 10}, 1: {"y": 20}} + assert G.get_edge_data(0, 2, "a", default=default) == {"x": 100} + assert G.get_edge_data(0, 2, "b", default=default) == {"y": 200} + assert G.get_edge_data(0, 2, default=default) == {"a": {"x": 100}, "b": {"y": 200}} + assert G.get_edge_data(0, 3, 0, default=default) == {} + assert G.get_edge_data(0, 3, 1, default=default) == {} + assert G.get_edge_data(0, 3, 2, default=default) is default + assert G.get_edge_data(0, 3, default=default) == {0: {}, 1: {}} + assert G.has_edge(0, 1) + assert G.has_edge(0, 1, 0) + assert G.has_edge(0, 1, 1) + assert not G.has_edge(0, 1, 2) + assert not G.has_edge(0, 1, "a") + assert not G.has_edge(0, -1) + assert G.has_edge(0, 2) + assert G.has_edge(0, 2, "a") + assert G.has_edge(0, 2, "b") + assert not G.has_edge(0, 2, "c") + assert not G.has_edge(0, 2, 0) + assert G.has_edge(0, 3) + assert not G.has_edge(0, 0) + assert not G.has_edge(0, 0, 0) + + G = nx.MultiGraph() + G.add_edge(0, 1) + if test_nxcugraph: + G = nxcg.MultiGraph(G) + assert G.get_edge_data(0, 1, default=default) == {0: {}} + assert G.get_edge_data(0, 1, 0, default=default) == {} + assert G.get_edge_data(0, 1, 1, default=default) is default + assert G.get_edge_data(0, 1, "b", default=default) is default + assert G.get_edge_data(-1, 2, default=default) is default + assert G.has_edge(0, 1) + assert G.has_edge(0, 1, 0) + assert not G.has_edge(0, 1, 1) + assert not G.has_edge(0, 1, "a") diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 98adbb439..2478a02df 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -32,6 +32,7 @@ classifiers = [ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", + "numpy>=1.21", "pylibcugraph==23.12.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. @@ -109,7 +110,7 @@ addopts = [ # "-ra", # Print summary of all fails/errors "--benchmark-warmup=off", "--benchmark-max-time=0", - "--benchmark-min-rounds=3", + "--benchmark-min-rounds=1", "--benchmark-columns=min,median,max", ] From 95fb896b0d6e3cb68b7d12da9f0cf90821714dd1 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 23 Oct 2023 17:14:44 -0500 Subject: [PATCH 279/384] nx-cugraph: xfail test_louvain.py:test_threshold in Python 3.9 (#3944) This test is flaky b/c Louvain in PLC does not have a seed or random state parameter. This test holds the seed fixed and changes the threshold, but clearly this may not work if we can't set the seed. Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Naim (https://github.com/naimnv) URL: https://github.com/rapidsai/cugraph/pull/3944 --- python/nx-cugraph/nx_cugraph/interface.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 124b86a4a..a7b88b72e 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -12,6 +12,8 @@ # limitations under the License. from __future__ import annotations +import sys + import networkx as nx import nx_cugraph as nxcg @@ -180,6 +182,11 @@ def key(testpath): ): "self-loops not handled in Louvain", } ) + if sys.version_info[:2] == (3, 9): + # This test is sensitive to RNG, which depends on Python version + xfail[ + key("test_louvain.py:test_threshold") + ] = "Louvain does not support seed parameter" for item in items: kset = set(item.keywords) From 53ddf09e76a796988e13980a0c8502affad6a4a5 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 25 Oct 2023 16:16:00 -0500 Subject: [PATCH 280/384] nx-cugraph: add k_truss and degree centralities (#3945) New algorithms: - `degree_centrality` - `in_degree_centrality` - `k_truss` - `number_of_selfloops` - `out_degree_centrality` Also, rename `row_indices, col_indices` to `src_indices, dst_indices` Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3945 --- python/nx-cugraph/_nx_cugraph/__init__.py | 5 + python/nx-cugraph/lint.yaml | 6 +- .../nx_cugraph/algorithms/__init__.py | 1 + .../algorithms/centrality/__init__.py | 1 + .../algorithms/centrality/degree_alg.py | 48 ++++++++ .../algorithms/community/louvain.py | 2 +- .../nx-cugraph/nx_cugraph/algorithms/core.py | 96 ++++++++++++++++ .../nx_cugraph/algorithms/isolate.py | 8 +- .../nx-cugraph/nx_cugraph/classes/__init__.py | 5 +- .../nx-cugraph/nx_cugraph/classes/digraph.py | 13 ++- .../nx-cugraph/nx_cugraph/classes/function.py | 23 ++++ python/nx-cugraph/nx_cugraph/classes/graph.py | 105 ++++++++++-------- .../nx_cugraph/classes/multigraph.py | 64 ++++++----- python/nx-cugraph/nx_cugraph/convert.py | 38 +++---- .../nx_cugraph/tests/test_convert.py | 32 +++--- .../nx_cugraph/tests/test_match_api.py | 3 + python/nx-cugraph/pyproject.toml | 5 +- 17 files changed, 330 insertions(+), 125 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/core.py create mode 100644 python/nx-cugraph/nx_cugraph/classes/function.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 886d7a19f..965b5b232 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -30,11 +30,16 @@ "functions": { # BEGIN: functions "betweenness_centrality", + "degree_centrality", "edge_betweenness_centrality", + "in_degree_centrality", "is_isolate", "isolates", + "k_truss", "louvain_communities", "number_of_isolates", + "number_of_selfloops", + "out_degree_centrality", # END: functions }, "extra_docstrings": { diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 4f604fbeb..fef2cebc7 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -45,12 +45,12 @@ repos: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.9.1 + rev: 23.10.0 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.292 + rev: v0.1.1 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.292 + rev: v0.1.1 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index dfd9adfc6..22600bfdc 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -12,4 +12,5 @@ # limitations under the License. from . import centrality, community from .centrality import * +from .core import * from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py index 2ac6242e8..af91f2278 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py @@ -11,3 +11,4 @@ # See the License for the specific language governing permissions and # limitations under the License. from .betweenness import * +from .degree_alg import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py new file mode 100644 index 000000000..0b2fd24af --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nx_cugraph.convert import _to_directed_graph, _to_graph +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +__all__ = ["degree_centrality", "in_degree_centrality", "out_degree_centrality"] + + +@networkx_algorithm +def degree_centrality(G): + G = _to_graph(G) + if len(G) <= 1: + return dict.fromkeys(G, 1) + deg = G._degrees_array() + centrality = deg * (1 / (len(G) - 1)) + return G._nodearray_to_dict(centrality) + + +@not_implemented_for("undirected") +@networkx_algorithm +def in_degree_centrality(G): + G = _to_directed_graph(G) + if len(G) <= 1: + return dict.fromkeys(G, 1) + deg = G._in_degrees_array() + centrality = deg * (1 / (len(G) - 1)) + return G._nodearray_to_dict(centrality) + + +@not_implemented_for("undirected") +@networkx_algorithm +def out_degree_centrality(G): + G = _to_directed_graph(G) + if len(G) <= 1: + return dict.fromkeys(G, 1) + deg = G._out_degrees_array() + centrality = deg * (1 / (len(G) - 1)) + return G._nodearray_to_dict(centrality) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index dc209870c..62261d109 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -42,7 +42,7 @@ def louvain_communities( # NetworkX allows both directed and undirected, but cugraph only allows undirected. seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) - if G.row_indices.size == 0: + if G.src_indices.size == 0: # TODO: PLC doesn't handle empty graphs gracefully! return [{key} for key in G._nodeiter_to_iter(range(len(G)))] if max_level is None: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py new file mode 100644 index 000000000..0a64dd71c --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -0,0 +1,96 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +import nx_cugraph as nxcg +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +__all__ = ["k_truss"] + + +@not_implemented_for("directed") +@not_implemented_for("multigraph") +@networkx_algorithm +def k_truss(G, k): + if is_nx := isinstance(G, nx.Graph): + G = nxcg.from_networkx(G, preserve_all_attrs=True) + if nxcg.number_of_selfloops(G) > 0: + raise nx.NetworkXError( + "Input graph has self loops which is not permitted; " + "Consider using G.remove_edges_from(nx.selfloop_edges(G))." + ) + # TODO: create renumbering helper function(s) + if k < 3: + # k-truss graph is comprised of nodes incident on k-2 triangles, so k<3 is a + # boundary condition. Here, all we need to do is drop nodes with zero degree. + # Technically, it would be okay to delete this branch of code, because + # plc.k_truss_subgraph behaves the same for 0 <= k < 3. We keep this branch b/c + # it's faster and has an "early return" if there are no nodes with zero degree. + degrees = G._degrees_array() + # Renumber step 0: node indices + node_indices = degrees.nonzero()[0] + if degrees.size == node_indices.size: + # No change + return G if is_nx else G.copy() + src_indices = G.src_indices + dst_indices = G.dst_indices + # Renumber step 1: edge values (no changes needed) + edge_values = {key: val.copy() for key, val in G.edge_values.items()} + edge_masks = {key: val.copy() for key, val in G.edge_masks.items()} + else: + # int dtype for edge_indices would be preferred + edge_indices = cp.arange(G.src_indices.size, dtype=np.float64) + src_indices, dst_indices, edge_indices, _ = plc.k_truss_subgraph( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(edge_array=edge_indices), + k=k, + do_expensive_check=False, + ) + # Renumber step 0: node indices + node_indices = cp.unique(cp.concatenate([src_indices, dst_indices])) + # Renumber step 1: edge values + edge_indices = edge_indices.astype(np.int64) + edge_values = {key: val[edge_indices] for key, val in G.edge_values.items()} + edge_masks = {key: val[edge_indices] for key, val in G.edge_masks.items()} + # Renumber step 2: edge indices + mapper = cp.zeros(len(G), src_indices.dtype) + mapper[node_indices] = cp.arange(node_indices.size, dtype=mapper.dtype) + src_indices = mapper[src_indices] + dst_indices = mapper[dst_indices] + # Renumber step 3: node values + node_values = {key: val[node_indices] for key, val in G.node_values.items()} + node_masks = {key: val[node_indices] for key, val in G.node_masks.items()} + # Renumber step 4: key_to_id + if (id_to_key := G.id_to_key) is not None: + key_to_id = { + id_to_key[old_index]: new_index + for new_index, old_index in enumerate(node_indices.tolist()) + } + else: + key_to_id = None + # Same as calling `G.from_coo`, but use __class__ to indicate it's a classmethod. + new_graph = G.__class__.from_coo( + node_indices.size, + src_indices, + dst_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + ) + new_graph.graph.update(G.graph) + return new_graph diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py index 774627e84..d32223fb3 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py @@ -30,18 +30,18 @@ def is_isolate(G, n): G = _to_graph(G) index = n if G.key_to_id is None else G.key_to_id[n] return not ( - (G.row_indices == index).any().tolist() + (G.src_indices == index).any().tolist() or G.is_directed() - and (G.col_indices == index).any().tolist() + and (G.dst_indices == index).any().tolist() ) def _mark_isolates(G) -> cp.ndarray[bool]: """Return a boolean mask array indicating indices of isolated nodes.""" mark_isolates = cp.ones(len(G), bool) - mark_isolates[G.row_indices] = False + mark_isolates[G.src_indices] = False if G.is_directed(): - mark_isolates[G.col_indices] = False + mark_isolates[G.dst_indices] = False return mark_isolates diff --git a/python/nx-cugraph/nx_cugraph/classes/__init__.py b/python/nx-cugraph/nx_cugraph/classes/__init__.py index 9916bcbe2..19a5357da 100644 --- a/python/nx-cugraph/nx_cugraph/classes/__init__.py +++ b/python/nx-cugraph/nx_cugraph/classes/__init__.py @@ -11,7 +11,8 @@ # See the License for the specific language governing permissions and # limitations under the License. from .graph import Graph +from .digraph import DiGraph from .multigraph import MultiGraph +from .multidigraph import MultiDiGraph -from .digraph import DiGraph # isort:skip -from .multidigraph import MultiDiGraph # isort:skip +from .function import * diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index 72a1bff21..52ea2334c 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -14,6 +14,7 @@ from typing import TYPE_CHECKING +import cupy as cp import networkx as nx import nx_cugraph as nxcg @@ -48,7 +49,7 @@ def number_of_edges( ) -> int: if u is not None or v is not None: raise NotImplementedError - return self.row_indices.size + return self.src_indices.size ########################## # NetworkX graph methods # @@ -59,3 +60,13 @@ def reverse(self, copy: bool = True) -> DiGraph: return self._copy(not copy, self.__class__, reverse=True) # Many more methods to implement... + + ################### + # Private methods # + ################### + + def _in_degrees_array(self): + return cp.bincount(self.dst_indices, minlength=self._N) + + def _out_degrees_array(self): + return cp.bincount(self.src_indices, minlength=self._N) diff --git a/python/nx-cugraph/nx_cugraph/classes/function.py b/python/nx-cugraph/nx_cugraph/classes/function.py new file mode 100644 index 000000000..633e4abd7 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/function.py @@ -0,0 +1,23 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import networkx_algorithm + +__all__ = ["number_of_selfloops"] + + +@networkx_algorithm +def number_of_selfloops(G): + G = _to_graph(G) + is_selfloop = G.src_indices == G.dst_indices + return is_selfloop.sum().tolist() diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index f1e85c836..166b6b9dc 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -52,8 +52,8 @@ class Graph: # Not networkx properties # We store edge data in COO format with {row,col}_indices and edge_values. - row_indices: cp.ndarray[IndexValue] - col_indices: cp.ndarray[IndexValue] + src_indices: cp.ndarray[IndexValue] + dst_indices: cp.ndarray[IndexValue] edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] edge_masks: dict[AttrKey, cp.ndarray[bool]] node_values: dict[AttrKey, cp.ndarray[NodeValue]] @@ -70,8 +70,8 @@ class Graph: def from_coo( cls, N: int, - row_indices: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -82,8 +82,8 @@ def from_coo( **attr, ) -> Graph: new_graph = object.__new__(cls) - new_graph.row_indices = row_indices - new_graph.col_indices = col_indices + new_graph.src_indices = src_indices + new_graph.dst_indices = dst_indices new_graph.edge_values = {} if edge_values is None else dict(edge_values) new_graph.edge_masks = {} if edge_masks is None else dict(edge_masks) new_graph.node_values = {} if node_values is None else dict(node_values) @@ -93,9 +93,9 @@ def from_coo( new_graph._N = op.index(N) # Ensure N is integral new_graph.graph = new_graph.graph_attr_dict_factory() new_graph.graph.update(attr) - size = new_graph.row_indices.size + size = new_graph.src_indices.size # Easy and fast sanity checks - if size != new_graph.col_indices.size: + if size != new_graph.dst_indices.size: raise ValueError for attr in ["edge_values", "edge_masks"]: if datadict := getattr(new_graph, attr): @@ -117,7 +117,7 @@ def from_coo( def from_csr( cls, indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -128,14 +128,14 @@ def from_csr( **attr, ) -> Graph: N = indptr.size - 1 - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -149,7 +149,7 @@ def from_csr( def from_csc( cls, indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -160,14 +160,14 @@ def from_csc( **attr, ) -> Graph: N = indptr.size - 1 - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -183,7 +183,7 @@ def from_dcsr( N: int, compressed_rows: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -193,14 +193,14 @@ def from_dcsr( id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_rows.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -216,7 +216,7 @@ def from_dcsc( N: int, compressed_cols: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -226,14 +226,14 @@ def from_dcsc( id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_cols.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -343,8 +343,8 @@ def clear(self) -> None: self.node_values.clear() self.node_masks.clear() self.graph.clear() - self.row_indices = cp.empty(0, self.row_indices.dtype) - self.col_indices = cp.empty(0, self.col_indices.dtype) + self.src_indices = cp.empty(0, self.src_indices.dtype) + self.dst_indices = cp.empty(0, self.dst_indices.dtype) self._N = 0 self.key_to_id = None self._id_to_key = None @@ -353,8 +353,8 @@ def clear(self) -> None: def clear_edges(self) -> None: self.edge_values.clear() self.edge_masks.clear() - self.row_indices = cp.empty(0, self.row_indices.dtype) - self.col_indices = cp.empty(0, self.col_indices.dtype) + self.src_indices = cp.empty(0, self.src_indices.dtype) + self.dst_indices = cp.empty(0, self.dst_indices.dtype) @networkx_api def copy(self, as_view: bool = False) -> Graph: @@ -377,7 +377,7 @@ def get_edge_data( return default except TypeError: return default - index = cp.nonzero((self.row_indices == u) & (self.col_indices == v))[0] + index = cp.nonzero((self.src_indices == u) & (self.dst_indices == v))[0] if index.size == 0: return default [index] = index.tolist() @@ -397,7 +397,7 @@ def has_edge(self, u: NodeKey, v: NodeKey) -> bool: v = self.key_to_id[v] except KeyError: return False - return bool(((self.row_indices == u) & (self.col_indices == v)).any()) + return bool(((self.src_indices == u) & (self.dst_indices == v)).any()) @networkx_api def has_node(self, n: NodeKey) -> bool: @@ -431,8 +431,8 @@ def order(self) -> int: def size(self, weight: AttrKey | None = None) -> int: if weight is not None: raise NotImplementedError - # If no self-edges, then `self.row_indices.size // 2` - return int((self.row_indices <= self.col_indices).sum()) + # If no self-edges, then `self.src_indices.size // 2` + return int((self.src_indices <= self.dst_indices).sum()) @networkx_api def to_directed(self, as_view: bool = False) -> nxcg.DiGraph: @@ -455,9 +455,8 @@ def to_undirected(self, as_view: bool = False) -> Graph: def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): # DRY warning: see also MultiGraph._copy - indptr = self.indptr - row_indices = self.row_indices - col_indices = self.col_indices + src_indices = self.src_indices + dst_indices = self.dst_indices edge_values = self.edge_values edge_masks = self.edge_masks node_values = self.node_values @@ -465,9 +464,8 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): key_to_id = self.key_to_id id_to_key = None if key_to_id is None else self._id_to_key if not as_view: - indptr = indptr.copy() - row_indices = row_indices.copy() - col_indices = col_indices.copy() + src_indices = src_indices.copy() + dst_indices = dst_indices.copy() edge_values = {key: val.copy() for key, val in edge_values.items()} edge_masks = {key: val.copy() for key, val in edge_masks.items()} node_values = {key: val.copy() for key, val in node_values.items()} @@ -477,11 +475,11 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): if id_to_key is not None: id_to_key = id_to_key.copy() if reverse: - row_indices, col_indices = col_indices, row_indices + src_indices, dst_indices = dst_indices, src_indices rv = cls.from_coo( - indptr, - row_indices, - col_indices, + self._N, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -502,8 +500,11 @@ def _get_plc_graph( edge_dtype: Dtype | None = None, *, store_transposed: bool = False, + edge_array: cp.ndarray[EdgeValue] | None = None, ): - if edge_attr is None: + if edge_array is not None: + pass + elif edge_attr is None: edge_array = None elif edge_attr not in self.edge_values: raise KeyError("Graph has no edge attribute {edge_attr!r}") @@ -532,14 +533,20 @@ def _get_plc_graph( is_multigraph=self.is_multigraph(), is_symmetric=not self.is_directed(), ), - src_or_offset_array=self.row_indices, - dst_or_index_array=self.col_indices, + src_or_offset_array=self.src_indices, + dst_or_index_array=self.dst_indices, weight_array=edge_array, store_transposed=store_transposed, renumber=False, do_expensive_check=False, ) + def _degrees_array(self): + degrees = cp.bincount(self.src_indices, minlength=self._N) + if self.is_directed(): + degrees += cp.bincount(self.dst_indices, minlength=self._N) + return degrees + def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: """Convert an iterable of node IDs to an iterable of node keys.""" if (id_to_key := self.id_to_key) is not None: @@ -551,6 +558,14 @@ def _nodearray_to_list(self, node_ids: cp.ndarray[IndexValue]) -> list[NodeKey]: return node_ids.tolist() return list(self._nodeiter_to_iter(node_ids.tolist())) + def _nodearray_to_dict( + self, values: cp.ndarray[NodeValue] + ) -> dict[NodeKey, NodeValue]: + it = enumerate(values.tolist()) + if (id_to_key := self.id_to_key) is not None: + return {id_to_key[key]: val for key, val in it} + return dict(it) + def _nodearrays_to_dict( self, node_ids: cp.ndarray[IndexValue], values: cp.ndarray[NodeValue] ) -> dict[NodeKey, NodeValue]: diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py index 499ca7ce2..3d90861a3 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -67,8 +67,8 @@ class MultiGraph(Graph): def from_coo( cls, N: int, - row_indices: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -82,8 +82,8 @@ def from_coo( ) -> MultiGraph: new_graph = super().from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -97,7 +97,7 @@ def from_coo( # Easy and fast sanity checks if ( new_graph.edge_keys is not None - and len(new_graph.edge_keys) != row_indices.size + and len(new_graph.edge_keys) != src_indices.size ): raise ValueError return new_graph @@ -106,7 +106,7 @@ def from_coo( def from_csr( cls, indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -119,14 +119,14 @@ def from_csr( **attr, ) -> MultiGraph: N = indptr.size - 1 - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -142,7 +142,7 @@ def from_csr( def from_csc( cls, indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -155,14 +155,14 @@ def from_csc( **attr, ) -> MultiGraph: N = indptr.size - 1 - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -180,7 +180,7 @@ def from_dcsr( N: int, compressed_rows: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -192,14 +192,14 @@ def from_dcsr( edge_keys: list[EdgeKey] | None = None, **attr, ) -> MultiGraph: - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_rows.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -217,7 +217,7 @@ def from_dcsc( N: int, compressed_cols: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -229,14 +229,14 @@ def from_dcsc( edge_keys: list[EdgeKey] | None = None, **attr, ) -> Graph: - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_cols.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -330,7 +330,7 @@ def get_edge_data( return default except TypeError: return default - mask = (self.row_indices == u) & (self.col_indices == v) + mask = (self.src_indices == u) & (self.dst_indices == v) if not mask.any(): return default if self.edge_keys is None: @@ -376,7 +376,7 @@ def has_edge(self, u: NodeKey, v: NodeKey, key: EdgeKey | None = None) -> bool: v = self.key_to_id[v] except KeyError: return False - mask = (self.row_indices == u) & (self.col_indices == v) + mask = (self.src_indices == u) & (self.dst_indices == v) if key is None or (self.edge_indices is None and self.edge_keys is None): return bool(mask.any()) if self.edge_keys is None: @@ -405,9 +405,8 @@ def to_undirected(self, as_view: bool = False) -> MultiGraph: def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): # DRY warning: see also Graph._copy - indptr = self.indptr - row_indices = self.row_indices - col_indices = self.col_indices + src_indices = self.src_indices + dst_indices = self.dst_indices edge_indices = self.edge_indices edge_values = self.edge_values edge_masks = self.edge_masks @@ -417,9 +416,8 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): id_to_key = None if key_to_id is None else self._id_to_key edge_keys = self.edge_keys if not as_view: - indptr = indptr.copy() - row_indices = row_indices.copy() - col_indices = col_indices.copy() + src_indices = src_indices.copy() + dst_indices = dst_indices.copy() edge_indices = edge_indices.copy() edge_values = {key: val.copy() for key, val in edge_values.items()} edge_masks = {key: val.copy() for key, val in edge_masks.items()} @@ -432,11 +430,11 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): if edge_keys is not None: edge_keys = edge_keys.copy() if reverse: - row_indices, col_indices = col_indices, row_indices + src_indices, dst_indices = dst_indices, src_indices rv = cls.from_coo( - indptr, - row_indices, - col_indices, + self._N, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index e82286e5e..d117c8e5c 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -266,12 +266,12 @@ def from_networkx( else: col_iter = map(key_to_id.__getitem__, col_iter) if graph.is_multigraph(): - col_indices = np.fromiter(col_iter, np.int32) + dst_indices = np.fromiter(col_iter, np.int32) num_multiedges = np.fromiter( map(len, concat(map(dict.values, adj.values()))), np.int32 ) # cp.repeat is slow to use here, so use numpy instead - col_indices = cp.array(np.repeat(col_indices, num_multiedges)) + dst_indices = cp.array(np.repeat(dst_indices, num_multiedges)) # Determine edge keys and edge ids for multigraphs edge_keys = list(concat(concat(map(dict.values, adj.values())))) edge_indices = cp.fromiter( @@ -281,7 +281,7 @@ def from_networkx( if edge_keys == edge_indices.tolist(): edge_keys = None # Prefer edge_indices else: - col_indices = cp.fromiter(col_iter, np.int32) + dst_indices = cp.fromiter(col_iter, np.int32) edge_values = {} edge_masks = {} @@ -353,12 +353,12 @@ def from_networkx( # if vals.ndim > 1: ... # cp.repeat is slow to use here, so use numpy instead - row_indices = np.repeat( + src_indices = np.repeat( np.arange(N, dtype=np.int32), np.fromiter(map(len, adj.values()), np.int32) ) if graph.is_multigraph(): - row_indices = np.repeat(row_indices, num_multiedges) - row_indices = cp.array(row_indices) + src_indices = np.repeat(src_indices, num_multiedges) + src_indices = cp.array(src_indices) node_values = {} node_masks = {} @@ -405,8 +405,8 @@ def from_networkx( klass = nxcg.MultiGraph rv = klass.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -422,8 +422,8 @@ def from_networkx( klass = nxcg.Graph rv = klass.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -496,23 +496,23 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: else: rv.add_nodes_from(range(len(G))) - row_indices = G.row_indices - col_indices = G.col_indices + src_indices = G.src_indices + dst_indices = G.dst_indices edge_values = G.edge_values edge_masks = G.edge_masks if edge_values and not G.is_directed(): # Only add upper triangle of the adjacency matrix so we don't double-add edges - mask = row_indices <= col_indices - row_indices = row_indices[mask] - col_indices = col_indices[mask] + mask = src_indices <= dst_indices + src_indices = src_indices[mask] + dst_indices = dst_indices[mask] edge_values = {k: v[mask] for k, v in edge_values.items()} if edge_masks: edge_masks = {k: v[mask] for k, v in edge_masks.items()} - row_indices = row_iter = row_indices.tolist() - col_indices = col_iter = col_indices.tolist() + src_indices = row_iter = src_indices.tolist() + dst_indices = col_iter = dst_indices.tolist() if id_to_key is not None: - row_iter = map(id_to_key.__getitem__, row_indices) - col_iter = map(id_to_key.__getitem__, col_indices) + row_iter = map(id_to_key.__getitem__, src_indices) + col_iter = map(id_to_key.__getitem__, dst_indices) if G.is_multigraph() and (G.edge_keys is not None or G.edge_indices is not None): if G.edge_keys is not None: edge_keys = G.edge_keys diff --git a/python/nx-cugraph/nx_cugraph/tests/test_convert.py b/python/nx-cugraph/nx_cugraph/tests/test_convert.py index 83820f762..1a71b7968 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_convert.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_convert.py @@ -71,8 +71,8 @@ def test_convert(graph_class): ]: # All edges have "x" attribute, so all kwargs are equivalent Gcg = nxcg.from_networkx(G, **kwargs) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) cp.testing.assert_array_equal(Gcg.edge_values["x"], [2, 2]) assert len(Gcg.edge_values) == 1 assert Gcg.edge_masks == {} @@ -86,8 +86,8 @@ def test_convert(graph_class): # Structure-only graph (no edge attributes) Gcg = nxcg.from_networkx(G, preserve_node_attrs=True) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) cp.testing.assert_array_equal(Gcg.node_values["foo"], [10, 20]) assert Gcg.edge_values == Gcg.edge_masks == {} H = nxcg.to_networkx(Gcg) @@ -99,8 +99,8 @@ def test_convert(graph_class): # Fill completely missing attribute with default value Gcg = nxcg.from_networkx(G, edge_attrs={"y": 0}) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) cp.testing.assert_array_equal(Gcg.edge_values["y"], [0, 0]) assert len(Gcg.edge_values) == 1 assert Gcg.edge_masks == Gcg.node_values == Gcg.node_masks == {} @@ -111,8 +111,8 @@ def test_convert(graph_class): # If attribute is completely missing (and no default), then just ignore it Gcg = nxcg.from_networkx(G, edge_attrs={"y": None}) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == [] H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {})] @@ -123,8 +123,8 @@ def test_convert(graph_class): # Some edges are missing 'x' attribute; need to use a mask for kwargs in [{"preserve_edge_attrs": True}, {"edge_attrs": {"x": None}}]: Gcg = nxcg.from_networkx(G, **kwargs) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == ["x"] cp.testing.assert_array_equal(Gcg.edge_masks["x"], [True, False, True, False]) cp.testing.assert_array_equal(Gcg.edge_values["x"][Gcg.edge_masks["x"]], [2, 2]) @@ -160,8 +160,8 @@ def test_convert(graph_class): ]: Gcg = nxcg.from_networkx(G, **kwargs) assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.edge_values["x"], [1, 2, 1, 2]) assert sorted(Gcg.edge_masks) == ["y"] cp.testing.assert_array_equal(Gcg.edge_masks["y"], [False, True, False, True]) @@ -181,8 +181,8 @@ def test_convert(graph_class): ]: Gcg = nxcg.from_networkx(G, **kwargs) assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["foo"], [100, 200, 300]) assert sorted(Gcg.node_masks) == ["bar"] cp.testing.assert_array_equal(Gcg.node_masks["bar"], [False, True, False]) @@ -202,8 +202,8 @@ def test_convert(graph_class): ]: Gcg = nxcg.from_networkx(G, **kwargs) assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["bar"], [0, 1000, 0]) assert Gcg.node_masks == {} diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py index ecfda1397..a654ff343 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py @@ -31,6 +31,9 @@ def test_match_signature_and_names(): if is_nx_30_or_31 and name in {"louvain_communities"}: continue + if name not in nx_backends._registered_algorithms: + print(f"{name} not dispatched from networkx") + continue dispatchable_func = nx_backends._registered_algorithms[name] # nx version >=3.2 uses orig_func, version >=3.0,<3.2 uses _orig_func if is_nx_30_or_31: diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 2478a02df..9fec8fa02 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -81,7 +81,10 @@ float_to_top = true default_section = "THIRDPARTY" known_first_party = "nx_cugraph" line_length = 88 -extend_skip_glob = ["nx_cugraph/__init__.py"] +extend_skip_glob = [ + "nx_cugraph/__init__.py", + "nx_cugraph/classes/__init__.py", +] [tool.pytest.ini_options] minversion = "6.0" From f7e821534ebdaa6f9626baca80c8769eab382984 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 30 Oct 2023 14:01:25 -0500 Subject: [PATCH 281/384] nx-cugraph: handle seed argument in edge_betweenness_centrality (#3943) CC @rlratzel who brought this to my attention and can test this fix Authors: - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3943 --- .../nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py | 1 + 1 file changed, 1 insertion(+) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py index 104ac8741..210e1f0a2 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py @@ -53,6 +53,7 @@ def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=No raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" ) + seed = _seed_to_int(seed) G = _to_graph(G, weight) src_ids, dst_ids, values, _edge_ids = plc.edge_betweenness_centrality( resource_handle=plc.ResourceHandle(), From 27263cbda5816f1a379f79e929b1e966f52671aa Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 31 Oct 2023 14:10:20 -0500 Subject: [PATCH 282/384] Add many graph generators to nx-cugraph (#3954) Also, better handle dtypes for edge values passed to pylibcugraph, which only takes float32 and float64 atm. I also defined `index_dtype` (currently int32) to globally control the dtype of indices. Authors: - Erik Welch (https://github.com/eriknw) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3954 --- python/nx-cugraph/_nx_cugraph/__init__.py | 45 ++ python/nx-cugraph/lint.yaml | 8 +- python/nx-cugraph/nx_cugraph/__init__.py | 8 +- .../nx_cugraph/algorithms/__init__.py | 3 +- .../algorithms/bipartite/__init__.py | 13 + .../algorithms/bipartite/generators.py | 62 ++ .../nx-cugraph/nx_cugraph/algorithms/core.py | 11 +- python/nx-cugraph/nx_cugraph/classes/graph.py | 188 +++++- .../nx_cugraph/classes/multidigraph.py | 5 + .../nx_cugraph/classes/multigraph.py | 74 ++- python/nx-cugraph/nx_cugraph/convert.py | 78 ++- .../nx-cugraph/nx_cugraph/convert_matrix.py | 146 ++++ .../nx_cugraph/generators/__init__.py | 16 + .../nx_cugraph/generators/_utils.py | 136 ++++ .../nx_cugraph/generators/classic.py | 423 ++++++++++++ .../nx_cugraph/generators/community.py | 45 ++ .../nx-cugraph/nx_cugraph/generators/small.py | 622 ++++++++++++++++++ .../nx_cugraph/generators/social.py | 294 +++++++++ python/nx-cugraph/nx_cugraph/interface.py | 35 + .../nx_cugraph/tests/test_generators.py | 277 ++++++++ .../nx-cugraph/nx_cugraph/tests/test_utils.py | 87 +++ python/nx-cugraph/nx_cugraph/typing.py | 8 + python/nx-cugraph/nx_cugraph/utils/misc.py | 72 +- python/nx-cugraph/pyproject.toml | 3 +- 24 files changed, 2573 insertions(+), 86 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py create mode 100644 python/nx-cugraph/nx_cugraph/convert_matrix.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/_utils.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/classic.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/community.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/small.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/social.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_generators.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_utils.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 965b5b232..af1df0464 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -29,23 +29,68 @@ # "description": "TODO", "functions": { # BEGIN: functions + "barbell_graph", "betweenness_centrality", + "bull_graph", + "caveman_graph", + "chvatal_graph", + "circular_ladder_graph", + "complete_bipartite_graph", + "complete_graph", + "complete_multipartite_graph", + "cubical_graph", + "cycle_graph", + "davis_southern_women_graph", "degree_centrality", + "desargues_graph", + "diamond_graph", + "dodecahedral_graph", "edge_betweenness_centrality", + "empty_graph", + "florentine_families_graph", + "from_pandas_edgelist", + "from_scipy_sparse_array", + "frucht_graph", + "heawood_graph", + "house_graph", + "house_x_graph", + "icosahedral_graph", "in_degree_centrality", "is_isolate", "isolates", "k_truss", + "karate_club_graph", + "krackhardt_kite_graph", + "ladder_graph", + "les_miserables_graph", + "lollipop_graph", "louvain_communities", + "moebius_kantor_graph", + "null_graph", "number_of_isolates", "number_of_selfloops", + "octahedral_graph", "out_degree_centrality", + "pappus_graph", + "path_graph", + "petersen_graph", + "sedgewick_maze_graph", + "star_graph", + "tadpole_graph", + "tetrahedral_graph", + "trivial_graph", + "truncated_cube_graph", + "truncated_tetrahedron_graph", + "turan_graph", + "tutte_graph", + "wheel_graph", # END: functions }, "extra_docstrings": { # BEGIN: extra_docstrings "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", + "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", "louvain_communities": "`seed` parameter is currently ignored.", # END: extra_docstrings }, diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index fef2cebc7..01a806e61 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -45,12 +45,12 @@ repos: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.10.0 + rev: 23.10.1 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.1 + rev: v0.1.3 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -58,7 +58,7 @@ repos: rev: 6.1.0 hooks: - id: flake8 - args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501'] # Why is this necessary? + args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=SIM105'] # Why is this necessary? additional_dependencies: &flake8_dependencies # These versions need updated manually - flake8==6.1.0 @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.1 + rev: v0.1.3 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 1eaf3ec6b..25d442122 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -20,11 +20,11 @@ from . import convert from .convert import * -# from . import convert_matrix -# from .convert_matrix import * +from . import convert_matrix +from .convert_matrix import * -# from . import generators -# from .generators import * +from . import generators +from .generators import * from . import algorithms from .algorithms import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 22600bfdc..69feb8f64 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,7 +10,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import centrality, community +from . import bipartite, centrality, community +from .bipartite import complete_bipartite_graph from .centrality import * from .core import * from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py new file mode 100644 index 000000000..062be973d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .generators import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py new file mode 100644 index 000000000..1d3e762b4 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py @@ -0,0 +1,62 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from numbers import Integral + +import cupy as cp +import networkx as nx +import numpy as np + +from nx_cugraph.generators._utils import _create_using_class, _number_and_nodes +from nx_cugraph.utils import index_dtype, networkx_algorithm, nodes_or_number + +__all__ = [ + "complete_bipartite_graph", +] + + +@nodes_or_number([0, 1]) +@networkx_algorithm +def complete_bipartite_graph(n1, n2, create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + orig_n1, unused_nodes1 = n1 + orig_n2, unused_nodes2 = n2 + n1, nodes1 = _number_and_nodes(n1) + n2, nodes2 = _number_and_nodes(n2) + all_indices = cp.indices((n1, n2), dtype=index_dtype) + indices0 = all_indices[0].ravel() + indices1 = all_indices[1].ravel() + n1 + del all_indices + src_indices = cp.hstack((indices0, indices1)) + dst_indices = cp.hstack((indices1, indices0)) + bipartite = cp.zeros(n1 + n2, np.int8) + bipartite[n1:] = 1 + if isinstance(orig_n1, Integral) and isinstance(orig_n2, Integral): + nodes = None + else: + nodes = list(range(n1)) if nodes1 is None else nodes1 + nodes.extend(range(n2) if nodes2 is None else nodes2) + if len(set(nodes)) != len(nodes): + raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes") + G = graph_class.from_coo( + n1 + n2, + src_indices, + dst_indices, + node_values={"bipartite": bipartite}, + id_to_key=nodes, + name=f"complete_bipartite_graph({orig_n1}, {orig_n2})", + ) + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index 0a64dd71c..33e797935 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -12,11 +12,10 @@ # limitations under the License. import cupy as cp import networkx as nx -import numpy as np import pylibcugraph as plc import nx_cugraph as nxcg -from nx_cugraph.utils import networkx_algorithm, not_implemented_for +from nx_cugraph.utils import _get_int_dtype, networkx_algorithm, not_implemented_for __all__ = ["k_truss"] @@ -51,8 +50,8 @@ def k_truss(G, k): edge_values = {key: val.copy() for key, val in G.edge_values.items()} edge_masks = {key: val.copy() for key, val in G.edge_masks.items()} else: - # int dtype for edge_indices would be preferred - edge_indices = cp.arange(G.src_indices.size, dtype=np.float64) + edge_dtype = _get_int_dtype(G.src_indices.size - 1) + edge_indices = cp.arange(G.src_indices.size, dtype=edge_dtype) src_indices, dst_indices, edge_indices, _ = plc.k_truss_subgraph( resource_handle=plc.ResourceHandle(), graph=G._get_plc_graph(edge_array=edge_indices), @@ -62,7 +61,9 @@ def k_truss(G, k): # Renumber step 0: node indices node_indices = cp.unique(cp.concatenate([src_indices, dst_indices])) # Renumber step 1: edge values - edge_indices = edge_indices.astype(np.int64) + if edge_indices.dtype != edge_dtype: + # The returned edge_indices may have different dtype (and float) + edge_indices = edge_indices.astype(edge_dtype) edge_values = {key: val[edge_indices] for key, val in G.edge_values.items()} edge_masks = {key: val[edge_indices] for key, val in G.edge_masks.items()} # Renumber step 2: edge indices diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 166b6b9dc..2048c4c3d 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -14,7 +14,7 @@ import operator as op from copy import deepcopy -from typing import TYPE_CHECKING, ClassVar +from typing import TYPE_CHECKING import cupy as cp import networkx as nx @@ -23,8 +23,11 @@ import nx_cugraph as nxcg +from ..utils import index_dtype + if TYPE_CHECKING: # pragma: no cover from collections.abc import Iterable, Iterator + from typing import ClassVar from nx_cugraph.typing import ( AttrKey, @@ -34,6 +37,7 @@ IndexValue, NodeKey, NodeValue, + any_ndarray, ) __all__ = ["Graph"] @@ -51,17 +55,38 @@ class Graph: graph_attr_dict_factory: ClassVar[type] = dict # Not networkx properties - # We store edge data in COO format with {row,col}_indices and edge_values. + # We store edge data in COO format with {src,dst}_indices and edge_values. src_indices: cp.ndarray[IndexValue] dst_indices: cp.ndarray[IndexValue] edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] edge_masks: dict[AttrKey, cp.ndarray[bool]] - node_values: dict[AttrKey, cp.ndarray[NodeValue]] - node_masks: dict[AttrKey, cp.ndarray[bool]] + node_values: dict[AttrKey, any_ndarray[NodeValue]] + node_masks: dict[AttrKey, any_ndarray[bool]] key_to_id: dict[NodeKey, IndexValue] | None _id_to_key: list[NodeKey] | None _N: int + # Used by graph._get_plc_graph + _plc_type_map: ClassVar[dict[np.dtype, np.dtype]] = { + # signed int + np.dtype(np.int8): np.dtype(np.float32), + np.dtype(np.int16): np.dtype(np.float32), + np.dtype(np.int32): np.dtype(np.float64), + np.dtype(np.int64): np.dtype(np.float64), # raise if abs(x) > 2**53 + # unsigned int + np.dtype(np.uint8): np.dtype(np.float32), + np.dtype(np.uint16): np.dtype(np.float32), + np.dtype(np.uint32): np.dtype(np.float64), + np.dtype(np.uint64): np.dtype(np.float64), # raise if x > 2**53 + # other + np.dtype(np.bool_): np.dtype(np.float16), + np.dtype(np.float16): np.dtype(np.float32), + } + _plc_allowed_edge_types: ClassVar[set[np.dtype]] = { + np.dtype(np.float32), + np.dtype(np.float64), + } + #################### # Creation methods # #################### @@ -74,8 +99,8 @@ def from_coo( dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -111,6 +136,27 @@ def from_coo( raise ValueError if new_graph._id_to_key is not None and len(new_graph._id_to_key) != N: raise ValueError + if new_graph._id_to_key is not None and new_graph.key_to_id is None: + try: + new_graph.key_to_id = dict(zip(new_graph._id_to_key, range(N))) + except TypeError as exc: + raise ValueError("Bad type of a node value") from exc + if new_graph.src_indices.dtype != index_dtype: + src_indices = new_graph.src_indices.astype(index_dtype) + if not (new_graph.src_indices == src_indices).all(): + raise ValueError( + f"Unable to convert src_indices to {src_indices.dtype.name} " + f"(got {new_graph.src_indices.dtype.name})." + ) + new_graph.src_indices = src_indices + if new_graph.dst_indices.dtype != index_dtype: + dst_indices = new_graph.dst_indices.astype(index_dtype) + if not (new_graph.dst_indices == dst_indices).all(): + raise ValueError( + f"Unable to convert dst_indices to {dst_indices.dtype.name} " + f"(got {new_graph.dst_indices.dtype.name})." + ) + new_graph.dst_indices = dst_indices return new_graph @classmethod @@ -120,8 +166,8 @@ def from_csr( dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -130,7 +176,7 @@ def from_csr( N = indptr.size - 1 src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -152,8 +198,8 @@ def from_csc( src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -162,7 +208,7 @@ def from_csc( N = indptr.size - 1 dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -181,13 +227,13 @@ def from_csc( def from_dcsr( cls, N: int, - compressed_rows: cp.ndarray[IndexValue], + compressed_srcs: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -195,7 +241,7 @@ def from_dcsr( ) -> Graph: src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_rows.get(), cp.diff(indptr).get()) + np.repeat(compressed_srcs.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -214,13 +260,13 @@ def from_dcsr( def from_dcsc( cls, N: int, - compressed_cols: cp.ndarray[IndexValue], + compressed_dsts: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -228,7 +274,7 @@ def from_dcsc( ) -> Graph: dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_cols.get(), cp.diff(indptr).get()) + np.repeat(compressed_dsts.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -245,7 +291,9 @@ def from_dcsc( def __new__(cls, incoming_graph_data=None, **attr) -> Graph: if incoming_graph_data is None: - new_graph = cls.from_coo(0, cp.empty(0, np.int32), cp.empty(0, np.int32)) + new_graph = cls.from_coo( + 0, cp.empty(0, index_dtype), cp.empty(0, index_dtype) + ) elif incoming_graph_data.__class__ is cls: new_graph = incoming_graph_data.copy() elif incoming_graph_data.__class__ is cls.to_networkx_class(): @@ -336,6 +384,17 @@ def __len__(self) -> int: # NetworkX graph methods # ########################## + @networkx_api + def add_nodes_from(self, nodes_for_adding: Iterable[NodeKey], **attr) -> None: + if self._N != 0: + raise NotImplementedError( + "add_nodes_from is not implemented for graph that already has nodes." + ) + G = self.to_networkx_class()() + G.add_nodes_from(nodes_for_adding, **attr) + G = nxcg.from_networkx(G, preserve_node_attrs=True) + self._become(G) + @networkx_api def clear(self) -> None: self.edge_values.clear() @@ -522,11 +581,38 @@ def _get_plc_graph( # Mask is all True; don't need anymore del self.edge_masks[edge_attr] edge_array = self.edge_values[edge_attr] + if edge_array is not None: + if edge_dtype is not None: + edge_dtype = np.dtype(edge_dtype) + if edge_array.dtype != edge_dtype: + edge_array = edge_array.astype(edge_dtype) + # PLC doesn't handle int edge weights right now, so cast int to float + if edge_array.dtype in self._plc_type_map: + if edge_array.dtype == np.int64: + if (val := edge_array.max().tolist()) > 2**53: + raise ValueError( + f"Integer value of value is too large (> 2**53): {val}; " + "pylibcugraph only supports float16 and float32 dtypes." + ) + if (val := edge_array.min().tolist()) < -(2**53): + raise ValueError( + f"Integer value of value is small large (< -2**53): {val}; " + "pylibcugraph only supports float16 and float32 dtypes." + ) + elif ( + edge_array.dtype == np.uint64 + and edge_array.max().tolist() > 2**53 + ): + raise ValueError( + f"Integer value of value is too large (> 2**53): {val}; " + "pylibcugraph only supports float16 and float32 dtypes." + ) + # Consider warning here if we add algorithms that may + # introduce roundoff errors when using floats as ints. + edge_array = edge_array.astype(self._plc_type_map[edge_array.dtype]) + elif edge_array.dtype not in self._plc_allowed_edge_types: + raise TypeError(edge_array.dtype) # Should we cache PLC graph? - if edge_dtype is not None: - edge_dtype = np.dtype(edge_dtype) - if edge_array.dtype != edge_dtype: - edge_array = edge_array.astype(edge_dtype) return plc.SGGraph( resource_handle=plc.ResourceHandle(), graph_properties=plc.GraphProperties( @@ -541,12 +627,60 @@ def _get_plc_graph( do_expensive_check=False, ) + def _sort_edge_indices(self, primary="src"): + # DRY warning: see also MultiGraph._sort_edge_indices + if primary == "src": + stacked = cp.vstack((self.dst_indices, self.src_indices)) + elif primary == "dst": + stacked = cp.vstack((self.src_indices, self.dst_indices)) + else: + raise ValueError( + f'Bad `primary` argument; expected "src" or "dst", got {primary!r}' + ) + indices = cp.lexsort(stacked) + if (cp.diff(indices) > 0).all(): + # Already sorted + return + self.src_indices = self.src_indices[indices] + self.dst_indices = self.dst_indices[indices] + self.edge_values.update( + {key: val[indices] for key, val in self.edge_values.items()} + ) + self.edge_masks.update( + {key: val[indices] for key, val in self.edge_masks.items()} + ) + + def _become(self, other: Graph): + if self.__class__ is not other.__class__: + raise TypeError( + "Attempting to update graph inplace with graph of different type!" + ) + self.clear() + edge_values = self.edge_values + edge_masks = self.edge_masks + node_values = self.node_values + node_masks = self.node_masks + graph = self.graph + edge_values.update(other.edge_values) + edge_masks.update(other.edge_masks) + node_values.update(other.node_values) + node_masks.update(other.node_masks) + graph.update(other.graph) + self.__dict__.update(other.__dict__) + self.edge_values = edge_values + self.edge_masks = edge_masks + self.node_values = node_values + self.node_masks = node_masks + self.graph = graph + return self + def _degrees_array(self): degrees = cp.bincount(self.src_indices, minlength=self._N) if self.is_directed(): degrees += cp.bincount(self.dst_indices, minlength=self._N) return degrees + # Data conversions def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: """Convert an iterable of node IDs to an iterable of node keys.""" if (id_to_key := self.id_to_key) is not None: @@ -567,7 +701,7 @@ def _nodearray_to_dict( return dict(it) def _nodearrays_to_dict( - self, node_ids: cp.ndarray[IndexValue], values: cp.ndarray[NodeValue] + self, node_ids: cp.ndarray[IndexValue], values: any_ndarray[NodeValue] ) -> dict[NodeKey, NodeValue]: it = zip(node_ids.tolist(), values.tolist()) if (id_to_key := self.id_to_key) is not None: @@ -597,7 +731,7 @@ def _dict_to_nodearrays( indices_iter = d else: indices_iter = map(self.key_to_id.__getitem__, d) - node_ids = cp.fromiter(indices_iter, np.int32) + node_ids = cp.fromiter(indices_iter, index_dtype) if dtype is None: values = cp.array(list(d.values())) else: diff --git a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py index 5629e2c9c..2c7bfc007 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py @@ -25,6 +25,11 @@ class MultiDiGraph(MultiGraph, DiGraph): + @classmethod + @networkx_api + def is_directed(cls) -> bool: + return True + @classmethod def to_networkx_class(cls) -> type[nx.MultiDiGraph]: return nx.MultiDiGraph diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py index 3d90861a3..23466dc7d 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -21,6 +21,7 @@ import nx_cugraph as nxcg +from ..utils import index_dtype from .graph import Graph if TYPE_CHECKING: @@ -31,6 +32,7 @@ IndexValue, NodeKey, NodeValue, + any_ndarray, ) __all__ = ["MultiGraph"] @@ -43,11 +45,11 @@ class MultiGraph(Graph): # Not networkx properties - # In a MultiGraph, each edge has a unique `(row, col, key)` key. + # In a MultiGraph, each edge has a unique `(src, dst, key)` key. # By default, `key` is 0 if possible, else 1, else 2, etc. # This key can be any hashable Python object in NetworkX. # We don't use a dict for our data structure here, because - # that would require a `(row, col, key)` key. + # that would require a `(src, dst, key)` key. # Instead, we keep `edge_keys` and/or `edge_indices`. # `edge_keys` is the list of Python objects for each edge. # `edge_indices` is for the common case of default multiedge keys, @@ -72,8 +74,8 @@ def from_coo( edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -110,8 +112,8 @@ def from_csr( edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -121,7 +123,7 @@ def from_csr( N = indptr.size - 1 src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -146,8 +148,8 @@ def from_csc( edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -157,7 +159,7 @@ def from_csc( N = indptr.size - 1 dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -178,14 +180,14 @@ def from_csc( def from_dcsr( cls, N: int, - compressed_rows: cp.ndarray[IndexValue], + compressed_srcs: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -194,7 +196,7 @@ def from_dcsr( ) -> MultiGraph: src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_rows.get(), cp.diff(indptr).get()) + np.repeat(compressed_srcs.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -215,14 +217,14 @@ def from_dcsr( def from_dcsc( cls, N: int, - compressed_cols: cp.ndarray[IndexValue], + compressed_dsts: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], src_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -231,7 +233,7 @@ def from_dcsc( ) -> Graph: dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_cols.get(), cp.diff(indptr).get()) + np.repeat(compressed_dsts.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -449,3 +451,39 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): else: rv.graph.update(deepcopy(self.graph)) return rv + + def _sort_edge_indices(self, primary="src"): + # DRY warning: see also Graph._sort_edge_indices + if self.edge_indices is None and self.edge_keys is None: + return super()._sort_edge_indices(primary=primary) + if primary == "src": + if self.edge_indices is None: + stacked = (self.dst_indices, self.src_indices) + else: + stacked = (self.edge_indices, self.dst_indices, self.src_indices) + elif primary == "dst": + if self.edge_indices is None: + stacked = (self.src_indices, self.dst_indices) + else: + stacked = (self.edge_indices, self.dst_indices, self.src_indices) + else: + raise ValueError( + f'Bad `primary` argument; expected "src" or "dst", got {primary!r}' + ) + indices = cp.lexsort(cp.vstack(stacked)) + if (cp.diff(indices) > 0).all(): + # Already sorted + return + self.src_indices = self.src_indices[indices] + self.dst_indices = self.dst_indices[indices] + self.edge_values.update( + {key: val[indices] for key, val in self.edge_values.items()} + ) + self.edge_masks.update( + {key: val[indices] for key, val in self.edge_masks.items()} + ) + if self.edge_indices is not None: + self.edge_indices = self.edge_indices[indices] + if self.edge_keys is not None: + edge_keys = self.edge_keys + self.edge_keys = [edge_keys[i] for i in indices.tolist()] diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index d117c8e5c..3c0814370 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -24,8 +24,10 @@ import nx_cugraph as nxcg +from .utils import index_dtype + if TYPE_CHECKING: # pragma: no cover - from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue + from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue, any_ndarray __all__ = [ "from_networkx", @@ -256,7 +258,7 @@ def from_networkx( node_attrs[attr] = REQUIRED key_to_id = dict(zip(adj, range(N))) - col_iter = concat(adj.values()) + dst_iter = concat(adj.values()) try: no_renumber = all(k == v for k, v in key_to_id.items()) except Exception: @@ -264,11 +266,11 @@ def from_networkx( if no_renumber: key_to_id = None else: - col_iter = map(key_to_id.__getitem__, col_iter) + dst_iter = map(key_to_id.__getitem__, dst_iter) if graph.is_multigraph(): - dst_indices = np.fromiter(col_iter, np.int32) + dst_indices = np.fromiter(dst_iter, index_dtype) num_multiedges = np.fromiter( - map(len, concat(map(dict.values, adj.values()))), np.int32 + map(len, concat(map(dict.values, adj.values()))), index_dtype ) # cp.repeat is slow to use here, so use numpy instead dst_indices = cp.array(np.repeat(dst_indices, num_multiedges)) @@ -276,12 +278,12 @@ def from_networkx( edge_keys = list(concat(concat(map(dict.values, adj.values())))) edge_indices = cp.fromiter( concat(map(range, map(len, concat(map(dict.values, adj.values()))))), - np.int32, + index_dtype, ) if edge_keys == edge_indices.tolist(): edge_keys = None # Prefer edge_indices else: - dst_indices = cp.fromiter(col_iter, np.int32) + dst_indices = cp.fromiter(dst_iter, index_dtype) edge_values = {} edge_masks = {} @@ -354,7 +356,8 @@ def from_networkx( # cp.repeat is slow to use here, so use numpy instead src_indices = np.repeat( - np.arange(N, dtype=np.int32), np.fromiter(map(len, adj.values()), np.int32) + np.arange(N, dtype=index_dtype), + np.fromiter(map(len, adj.values()), index_dtype), ) if graph.is_multigraph(): src_indices = np.repeat(src_indices, num_multiedges) @@ -383,8 +386,18 @@ def from_networkx( or present for node_id in adj ) - node_masks[node_attr] = cp.fromiter(iter_mask, bool) - node_values[node_attr] = cp.array(vals, dtype) + # Node values may be numpy or cupy arrays (useful for str, object, etc). + # Someday we'll let the user choose np or cp, and support edge values. + node_mask = np.fromiter(iter_mask, bool) + node_value = np.array(vals, dtype) + try: + node_value = cp.array(node_value) + except ValueError: + pass + else: + node_mask = cp.array(node_mask) + node_values[node_attr] = node_value + node_masks[node_attr] = node_mask # if vals.ndim > 1: ... else: if node_default is REQUIRED: @@ -393,10 +406,17 @@ def from_networkx( iter_values = ( nodes[node_id].get(node_attr, node_default) for node_id in adj ) + # Node values may be numpy or cupy arrays (useful for str, object, etc). + # Someday we'll let the user choose np or cp, and support edge values. if dtype is None: - node_values[node_attr] = cp.array(list(iter_values)) + node_value = np.array(list(iter_values)) else: - node_values[node_attr] = cp.fromiter(iter_values, dtype) + node_value = np.fromiter(iter_values, dtype) + try: + node_value = cp.array(node_value) + except ValueError: + pass + node_values[node_attr] = node_value # if vals.ndim > 1: ... if graph.is_multigraph(): if graph.is_directed() or as_directed: @@ -436,8 +456,8 @@ def from_networkx( def _iter_attr_dicts( - values: dict[AttrKey, cp.ndarray[EdgeValue | NodeValue]], - masks: dict[AttrKey, cp.ndarray[bool]], + values: dict[AttrKey, any_ndarray[EdgeValue | NodeValue]], + masks: dict[AttrKey, any_ndarray[bool]], ): full_attrs = list(values.keys() - masks.keys()) if full_attrs: @@ -463,7 +483,7 @@ def _iter_attr_dicts( return full_dicts -def to_networkx(G: nxcg.Graph) -> nx.Graph: +def to_networkx(G: nxcg.Graph, *, sort_edges: bool = False) -> nx.Graph: """Convert a nx_cugraph graph to networkx graph. All edge and node attributes and ``G.graph`` properties are converted. @@ -471,6 +491,11 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: Parameters ---------- G : nx_cugraph.Graph + sort_edges : bool, default False + Whether to sort the edge data of the input graph by (src, dst) indices + before converting. This can be useful to convert to networkx graphs + that iterate over edges consistently since edges are stored in dicts + in the order they were added. Returns ------- @@ -482,6 +507,8 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: """ rv = G.to_networkx_class()() id_to_key = G.id_to_key + if sort_edges: + G._sort_edge_indices() node_values = G.node_values node_masks = G.node_masks @@ -500,19 +527,20 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: dst_indices = G.dst_indices edge_values = G.edge_values edge_masks = G.edge_masks - if edge_values and not G.is_directed(): + if not G.is_directed(): # Only add upper triangle of the adjacency matrix so we don't double-add edges mask = src_indices <= dst_indices src_indices = src_indices[mask] dst_indices = dst_indices[mask] - edge_values = {k: v[mask] for k, v in edge_values.items()} + if edge_values: + edge_values = {k: v[mask] for k, v in edge_values.items()} if edge_masks: edge_masks = {k: v[mask] for k, v in edge_masks.items()} - src_indices = row_iter = src_indices.tolist() - dst_indices = col_iter = dst_indices.tolist() + src_indices = src_iter = src_indices.tolist() + dst_indices = dst_iter = dst_indices.tolist() if id_to_key is not None: - row_iter = map(id_to_key.__getitem__, src_indices) - col_iter = map(id_to_key.__getitem__, dst_indices) + src_iter = map(id_to_key.__getitem__, src_indices) + dst_iter = map(id_to_key.__getitem__, dst_indices) if G.is_multigraph() and (G.edge_keys is not None or G.edge_indices is not None): if G.edge_keys is not None: edge_keys = G.edge_keys @@ -520,14 +548,14 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: edge_keys = G.edge_indices.tolist() if edge_values: full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) - rv.add_edges_from(zip(row_iter, col_iter, edge_keys, full_edge_dicts)) + rv.add_edges_from(zip(src_iter, dst_iter, edge_keys, full_edge_dicts)) else: - rv.add_edges_from(zip(row_iter, col_iter, edge_keys)) + rv.add_edges_from(zip(src_iter, dst_iter, edge_keys)) elif edge_values: full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) - rv.add_edges_from(zip(row_iter, col_iter, full_edge_dicts)) + rv.add_edges_from(zip(src_iter, dst_iter, full_edge_dicts)) else: - rv.add_edges_from(zip(row_iter, col_iter)) + rv.add_edges_from(zip(src_iter, dst_iter)) rv.graph.update(G.graph) return rv diff --git a/python/nx-cugraph/nx_cugraph/convert_matrix.py b/python/nx-cugraph/nx_cugraph/convert_matrix.py new file mode 100644 index 000000000..6c8b8fb4a --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/convert_matrix.py @@ -0,0 +1,146 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np + +from .generators._utils import _create_using_class +from .utils import index_dtype, networkx_algorithm + +__all__ = [ + "from_pandas_edgelist", + "from_scipy_sparse_array", +] + + +@networkx_algorithm +def from_pandas_edgelist( + df, + source="source", + target="target", + edge_attr=None, + create_using=None, + edge_key=None, +): + """cudf.DataFrame inputs also supported.""" + graph_class, inplace = _create_using_class(create_using) + src_array = df[source].to_numpy() + dst_array = df[target].to_numpy() + # Renumber step 0: node keys + nodes = np.unique(np.concatenate([src_array, dst_array])) + N = nodes.size + kwargs = {} + if N > 0 and ( + nodes[0] != 0 + or nodes[N - 1] != N - 1 + or ( + nodes.dtype.kind not in {"i", "u"} + and not (nodes == np.arange(N, dtype=np.int64)).all() + ) + ): + # We need to renumber indices--np.searchsorted to the rescue! + kwargs["id_to_key"] = nodes.tolist() + src_indices = cp.array(np.searchsorted(nodes, src_array), index_dtype) + dst_indices = cp.array(np.searchsorted(nodes, dst_array), index_dtype) + else: + src_indices = cp.array(src_array) + dst_indices = cp.array(dst_array) + + if not graph_class.is_directed(): + # Symmetrize the edges + mask = src_indices != dst_indices + if mask.all(): + mask = None + src_indices, dst_indices = ( + cp.hstack( + (src_indices, dst_indices[mask] if mask is not None else dst_indices) + ), + cp.hstack( + (dst_indices, src_indices[mask] if mask is not None else src_indices) + ), + ) + + if edge_attr is not None: + # Additional columns requested for edge data + if edge_attr is True: + attr_col_headings = df.columns.difference({source, target}).to_list() + elif isinstance(edge_attr, (list, tuple)): + attr_col_headings = edge_attr + else: + attr_col_headings = [edge_attr] + if len(attr_col_headings) == 0: + raise nx.NetworkXError( + "Invalid edge_attr argument: No columns found with name: " + f"{attr_col_headings}" + ) + try: + edge_values = { + key: cp.array(val.to_numpy()) + for key, val in df[attr_col_headings].items() + } + except (KeyError, TypeError) as exc: + raise nx.NetworkXError(f"Invalid edge_attr argument: {edge_attr}") from exc + + if not graph_class.is_directed(): + # Symmetrize the edges + edge_values = { + key: cp.hstack((val, val[mask] if mask is not None else val)) + for key, val in edge_values.items() + } + kwargs["edge_values"] = edge_values + + if graph_class.is_multigraph() and edge_key is not None: + try: + edge_keys = df[edge_key].to_list() + except (KeyError, TypeError) as exc: + raise nx.NetworkXError( + f"Invalid edge_key argument: {edge_key}" + ) from exc + if not graph_class.is_directed(): + # Symmetrize the edges + edge_keys = cp.hstack( + (edge_keys, edge_keys[mask] if mask is not None else edge_keys) + ) + kwargs["edge_keys"] = edge_keys + + G = graph_class.from_coo(N, src_indices, dst_indices, **kwargs) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def from_scipy_sparse_array( + A, parallel_edges=False, create_using=None, edge_attribute="weight" +): + graph_class, inplace = _create_using_class(create_using) + m, n = A.shape + if m != n: + raise nx.NetworkXError(f"Adjacency matrix not square: nx,ny={A.shape}") + if A.format != "coo": + A = A.tocoo() + if A.dtype.kind in {"i", "u"} and graph_class.is_multigraph() and parallel_edges: + src_indices = cp.array(np.repeat(A.row, A.data), index_dtype) + dst_indices = cp.array(np.repeat(A.col, A.data), index_dtype) + weight = cp.empty(src_indices.size, A.data.dtype) + weight[:] = 1 + else: + src_indices = cp.array(A.row, index_dtype) + dst_indices = cp.array(A.col, index_dtype) + weight = cp.array(A.data) + G = graph_class.from_coo( + n, src_indices, dst_indices, edge_values={"weight": weight} + ) + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/generators/__init__.py b/python/nx-cugraph/nx_cugraph/generators/__init__.py new file mode 100644 index 000000000..c1834a4de --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .classic import * +from .community import * +from .small import * +from .social import * diff --git a/python/nx-cugraph/nx_cugraph/generators/_utils.py b/python/nx-cugraph/nx_cugraph/generators/_utils.py new file mode 100644 index 000000000..e38ace5b2 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/_utils.py @@ -0,0 +1,136 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import operator as op + +import cupy as cp +import networkx as nx + +import nx_cugraph as nxcg + +from ..utils import index_dtype + +# 3.2.1 fixed some issues in generators that occur in 3.2 and earlier +_IS_NX32_OR_LESS = (nxver := nx.__version__)[:3] <= "3.2" and ( + len(nxver) <= 3 or nxver[3] != "." and not nxver[3].isdigit() +) + + +def _ensure_int(n): + """Ensure n is integral.""" + return op.index(n) + + +def _ensure_nonnegative_int(n): + """Ensure n is a nonnegative integer.""" + n = op.index(n) + if n < 0: + raise nx.NetworkXError(f"Negative number of nodes not valid: {n}") + return n + + +def _complete_graph_indices(n): + all_indices = cp.indices((n, n), dtype=index_dtype) + src_indices = all_indices[0].ravel() + dst_indices = all_indices[1].ravel() + del all_indices + mask = src_indices != dst_indices + return (src_indices[mask], dst_indices[mask]) + + +def _common_small_graph(n, nodes, create_using, *, allow_directed=True): + """Create a "common graph" for small n. + + n == 0: empty graph + n == 1: empty graph + n == 2: complete graph + n > 2: undefined + """ + graph_class, inplace = _create_using_class(create_using) + if not allow_directed and graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if n < 2: + G = graph_class.from_coo( + n, cp.empty(0, index_dtype), cp.empty(0, index_dtype), id_to_key=nodes + ) + else: + G = graph_class.from_coo( + n, + cp.arange(2, dtype=index_dtype), + cp.array([1, 0], index_dtype), + id_to_key=nodes, + ) + if inplace: + return create_using._become(G) + return G + + +def _create_using_class(create_using, *, default=nxcg.Graph): + """Handle ``create_using`` argument and return a Graph type from nx_cugraph.""" + inplace = False + if create_using is None: + G = default() + elif isinstance(create_using, type): + G = create_using() + elif not hasattr(create_using, "is_directed") or not hasattr( + create_using, "is_multigraph" + ): + raise TypeError("create_using is not a valid graph type or instance") + elif not isinstance(create_using, nxcg.Graph): + raise NotImplementedError( + f"create_using with object of type {type(create_using)} is not supported " + "by the cugraph backend; only nx_cugraph.Graph objects are allowed." + ) + else: + inplace = True + G = create_using + G.clear() + if not isinstance(G, nxcg.Graph): + if G.is_multigraph(): + if G.is_directed(): + graph_class = nxcg.MultiDiGraph + else: + graph_class = nxcg.MultiGraph + elif G.is_directed(): + graph_class = nxcg.DiGraph + else: + graph_class = nxcg.Graph + if G.__class__ not in {nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph}: + raise NotImplementedError( + f"create_using with type {type(G)} is not supported by the cugraph " + "backend; only standard networkx or nx_cugraph Graph objects are " + "allowed (but not customized subclasses derived from them)." + ) + else: + graph_class = G.__class__ + return graph_class, inplace + + +def _number_and_nodes(n_and_nodes): + n, nodes = n_and_nodes + try: + n = op.index(n) + except TypeError: + n = len(nodes) + if n < 0: + raise nx.NetworkXError(f"Negative number of nodes not valid: {n}") + if not isinstance(nodes, list): + nodes = list(nodes) + if not nodes: + return (n, None) + if nodes[0] == 0 and nodes[n - 1] == n - 1: + try: + if nodes == list(range(n)): + return (n, None) + except Exception: + pass + return (n, nodes) diff --git a/python/nx-cugraph/nx_cugraph/generators/classic.py b/python/nx-cugraph/nx_cugraph/generators/classic.py new file mode 100644 index 000000000..b196c2323 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/classic.py @@ -0,0 +1,423 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import itertools +from numbers import Integral + +import cupy as cp +import networkx as nx +import numpy as np + +import nx_cugraph as nxcg + +from ..utils import _get_int_dtype, index_dtype, networkx_algorithm, nodes_or_number +from ._utils import ( + _IS_NX32_OR_LESS, + _common_small_graph, + _complete_graph_indices, + _create_using_class, + _ensure_int, + _ensure_nonnegative_int, + _number_and_nodes, +) + +__all__ = [ + "barbell_graph", + "circular_ladder_graph", + "complete_graph", + "complete_multipartite_graph", + "cycle_graph", + "empty_graph", + "ladder_graph", + "lollipop_graph", + "null_graph", + "path_graph", + "star_graph", + "tadpole_graph", + "trivial_graph", + "turan_graph", + "wheel_graph", +] + +concat = itertools.chain.from_iterable + + +@networkx_algorithm +def barbell_graph(m1, m2, create_using=None): + # Like two complete graphs and a path_graph + m1 = _ensure_nonnegative_int(m1) + if m1 < 2: + raise nx.NetworkXError("Invalid graph description, m1 should be >=2") + m2 = _ensure_nonnegative_int(m2) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_bell1, dst_bell1 = _complete_graph_indices(m1) + src_bell2 = src_bell1 + (m1 + m2) + dst_bell2 = dst_bell1 + (m1 + m2) + if m2 == 0: + src_bar = cp.array([m1 - 1, m1], index_dtype) + dst_bar = cp.array([m1, m1 - 1], index_dtype) + else: + src_bar = cp.arange(2 * m1 - 1, 2 * m1 + 2 * m2 + 1, dtype=index_dtype) // 2 + dst_bar = ( + cp.arange(m1 - 1, m1 + m2 + 1, dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + src_indices = cp.hstack((src_bell1, src_bar, src_bell2)) + dst_indices = cp.hstack((dst_bell1, dst_bar, dst_bell2)) + G = graph_class.from_coo(2 * m1 + m2, src_indices, dst_indices) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def circular_ladder_graph(n, create_using=None): + return _ladder_graph(n, create_using, is_circular=True) + + +@nodes_or_number(0) +@networkx_algorithm +def complete_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + if n < 3: + return _common_small_graph(n, nodes, create_using) + graph_class, inplace = _create_using_class(create_using) + src_indices, dst_indices = _complete_graph_indices(n) + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def complete_multipartite_graph(*subset_sizes): + if not subset_sizes: + return nxcg.Graph() + try: + subset_sizes = [_ensure_int(size) for size in subset_sizes] + except TypeError: + subsets = [list(subset) for subset in subset_sizes] + subset_sizes = [len(subset) for subset in subsets] + nodes = list(concat(subsets)) + else: + subsets = nodes = None + try: + subset_sizes = [_ensure_nonnegative_int(size) for size in subset_sizes] + except nx.NetworkXError: + if _IS_NX32_OR_LESS: + raise NotImplementedError("Negative number of nodes is not supported") + raise + L1 = [] + L2 = [] + total = 0 + for size in subset_sizes: + all_indices = cp.indices((total, size), dtype=index_dtype) + L1.append(all_indices[0].ravel()) + L2.append(all_indices[1].ravel() + total) + total += size + src_indices = cp.hstack(L1 + L2) + dst_indices = cp.hstack(L2 + L1) + subsets_array = cp.array( + np.repeat( + np.arange(len(subset_sizes), dtype=_get_int_dtype(len(subset_sizes) - 1)), + subset_sizes, + ) + ) + return nxcg.Graph.from_coo( + subsets_array.size, + src_indices, + dst_indices, + node_values={"subset": subsets_array}, + id_to_key=nodes, + ) + + +@nodes_or_number(0) +@networkx_algorithm +def cycle_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if n == 1: + src_indices = cp.zeros(1, index_dtype) + dst_indices = cp.zeros(1, index_dtype) + elif n == 2 and graph_class.is_multigraph() and not graph_class.is_directed(): + # This is kind of a peculiar edge case + src_indices = cp.array([0, 0, 1, 1], index_dtype) + dst_indices = cp.array([1, 1, 0, 0], index_dtype) + elif n < 3: + return _common_small_graph(n, nodes, create_using) + elif graph_class.is_directed(): + src_indices = cp.arange(n, dtype=index_dtype) + dst_indices = cp.arange(1, n + 1, dtype=index_dtype) + dst_indices[-1] = 0 + else: + src_indices = cp.arange(2 * n, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange(n, dtype=index_dtype)[:, None] + cp.array([-1, 1], index_dtype) + ).ravel() + dst_indices[0] = n - 1 + dst_indices[-1] = 0 + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@nodes_or_number(0) +@networkx_algorithm +def empty_graph(n=0, create_using=None, default=nx.Graph): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using, default=default) + G = graph_class.from_coo( + n, cp.empty(0, index_dtype), cp.empty(0, index_dtype), id_to_key=nodes + ) + if inplace: + return create_using._become(G) + return G + + +def _ladder_graph(n, create_using, *, is_circular=False): + # Like path path_graph with extra arange, and middle link missing + n = _ensure_nonnegative_int(n) + if n < 2: + if not is_circular: + return _common_small_graph(2 * n, None, create_using, allow_directed=False) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if n == 1: + src_indices = cp.array([0, 1, 0, 1], index_dtype) + dst_indices = cp.array([0, 0, 1, 1], index_dtype) + nodes = None + elif graph_class.is_multigraph(): + src_indices = cp.array([0, 0, 1, 1], index_dtype) + dst_indices = cp.array([1, 1, 0, 0], index_dtype) + nodes = [0, -1] + else: + src_indices = cp.array([0, 1], index_dtype) + dst_indices = cp.array([1, 0], index_dtype) + nodes = [0, -1] + G = graph_class.from_coo(2, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + path_src = cp.arange(1, 2 * n - 1, dtype=index_dtype) // 2 + path_dst = ( + cp.arange(n, dtype=index_dtype)[:, None] + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + srcs = [path_src, path_src + n, cp.arange(2 * n, dtype=index_dtype)] + dsts = [ + path_dst, + path_dst + n, + cp.arange(n, 2 * n, dtype=index_dtype), + cp.arange(0, n, dtype=index_dtype), + ] + if is_circular and (n > 2 or graph_class.is_multigraph()): + srcs.append(cp.array([0, n - 1, n, 2 * n - 1], index_dtype)) + dsts.append(cp.array([n - 1, 0, 2 * n - 1, n], index_dtype)) + src_indices = cp.hstack(srcs) + dst_indices = cp.hstack(dsts) + G = graph_class.from_coo(2 * n, src_indices, dst_indices) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def ladder_graph(n, create_using=None): + return _ladder_graph(n, create_using) + + +@nodes_or_number([0, 1]) +@networkx_algorithm +def lollipop_graph(m, n, create_using=None): + # Like complete_graph then path_graph + orig_m, unused_nodes_m = m + orig_n, unused_nodes_n = n + m, m_nodes = _number_and_nodes(m) + if m < 2: + raise nx.NetworkXError( + "Invalid description: m should indicate at least 2 nodes" + ) + n, n_nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + msrc_indices, mdst_indices = _complete_graph_indices(m) + nsrc_indices = cp.arange(2 * m - 1, 2 * m + 2 * n - 1, dtype=index_dtype) // 2 + ndst_indices = ( + cp.arange(m - 1, m + n, dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + src_indices = cp.hstack((msrc_indices, nsrc_indices)) + dst_indices = cp.hstack((mdst_indices, ndst_indices)) + if isinstance(orig_m, Integral) and isinstance(orig_n, Integral): + nodes = None + else: + nodes = list(range(m)) if m_nodes is None else m_nodes + nodes.extend(range(n) if n_nodes is None else n_nodes) + if len(set(nodes)) != len(nodes): + raise nx.NetworkXError("Nodes must be distinct in containers m and n") + G = graph_class.from_coo(m + n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def null_graph(create_using=None): + return _common_small_graph(0, None, create_using) + + +@nodes_or_number(0) +@networkx_algorithm +def path_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.arange(n - 1, dtype=index_dtype) + dst_indices = cp.arange(1, n, dtype=index_dtype) + elif n < 3: + return _common_small_graph(n, nodes, create_using) + else: + src_indices = cp.arange(1, 2 * n - 1, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange(n, dtype=index_dtype)[:, None] + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@nodes_or_number(0) +@networkx_algorithm +def star_graph(n, create_using=None): + orig_n, orig_nodes = n + n, nodes = _number_and_nodes(n) + # star_graph behaves differently whether the input was an int or iterable + if isinstance(orig_n, Integral): + if nodes is not None: + nodes.append(n) + n += 1 + if n < 3: + return _common_small_graph(n, nodes, create_using, allow_directed=False) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + flat = cp.zeros(n - 1, index_dtype) + ramp = cp.arange(1, n, dtype=index_dtype) + src_indices = cp.hstack((flat, ramp)) + dst_indices = cp.hstack((ramp, flat)) + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@nodes_or_number([0, 1]) +@networkx_algorithm +def tadpole_graph(m, n, create_using=None): + orig_m, unused_nodes_m = m + orig_n, unused_nodes_n = n + m, m_nodes = _number_and_nodes(m) + if m < 2: + raise nx.NetworkXError( + "Invalid description: m should indicate at least 2 nodes" + ) + n, n_nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if isinstance(orig_m, Integral) and isinstance(orig_n, Integral): + nodes = None + else: + nodes = list(range(m)) if m_nodes is None else m_nodes + nodes.extend(range(n) if n_nodes is None else n_nodes) + if m == 2 and not graph_class.is_multigraph(): + src_indices = cp.arange(1, 2 * (m + n) - 1, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange((m + n), dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + else: + src_indices = cp.arange(2 * (m + n), dtype=index_dtype) // 2 + dst_indices = ( + cp.arange((m + n), dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel() + dst_indices[0] = m - 1 + dst_indices[-1] = 0 + G = graph_class.from_coo(m + n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def trivial_graph(create_using=None): + return _common_small_graph(1, None, create_using) + + +@networkx_algorithm +def turan_graph(n, r): + if not 1 <= r <= n: + raise nx.NetworkXError("Must satisfy 1 <= r <= n") + n_div_r, n_mod_r = divmod(n, r) + partitions = [n_div_r] * (r - n_mod_r) + [n_div_r + 1] * n_mod_r + return complete_multipartite_graph(*partitions) + + +@nodes_or_number(0) +@networkx_algorithm +def wheel_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if n < 2: + G = graph_class.from_coo( + n, cp.empty(0, index_dtype), cp.empty(0, index_dtype), id_to_key=nodes + ) + else: + # Like star_graph + flat = cp.zeros(n - 1, index_dtype) + ramp = cp.arange(1, n, dtype=index_dtype) + # Like cycle_graph + if n < 3: + src_indices = cp.empty(0, index_dtype) + dst_indices = cp.empty(0, index_dtype) + elif n > 3: + src_indices = cp.arange(2, 2 * n, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange(1, n, dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel() + dst_indices[-1] = 1 + dst_indices[0] = n - 1 + elif graph_class.is_multigraph(): + src_indices = cp.array([1, 1, 2, 2], index_dtype) + dst_indices = cp.array([2, 2, 1, 1], index_dtype) + else: + src_indices = cp.array([1, 2], index_dtype) + dst_indices = cp.array([2, 1], index_dtype) + src_indices = cp.hstack((flat, ramp, src_indices)) + dst_indices = cp.hstack((ramp, flat, dst_indices)) + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/generators/community.py b/python/nx-cugraph/nx_cugraph/generators/community.py new file mode 100644 index 000000000..e5cb03e8c --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/community.py @@ -0,0 +1,45 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp + +import nx_cugraph as nxcg + +from ..utils import networkx_algorithm +from ._utils import ( + _common_small_graph, + _complete_graph_indices, + _ensure_int, + _ensure_nonnegative_int, +) + +__all__ = [ + "caveman_graph", +] + + +@networkx_algorithm +def caveman_graph(l, k): # noqa: E741 + l = _ensure_int(l) # noqa: E741 + k = _ensure_int(k) + N = _ensure_nonnegative_int(k * l) + if l == 0 or k < 1: + return _common_small_graph(N, None, None) + k = _ensure_nonnegative_int(k) + src_clique, dst_clique = _complete_graph_indices(k) + src_cliques = [src_clique] + dst_cliques = [dst_clique] + src_cliques.extend(src_clique + i * k for i in range(1, l)) + dst_cliques.extend(dst_clique + i * k for i in range(1, l)) + src_indices = cp.hstack(src_cliques) + dst_indices = cp.hstack(dst_cliques) + return nxcg.Graph.from_coo(l * k, src_indices, dst_indices) diff --git a/python/nx-cugraph/nx_cugraph/generators/small.py b/python/nx-cugraph/nx_cugraph/generators/small.py new file mode 100644 index 000000000..b9a189c31 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/small.py @@ -0,0 +1,622 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx + +import nx_cugraph as nxcg + +from ..utils import index_dtype, networkx_algorithm +from ._utils import _IS_NX32_OR_LESS, _create_using_class + +__all__ = [ + "bull_graph", + "chvatal_graph", + "cubical_graph", + "desargues_graph", + "diamond_graph", + "dodecahedral_graph", + "frucht_graph", + "heawood_graph", + "house_graph", + "house_x_graph", + "icosahedral_graph", + "krackhardt_kite_graph", + "moebius_kantor_graph", + "octahedral_graph", + "pappus_graph", + "petersen_graph", + "sedgewick_maze_graph", + "tetrahedral_graph", + "truncated_cube_graph", + "truncated_tetrahedron_graph", + "tutte_graph", +] + + +@networkx_algorithm +def bull_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array([0, 0, 1, 1, 1, 2, 2, 2, 3, 4], index_dtype) + dst_indices = cp.array([1, 2, 0, 2, 3, 0, 1, 4, 1, 2], index_dtype) + G = graph_class.from_coo(5, src_indices, dst_indices, name="Bull Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def chvatal_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, + 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, + 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 4, 6, 9, 0, 2, 5, 7, 1, 3, 6, 8, 2, 4, 7, 9, 0, 3, 5, 8, 1, 4, 10, 11, + 0, 2, 10, 11, 1, 3, 8, 11, 2, 4, 7, 10, 0, 3, 10, 11, 5, 6, 8, 9, 5, 6, + 7, 9, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(12, src_indices, dst_indices, name="Chvatal Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def cubical_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array( + [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7], + index_dtype, + ) + dst_indices = cp.array( + [1, 3, 4, 0, 2, 7, 1, 3, 6, 0, 2, 5, 0, 5, 7, 3, 4, 6, 2, 5, 7, 1, 4, 6], + index_dtype, + ) + name = ("Platonic Cubical Graph",) if _IS_NX32_OR_LESS else "Platonic Cubical Graph" + G = graph_class.from_coo(8, src_indices, dst_indices, name=name) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def desargues_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 19, 0, 2, 16, 1, 3, 11, 2, 4, 14, 3, 5, 9, 0, 4, 6, 5, 7, 15, 6, 8, + 18, 7, 9, 13, 4, 8, 10, 9, 11, 19, 2, 10, 12, 11, 13, 17, 8, 12, 14, 3, + 13, 15, 6, 14, 16, 1, 15, 17, 12, 16, 18, 7, 17, 19, 0, 10, 18, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], + index_dtype, + ) + dst_indices_extra = cp.array( + [5, 16, 11, 14, 9, 0, 15, 18, 13, 4, 19, 2, 17, 8, 3, 6, 1, 12, 7, 10], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(20, src_indices, dst_indices, name="Desargues Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def diamond_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array([0, 0, 1, 1, 1, 2, 2, 2, 3, 3], index_dtype) + dst_indices = cp.array([1, 2, 0, 2, 3, 0, 1, 3, 1, 2], index_dtype) + G = graph_class.from_coo(4, src_indices, dst_indices, name="Diamond Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def dodecahedral_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 10, 19, 0, 2, 8, 1, 3, 6, 2, 4, 19, 3, 5, 17, 4, 6, 15, 2, 5, 7, 6, 8, + 14, 1, 7, 9, 8, 10, 13, 0, 9, 11, 10, 12, 18, 11, 13, 16, 9, 12, 14, 7, + 13, 15, 5, 14, 16, 12, 15, 17, 4, 16, 18, 11, 17, 19, 0, 3, 18, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], + index_dtype, + ) + dst_indices_extra = cp.array( + [10, 8, 6, 19, 17, 15, 2, 14, 1, 13, 0, 18, 16, 9, 7, 5, 12, 4, 11, 3], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(20, src_indices, dst_indices, name="Dodecahedral Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def frucht_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.array( + [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 8, 8, 10], + index_dtype, + ) + dst_indices = cp.array( + [1, 7, 2, 7, 3, 8, 4, 9, 5, 9, 6, 10, 0, 10, 11, 9, 11, 11], + index_dtype, + ) + else: + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, + 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 6, 7, 0, 2, 7, 1, 3, 8, 2, 4, 9, 3, 5, 9, 4, 6, 10, 0, 5, 10, 0, + 1, 11, 2, 9, 11, 3, 4, 8, 5, 6, 11, 7, 8, 10, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(12, src_indices, dst_indices, name="Frucht Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def heawood_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 13, 0, 2, 10, 1, 3, 7, 2, 4, 12, 3, 5, 9, 0, 4, 6, 5, 7, 11, 2, 6, + 8, 7, 9, 13, 4, 8, 10, 1, 9, 11, 6, 10, 12, 3, 11, 13, 0, 8, 12, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], + index_dtype, + ) + dst_indices_extra = cp.array( + [5, 10, 7, 12, 9, 0, 11, 2, 13, 4, 1, 6, 3, 8], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(14, src_indices, dst_indices, name="Heawood Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def house_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array([0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4], index_dtype) + dst_indices = cp.array([1, 2, 0, 3, 0, 3, 4, 1, 2, 4, 2, 3], index_dtype) + G = graph_class.from_coo(5, src_indices, dst_indices, name="House Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def house_x_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array( + [0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4], index_dtype + ) + dst_indices = cp.array( + [1, 2, 3, 0, 2, 3, 0, 1, 3, 4, 0, 1, 2, 4, 2, 3], index_dtype + ) + G = graph_class.from_coo( + 5, src_indices, dst_indices, name="House-with-X-inside Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def icosahedral_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, + 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, + 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 7, 8, 11, 0, 2, 5, 6, 8, 1, 3, 6, 8, 9, 2, 4, 6, 9, 10, 3, 5, 6, + 10, 11, 0, 1, 4, 6, 11, 1, 2, 3, 4, 5, 0, 8, 9, 10, 11, 0, 1, 2, 7, 9, 2, + 3, 7, 8, 10, 3, 4, 7, 9, 11, 0, 4, 5, 7, 10, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo( + 12, src_indices, dst_indices, name="Platonic Icosahedral Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def krackhardt_kite_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, + 5, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 3, 5, 0, 3, 4, 6, 0, 3, 5, 0, 1, 2, 4, 5, 6, 1, 3, 6, 0, 2, 3, 6, + 7, 1, 3, 4, 5, 7, 5, 6, 8, 7, 9, 8, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo( + 10, src_indices, dst_indices, name="Krackhardt Kite Social Network" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def moebius_kantor_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, + 14, 14, 15, 15, 15, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 15, 0, 2, 12, 1, 3, 7, 2, 4, 14, 3, 5, 9, 0, 4, 6, 5, 7, 11, 2, 6, + 8, 7, 9, 13, 4, 8, 10, 9, 11, 15, 6, 10, 12, 1, 11, 13, 8, 12, 14, 3, 13, + 15, 0, 10, 14, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], + index_dtype, + ) + dst_indices_extra = cp.array( + [5, 12, 7, 14, 9, 0, 11, 2, 13, 4, 15, 6, 1, 8, 3, 10], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(16, src_indices, dst_indices, name="Moebius-Kantor Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def octahedral_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array( + [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5], + index_dtype, + ) + dst_indices = cp.array( + [1, 2, 3, 4, 0, 2, 3, 5, 0, 1, 4, 5, 0, 1, 4, 5, 0, 2, 3, 5, 1, 2, 3, 4], + index_dtype, + ) + G = graph_class.from_coo( + 6, src_indices, dst_indices, name="Platonic Octahedral Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def pappus_graph(): + # This can also be defined w.r.t. LCF_graph + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 17, 0, 2, 8, 1, 3, 13, 2, 4, 10, 3, 5, 15, 0, 4, 6, 5, 7, 11, 6, 8, + 14, 1, 7, 9, 8, 10, 16, 3, 9, 11, 6, 10, 12, 11, 13, 17, 2, 12, 14, 7, + 13, 15, 4, 14, 16, 9, 15, 17, 0, 12, 16, + ], + index_dtype, + ) + # fmt: on + return nxcg.Graph.from_coo(18, src_indices, dst_indices, name="Pappus Graph") + + +@networkx_algorithm +def petersen_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 4, 5, 0, 2, 6, 1, 3, 7, 2, 4, 8, 0, 3, 9, 0, 7, 8, 1, 8, 9, 2, 5, 9, + 3, 5, 6, 4, 6, 7, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(10, src_indices, dst_indices, name="Petersen Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def sedgewick_maze_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.array([0, 0, 0, 1, 2, 3, 3, 4, 4, 4], index_dtype) + dst_indices = cp.array([2, 5, 7, 7, 6, 4, 5, 5, 6, 7], index_dtype) + else: + src_indices = cp.array( + [0, 0, 0, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 7], + index_dtype, + ) + dst_indices = cp.array( + [2, 5, 7, 7, 0, 6, 4, 5, 3, 5, 6, 7, 0, 3, 4, 2, 4, 0, 1, 4], + index_dtype, + ) + G = graph_class.from_coo(8, src_indices, dst_indices, name="Sedgewick Maze") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def tetrahedral_graph(create_using=None): + # This can also be defined w.r.t. complete_graph + graph_class, inplace = _create_using_class(create_using) + src_indices = cp.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3], index_dtype) + dst_indices = cp.array([1, 2, 3, 0, 2, 3, 0, 1, 3, 0, 1, 2], index_dtype) + name = ( + "Platonic Tetrahedral graph" + if _IS_NX32_OR_LESS + else "Platonic Tetrahedral Graph" + ) + G = graph_class.from_coo(4, src_indices, dst_indices, name=name) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def truncated_cube_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, + 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 4, 0, 11, 14, 0, 3, 4, 2, 6, 8, 0, 2, 5, 4, 16, 18, 3, 7, 8, 6, 10, + 12, 3, 6, 9, 8, 17, 20, 7, 11, 12, 1, 10, 14, 7, 10, 13, 12, 21, 22, 1, + 11, 15, 14, 19, 23, 5, 17, 18, 9, 16, 20, 5, 16, 19, 15, 18, 23, 9, 17, + 21, 13, 20, 22, 13, 21, 23, 15, 19, 22, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(24, src_indices, dst_indices, name="Truncated Cube Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def truncated_tetrahedron_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.array( + [0, 0, 0, 1, 1, 2, 3, 3, 4, 4, 5, 5, 6, 7, 8, 8, 9, 10], index_dtype + ) + dst_indices = cp.array( + [1, 2, 9, 2, 6, 3, 4, 11, 5, 11, 6, 7, 7, 8, 9, 10, 10, 11], index_dtype + ) + else: + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, + 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 9, 0, 2, 6, 0, 1, 3, 2, 4, 11, 3, 5, 11, 4, 6, 7, 1, 5, 7, 5, + 6, 8, 7, 9, 10, 0, 8, 10, 8, 9, 11, 3, 4, 10, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo( + 12, src_indices, dst_indices, name="Truncated Tetrahedron Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def tutte_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, + 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, + 26, 27, 27, 27, 28, 28, 28, 29, 29, 29, 30, 30, 30, 31, 31, 31, 32, 32, + 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 36, 36, 36, 37, 37, 37, 38, 38, + 38, 39, 39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, + 44, 45, 45, 45, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 3, 0, 4, 26, 0, 10, 11, 0, 18, 19, 1, 5, 33, 4, 6, 29, 5, 7, 27, 6, + 8, 14, 7, 9, 38, 8, 10, 37, 2, 9, 39, 2, 12, 39, 11, 13, 35, 12, 14, 15, + 7, 13, 34, 13, 16, 22, 15, 17, 44, 16, 18, 43, 3, 17, 45, 3, 20, 45, 19, + 21, 41, 20, 22, 23, 15, 21, 40, 21, 24, 27, 23, 25, 32, 24, 26, 31, 1, + 25, 33, 6, 23, 28, 27, 29, 32, 5, 28, 30, 29, 31, 33, 25, 30, 32, 24, 28, + 31, 4, 26, 30, 14, 35, 38, 12, 34, 36, 35, 37, 39, 9, 36, 38, 8, 34, 37, + 10, 11, 36, 22, 41, 44, 20, 40, 42, 41, 43, 45, 17, 42, 44, 16, 40, 43, + 18, 19, 42, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(46, src_indices, dst_indices, name="Tutte's Graph") + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/generators/social.py b/python/nx-cugraph/nx_cugraph/generators/social.py new file mode 100644 index 000000000..3c936d07a --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/social.py @@ -0,0 +1,294 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import numpy as np + +import nx_cugraph as nxcg + +from ..utils import index_dtype, networkx_algorithm + +__all__ = [ + "davis_southern_women_graph", + "florentine_families_graph", + "karate_club_graph", + "les_miserables_graph", +] + + +@networkx_algorithm +def davis_southern_women_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, + 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, + 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, + 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, + 16, 16, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 20, 21, 21, + 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, + 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, + 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, + 27, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 30, 30, 30, + 31, 31, 31, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 18, 19, 20, 21, 22, 23, 25, 26, 18, 19, 20, 22, 23, 24, 25, 19, 20, 21, + 22, 23, 24, 25, 26, 18, 20, 21, 22, 23, 24, 25, 20, 21, 22, 24, 20, 22, + 23, 25, 22, 23, 24, 25, 23, 25, 26, 22, 24, 25, 26, 24, 25, 26, 29, 25, + 26, 27, 29, 25, 26, 27, 29, 30, 31, 24, 25, 26, 27, 29, 30, 31, 23, 24, + 26, 27, 28, 29, 30, 31, 24, 25, 27, 28, 29, 25, 26, 26, 28, 26, 28, 0, 1, + 3, 0, 1, 2, 0, 1, 2, 3, 4, 5, 0, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 8, 0, 1, + 2, 3, 5, 6, 7, 13, 1, 2, 3, 4, 6, 8, 9, 12, 13, 14, 0, 1, 2, 3, 5, 6, 7, + 8, 9, 10, 11, 12, 14, 15, 0, 2, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 10, + 11, 12, 13, 14, 13, 14, 16, 17, 9, 10, 11, 12, 13, 14, 11, 12, 13, 11, + 12, 13, + ], + index_dtype, + ) + bipartite = cp.array( + [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, + ], + np.int8, + ) + women = [ + "Evelyn Jefferson", "Laura Mandeville", "Theresa Anderson", "Brenda Rogers", + "Charlotte McDowd", "Frances Anderson", "Eleanor Nye", "Pearl Oglethorpe", + "Ruth DeSand", "Verne Sanderson", "Myra Liddel", "Katherina Rogers", + "Sylvia Avondale", "Nora Fayette", "Helen Lloyd", "Dorothy Murchison", + "Olivia Carleton", "Flora Price", + ] + events = [ + "E1", "E2", "E3", "E4", "E5", "E6", "E7", "E8", "E9", "E10", "E11", "E12", + "E13", "E14", + ] + # fmt: on + return nxcg.Graph.from_coo( + 32, + src_indices, + dst_indices, + node_values={"bipartite": bipartite}, + id_to_key=women + events, + top=women, + bottom=events, + ) + + +@networkx_algorithm +def florentine_families_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 5, 6, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, + 9, 10, 10, 10, 11, 11, 11, 12, 12, 13, 13, 13, 13, 14, 14, 14, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 8, 5, 6, 8, 4, 8, 6, 10, 13, 2, 10, 13, 1, 1, 3, 7, 14, 6, 0, 1, 2, 11, + 12, 14, 12, 3, 4, 13, 8, 13, 14, 8, 9, 3, 4, 10, 11, 6, 8, 11, + ], + index_dtype, + ) + nodes = [ + "Acciaiuoli", "Albizzi", "Barbadori", "Bischeri", "Castellani", "Ginori", + "Guadagni", "Lamberteschi", "Medici", "Pazzi", "Peruzzi", "Ridolfi", + "Salviati", "Strozzi", "Tornabuoni" + ] + # fmt: on + return nxcg.Graph.from_coo(15, src_indices, dst_indices, id_to_key=nodes) + + +@networkx_algorithm +def karate_club_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, + 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 10, 10, 10, 11, 12, 12, 13, + 13, 13, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, 20, + 20, 21, 21, 22, 22, 23, 23, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, + 27, 27, 27, 27, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30, 31, 31, 31, + 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, + 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 17, 19, 21, 31, 0, 2, 3, 7, 13, + 17, 19, 21, 30, 0, 1, 3, 7, 8, 9, 13, 27, 28, 32, 0, 1, 2, 7, 12, 13, 0, + 6, 10, 0, 6, 10, 16, 0, 4, 5, 16, 0, 1, 2, 3, 0, 2, 30, 32, 33, 2, 33, + 0, 4, 5, 0, 0, 3, 0, 1, 2, 3, 33, 32, 33, 32, 33, 5, 6, 0, 1, 32, 33, 0, + 1, 33, 32, 33, 0, 1, 32, 33, 25, 27, 29, 32, 33, 25, 27, 31, 23, 24, 31, + 29, 33, 2, 23, 24, 33, 2, 31, 33, 23, 26, 32, 33, 1, 8, 32, 33, 0, 24, + 25, 28, 32, 33, 2, 8, 14, 15, 18, 20, 22, 23, 29, 30, 31, 33, 8, 9, 13, + 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30, 31, 32, + ], + index_dtype, + ) + weights = cp.array( + [ + 4, 5, 3, 3, 3, 3, 2, 2, 2, 3, 1, 3, 2, 2, 2, 2, 4, 6, 3, 4, 5, 1, 2, 2, + 2, 5, 6, 3, 4, 5, 1, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 5, 3, 3, + 3, 2, 5, 3, 2, 4, 4, 3, 2, 5, 3, 3, 4, 1, 2, 2, 3, 3, 3, 1, 3, 3, 5, 3, + 3, 3, 3, 2, 3, 4, 3, 3, 2, 1, 1, 2, 2, 2, 1, 3, 1, 2, 2, 2, 3, 5, 4, 3, + 5, 4, 2, 3, 2, 5, 2, 7, 4, 2, 2, 4, 3, 4, 2, 2, 2, 3, 4, 4, 2, 2, 3, 3, + 3, 2, 2, 7, 2, 4, 4, 2, 3, 3, 3, 1, 3, 2, 5, 4, 3, 4, 5, 4, 2, 3, 2, 4, + 2, 1, 1, 3, 4, 2, 4, 2, 2, 3, 4, 5, + ], + np.int8, + ) + # For now, cupy doesn't handle str dtypes and we primarily handle cupy arrays. + # We try to support numpy arrays for node values, so let's use numpy here. + clubs = np.array([ + "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", + "Mr. Hi", "Mr. Hi", "Officer", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", + "Officer", "Officer", "Mr. Hi", "Mr. Hi", "Officer", "Mr. Hi", "Officer", + "Mr. Hi", "Officer", "Officer", "Officer", "Officer", "Officer", "Officer", + "Officer", "Officer", "Officer", "Officer", "Officer", "Officer", + ]) + # fmt: on + return nxcg.Graph.from_coo( + 34, + src_indices, + dst_indices, + edge_values={"weight": weights}, + node_values={"club": clubs}, + name="Zachary's Karate Club", + ) + + +@networkx_algorithm +def les_miserables_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, + 10, 10, 10, 10, 11, 12, 12, 12, 12, 12, 12, 13, 13, 14, 14, 15, 15, 15, + 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, + 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 23, 23, 23, + 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, + 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, + 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, + 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, + 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 32, 33, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, + 35, 35, 35, 35, 35, 35, 35, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, + 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, + 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 42, 42, 42, 42, 42, + 42, 43, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 46, 46, 46, 46, 46, + 46, 46, 46, 46, 46, 46, 47, 47, 48, 48, 49, 49, 49, 49, 49, 49, 49, 49, + 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50, 51, 51, 51, 51, + 51, 51, 51, 52, 53, 53, 54, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 57, + 57, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59, 59, + 59, 59, 59, 60, 60, 61, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 63, 64, + 65, 65, 66, 66, 66, 67, 67, 67, 67, 67, 67, 67, 67, 67, 68, 69, 69, 69, + 69, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 71, + 71, 71, 71, 71, 71, 71, 71, 71, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73, + 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, + 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 74, 74, 75, 75, 75, 76, 76, + 76, 76, 76, 76, 76, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 25, 58, 70, 9, 15, 25, 31, 37, 39, 58, 59, 70, 73, 6, 17, 21, 24, 30, 31, + 35, 40, 46, 49, 55, 67, 8, 10, 12, 16, 27, 39, 42, 73, 34, 49, 23, 26, + 27, 29, 44, 71, 76, 2, 17, 21, 24, 30, 31, 35, 40, 46, 49, 55, 67, 73, + 70, 3, 10, 12, 16, 42, 73, 1, 15, 25, 31, 37, 59, 70, 3, 8, 12, 16, 42, + 73, 62, 3, 8, 10, 16, 42, 73, 14, 31, 13, 31, 1, 9, 24, 25, 37, 39, 58, + 59, 70, 73, 3, 8, 10, 12, 42, 73, 2, 6, 21, 24, 30, 31, 35, 40, 46, 49, + 67, 34, 39, 45, 49, 51, 58, 70, 71, 72, 73, 75, 62, 62, 2, 6, 17, 24, 25, + 30, 31, 35, 40, 46, 49, 55, 67, 62, 5, 26, 27, 29, 44, 71, 76, 2, 6, 15, + 17, 21, 30, 31, 35, 39, 40, 46, 49, 55, 67, 73, 0, 1, 9, 15, 21, 37, 46, + 49, 58, 59, 70, 5, 23, 27, 29, 44, 71, 76, 3, 5, 23, 26, 29, 39, 44, 48, + 58, 65, 69, 70, 71, 73, 76, 36, 39, 60, 73, 5, 23, 26, 27, 44, 71, 76, 2, + 6, 17, 21, 24, 31, 35, 40, 46, 49, 67, 1, 2, 6, 9, 13, 14, 17, 21, 24, + 30, 35, 37, 39, 40, 46, 49, 53, 55, 59, 67, 70, 73, 62, 73, 4, 18, 45, + 47, 49, 51, 73, 2, 6, 17, 21, 24, 30, 31, 40, 55, 67, 28, 1, 9, 15, 25, + 31, 39, 58, 59, 70, 73, 73, 1, 3, 15, 18, 24, 27, 28, 31, 37, 58, 59, 69, + 70, 72, 73, 74, 75, 2, 6, 17, 21, 24, 30, 31, 35, 46, 49, 55, 67, 53, 3, + 8, 10, 12, 16, 73, 73, 5, 23, 26, 27, 29, 71, 76, 18, 34, 49, 51, 2, 6, + 17, 21, 24, 25, 30, 31, 40, 49, 61, 34, 58, 27, 73, 2, 4, 6, 17, 18, 21, + 24, 25, 30, 31, 34, 40, 45, 46, 51, 66, 70, 71, 73, 56, 62, 73, 18, 34, + 45, 49, 52, 57, 73, 51, 31, 41, 73, 2, 6, 21, 24, 31, 35, 40, 50, 62, 73, + 51, 66, 0, 1, 15, 18, 25, 27, 37, 39, 47, 70, 73, 1, 9, 15, 25, 31, 37, + 39, 70, 73, 28, 73, 46, 11, 19, 20, 22, 32, 50, 56, 63, 64, 73, 62, 62, + 27, 69, 49, 57, 70, 2, 6, 17, 21, 24, 30, 31, 35, 40, 73, 27, 39, 65, 73, + 0, 1, 7, 9, 15, 18, 25, 27, 31, 37, 39, 49, 58, 59, 66, 73, 5, 18, 23, + 26, 27, 29, 44, 49, 76, 18, 39, 73, 1, 3, 6, 8, 10, 12, 15, 16, 18, 24, + 27, 28, 31, 33, 34, 37, 38, 39, 42, 43, 48, 49, 50, 51, 54, 56, 58, 59, + 60, 62, 68, 69, 70, 72, 74, 75, 39, 73, 18, 39, 73, 5, 23, 26, 27, 29, + 44, 71, + ], + index_dtype, + ) + weights = cp.array( + [ + 2, 1, 2, 3, 4, 1, 1, 6, 2, 1, 2, 6, 1, 4, 5, 6, 4, 3, 5, 1, 5, 2, 1, 1, + 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 3, 4, 3, 4, 4, 4, 3, 4, 9, 12, 10, 6, 5, + 3, 7, 1, 5, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 3, 1, 1, 1, 3, 1, 3, 2, 2, 2, + 2, 3, 3, 1, 1, 2, 2, 2, 2, 2, 3, 2, 3, 2, 4, 1, 1, 1, 4, 1, 1, 2, 4, 1, + 1, 2, 2, 2, 2, 2, 5, 9, 13, 15, 5, 6, 1, 5, 2, 5, 2, 3, 1, 1, 21, 2, 4, + 1, 1, 2, 31, 1, 2, 1, 6, 12, 13, 17, 1, 6, 7, 2, 5, 2, 9, 1, 3, 1, 3, 3, + 4, 5, 3, 3, 4, 4, 10, 1, 15, 17, 6, 7, 3, 6, 5, 1, 7, 1, 4, 4, 2, 1, 1, + 1, 1, 1, 1, 5, 2, 1, 3, 4, 3, 3, 3, 4, 4, 3, 1, 3, 4, 3, 4, 5, 3, 2, 2, + 1, 2, 1, 3, 9, 4, 2, 1, 3, 8, 4, 5, 3, 4, 3, 3, 4, 3, 6, 5, 6, 6, 2, 1, + 5, 1, 1, 2, 1, 5, 5, 1, 2, 2, 6, 7, 7, 2, 1, 1, 1, 3, 1, 4, 2, 1, 1, 1, + 1, 1, 1, 1, 1, 3, 1, 1, 12, 9, 2, 1, 3, 1, 2, 3, 1, 1, 2, 1, 1, 2, 6, 3, + 4, 1, 1, 1, 1, 2, 5, 1, 1, 2, 1, 1, 1, 6, 5, 1, 1, 1, 1, 1, 1, 5, 1, 17, + 1, 1, 5, 7, 5, 5, 5, 5, 3, 2, 1, 2, 1, 2, 1, 2, 2, 3, 2, 2, 3, 1, 4, 3, + 4, 3, 3, 4, 3, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1, + 1, 1, 5, 5, 21, 9, 7, 5, 1, 4, 12, 2, 1, 1, 6, 1, 2, 1, 19, 6, 8, 3, 2, + 9, 2, 6, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 10, 3, 1, 1, 1, 1, + 1, 4, 2, 2, 1, 1, 1, 13, 7, 2, 1, 2, 1, 1, 2, 1, 1, 1, 3, 1, 3, 1, 2, 1, + 1, 1, 8, 10, 1, 1, 5, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 3, 4, 2, 1, 1, 2, 1, + 2, 1, 2, 3, 2, 6, 1, 3, 4, 1, 3, 1, 1, 5, 5, 2, 13, 1, 1, 12, 4, 1, 3, 4, + 3, 3, 4, 1, 3, 2, 1, 1, 1, 2, 1, 2, 3, 2, 1, 2, 31, 4, 9, 8, 1, 1, 2, 1, + 1, 17, 3, 1, 1, 19, 3, 2, 1, 3, 7, 1, 1, 5, 1, 3, 12, 1, 2, 3, 1, 2, 1, + 1, 3, 3, 4, 3, 4, 4, 3, 3, + ], + np.int8, + ) + nodes = [ + "Anzelma", "Babet", "Bahorel", "Bamatabois", "BaronessT", "Blacheville", + "Bossuet", "Boulatruelle", "Brevet", "Brujon", "Champmathieu", + "Champtercier", "Chenildieu", "Child1", "Child2", "Claquesous", + "Cochepaille", "Combeferre", "Cosette", "Count", "CountessDeLo", + "Courfeyrac", "Cravatte", "Dahlia", "Enjolras", "Eponine", "Fameuil", + "Fantine", "Fauchelevent", "Favourite", "Feuilly", "Gavroche", "Geborand", + "Gervais", "Gillenormand", "Grantaire", "Gribier", "Gueulemer", "Isabeau", + "Javert", "Joly", "Jondrette", "Judge", "Labarre", "Listolier", + "LtGillenormand", "Mabeuf", "Magnon", "Marguerite", "Marius", + "MlleBaptistine", "MlleGillenormand", "MlleVaubois", "MmeBurgon", "MmeDeR", + "MmeHucheloup", "MmeMagloire", "MmePontmercy", "MmeThenardier", + "Montparnasse", "MotherInnocent", "MotherPlutarch", "Myriel", "Napoleon", + "OldMan", "Perpetue", "Pontmercy", "Prouvaire", "Scaufflaire", "Simplice", + "Thenardier", "Tholomyes", "Toussaint", "Valjean", "Woman1", "Woman2", + "Zephine", + ] + # fmt: on + return nxcg.Graph.from_coo( + 77, src_indices, dst_indices, edge_values={"weight": weights}, id_to_key=nodes + ) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index a7b88b72e..fd0b1483d 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -65,6 +65,7 @@ def key(testpath): no_weights = "weighted implementation not currently supported" no_multigraph = "multigraphs not currently supported" louvain_different = "Louvain may be different due to RNG" + no_string_dtype = "string edge values not currently supported" xfail = {} @@ -187,6 +188,40 @@ def key(testpath): xfail[ key("test_louvain.py:test_threshold") ] = "Louvain does not support seed parameter" + if nxver.major == 3 and nxver.minor >= 2: + xfail.update( + { + key( + "test_convert_pandas.py:TestConvertPandas." + "test_from_edgelist_multi_attr_incl_target" + ): no_string_dtype, + key( + "test_convert_pandas.py:TestConvertPandas." + "test_from_edgelist_multidigraph_and_edge_attr" + ): no_string_dtype, + key( + "test_convert_pandas.py:TestConvertPandas." + "test_from_edgelist_int_attr_name" + ): no_string_dtype, + } + ) + if nxver.minor == 2: + different_iteration_order = "Different graph data iteration order" + xfail.update( + { + key( + "test_cycles.py:TestMinimumCycleBasis." + "test_gh6787_and_edge_attribute_names" + ): different_iteration_order, + key( + "test_euler.py:TestEulerianCircuit." + "test_eulerian_circuit_cycle" + ): different_iteration_order, + key( + "test_gml.py:TestGraph.test_special_float_label" + ): different_iteration_order, + } + ) for item in items: kset = set(item.keywords) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_generators.py b/python/nx-cugraph/nx_cugraph/tests/test_generators.py new file mode 100644 index 000000000..511f8dcd8 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_generators.py @@ -0,0 +1,277 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import numpy as np +import pytest +from packaging.version import parse + +import nx_cugraph as nxcg + +nxver = parse(nx.__version__) + + +def assert_graphs_equal(Gnx, Gcg): + assert isinstance(Gnx, nx.Graph) + assert isinstance(Gcg, nxcg.Graph) + assert Gnx.number_of_nodes() == Gcg.number_of_nodes() + assert Gnx.number_of_edges() == Gcg.number_of_edges() + assert Gnx.is_directed() == Gcg.is_directed() + assert Gnx.is_multigraph() == Gcg.is_multigraph() + G = nxcg.to_networkx(Gcg) + rv = nx.utils.graphs_equal(G, Gnx) + if not rv: + print("GRAPHS ARE NOT EQUAL!") + assert sorted(G) == sorted(Gnx) + assert sorted(G._adj) == sorted(Gnx._adj) + assert sorted(G._node) == sorted(Gnx._node) + for k in sorted(G._adj): + print(k, sorted(G._adj[k]), sorted(Gnx._adj[k])) + print(nx.to_scipy_sparse_array(G).todense()) + print(nx.to_scipy_sparse_array(Gnx).todense()) + print(G.graph) + print(Gnx.graph) + assert rv + + +if nxver.major == 3 and nxver.minor < 2: + pytest.skip("Need NetworkX >=3.2 to test generators", allow_module_level=True) + + +def compare(name, create_using, *args, is_vanilla=False): + exc1 = exc2 = None + func = getattr(nx, name) + if isinstance(create_using, nxcg.Graph): + nx_create_using = nxcg.to_networkx(create_using) + elif isinstance(create_using, type) and issubclass(create_using, nxcg.Graph): + nx_create_using = create_using.to_networkx_class() + elif isinstance(create_using, nx.Graph): + nx_create_using = create_using.copy() + else: + nx_create_using = create_using + try: + if is_vanilla: + G = func(*args) + else: + G = func(*args, create_using=nx_create_using) + except Exception as exc: + exc1 = exc + try: + if is_vanilla: + Gcg = func(*args, backend="cugraph") + else: + Gcg = func(*args, create_using=create_using, backend="cugraph") + except ZeroDivisionError: + raise + except NotImplementedError as exc: + if name in {"complete_multipartite_graph"}: # nx.__version__[:3] <= "3.2" + return + exc2 = exc + except Exception as exc: + if exc1 is None: # pragma: no cover (debug) + raise + exc2 = exc + if exc1 is not None or exc2 is not None: + assert type(exc1) is type(exc2) + else: + assert_graphs_equal(G, Gcg) + + +N = list(range(-1, 5)) +CREATE_USING = [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] +COMPLETE_CREATE_USING = [ + nx.Graph, + nx.DiGraph, + nx.MultiGraph, + nx.MultiDiGraph, + nxcg.Graph, + nxcg.DiGraph, + nxcg.MultiGraph, + nxcg.MultiDiGraph, + # These raise NotImplementedError + # nx.Graph(), + # nx.DiGraph(), + # nx.MultiGraph(), + # nx.MultiDiGraph(), + nxcg.Graph(), + nxcg.DiGraph(), + nxcg.MultiGraph(), + nxcg.MultiDiGraph(), + None, + object, # Bad input + 7, # Bad input +] +GENERATORS_NOARG = [ + # classic + "null_graph", + "trivial_graph", + # small + "bull_graph", + "chvatal_graph", + "cubical_graph", + "desargues_graph", + "diamond_graph", + "dodecahedral_graph", + "frucht_graph", + "heawood_graph", + "house_graph", + "house_x_graph", + "icosahedral_graph", + "krackhardt_kite_graph", + "moebius_kantor_graph", + "octahedral_graph", + "petersen_graph", + "sedgewick_maze_graph", + "tetrahedral_graph", + "truncated_cube_graph", + "truncated_tetrahedron_graph", + "tutte_graph", +] +GENERATORS_NOARG_VANILLA = [ + # classic + "complete_multipartite_graph", + # small + "pappus_graph", + # social + "davis_southern_women_graph", + "florentine_families_graph", + "karate_club_graph", + "les_miserables_graph", +] +GENERATORS_N = [ + # classic + "circular_ladder_graph", + "complete_graph", + "cycle_graph", + "empty_graph", + "ladder_graph", + "path_graph", + "star_graph", + "wheel_graph", +] +GENERATORS_M_N = [ + # classic + "barbell_graph", + "lollipop_graph", + "tadpole_graph", + # bipartite + "complete_bipartite_graph", +] +GENERATORS_M_N_VANILLA = [ + # classic + "complete_multipartite_graph", + "turan_graph", + # community + "caveman_graph", +] + + +@pytest.mark.parametrize("name", GENERATORS_NOARG) +@pytest.mark.parametrize("create_using", COMPLETE_CREATE_USING) +def test_generator_noarg(name, create_using): + print(name, create_using, type(create_using)) + if isinstance(create_using, nxcg.Graph) and name in { + # fmt: off + "bull_graph", "chvatal_graph", "cubical_graph", "diamond_graph", + "house_graph", "house_x_graph", "icosahedral_graph", "krackhardt_kite_graph", + "octahedral_graph", "petersen_graph", "truncated_cube_graph", "tutte_graph", + # fmt: on + }: + # The _raise_on_directed decorator used in networkx doesn't like our graphs. + if create_using.is_directed(): + with pytest.raises(AssertionError): + compare(name, create_using) + else: + with pytest.raises(TypeError): + compare(name, create_using) + else: + compare(name, create_using) + + +@pytest.mark.parametrize("name", GENERATORS_NOARG_VANILLA) +def test_generator_noarg_vanilla(name): + print(name) + compare(name, None, is_vanilla=True) + + +@pytest.mark.parametrize("name", GENERATORS_N) +@pytest.mark.parametrize("n", N) +@pytest.mark.parametrize("create_using", CREATE_USING) +def test_generator_n(name, n, create_using): + print(name, n, create_using) + compare(name, create_using, n) + + +@pytest.mark.parametrize("name", GENERATORS_N) +@pytest.mark.parametrize("n", [1, 4]) +@pytest.mark.parametrize("create_using", COMPLETE_CREATE_USING) +def test_generator_n_complete(name, n, create_using): + print(name, n, create_using) + compare(name, create_using, n) + + +@pytest.mark.parametrize("name", GENERATORS_M_N) +@pytest.mark.parametrize("create_using", CREATE_USING) +@pytest.mark.parametrize("m", N) +@pytest.mark.parametrize("n", N) +def test_generator_m_n(name, create_using, m, n): + print(name, m, n, create_using) + compare(name, create_using, m, n) + + +@pytest.mark.parametrize("name", GENERATORS_M_N_VANILLA) +@pytest.mark.parametrize("m", N) +@pytest.mark.parametrize("n", N) +def test_generator_m_n_vanilla(name, m, n): + print(name, m, n) + compare(name, None, m, n, is_vanilla=True) + + +@pytest.mark.parametrize("name", GENERATORS_M_N) +@pytest.mark.parametrize("create_using", COMPLETE_CREATE_USING) +@pytest.mark.parametrize("m", [4]) +@pytest.mark.parametrize("n", [4]) +def test_generator_m_n_complete(name, create_using, m, n): + print(name, m, n, create_using) + compare(name, create_using, m, n) + + +@pytest.mark.parametrize("name", GENERATORS_M_N_VANILLA) +@pytest.mark.parametrize("m", [4]) +@pytest.mark.parametrize("n", [4]) +def test_generator_m_n_complete_vanilla(name, m, n): + print(name, m, n) + compare(name, None, m, n, is_vanilla=True) + + +def test_bad_lollipop_graph(): + compare("lollipop_graph", None, [0, 1], [1, 2]) + + +def test_can_convert_karate_club(): + # Karate club graph has string node values. + # This really tests conversions, but it's here so we can use `assert_graphs_equal`. + G = nx.karate_club_graph() + G.add_node(0, foo="bar") # string dtype with a mask + G.add_node(1, object=object()) # haha + Gcg = nxcg.from_networkx(G, preserve_all_attrs=True) + assert_graphs_equal(G, Gcg) + Gnx = nxcg.to_networkx(Gcg) + assert nx.utils.graphs_equal(G, Gnx) + assert isinstance(Gcg.node_values["club"], np.ndarray) + assert Gcg.node_values["club"].dtype.kind == "U" + assert isinstance(Gcg.node_values["foo"], np.ndarray) + assert isinstance(Gcg.node_masks["foo"], np.ndarray) + assert Gcg.node_values["foo"].dtype.kind == "U" + assert isinstance(Gcg.node_values["object"], np.ndarray) + assert Gcg.node_values["object"].dtype.kind == "O" + assert isinstance(Gcg.node_masks["object"], np.ndarray) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_utils.py b/python/nx-cugraph/nx_cugraph/tests/test_utils.py new file mode 100644 index 000000000..fdd0c9199 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_utils.py @@ -0,0 +1,87 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import pytest + +from nx_cugraph.utils import _get_int_dtype + + +def test_get_int_dtype(): + uint8 = np.dtype(np.uint8) + uint16 = np.dtype(np.uint16) + uint32 = np.dtype(np.uint32) + uint64 = np.dtype(np.uint64) + # signed + cur = np.iinfo(np.int8) + for val in [cur.min, cur.min + 1, -1, 0, 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) == np.int8 + assert _get_int_dtype(val, signed=True) == np.int8 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) == np.uint8 + assert _get_int_dtype(val + 1, unsigned=True) == np.uint8 + prev = cur + cur = np.iinfo(np.int16) + for val in [cur.min, cur.min + 1, prev.min - 1, prev.max + 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) != prev.dtype + assert _get_int_dtype(val, signed=True) == np.int16 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) in {uint8, uint16} + assert _get_int_dtype(val + 1, unsigned=True) in {uint8, uint16} + prev = cur + cur = np.iinfo(np.int32) + for val in [cur.min, cur.min + 1, prev.min - 1, prev.max + 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) != prev.dtype + assert _get_int_dtype(val, signed=True) == np.int32 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) in {uint16, uint32} + assert _get_int_dtype(val + 1, unsigned=True) in {uint16, uint32} + prev = cur + cur = np.iinfo(np.int64) + for val in [cur.min, cur.min + 1, prev.min - 1, prev.max + 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) != prev.dtype + assert _get_int_dtype(val, signed=True) == np.int64 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) in {uint32, uint64} + assert _get_int_dtype(val + 1, unsigned=True) in {uint32, uint64} + with pytest.raises(ValueError, match="Value is too"): + _get_int_dtype(cur.min - 1, signed=True) + with pytest.raises(ValueError, match="Value is too"): + _get_int_dtype(cur.max + 1, signed=True) + + # unsigned + cur = np.iinfo(np.uint8) + for val in [0, 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) == (np.uint8 if val > 1 else np.int8) + assert _get_int_dtype(val, unsigned=True) == np.uint8 + assert _get_int_dtype(cur.max + 1) == np.int16 + cur = np.iinfo(np.uint16) + for val in [cur.max - 1, cur.max]: + assert _get_int_dtype(val, unsigned=True) == np.uint16 + assert _get_int_dtype(cur.max + 1) == np.int32 + cur = np.iinfo(np.uint32) + for val in [cur.max - 1, cur.max]: + assert _get_int_dtype(val, unsigned=True) == np.uint32 + assert _get_int_dtype(cur.max + 1) == np.int64 + cur = np.iinfo(np.uint64) + for val in [cur.max - 1, cur.max]: + assert _get_int_dtype(val, unsigned=True) == np.uint64 + with pytest.raises(ValueError, match="Value is incompatible"): + _get_int_dtype(cur.min - 1, unsigned=True) + with pytest.raises(ValueError, match="Value is too"): + _get_int_dtype(cur.max + 1, unsigned=True) + + # API + with pytest.raises(TypeError, match="incompatible"): + _get_int_dtype(7, signed=True, unsigned=True) + assert _get_int_dtype(7, signed=True, unsigned=False) == np.int8 + assert _get_int_dtype(7, signed=False, unsigned=True) == np.uint8 diff --git a/python/nx-cugraph/nx_cugraph/typing.py b/python/nx-cugraph/nx_cugraph/typing.py index d3045ab46..b419a9085 100644 --- a/python/nx-cugraph/nx_cugraph/typing.py +++ b/python/nx-cugraph/nx_cugraph/typing.py @@ -15,6 +15,9 @@ from collections.abc import Hashable from typing import TypeVar +import cupy as cp +import numpy as np + AttrKey = TypeVar("AttrKey", bound=Hashable) EdgeKey = TypeVar("EdgeKey", bound=Hashable) NodeKey = TypeVar("NodeKey", bound=Hashable) @@ -23,3 +26,8 @@ NodeValue = TypeVar("NodeValue") IndexValue = TypeVar("IndexValue") Dtype = TypeVar("Dtype") + + +class any_ndarray: + def __class_getitem__(cls, item): + return cp.ndarray[item] | np.ndarray[item] diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 72e4094b8..9683df5e7 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -12,13 +12,19 @@ # limitations under the License. from __future__ import annotations +import itertools import operator as op import sys from random import Random +from typing import SupportsIndex import cupy as cp +import numpy as np -__all__ = ["_groupby", "_seed_to_int"] +__all__ = ["index_dtype", "_groupby", "_seed_to_int", "_get_int_dtype"] + +# This may switch to np.uint32 at some point +index_dtype = np.int32 def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: @@ -58,3 +64,67 @@ def _seed_to_int(seed: int | Random | None) -> int: if isinstance(seed, Random): return seed.randint(0, sys.maxsize) return op.index(seed) # Ensure seed is integral + + +def _get_int_dtype( + val: SupportsIndex, *, signed: bool | None = None, unsigned: bool | None = None +): + """Determine the smallest integer dtype that can store the integer ``val``. + + If signed or unsigned are unspecified, then signed integers are preferred + unless the value can be represented by a smaller unsigned integer. + + Raises + ------ + ValueError : If the value cannot be represented with an int dtype. + """ + # This is similar in spirit to `np.min_scalar_type` + if signed is not None: + if unsigned is not None and (not signed) is (not unsigned): + raise TypeError( + f"signed (={signed}) and unsigned (={unsigned}) keyword arguments " + "are incompatible." + ) + signed = bool(signed) + unsigned = not signed + elif unsigned is not None: + unsigned = bool(unsigned) + signed = not unsigned + + val = op.index(val) # Ensure val is integral + if val < 0: + if unsigned: + raise ValueError(f"Value is incompatible with unsigned int: {val}.") + signed = True + unsigned = False + + if signed is not False: + # Number of bytes (and a power of two) + signed_nbytes = (val + (val < 0)).bit_length() // 8 + 1 + signed_nbytes = next( + filter( + signed_nbytes.__le__, + itertools.accumulate(itertools.repeat(2), op.mul, initial=1), + ) + ) + if unsigned is not False: + # Number of bytes (and a power of two) + unsigned_nbytes = (val.bit_length() + 7) // 8 + unsigned_nbytes = next( + filter( + unsigned_nbytes.__le__, + itertools.accumulate(itertools.repeat(2), op.mul, initial=1), + ) + ) + if signed is None and unsigned is None: + # Prefer signed int if same size + signed = signed_nbytes <= unsigned_nbytes + + if signed: + dtype_string = f"i{signed_nbytes}" + else: + dtype_string = f"u{unsigned_nbytes}" + try: + return np.dtype(dtype_string) + except TypeError as exc: + raise ValueError("Value is too large to store as integer: {val}") from exc diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 9fec8fa02..3d3029da2 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -160,7 +160,8 @@ ignore = [ # "SIM300", # Yoda conditions are discouraged, use ... instead (Note: we're not this picky) # "SIM401", # Use dict.get ... instead of if-else-block (Note: if-else better for coverage and sometimes clearer) # "TRY004", # Prefer `TypeError` exception for invalid type (Note: good advice, but not worth the nuisance) - # "TRY200", # Use `raise from` to specify exception cause (Note: sometimes okay to raise original exception) + "B904", # Bare `raise` inside exception clause (like TRY200; sometimes okay) + "TRY200", # Use `raise from` to specify exception cause (Note: sometimes okay to raise original exception) # Intentionally ignored "A003", # Class attribute ... is shadowing a python builtin From 01656828baca9c9ef4ea1df402604acd422e8e53 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 31 Oct 2023 17:50:52 -0500 Subject: [PATCH 283/384] nx-cugraph: add CC for undirected graphs to fix k-truss (#3965) Fixes #3963 and add `connected_components`, `is_connected`, `node_connected_component`, and `number_connected_components`. Also updated `_groupby` to handle groups that are not consecutive integers starting with 0. Also, `plc.weakly_connected_components` does not handle isolated nodes well, and I needed to handle this at the Python layer as was done in #3897 Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3965 --- python/nx-cugraph/_nx_cugraph/__init__.py | 8 ++ .../nx_cugraph/algorithms/__init__.py | 3 +- .../algorithms/community/louvain.py | 2 +- .../algorithms/components/__init__.py | 13 ++ .../algorithms/components/connected.py | 130 ++++++++++++++++++ .../nx-cugraph/nx_cugraph/algorithms/core.py | 10 ++ python/nx-cugraph/nx_cugraph/classes/graph.py | 5 + python/nx-cugraph/nx_cugraph/interface.py | 9 ++ .../nx_cugraph/tests/test_ktruss.py | 30 ++++ .../nx-cugraph/nx_cugraph/utils/decorators.py | 5 +- python/nx-cugraph/nx_cugraph/utils/misc.py | 44 ++++-- python/nx-cugraph/pyproject.toml | 1 + 12 files changed, 243 insertions(+), 17 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/components/connected.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_ktruss.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index af1df0464..8ef976aab 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -38,6 +38,7 @@ "complete_bipartite_graph", "complete_graph", "complete_multipartite_graph", + "connected_components", "cubical_graph", "cycle_graph", "davis_southern_women_graph", @@ -56,6 +57,7 @@ "house_x_graph", "icosahedral_graph", "in_degree_centrality", + "is_connected", "is_isolate", "isolates", "k_truss", @@ -66,7 +68,9 @@ "lollipop_graph", "louvain_communities", "moebius_kantor_graph", + "node_connected_component", "null_graph", + "number_connected_components", "number_of_isolates", "number_of_selfloops", "octahedral_graph", @@ -91,6 +95,10 @@ "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", + "k_truss": ( + "Currently raises `NotImplementedError` for graphs with more than one connected\n" + "component when k >= 3. We expect to fix this soon." + ), "louvain_communities": "`seed` parameter is currently ignored.", # END: extra_docstrings }, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 69feb8f64..87b1967fa 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,8 +10,9 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import bipartite, centrality, community +from . import bipartite, centrality, community, components from .bipartite import complete_bipartite_graph from .centrality import * +from .components import * from .core import * from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index 62261d109..45a3429d2 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -62,7 +62,7 @@ def louvain_communities( resolution=resolution, do_expensive_check=False, ) - groups = _groupby(clusters, vertices) + groups = _groupby(clusters, vertices, groups_are_canonical=True) rv = [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix isolates = _isolates(G) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py new file mode 100644 index 000000000..26816ef36 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .connected import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py new file mode 100644 index 000000000..41f3457d5 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py @@ -0,0 +1,130 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import itertools + +import cupy as cp +import networkx as nx +import pylibcugraph as plc + +from nx_cugraph.convert import _to_undirected_graph +from nx_cugraph.utils import _groupby, networkx_algorithm, not_implemented_for + +from ..isolate import _isolates + +__all__ = [ + "number_connected_components", + "connected_components", + "is_connected", + "node_connected_component", +] + + +@not_implemented_for("directed") +@networkx_algorithm +def number_connected_components(G): + return sum(1 for _ in connected_components(G)) + # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL + # G = _to_undirected_graph(G) + # unused_node_ids, labels = plc.weakly_connected_components( + # resource_handle=plc.ResourceHandle(), + # graph=G._get_plc_graph(), + # offsets=None, + # indices=None, + # weights=None, + # labels=None, + # do_expensive_check=False, + # ) + # return cp.unique(labels).size + + +@number_connected_components._can_run +def _(G): + # NetworkX <= 3.2.1 does not check directedness for us + try: + return not G.is_directed() + except Exception: + return False + + +@not_implemented_for("directed") +@networkx_algorithm +def connected_components(G): + G = _to_undirected_graph(G) + if G.src_indices.size == 0: + # TODO: PLC doesn't handle empty graphs (or isolated nodes) gracefully! + return [{key} for key in G._nodeiter_to_iter(range(len(G)))] + node_ids, labels = plc.weakly_connected_components( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + offsets=None, + indices=None, + weights=None, + labels=None, + do_expensive_check=False, + ) + groups = _groupby(labels, node_ids) + it = (G._nodearray_to_set(connected_ids) for connected_ids in groups.values()) + # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix + isolates = _isolates(G) + if isolates.size > 0: + isolates = isolates[isolates > node_ids.max()] + if isolates.size > 0: + it = itertools.chain( + it, ({node} for node in G._nodearray_to_list(isolates)) + ) + return it + + +@not_implemented_for("directed") +@networkx_algorithm +def is_connected(G): + G = _to_undirected_graph(G) + if len(G) == 0: + raise nx.NetworkXPointlessConcept( + "Connectivity is undefined for the null graph." + ) + for community in connected_components(G): + return len(community) == len(G) + raise RuntimeError # pragma: no cover + # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL + # unused_node_ids, labels = plc.weakly_connected_components( + # resource_handle=plc.ResourceHandle(), + # graph=G._get_plc_graph(), + # offsets=None, + # indices=None, + # weights=None, + # labels=None, + # do_expensive_check=False, + # ) + # return labels.size == len(G) and cp.unique(labels).size == 1 + + +@not_implemented_for("directed") +@networkx_algorithm +def node_connected_component(G, n): + # We could also do plain BFS from n + G = _to_undirected_graph(G) + node_id = n if G.key_to_id is None else G.key_to_id[n] + node_ids, labels = plc.weakly_connected_components( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + offsets=None, + indices=None, + weights=None, + labels=None, + do_expensive_check=False, + ) + indices = cp.nonzero(node_ids == node_id)[0] + if indices.size == 0: + return {n} + return G._nodearray_to_set(node_ids[labels == labels[indices[0]]]) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index 33e797935..2219388bc 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -24,6 +24,10 @@ @not_implemented_for("multigraph") @networkx_algorithm def k_truss(G, k): + """ + Currently raises `NotImplementedError` for graphs with more than one connected + component when k >= 3. We expect to fix this soon. + """ if is_nx := isinstance(G, nx.Graph): G = nxcg.from_networkx(G, preserve_all_attrs=True) if nxcg.number_of_selfloops(G) > 0: @@ -31,6 +35,7 @@ def k_truss(G, k): "Input graph has self loops which is not permitted; " "Consider using G.remove_edges_from(nx.selfloop_edges(G))." ) + # TODO: create renumbering helper function(s) if k < 3: # k-truss graph is comprised of nodes incident on k-2 triangles, so k<3 is a @@ -49,6 +54,11 @@ def k_truss(G, k): # Renumber step 1: edge values (no changes needed) edge_values = {key: val.copy() for key, val in G.edge_values.items()} edge_masks = {key: val.copy() for key, val in G.edge_masks.items()} + elif (ncc := nxcg.number_connected_components(G)) > 1: + raise NotImplementedError( + "nx_cugraph.k_truss does not yet work on graphs with more than one " + f"connected component (this graph has {ncc}). We expect to fix this soon." + ) else: edge_dtype = _get_int_dtype(G.src_indices.size - 1) edge_indices = cp.arange(G.src_indices.size, dtype=edge_dtype) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 2048c4c3d..23004651f 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -692,6 +692,11 @@ def _nodearray_to_list(self, node_ids: cp.ndarray[IndexValue]) -> list[NodeKey]: return node_ids.tolist() return list(self._nodeiter_to_iter(node_ids.tolist())) + def _nodearray_to_set(self, node_ids: cp.ndarray[IndexValue]) -> set[NodeKey]: + if self.key_to_id is None: + return set(node_ids.tolist()) + return set(self._nodeiter_to_iter(node_ids.tolist())) + def _nodearray_to_dict( self, values: cp.ndarray[NodeValue] ) -> dict[NodeKey, NodeValue]: diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index fd0b1483d..875f86210 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -223,11 +223,20 @@ def key(testpath): } ) + too_slow = "Too slow to run" + skip = { + key("test_tree_isomorphism.py:test_positive"): too_slow, + key("test_tree_isomorphism.py:test_negative"): too_slow, + } + for item in items: kset = set(item.keywords) for (test_name, keywords), reason in xfail.items(): if item.name == test_name and keywords.issubset(kset): item.add_marker(pytest.mark.xfail(reason=reason)) + for (test_name, keywords), reason in skip.items(): + if item.name == test_name and keywords.issubset(kset): + item.add_marker(pytest.mark.skip(reason=reason)) @classmethod def can_run(cls, name, args, kwargs): diff --git a/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py new file mode 100644 index 000000000..a3e4cee31 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py @@ -0,0 +1,30 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest + +import nx_cugraph as nxcg + + +@pytest.mark.parametrize( + "get_graph", [nx.florentine_families_graph, nx.les_miserables_graph] +) +def test_k_truss(get_graph): + Gnx = get_graph() + Gcg = nxcg.from_networkx(Gnx, preserve_all_attrs=True) + for k in range(10): + Hnx = nx.k_truss(Gnx, k) + Hcg = nxcg.k_truss(Gcg, k) + assert nx.utils.graphs_equal(Hnx, nxcg.to_networkx(Hcg)) + if Hnx.number_of_edges() == 0: + break diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py index 0f15d236e..0048aee51 100644 --- a/python/nx-cugraph/nx_cugraph/utils/decorators.py +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -13,6 +13,7 @@ from __future__ import annotations from functools import partial, update_wrapper +from textwrap import dedent from networkx.utils.decorators import nodes_or_number, not_implemented_for @@ -65,7 +66,9 @@ def __new__( ) instance.extra_params = extra_params # The docstring on our function is added to the NetworkX docstring. - instance.extra_doc = func.__doc__ + instance.extra_doc = ( + dedent(func.__doc__.lstrip("\n").rstrip()) if func.__doc__ else None + ) # Copy __doc__ from NetworkX if instance.name in _registered_algorithms: instance.__doc__ = _registered_algorithms[instance.name].__doc__ diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 9683df5e7..26f023bdc 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -21,40 +21,56 @@ import cupy as cp import numpy as np +try: + from itertools import pairwise # Python >=3.10 +except ImportError: + + def pairwise(it): + it = iter(it) + for prev in it: + for cur in it: + yield (prev, cur) + prev = cur + + __all__ = ["index_dtype", "_groupby", "_seed_to_int", "_get_int_dtype"] # This may switch to np.uint32 at some point index_dtype = np.int32 -def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: +def _groupby( + groups: cp.ndarray, values: cp.ndarray, groups_are_canonical: bool = False +) -> dict[int, cp.ndarray]: """Perform a groupby operation given an array of group IDs and array of values. Parameters ---------- groups : cp.ndarray Array that holds the group IDs. - Group IDs are assumed to be consecutive integers from 0. values : cp.ndarray Array of values to be grouped according to groups. Must be the same size as groups array. + groups_are_canonical : bool, default False + Whether the group IDs are consecutive integers beginning with 0. Returns ------- dict with group IDs as keys and cp.ndarray as values. """ - # It would actually be easy to support groups that aren't consecutive integers, - # but let's wait until we need it to implement it. - sorted_groups = cp.argsort(groups) - sorted_values = values[sorted_groups] - rv = {} - start = 0 - for i, end in enumerate( - [*(cp.nonzero(cp.diff(groups[sorted_groups]))[0] + 1).tolist(), groups.size] - ): - rv[i] = sorted_values[start:end] - start = end - return rv + if groups.size == 0: + return {} + sort_indices = cp.argsort(groups) + sorted_groups = groups[sort_indices] + sorted_values = values[sort_indices] + prepend = 1 if groups_are_canonical else sorted_groups[0] + 1 + left_bounds = cp.nonzero(cp.diff(sorted_groups, prepend=prepend))[0] + boundaries = pairwise(itertools.chain(left_bounds.tolist(), [groups.size])) + if groups_are_canonical: + it = enumerate(boundaries) + else: + it = zip(sorted_groups[left_bounds].tolist(), boundaries) + return {group: sorted_values[start:end] for group, (start, end) in it} def _seed_to_int(seed: int | Random | None) -> int: diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 3d3029da2..7e51efd4f 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -218,6 +218,7 @@ ignore = [ # Allow assert, print, RNG, and no docstring "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"] "_nx_cugraph/__init__.py" = ["E501"] +"nx_cugraph/algorithms/**/*py" = ["D205", "D401"] # Allow flexible docstrings for algorithms [tool.ruff.flake8-annotations] mypy-init-return = true From a57e688e4e51e5aa2846b1efa98f9b4e506f05d1 Mon Sep 17 00:00:00 2001 From: Divye Gala Date: Wed, 1 Nov 2023 19:19:22 -0400 Subject: [PATCH 284/384] Setup Consistent Nightly Versions for Pip and Conda (#3933) See https://github.com/rapidsai/rmm/pull/1347 Authors: - Divye Gala (https://github.com/divyegala) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3933 --- VERSION | 1 + python/nx-cugraph/nx_cugraph/VERSION | 1 + python/nx-cugraph/nx_cugraph/__init__.py | 2 +- python/nx-cugraph/nx_cugraph/_version.py | 26 ++++++++++++++++++++++++ python/nx-cugraph/pyproject.toml | 5 ++++- python/nx-cugraph/setup.py | 7 +++++-- 6 files changed, 38 insertions(+), 4 deletions(-) create mode 100644 VERSION create mode 120000 python/nx-cugraph/nx_cugraph/VERSION create mode 100644 python/nx-cugraph/nx_cugraph/_version.py diff --git a/VERSION b/VERSION new file mode 100644 index 000000000..a193fff41 --- /dev/null +++ b/VERSION @@ -0,0 +1 @@ +23.12.00 diff --git a/python/nx-cugraph/nx_cugraph/VERSION b/python/nx-cugraph/nx_cugraph/VERSION new file mode 120000 index 000000000..d62dc733e --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/VERSION @@ -0,0 +1 @@ +../../../VERSION \ No newline at end of file diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 25d442122..3a8f0996e 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -29,4 +29,4 @@ from . import algorithms from .algorithms import * -__version__ = "23.12.00" +from nx_cugraph._version import __git_commit__, __version__ diff --git a/python/nx-cugraph/nx_cugraph/_version.py b/python/nx-cugraph/nx_cugraph/_version.py new file mode 100644 index 000000000..868a2e194 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/_version.py @@ -0,0 +1,26 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("nx_cugraph").joinpath("VERSION").read_text().strip() +) +__git_commit__ = "" diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 7e51efd4f..f309f4797 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "nx-cugraph" -version = "23.12.00" +dynamic = ["version"] description = "cugraph backend for NetworkX" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -61,6 +61,9 @@ cugraph = "_nx_cugraph:get_info" [tool.setuptools] license-files = ["LICENSE"] +[tool.setuptools.dynamic] +version = {file = "nx_cugraph/VERSION"} + [tool.setuptools.packages.find] include = [ "nx_cugraph*", diff --git a/python/nx-cugraph/setup.py b/python/nx-cugraph/setup.py index 87c0e1064..c4ab53592 100644 --- a/python/nx-cugraph/setup.py +++ b/python/nx-cugraph/setup.py @@ -10,6 +10,9 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from setuptools import setup +from setuptools import find_packages, setup -setup() +packages = find_packages(include=["nx_cugraph*"]) +setup( + package_data={key: ["VERSION"] for key in packages}, +) From 885c064806823c4acaaaa76f0b27525281215abe Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 8 Nov 2023 10:51:33 -0600 Subject: [PATCH 285/384] Updates README file to include nx-cugraph user documentation, adds nx-cugraph to main README (#3984) * Updates the nx-cugraph README file to include nx-cugraph user documentation * Adds nx-cugraph and new News section to main README * Updates software strack diagram to remove DGL and PyG since they are not currently using PropertyGraph or cugraph-service Authors: - Rick Ratzel (https://github.com/rlratzel) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Brad Rees (https://github.com/BradReesWork) --- python/nx-cugraph/README.md | 135 +++++++++++++++++++++++++++++++----- 1 file changed, 119 insertions(+), 16 deletions(-) diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index ab267e5a7..148fb2f09 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -2,31 +2,134 @@ ## Description [RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends) -with minimal dependencies (`networkx`, `cupy`, and `pylibcugraph`) to run graph algorithms on the GPU. +to run supported algorithms with GPU acceleration. -### Contribute +## System Requirements -Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.12 -and [building from source](https://docs.rapids.ai/api/cugraph/stable/installation/source_build/), then build nx-cugraph in develop (i.e., editable) mode: -``` -$ ./build.sh nx-cugraph --pydevelop -``` +nx-cugraph requires the following: + + * NVIDIA GPU, Pascal architecture or later + * CUDA 11.2, 11.4, 11.5, 11.8, or 12.0 + * Python versions 3.9, 3.10, or 3.11 + * NetworkX >= version 3.2 -### Run tests +More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).. -Run nx-cugraph tests from `cugraph/python/nx-cugraph` directory: +## Installation + +nx-cugraph can be installed using either conda or pip. + +### conda ``` -$ pytest +conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph ``` -Run nx-cugraph benchmarks: +### pip ``` -$ pytest --bench +python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com ``` -Run networkx tests (requires networkx version 3.2): +Notes: + + * Nightly wheel builds will not be available until the 23.12 release, therefore the index URL for the stable release version is being used in the pip install command above. + * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start). + +## Enabling nx-cugraph + +NetworkX will use nx-cugraph as the graph analytics backend if any of the +following are are used: + +### `NETWORKX_AUTOMATIC_BACKENDS` environment variable. +The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports. +Set `NETWORKX_AUTOMATIC_BACKENDS=cugraph` to use nx-cugraph to GPU accelerate supported APIs with no code changes. +Example: ``` -$ ./run_nx_tests.sh +bash> NETWORKX_AUTOMATIC_BACKENDS=cugraph python my_networkx_script.py ``` -Additional arguments may be passed to pytest such as: + +### `backend=` keyword argument +To explicitly specify a particular backend for an API, use the `backend=` +keyword argument. This argument takes precedence over the +`NETWORKX_AUTOMATIC_BACKENDS` environment variable. This requires anyone +running code that uses the `backend=` keyword argument to have the specified +backend installed. + +Example: ``` -$ ./run_nx_tests.sh -x --sw -k betweenness +nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph") ``` + +### Type-based dispatching + +NetworkX also supports automatically dispatching to backends associated with +specific graph types. Like the `backend=` keyword argument example above, this +requires the user to write code for a specific backend, and therefore requires +the backend to be installed, but has the advantage of ensuring a particular +behavior without the potential for runtime conversions. + +To use type-based dispatching with nx-cugraph, the user must import the backend +directly in their code to access the utilities provided to create a Graph +instance specifically for the nx-cugraph backend. + +Example: +``` +import networkx as nx +import nx_cugraph as nxcg + +G = nx.Graph() +... +nxcg_G = nxcg.from_networkx(G) # conversion happens once here +nx.betweenness_centrality(nxcg_G, k=1000) # nxcg Graph type causes cugraph backend + # to be used, no conversion necessary +``` + +## Supported Algorithms + +The nx-cugraph backend to NetworkX connects +[pylibcugraph](../../readme_pages/pylibcugraph.md) (cuGraph's low-level python +interface to its CUDA-based graph analytics library) and +[CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's +familiar and easy-to-use API. + +Below is the list of algorithms (many listed using pylibcugraph names), +available today in pylibcugraph or implemented using CuPy, that are or will be +supported in nx-cugraph. + +| feature/algo | release/target version | +| ----- | ----- | +| analyze_clustering_edge_cut | ? | +| analyze_clustering_modularity | ? | +| analyze_clustering_ratio_cut | ? | +| balanced_cut_clustering | ? | +| betweenness_centrality | 23.10 | +| bfs | ? | +| core_number | ? | +| degree_centrality | 23.12 | +| ecg | ? | +| edge_betweenness_centrality | 23.10 | +| ego_graph | ? | +| eigenvector_centrality | 23.12 | +| get_two_hop_neighbors | ? | +| hits | 23.12 | +| in_degree_centrality | 23.12 | +| induced_subgraph | ? | +| jaccard_coefficients | ? | +| katz_centrality | 23.12 | +| k_core | ? | +| k_truss_subgraph | 23.12 | +| leiden | ? | +| louvain | 23.10 | +| node2vec | ? | +| out_degree_centrality | 23.12 | +| overlap_coefficients | ? | +| pagerank | 23.12 | +| personalized_pagerank | ? | +| sorensen_coefficients | ? | +| spectral_modularity_maximization | ? | +| sssp | 23.12 | +| strongly_connected_components | ? | +| triangle_count | ? | +| uniform_neighbor_sample | ? | +| uniform_random_walks | ? | +| weakly_connected_components | ? | + +To request nx-cugraph backend support for a NetworkX API that is not listed +above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph). From 72c5ca80a027d4e03c695a0da9130f9b7cb3450d Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 8 Nov 2023 11:07:29 -0600 Subject: [PATCH 286/384] Fixes typos, updates link to NX backend docs. (#3989) * Fixes typos (repeated word, extra periods) * Updates link to the current docs on NetworkX backends Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Ray Douglass (https://github.com/raydouglass) --- python/nx-cugraph/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index 148fb2f09..273a6112d 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -1,7 +1,7 @@ # nx-cugraph ## Description -[RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends) +[RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/utils.html#backends) to run supported algorithms with GPU acceleration. ## System Requirements @@ -13,7 +13,7 @@ nx-cugraph requires the following: * Python versions 3.9, 3.10, or 3.11 * NetworkX >= version 3.2 -More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).. +More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req). ## Installation @@ -35,7 +35,7 @@ Notes: ## Enabling nx-cugraph NetworkX will use nx-cugraph as the graph analytics backend if any of the -following are are used: +following are used: ### `NETWORKX_AUTOMATIC_BACKENDS` environment variable. The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports. From cc7b17ea527cf5472757a7a215cc14117bc73651 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Tue, 14 Nov 2023 09:29:03 -0600 Subject: [PATCH 287/384] Adds update-version.sh support for proper versioning to cu version suffix pacakges and nx-cugraph meta-data files. (#3994) Adds `update-version.sh` support for: * nx-cugraph meta-data files * `cugraph-pyg`, `cugraph-dgl` env yaml files * `*-cu11`, `*-cu12` wheel dependencies in dependencies.yaml Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Brad Rees (https://github.com/BradReesWork) - Ray Douglass (https://github.com/raydouglass) --- python/nx-cugraph/_nx_cugraph/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 8ef976aab..26638d1e7 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -136,6 +136,7 @@ def get_info(): return d +# FIXME: can this use the standard VERSION file and update mechanism? __version__ = "23.12.00" if __name__ == "__main__": From 5fb4d97abef1c758550e7fad1371b03735dd3303 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Tue, 14 Nov 2023 11:31:50 -0500 Subject: [PATCH 288/384] v24.02 Updates [skip ci] --- VERSION | 2 +- docs/cugraph/source/conf.py | 4 ++-- python/nx-cugraph/_nx_cugraph/__init__.py | 4 ++-- python/nx-cugraph/pyproject.toml | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/VERSION b/VERSION index a193fff41..3c6c5e2b7 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -23.12.00 +24.02.00 diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 470086b4f..940d21442 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -77,9 +77,9 @@ # built documents. # # The short X.Y version. -version = '23.12' +version = '24.02' # The full version, including alpha/beta/rc tags. -release = '23.12.00' +release = '24.02.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 26638d1e7..2e5a16aa4 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -24,7 +24,7 @@ "backend_name": "cugraph", "project": "nx-cugraph", "package": "nx_cugraph", - "url": "https://github.com/rapidsai/cugraph/tree/branch-23.12/python/nx-cugraph", + "url": "https://github.com/rapidsai/cugraph/tree/branch-24.02/python/nx-cugraph", "short_summary": "GPU-accelerated backend.", # "description": "TODO", "functions": { @@ -137,7 +137,7 @@ def get_info(): # FIXME: can this use the standard VERSION file and update mechanism? -__version__ = "23.12.00" +__version__ = "24.02.00" if __name__ == "__main__": from pathlib import Path diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index f309f4797..b29578b03 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -33,7 +33,7 @@ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", "numpy>=1.21", - "pylibcugraph==23.12.*", + "pylibcugraph==24.2.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. [project.optional-dependencies] From 3f1619828998b460ed67845bd1538da5295f1b1c Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 20 Nov 2023 17:36:50 -0600 Subject: [PATCH 289/384] nx-cugraph: add SSSP (unweighted) (#3976) There are many more traversal algorithms to implement, but these get us started! Authors: - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3976 --- python/nx-cugraph/_nx_cugraph/__init__.py | 2 + .../nx_cugraph/algorithms/__init__.py | 3 +- .../algorithms/shortest_paths/__init__.py | 13 +++++ .../algorithms/shortest_paths/unweighted.py | 53 +++++++++++++++++++ python/nx-cugraph/nx_cugraph/classes/graph.py | 9 +++- python/nx-cugraph/nx_cugraph/interface.py | 2 + 6 files changed, 79 insertions(+), 3 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 26638d1e7..910db1bc3 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -79,6 +79,8 @@ "path_graph", "petersen_graph", "sedgewick_maze_graph", + "single_source_shortest_path_length", + "single_target_shortest_path_length", "star_graph", "tadpole_graph", "tetrahedral_graph", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 87b1967fa..32cd6f31a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,9 +10,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import bipartite, centrality, community, components +from . import bipartite, centrality, community, components, shortest_paths from .bipartite import complete_bipartite_graph from .centrality import * from .components import * from .core import * from .isolate import * +from .shortest_paths import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py new file mode 100644 index 000000000..b7d6b7421 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .unweighted import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py new file mode 100644 index 000000000..3413a637b --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py @@ -0,0 +1,53 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import index_dtype, networkx_algorithm + +__all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"] + + +@networkx_algorithm +def single_source_shortest_path_length(G, source, cutoff=None): + return _single_shortest_path_length(G, source, cutoff, "Source") + + +@networkx_algorithm +def single_target_shortest_path_length(G, target, cutoff=None): + return _single_shortest_path_length(G, target, cutoff, "Target") + + +def _single_shortest_path_length(G, source, cutoff, kind): + G = _to_graph(G) + if source not in G: + raise nx.NodeNotFound(f"{kind} {source} is not in G") + if G.src_indices.size == 0: + return {source: 0} + if cutoff is None: + cutoff = -1 + src_index = source if G.key_to_id is None else G.key_to_id[source] + distances, predecessors, node_ids = plc.bfs( + handle=plc.ResourceHandle(), + graph=G._get_plc_graph(switch_indices=kind == "Target"), + sources=cp.array([src_index], index_dtype), + direction_optimizing=False, # True for undirected only; what's recommended? + depth_limit=cutoff, + compute_predecessors=False, + do_expensive_check=False, + ) + mask = distances != np.iinfo(distances.dtype).max + return G._nodearrays_to_dict(node_ids[mask], distances[mask]) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 23004651f..fea318e03 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -559,6 +559,7 @@ def _get_plc_graph( edge_dtype: Dtype | None = None, *, store_transposed: bool = False, + switch_indices: bool = False, edge_array: cp.ndarray[EdgeValue] | None = None, ): if edge_array is not None: @@ -613,14 +614,18 @@ def _get_plc_graph( elif edge_array.dtype not in self._plc_allowed_edge_types: raise TypeError(edge_array.dtype) # Should we cache PLC graph? + src_indices = self.src_indices + dst_indices = self.dst_indices + if switch_indices: + src_indices, dst_indices = dst_indices, src_indices return plc.SGGraph( resource_handle=plc.ResourceHandle(), graph_properties=plc.GraphProperties( is_multigraph=self.is_multigraph(), is_symmetric=not self.is_directed(), ), - src_or_offset_array=self.src_indices, - dst_or_index_array=self.dst_indices, + src_or_offset_array=src_indices, + dst_or_index_array=dst_indices, weight_array=edge_array, store_transposed=store_transposed, renumber=False, diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 875f86210..8903fdc54 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -224,9 +224,11 @@ def key(testpath): ) too_slow = "Too slow to run" + maybe_oom = "out of memory in CI" skip = { key("test_tree_isomorphism.py:test_positive"): too_slow, key("test_tree_isomorphism.py:test_negative"): too_slow, + key("test_efficiency.py:TestEfficiency.test_using_ego_graph"): maybe_oom, } for item in items: From 7124054c5513b2b362444ccaedca393a09516425 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 21 Nov 2023 09:48:41 -0500 Subject: [PATCH 290/384] adding C/C++ API docs (#3938) adding C and C++ API Adding WholeGraph APIs Adding cugraph-ops APIs closes #3406 Authors: - Brad Rees (https://github.com/BradReesWork) - GALI PREM SAGAR (https://github.com/galipremsagar) - Ralph Liu (https://github.com/nv-rliu) - Don Acosta (https://github.com/acostadon) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3938 --- .gitignore | 4 +- docs/cugraph/Makefile | 2 +- .../cugraph-ops/bipartite_operators.rst | 16 -- .../api_docs/cugraph-ops/c_cpp/index.rst | 3 + .../api_docs/cugraph-ops/fg_operators.rst | 83 ------- .../api_docs/cugraph-ops/graph_types.rst | 33 --- .../source/api_docs/cugraph-ops/index.rst | 11 +- .../api_docs/cugraph-ops/mfg_operators.rst | 31 --- .../cugraph-ops/{ => python}/dimenet.rst | 4 +- .../cugraph-ops/python/graph_types.rst | 34 +++ .../api_docs/cugraph-ops/python/index.rst | 13 + .../api_docs/cugraph-ops/python/operators.rst | 93 +++++++ .../cugraph-ops/{ => python}/pytorch.rst | 22 +- .../api_docs/cugraph-ops/static_operators.rst | 16 -- .../api_docs/cugraph-pyg/cugraph_pyg.rst | 4 +- .../source/api_docs/cugraph_c/c_and_cpp.rst | 4 - .../source/api_docs/cugraph_c/centrality.rst | 51 ++++ .../source/api_docs/cugraph_c/community.rst | 63 +++++ .../source/api_docs/cugraph_c/core.rst | 21 ++ .../source/api_docs/cugraph_c/index.rst | 16 ++ .../source/api_docs/cugraph_c/labeling.rst | 20 ++ .../source/api_docs/cugraph_c/sampling.rst | 37 +++ .../source/api_docs/cugraph_c/similarity.rst | 25 ++ .../source/api_docs/cugraph_c/traversal.rst | 30 +++ docs/cugraph/source/api_docs/index.rst | 33 ++- .../service/cugraph_service_client.rst | 2 +- .../service/cugraph_service_server.rst | 2 +- .../source/api_docs/wholegraph/index.rst | 11 + .../wholegraph/libwholegraph/index.rst | 228 ++++++++++++++++++ .../wholegraph/pylibwholegraph/index.rst | 38 +++ docs/cugraph/source/basics/cugraph_intro.md | 9 +- docs/cugraph/source/conf.py | 5 +- .../source/graph_support/algorithms.md | 12 +- .../graph_support/algorithms/Centrality.md | 12 +- .../graph_support/algorithms/Similarity.md | 6 +- docs/cugraph/source/index.rst | 10 +- .../source/wholegraph/basics/index.rst | 11 + .../wholegraph/basics/wholegraph_intro.md | 135 +++++++++++ .../wholememory_implementation_details.md | 58 +++++ .../wholegraph/basics/wholememory_intro.md | 123 ++++++++++ .../imgs/device_chunked_wholememory_step1.png | Bin 0 -> 23136 bytes .../imgs/device_chunked_wholememory_step2.png | Bin 0 -> 28201 bytes .../device_continuous_wholememory_step1.png | Bin 0 -> 21107 bytes .../device_continuous_wholememory_step2.png | Bin 0 -> 26434 bytes .../imgs/distributed_wholememory.png | Bin 0 -> 23138 bytes .../wholegraph/imgs/general_wholememory.png | Bin 0 -> 14073 bytes .../imgs/host_mapped_wholememory_step1.png | Bin 0 -> 21733 bytes .../imgs/host_mapped_wholememory_step2.png | Bin 0 -> 32110 bytes .../wholegraph/imgs/wholememory_tensor.png | Bin 0 -> 40367 bytes docs/cugraph/source/wholegraph/index.rst | 14 ++ .../wholegraph/installation/container.md | 29 +++ .../installation/getting_wholegraph.md | 48 ++++ .../source/wholegraph/installation/index.rst | 9 + .../wholegraph/installation/source_build.md | 187 ++++++++++++++ 54 files changed, 1375 insertions(+), 243 deletions(-) delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst rename docs/cugraph/source/api_docs/cugraph-ops/{ => python}/dimenet.rst (89%) create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/python/index.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst rename docs/cugraph/source/api_docs/cugraph-ops/{ => python}/pytorch.rst (59%) delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/centrality.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/community.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/core.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/index.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/labeling.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/sampling.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/similarity.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/traversal.rst create mode 100644 docs/cugraph/source/api_docs/wholegraph/index.rst create mode 100644 docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst create mode 100644 docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst create mode 100644 docs/cugraph/source/wholegraph/basics/index.rst create mode 100644 docs/cugraph/source/wholegraph/basics/wholegraph_intro.md create mode 100644 docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md create mode 100644 docs/cugraph/source/wholegraph/basics/wholememory_intro.md create mode 100644 docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png create mode 100644 docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step2.png create mode 100644 docs/cugraph/source/wholegraph/imgs/device_continuous_wholememory_step1.png create mode 100644 docs/cugraph/source/wholegraph/imgs/device_continuous_wholememory_step2.png create mode 100644 docs/cugraph/source/wholegraph/imgs/distributed_wholememory.png create mode 100644 docs/cugraph/source/wholegraph/imgs/general_wholememory.png create mode 100644 docs/cugraph/source/wholegraph/imgs/host_mapped_wholememory_step1.png create mode 100644 docs/cugraph/source/wholegraph/imgs/host_mapped_wholememory_step2.png create mode 100644 docs/cugraph/source/wholegraph/imgs/wholememory_tensor.png create mode 100644 docs/cugraph/source/wholegraph/index.rst create mode 100644 docs/cugraph/source/wholegraph/installation/container.md create mode 100644 docs/cugraph/source/wholegraph/installation/getting_wholegraph.md create mode 100644 docs/cugraph/source/wholegraph/installation/index.rst create mode 100644 docs/cugraph/source/wholegraph/installation/source_build.md diff --git a/.gitignore b/.gitignore index c6bcf6965..358650cfc 100644 --- a/.gitignore +++ b/.gitignore @@ -84,8 +84,10 @@ datasets/* # Jupyter Notebooks .ipynb_checkpoints -## Doxygen +## Doxygen and Docs cpp/doxygen/html +docs/cugraph/lib* +docs/cugraph/api/* # created by Dask tests python/dask-worker-space diff --git a/docs/cugraph/Makefile b/docs/cugraph/Makefile index 32237aa2c..f92d0be69 100644 --- a/docs/cugraph/Makefile +++ b/docs/cugraph/Makefile @@ -2,7 +2,7 @@ # # You can set these variables from the command line. -SPHINXOPTS = +SPHINXOPTS = "-v" SPHINXBUILD = sphinx-build SPHINXPROJ = cugraph SOURCEDIR = source diff --git a/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst deleted file mode 100644 index e172309fa..000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst +++ /dev/null @@ -1,16 +0,0 @@ -============================= -Operators on Bipartite Graphs -============================= - -.. currentmodule:: pylibcugraphops - -Update Edges: Concatenation or Sum of Edge and Node Features ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.update_efeat_bipartite_e2e_concat_fwd - operators.update_efeat_bipartite_e2e_concat_bwd - - operators.update_efeat_bipartite_e2e_sum_fwd - operators.update_efeat_bipartite_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst new file mode 100644 index 000000000..5545bebe9 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst @@ -0,0 +1,3 @@ +cugraph-ops C++ API Reference +============================= + diff --git a/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst deleted file mode 100644 index 387844f68..000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst +++ /dev/null @@ -1,83 +0,0 @@ -======================== -Operators on Full Graphs -======================== - -.. currentmodule:: pylibcugraphops - -Simple Neighborhood Aggregator (SAGEConv) ------------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_simple_fg_n2n_fwd - operators.agg_simple_fg_n2n_bwd - operators.agg_simple_fg_e2n_fwd - operators.agg_simple_fg_e2n_bwd - operators.agg_simple_fg_n2n_e2n_fwd - operators.agg_simple_fg_n2n_e2n_bwd - - operators.agg_concat_fg_n2n_fwd - operators.agg_concat_fg_n2n_bwd - operators.agg_concat_fg_e2n_fwd - operators.agg_concat_fg_e2n_bwd - operators.agg_concat_fg_n2n_e2n_fwd - operators.agg_concat_fg_n2n_e2n_bwd - -Weighted Neighborhood Aggregation ---------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_weighted_fg_n2n_fwd - operators.agg_weighted_fg_n2n_bwd - operators.agg_concat_weighted_fg_n2n_fwd - operators.agg_concat_weighted_fg_n2n_bwd - -Heterogenous Aggregator using Basis Decomposition (RGCNConv) ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_hg_basis_fg_n2n_post_fwd - operators.agg_hg_basis_fg_n2n_post_bwd - -Graph Attention (GATConv/GATv2Conv) ------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.mha_gat_fg_n2n_fwd - operators.mha_gat_fg_n2n_bwd - operators.mha_gat_fg_n2n_efeat_fwd - operators.mha_gat_fg_n2n_efeat_bwd - - operators.mha_gat_v2_fg_n2n_fwd - operators.mha_gat_v2_fg_n2n_bwd - operators.mha_gat_v2_fg_n2n_efeat_fwd - operators.mha_gat_v2_fg_n2n_efeat_bwd - -Transformer-like Graph Attention (TransformerConv) --------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.mha_gat_v2_fg_n2n_fwd - operators.mha_gat_v2_fg_n2n_bwd - operators.mha_gat_v2_fg_n2n_efeat_fwd - operators.mha_gat_v2_fg_n2n_efeat_bwd - -Directional Message-Passing (DMPNN) ------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_dmpnn_fg_e2e_fwd - operators.agg_dmpnn_fg_e2e_bwd - -Graph Pooling -------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.pool_fg_n2s_fwd - operators.pool_fg_n2s_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst b/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst deleted file mode 100644 index 9289ce53e..000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst +++ /dev/null @@ -1,33 +0,0 @@ -=========== -Graph types -=========== - -.. currentmodule:: pylibcugraphops - -Message-Flow Graph (MFG) -------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - make_mfg_csr - -Heterogenous MFG ----------------- -.. autosummary:: - :toctree: ../api/ops/ - - make_mfg_csr_hg - -"Full" Graph (FG) ------------------ -.. autosummary:: - :toctree: ../api/ops/ - - make_fg_csr - -Heterogenous FG ---------------- -.. autosummary:: - :toctree: ../api/ops/ - - make_fg_csr_hg diff --git a/docs/cugraph/source/api_docs/cugraph-ops/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/index.rst index e2338dc18..fdfd5baab 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/index.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/index.rst @@ -1,4 +1,3 @@ -========================= cugraph-ops API reference ========================= @@ -8,11 +7,5 @@ This page provides a list of all publicly accessible modules, methods and classe :maxdepth: 2 :caption: API Documentation - graph_types - pytorch - mfg_operators - bipartite_operators - static_operators - fg_operators - dimenet - pytorch + python/index + c_cpp/index \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst deleted file mode 100644 index f3dd1faa2..000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst +++ /dev/null @@ -1,31 +0,0 @@ -================================ -Operators on Message-Flow Graphs -================================ - -.. currentmodule:: pylibcugraphops - -Simple Neighborhood Aggregator (SAGEConv) ------------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_simple_mfg_n2n_fwd - operators.agg_simple_mfg_n2n_bwd - operators.agg_concat_mfg_n2n_fwd - operators.agg_concat_mfg_n2n_bwd - -Graph Attention (GATConv) -------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.mha_gat_mfg_n2n_fwd - operators.mha_gat_mfg_n2n_bwd - -Heterogenous Aggregator using Basis Decomposition (RGCNConv) ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_hg_basis_mfg_n2n_post_fwd - operators.agg_hg_basis_mfg_n2n_post_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst similarity index 89% rename from docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst rename to docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst index b709464c7..6fadcc57b 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst @@ -7,7 +7,7 @@ Dimenet operators Radial Basis Functions ---------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops dimenet.radial_basis_fwd dimenet.radial_basis_bwd @@ -16,7 +16,7 @@ Radial Basis Functions Edge-to-Edge Aggregation ------------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops dimenet.agg_edge_to_edge_fwd dimenet.agg_edge_to_edge_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst new file mode 100644 index 000000000..141d40393 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst @@ -0,0 +1,34 @@ +=========== +Graph types +=========== + +.. currentmodule:: pylibcugraphops + + +CSC Graph +----------------- +.. autosummary:: + :toctree: ../../api/ops + + make_csc + +Heterogenous CSC Graph +---------------------- +.. autosummary:: + :toctree: ../../api/ops + + make_csc_hg + +Bipartite Graph +----------------- +.. autosummary:: + :toctree: ../../api/ops + + make_bipartite_csc + +Heterogenous Bipartite Graph +---------------------------- +.. autosummary:: + :toctree: ../../api/ops + + make_bipartite_csc_hg diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst new file mode 100644 index 000000000..082c7741f --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst @@ -0,0 +1,13 @@ +cugraph-ops Python API reference +================================ + +This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace. + +.. toctree:: + :maxdepth: 2 + :caption: API Documentation + + graph_types + operators + dimenet + pytorch diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst new file mode 100644 index 000000000..3e6664b2d --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst @@ -0,0 +1,93 @@ +============================= +Operators for Message-Passing +============================= + +.. currentmodule:: pylibcugraphops + +Simple Neighborhood Aggregator (SAGEConv) +----------------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_simple_n2n_fwd + operators.agg_simple_n2n_bwd + operators.agg_simple_e2n_fwd + operators.agg_simple_e2n_bwd + operators.agg_simple_n2n_e2n_fwd + operators.agg_simple_n2n_e2n_bwd + + operators.agg_concat_n2n_fwd + operators.agg_concat_n2n_bwd + operators.agg_concat_e2n_fwd + operators.agg_concat_e2n_bwd + operators.agg_concat_n2n_e2n_fwd + operators.agg_concat_n2n_e2n_bwd + + +Weighted Neighborhood Aggregation +--------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_weighted_n2n_fwd + operators.agg_weighted_n2n_bwd + operators.agg_concat_weighted_n2n_fwd + operators.agg_concat_weighted_n2n_bwd + +Heterogenous Aggregator using Basis Decomposition (RGCNConv) +------------------------------------------------------------ +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_hg_basis_n2n_post_fwd + operators.agg_hg_basis_n2n_post_bwd + +Graph Attention (GATConv/GATv2Conv) +----------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.mha_gat_n2n_fwd + operators.mha_gat_n2n_bwd + operators.mha_gat_n2n_efeat_fwd + operators.mha_gat_n2n_efeat_bwd + + operators.mha_gat_v2_n2n_fwd + operators.mha_gat_v2_n2n_bwd + operators.mha_gat_v2_n2n_efeat_fwd + operators.mha_gat_v2_n2n_efeat_bwd + +Transformer-like Graph Attention (TransformerConv) +-------------------------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.mha_gat_v2_n2n_fwd + operators.mha_gat_v2_n2n_bwd + operators.mha_gat_v2_n2n_efeat_fwd + operators.mha_gat_v2_n2n_efeat_bwd + +Directional Message-Passing (DMPNN) +----------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_dmpnn_e2e_fwd + operators.agg_dmpnn_e2e_bwd + +Update Edges: Concatenation or Sum of Edge and Node Features +------------------------------------------------------------ +.. autosummary:: + :toctree: ../../api/ops + + operators.update_efeat_e2e_concat_fwd + operators.update_efeat_e2e_concat_bwd + + operators.update_efeat_e2e_sum_fwd + operators.update_efeat_e2e_sum_bwd + + operators.update_efeat_e2e_concat_fwd + operators.update_efeat_e2e_concat_bwd + + operators.update_efeat_e2e_sum_fwd + operators.update_efeat_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst similarity index 59% rename from docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst rename to docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst index 83800fbc5..d2074df15 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst @@ -2,35 +2,35 @@ PyTorch Autograd Wrappers ========================== -.. currentmodule:: pylibcugraphops +.. currentmodule:: pylibcugraphops.pytorch Simple Neighborhood Aggregator (SAGEConv) ----------------------------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.agg_concat_n2n + operators.agg_concat_n2n Graph Attention (GATConv/GATv2Conv) ----------------------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.mha_gat_n2n - pytorch.operators.mha_gat_v2_n2n + operators.mha_gat_n2n + operators.mha_gat_v2_n2n Heterogenous Aggregator using Basis Decomposition (RGCNConv) ------------------------------------------------------------ .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.agg_hg_basis_n2n_post + operators.agg_hg_basis_n2n_post Update Edges: Concatenation or Sum of Edge and Node Features ------------------------------------------------------------ .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.update_efeat_bipartite_e2e - pytorch.operators.update_efeat_static_e2e + operators.update_efeat_e2e + operators.update_efeat_e2e diff --git a/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst deleted file mode 100644 index f3ecc068f..000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst +++ /dev/null @@ -1,16 +0,0 @@ -========================== -Operators on Static Graphs -========================== - -.. currentmodule:: pylibcugraphops - -Update Edges: Concatenation or Sum of Edge and Node Features ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.update_efeat_static_e2e_concat_fwd - operators.update_efeat_static_e2e_concat_bwd - - operators.update_efeat_static_e2e_sum_fwd - operators.update_efeat_static_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst index 2cd8969aa..f7d7f5f22 100644 --- a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst +++ b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst @@ -9,6 +9,6 @@ cugraph-pyg .. autosummary:: :toctree: ../api/cugraph-pyg/ - cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore - cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler +.. cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore +.. cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler diff --git a/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst b/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst deleted file mode 100644 index 34b812785..000000000 --- a/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst +++ /dev/null @@ -1,4 +0,0 @@ -CuGraph C and C++ API Links -=========================== - -coming soon - see https://docs.rapids.ai/api/libcugraph/nightly/ \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph_c/centrality.rst b/docs/cugraph/source/api_docs/cugraph_c/centrality.rst new file mode 100644 index 000000000..f34e26ad7 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/centrality.rst @@ -0,0 +1,51 @@ +Centrality +========== + +PageRank +-------- +.. doxygenfunction:: cugraph_pagerank + :project: libcugraph + +.. doxygenfunction:: cugraph_pagerank_allow_nonconvergence + :project: libcugraph + +Personalized PageRank +--------------------- +.. doxygenfunction:: cugraph_personalized_pagerank + :project: libcugraph + +.. doxygenfunction:: cugraph_personalized_pagerank_allow_nonconvergence + :project: libcugraph + +Eigenvector Centrality +---------------------- +.. doxygenfunction:: cugraph_eigenvector_centrality + :project: libcugraph + +Katz Centrality +--------------- +.. doxygenfunction:: cugraph_katz_centrality + :project: libcugraph + +Betweenness Centrality +---------------------- +.. doxygenfunction:: cugraph_betweenness_centrality + :project: libcugraph + +Edge Betweenness Centrality +--------------------------- +.. doxygenfunction:: cugraph_edge_betweenness_centrality + :project: libcugraph + +HITS Centrality +--------------- +.. doxygenfunction:: cugraph_hits + :project: libcugraph + +Centrality Support Functions +---------------------------- + .. doxygengroup:: centrality + :project: libcugraph + :members: + :content-only: + diff --git a/docs/cugraph/source/api_docs/cugraph_c/community.rst b/docs/cugraph/source/api_docs/cugraph_c/community.rst new file mode 100644 index 000000000..0bbfe365c --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/community.rst @@ -0,0 +1,63 @@ +Community +========= + +.. role:: py(code) + :language: c + :class: highlight + +``#include `` + +Triangle Counting +----------------- +.. doxygenfunction:: cugraph_triangle_count + :project: libcugraph + +Louvain +------- +.. doxygenfunction:: cugraph_louvain + :project: libcugraph + +Leiden +------ +.. doxygenfunction:: cugraph_leiden + :project: libcugraph + +ECG +--- +.. doxygenfunction:: cugraph_ecg + :project: libcugraph + +Extract Egonet +-------------- +.. doxygenfunction:: cugraph_extract_ego + :project: libcugraph + +Balanced Cut +------------ +.. doxygenfunction:: cugraph_balanced_cut_clustering + :project: libcugraph + +Spectral Clustering - Modularity Maximization +--------------------------------------------- +.. doxygenfunction:: cugraph_spectral_modularity_maximization + :project: libcugraph + +.. doxygenfunction:: cugraph_analyze_clustering_modularity + :project: libcugraph + +Spectral Clusteriong - Edge Cut +------------------------------- +.. doxygenfunction:: cugraph_analyze_clustering_edge_cut + :project: libcugraph + +.. doxygenfunction:: cugraph_analyze_clustering_ratio_cut + :project: libcugraph + + +Community Support Functions +--------------------------- + .. doxygengroup:: community + :project: libcugraph + :members: + :content-only: + diff --git a/docs/cugraph/source/api_docs/cugraph_c/core.rst b/docs/cugraph/source/api_docs/cugraph_c/core.rst new file mode 100644 index 000000000..34456c65e --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/core.rst @@ -0,0 +1,21 @@ +Core +==== + + +Core Number +----------- +.. doxygenfunction:: cugraph_core_number + :project: libcugraph + +K-Core +------ +.. doxygenfunction:: cugraph_k_core + :project: libcugraph + + +Core Support Functions +---------------------- + .. doxygengroup:: core + :project: libcugraph + :members: + :content-only: diff --git a/docs/cugraph/source/api_docs/cugraph_c/index.rst b/docs/cugraph/source/api_docs/cugraph_c/index.rst new file mode 100644 index 000000000..3dd37dbc3 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/index.rst @@ -0,0 +1,16 @@ +=========================== +cuGraph C API documentation +=========================== + + +.. toctree:: + :maxdepth: 3 + :caption: API Documentation + + centrality.rst + community.rst + core.rst + labeling.rst + sampling.rst + similarity.rst + traversal.rst diff --git a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst new file mode 100644 index 000000000..af105ee8f --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst @@ -0,0 +1,20 @@ +Components +========== + + +Weakly Connected Components +--------------------------- +.. doxygenfunction:: cugraph_weakly_connected_components + :project: libcugraph + +Strongly Connected Components +----------------------------- +.. doxygenfunction:: cugraph_strongly_connected_components + :project: libcugraph + +Support +------- + .. doxygengroup:: labeling + :project: libcugraph + :members: + :content-only: \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph_c/sampling.rst b/docs/cugraph/source/api_docs/cugraph_c/sampling.rst new file mode 100644 index 000000000..21b837daf --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/sampling.rst @@ -0,0 +1,37 @@ +Sampling +======== + +Uniform Random Walks +-------------------- +.. doxygenfunction:: cugraph_uniform_random_walks + :project: libcugraph + +Biased Random Walks +-------------------- +.. doxygenfunction:: cugraph_biased_random_walks + :project: libcugraph + +Random Walks via Node2Vec +------------------------- +.. doxygenfunction:: cugraph_node2vec_random_walks + :project: libcugraph + +Node2Vec +-------- +.. doxygenfunction:: cugraph_node2vec + :project: libcugraph + +Uniform Neighborhood Sampling +----------------------------- +.. doxygenfunction:: cugraph_uniform_neighbor_sample_with_edge_properties + :project: libcugraph + +.. doxygenfunction:: cugraph_uniform_neighbor_sample + :project: libcugraph + +Support +------- +.. doxygengroup:: samplingC + :project: libcugraph + :members: + :content-only: diff --git a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst new file mode 100644 index 000000000..fba07ad20 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst @@ -0,0 +1,25 @@ +Similarity +========== + + +Jaccard +------- +.. doxygenfunction:: cugraph_jaccard_coefficients + :project: libcugraph + +Sorensen +-------- +.. doxygenfunction:: cugraph_sorensen_coefficients + :project: libcugraph + +Overlap +------- +.. doxygenfunction:: cugraph_overlap_coefficients + :project: libcugraph + +Support +------- +.. doxygengroup:: similarity + :project: libcugraph + :members: + :content-only: \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst new file mode 100644 index 000000000..c90760e9e --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst @@ -0,0 +1,30 @@ +Traversal +========== + + +Breadth First Search (BFS) +-------------------------- +.. doxygenfunction:: cugraph_bfs + :project: libcugraph + +Single-Source Shortest-Path (SSSP) +---------------------------------- +.. doxygenfunction:: cugraph_sssp + :project: libcugraph + +Path Extraction +--------------- +.. doxygenfunction:: cugraph_extract_paths + :project: libcugraph + +Extract Max Path Length +----------------------- +.. doxygenfunction:: cugraph_extract_paths_result_get_max_path_length + :project: libcugraph + +Support +------- +.. doxygengroup:: traversal + :project: libcugraph + :members: + :content-only: \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 45f7210f5..74ca98bb9 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -1,16 +1,39 @@ -Python API reference -==================== +API Reference +============= This page provides a list of all publicly accessible Python modules with in the Graph collection +Core Graph API Documentation +---------------------------- + .. toctree:: - :maxdepth: 2 - :caption: Python API Documentation + :maxdepth: 3 + :caption: Core Graph API Documentation cugraph/index.rst plc/pylibcugraph.rst + cugraph_c/index.rst + cugraph_cpp/index.rst + +Graph Nerual Networks API Documentation +--------------------------------------- + +.. toctree:: + :maxdepth: 3 + :caption: Graph Nerual Networks API Documentation + cugraph-dgl/cugraph_dgl.rst cugraph-pyg/cugraph_pyg.rst - service/index.rst cugraph-ops/index.rst + wholegraph/index.rst + +Additional Graph Packages API Documentation +---------------------------------- + +.. toctree:: + :maxdepth: 3 + :caption: Additional Graph Packages API Documentation + + service/index.rst + diff --git a/docs/cugraph/source/api_docs/service/cugraph_service_client.rst b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst index 383b31d26..7e344d326 100644 --- a/docs/cugraph/source/api_docs/service/cugraph_service_client.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst @@ -9,7 +9,7 @@ cugraph-service .. autosummary:: :toctree: ../api/service/ - cugraph_service_client.client.RunAsyncioThread +.. cugraph_service_client.client.RunAsyncioThread cugraph_service_client.client.run_async cugraph_service_client.client.DeviceArrayAllocator cugraph_service_client.client.CugraphServiceClient diff --git a/docs/cugraph/source/api_docs/service/cugraph_service_server.rst b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst index a7e8b5475..09ca8360b 100644 --- a/docs/cugraph/source/api_docs/service/cugraph_service_server.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst @@ -9,6 +9,6 @@ cugraph-service .. autosummary:: :toctree: ../api/service/ - cugraph_service_server.cugraph_handler.call_algo +.. cugraph_service_server.cugraph_handler.call_algo cugraph_service_server.cugraph_handler.ExtensionServerFacade cugraph_service_server.cugraph_handler.CugraphHandler diff --git a/docs/cugraph/source/api_docs/wholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/index.rst new file mode 100644 index 000000000..80e231d46 --- /dev/null +++ b/docs/cugraph/source/api_docs/wholegraph/index.rst @@ -0,0 +1,11 @@ +WholeGraph API reference +======================== + +This page provides WholeGraph API reference + +.. toctree:: + :maxdepth: 2 + :caption: WholeGraph API Documentation + + libwholegraph/index.rst + pylibwholegraph/index.rst diff --git a/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst new file mode 100644 index 000000000..4ef68abef --- /dev/null +++ b/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst @@ -0,0 +1,228 @@ +===================== +libwholegraph API doc +===================== + +Doxygen WholeGraph C API documentation +-------------------------------------- +For doxygen documentation, please refer to `Doxygen Documentation <../../doxygen_docs/libwholegraph/html/index.html>`_ + +WholeGraph C API documentation +------------------------------ + +Library Level APIs +++++++++++++++++++ + +.. doxygenenum:: wholememory_error_code_t + :project: libwholegraph +.. doxygenfunction:: wholememory_init + :project: libwholegraph +.. doxygenfunction:: wholememory_finalize + :project: libwholegraph +.. doxygenfunction:: fork_get_device_count + :project: libwholegraph + +WholeMemory Communicator APIs ++++++++++++++++++++++++++++++ + +.. doxygentypedef:: wholememory_comm_t + :project: libwholegraph +.. doxygenstruct:: wholememory_unique_id_t + :project: libwholegraph +.. doxygenfunction:: wholememory_create_unique_id + :project: libwholegraph +.. doxygenfunction:: wholememory_create_communicator + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_communicator + :project: libwholegraph +.. doxygenfunction:: wholememory_communicator_get_rank + :project: libwholegraph +.. doxygenfunction:: wholememory_communicator_get_size + :project: libwholegraph +.. doxygenfunction:: wholememory_communicator_barrier + :project: libwholegraph + +WholeMemoryHandle APIs +++++++++++++++++++++++ + +.. doxygenenum:: wholememory_memory_type_t + :project: libwholegraph +.. doxygenenum:: wholememory_memory_location_t + :project: libwholegraph +.. doxygentypedef:: wholememory_handle_t + :project: libwholegraph +.. doxygenstruct:: wholememory_gref_t + :project: libwholegraph +.. doxygenfunction:: wholememory_malloc + :project: libwholegraph +.. doxygenfunction:: wholememory_free + :project: libwholegraph +.. doxygenfunction:: wholememory_get_communicator + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_type + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_location + :project: libwholegraph +.. doxygenfunction:: wholememory_get_total_size + :project: libwholegraph +.. doxygenfunction:: wholememory_get_data_granularity + :project: libwholegraph +.. doxygenfunction:: wholememory_get_local_memory + :project: libwholegraph +.. doxygenfunction:: wholememory_get_rank_memory + :project: libwholegraph +.. doxygenfunction:: wholememory_get_global_pointer + :project: libwholegraph +.. doxygenfunction:: wholememory_get_global_reference + :project: libwholegraph +.. doxygenfunction:: wholememory_determine_partition_plan + :project: libwholegraph +.. doxygenfunction:: wholememory_determine_entry_partition_plan + :project: libwholegraph +.. doxygenfunction:: wholememory_get_partition_plan + :project: libwholegraph +.. doxygenfunction:: wholememory_load_from_file + :project: libwholegraph +.. doxygenfunction:: wholememory_store_to_file + :project: libwholegraph + +WholeMemoryTensor APIs +++++++++++++++++++++++ + +.. doxygenenum:: wholememory_dtype_t + :project: libwholegraph +.. doxygenstruct:: wholememory_array_description_t + :project: libwholegraph +.. doxygenstruct:: wholememory_matrix_description_t + :project: libwholegraph +.. doxygenstruct:: wholememory_tensor_description_t + :project: libwholegraph +.. doxygentypedef:: wholememory_tensor_t + :project: libwholegraph +.. doxygenfunction:: wholememory_dtype_get_element_size + :project: libwholegraph +.. doxygenfunction:: wholememory_dtype_is_floating_number + :project: libwholegraph +.. doxygenfunction:: wholememory_dtype_is_integer_number + :project: libwholegraph +.. doxygenfunction:: wholememory_create_array_desc + :project: libwholegraph +.. doxygenfunction:: wholememory_create_matrix_desc + :project: libwholegraph +.. doxygenfunction:: wholememory_initialize_tensor_desc + :project: libwholegraph +.. doxygenfunction:: wholememory_copy_array_desc_to_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_copy_array_desc_to_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_copy_matrix_desc_to_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_convert_tensor_desc_to_array + :project: libwholegraph +.. doxygenfunction:: wholememory_convert_tensor_desc_to_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_element_count_from_array + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_size_from_array + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_element_count_from_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_size_from_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_element_count_from_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_size_from_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_unsqueeze_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_create_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_make_tensor_from_pointer + :project: libwholegraph +.. doxygenfunction:: wholememory_make_tensor_from_handle + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_has_handle + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_memory_handle + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_tensor_description + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_global_reference + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_map_local_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_data_pointer + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_entry_per_partition + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_subtensor + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_root + :project: libwholegraph + +Ops on WholeMemory Tensors +++++++++++++++++++++++++++ + +.. doxygenfunction:: wholememory_gather + :project: libwholegraph +.. doxygenfunction:: wholememory_scatter + :project: libwholegraph + +WholeTensorEmbedding APIs ++++++++++++++++++++++++++ + +.. doxygentypedef:: wholememory_embedding_cache_policy_t + :project: libwholegraph +.. doxygentypedef:: wholememory_embedding_optimizer_t + :project: libwholegraph +.. doxygentypedef:: wholememory_embedding_t + :project: libwholegraph +.. doxygenenum:: wholememory_access_type_t + :project: libwholegraph +.. doxygenenum:: wholememory_optimizer_type_t + :project: libwholegraph +.. doxygenfunction:: wholememory_create_embedding_optimizer + :project: libwholegraph +.. doxygenfunction:: wholememory_optimizer_set_parameter + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_embedding_optimizer + :project: libwholegraph +.. doxygenfunction:: wholememory_create_embedding_cache_policy + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_embedding_cache_policy + :project: libwholegraph +.. doxygenfunction:: wholememory_create_embedding + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_embedding + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_get_embedding_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_gather + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_gather_gradient_apply + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_get_optimizer_state_names + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_get_optimizer_state + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_writeback_cache + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_drop_all_cache + :project: libwholegraph + +Ops on graphs stored in WholeMemory ++++++++++++++++++++++++++++++++++++ + +.. doxygenfunction:: wholegraph_csr_unweighted_sample_without_replacement + :project: libwholegraph +.. doxygenfunction:: wholegraph_csr_weighted_sample_without_replacement + :project: libwholegraph + +Miscellaneous Ops for graph ++++++++++++++++++++++++++++ + +.. doxygenfunction:: graph_append_unique + :project: libwholegraph +.. doxygenfunction:: csr_add_self_loop + :project: libwholegraph diff --git a/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst new file mode 100644 index 000000000..67aab00ac --- /dev/null +++ b/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst @@ -0,0 +1,38 @@ +======================= +pylibwholegraph API doc +======================= + +.. currentmodule:: pylibwholegraph + +APIs +---- +.. autosummary:: + :toctree: ../../api/wg + + torch.initialize.init_torch_env + torch.initialize.init_torch_env_and_create_wm_comm + torch.initialize.finalize + torch.comm.WholeMemoryCommunicator + torch.comm.set_world_info + torch.comm.create_group_communicator + torch.comm.destroy_communicator + torch.comm.get_global_communicator + torch.comm.get_local_node_communicator + torch.comm.get_local_device_communicator + torch.tensor.WholeMemoryTensor + torch.tensor.create_wholememory_tensor + torch.tensor.create_wholememory_tensor_from_filelist + torch.tensor.destroy_wholememory_tensor + torch.embedding.WholeMemoryOptimizer + torch.embedding.create_wholememory_optimizer + torch.embedding.destroy_wholememory_optimizer + torch.embedding.WholeMemoryCachePolicy + torch.embedding.create_wholememory_cache_policy + torch.embedding.create_builtin_cache_policy + torch.embedding.destroy_wholememory_cache_policy + torch.embedding.WholeMemoryEmbedding + torch.embedding.create_embedding + torch.embedding.create_embedding_from_filelist + torch.embedding.destroy_embedding + torch.embedding.WholeMemoryEmbeddingModule + torch.graph_structure.GraphStructure diff --git a/docs/cugraph/source/basics/cugraph_intro.md b/docs/cugraph/source/basics/cugraph_intro.md index 068412950..10d14f8a0 100644 --- a/docs/cugraph/source/basics/cugraph_intro.md +++ b/docs/cugraph/source/basics/cugraph_intro.md @@ -21,7 +21,7 @@ call graph algorithms using data stored in a GPU DataFrame, NetworkX Graphs, or CuPy or SciPy sparse Matrix. -# Vision +## Vision The vision of RAPIDS cuGraph is to ___make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks___. This is a goal that many of us on the cuGraph team have been @@ -49,7 +49,7 @@ RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support multi-billion edge graphs. -# Terminology +## Terminology cuGraph is a collection of GPU accelerated graph algorithms and graph utility functions. The application of graph analysis covers a lot of areas. @@ -67,8 +67,7 @@ documentation we will mostly use the terms __Node__ and __Edge__ to better match NetworkX preferred term use, as well as other Python-based tools. At the CUDA/C layer, we favor the mathematical terms of __Vertex__ and __Edge__. -# Roadmap -GitHub does not provide a robust project management interface, and so a roadmap turns into simply a projection of when work will be completed and not a complete picture of everything that needs to be done. To capture the work that requires multiple steps, issues are labels as “EPIC” and include multiple subtasks that could span multiple releases. The EPIC will be in the release where work in expected to be completed. A better roadmap is being worked an image of the roadmap will be posted when ready. - * GitHub Project Board: https://github.com/rapidsai/cugraph/projects/28 + + \ No newline at end of file diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 470086b4f..3f7ef7deb 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -181,11 +181,10 @@ # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'cugraph', 'cugraph Documentation', - author, 'cugraph', 'One line description of project.', + author, 'cugraph', 'GPU-accelerated graph analysis.', 'Miscellaneous'), ] - # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'https://docs.python.org/': None} @@ -209,7 +208,9 @@ def setup(app): ) breathe_projects = { + 'libcugraph': os.environ['XML_DIR_LIBCUGRAPH'], 'libcugraphops': os.environ['XML_DIR_LIBCUGRAPHOPS'], 'libwholegraph': os.environ['XML_DIR_LIBWHOLEGRAPH'] } + breathe_default_project = "libcugraph" diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md index f6cb7c0d8..a1b80e927 100644 --- a/docs/cugraph/source/graph_support/algorithms.md +++ b/docs/cugraph/source/graph_support/algorithms.md @@ -22,7 +22,7 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | Category | Notebooks | Scale | Notes | | ----------------- | ---------------------------------- | ------------------- | --------------------------------------------------------------- | -| [Centrality](./algorithms/Centrality.md) | [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | | | +| [Centrality](./algorithms/Centrality.html ) | [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | | | | | [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) | __Multi-GPU__ | | | | [Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | __Multi-GPU__ | MG as of 23.06 | | | [Edge Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | __Multi-GPU__ | MG as of 23.08 | @@ -31,12 +31,12 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | Community | | | | | | [Leiden](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | MG as of 23.06 | | | [Louvain](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | | -| | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Single-GPU | MG planned for 23.10 | +| | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Single-GPU | MG planned for 24.02 | | | [Spectral-Clustering - Balanced Cut](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | | | [Spectral-Clustering - Modularity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | | | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipyn) | Single-GPU | | | | [Triangle Counting](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Triangle-Counting.ipynb) | __Multi-GPU__ | | -| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Single-GPU | MG planned for 23.10 | +| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Single-GPU | MG planned for 2024 | | Components | | | | | | [Weakly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | __Multi-GPU__ | | | | [Strongly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | Single-GPU | | @@ -55,7 +55,7 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | | | [Personal Pagerank]() | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) | | | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb) | __Multi-GPU__ | | -| [Link Prediction](./algorithms/Similarity.md) | | | | +| [Link Prediction](algorithms/Similarity.html) | | | | | | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | __Multi-GPU__ | Directed graph only | | | [Weighted Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | Single-GPU | | | | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) | **Multi-GPU** | | @@ -65,8 +65,8 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | | [Uniform Random Walks RW](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/sampling/RandomWalk.ipynb) | __Multi-GPU__ | | | | *Biased Random Walks (RW)* | --- | | | | Egonet | __Multi-GPU__ | | -| | Node2Vec | Single-GPU | | -| | Uniform Neighborhood sampling | __Multi-GPU__ | | +| | Node2Vec | __Multi-GPU__ | | +| | Neighborhood sampling | __Multi-GPU__ | | | Traversal | | | | | | Breadth First Search (BFS) | __Multi-GPU__ | with cutoff support [C++ README](cpp/src/traversal/README.md#BFS) | | | Single Source Shortest Path (SSSP) | __Multi-GPU__ | [C++ README](cpp/src/traversal/README.md#SSSP) | diff --git a/docs/cugraph/source/graph_support/algorithms/Centrality.md b/docs/cugraph/source/graph_support/algorithms/Centrality.md index e42bbe238..f82a1fe12 100644 --- a/docs/cugraph/source/graph_support/algorithms/Centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/Centrality.md @@ -1,7 +1,7 @@ # cuGraph Centrality Notebooks - + The RAPIDS cuGraph Centrality folder contains a collection of Jupyter Notebooks that demonstrate algorithms to identify and quantify the importance of vertices to the structure of the graph. In the diagram above, the highlighted vertices are highly important and are likely answers to questions like: @@ -15,13 +15,13 @@ But which vertices are most important? The answer depends on which measure/algor |Algorithm |Notebooks Containing |Description | | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -|[Degree Centrality](./degree_centrality.md)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb) |Measure based on counting direct connections for each vertex| -|[Betweenness Centrality](./betweenness_centrality.md)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb) |Number of shortest paths through the vertex| -|[Eigenvector Centrality](./eigenvector_centrality.md)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| -|[Katz Centrality](./katz_centrality.md)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | +|[Degree Centrality](./degree_centrality.html)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb) |Measure based on counting direct connections for each vertex| +|[Betweenness Centrality](./betweenness_centrality.html)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb) |Number of shortest paths through the vertex| +|[Eigenvector Centrality](./eigenvector_centrality.html)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| +|[Katz Centrality](./katz_centrality.html)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | |Pagerank|[Centrality](./Centrality.ipynb), [Pagerank](../../link_analysis/Pagerank.ipynb) |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices. | -[System Requirements](../../README.md#requirements) +[System Requirements](../../README.html#requirements) | Author Credit | Date | Update | cuGraph Version | Test Hardware | | --------------|------------|------------------|-----------------|----------------| diff --git a/docs/cugraph/source/graph_support/algorithms/Similarity.md b/docs/cugraph/source/graph_support/algorithms/Similarity.md index 450beb373..18c0a94d5 100644 --- a/docs/cugraph/source/graph_support/algorithms/Similarity.md +++ b/docs/cugraph/source/graph_support/algorithms/Similarity.md @@ -15,9 +15,9 @@ Manipulation of the data before or after the graph analytic is not covered here. |Algorithm |Notebooks Containing |Description | | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -|[Jaccard Smiliarity](./jaccard_similarity.md)| [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) || -|[Overlap Similarity](./overlap_similarity.md)| [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) || -|[Sorensen](./sorensen_coefficient.md)|[Sorensen Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb)|| +|[Jaccard Smiliarity](./jaccard_similarity.html)| [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) || +|[Overlap Similarity](./overlap_similarity.html)| [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) || +|[Sorensen](./sorensen_coefficient.html)|[Sorensen Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb)|| |Personal Pagerank|[Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) || diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index c5303c216..955eb6d54 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -25,12 +25,12 @@ RAPIDS Graph documentation * - :abbr:`libcugraph_etl (C++ renumbering function for strings)` - :abbr:`wholegraph (Shared memory-based GPU-accelerated GNN training)` - - .. - -| | +~~~~~~~~~~~~ +Introduction +~~~~~~~~~~~~ cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or @@ -39,6 +39,7 @@ even CuPy or SciPy sparse Matrices. Note: We are redoing all of our documents, please be patient as we update the docs and links +| .. toctree:: :maxdepth: 2 @@ -48,9 +49,8 @@ the docs and links installation/index tutorials/index graph_support/index + wholegraph/index references/index - dev_resources/index - releases/index api_docs/index Indices and tables diff --git a/docs/cugraph/source/wholegraph/basics/index.rst b/docs/cugraph/source/wholegraph/basics/index.rst new file mode 100644 index 000000000..429fe35d6 --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/index.rst @@ -0,0 +1,11 @@ +====== +Basics +====== + + +.. toctree:: + :maxdepth: 2 + + wholegraph_intro + wholememory_intro + wholememory_implementation_details diff --git a/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md b/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md new file mode 100644 index 000000000..360f8e0e3 --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md @@ -0,0 +1,135 @@ +# WholeGraph Introduction +WholeGraph helps train large-scale Graph Neural Networks(GNN). +WholeGraph provides underlying storage structure called WholeMemory. +WholeMemory is a Tensor like storage and provides multi-GPU support. +It is optimized for NVLink systems like DGX A100 servers. +By working together with cuGraph, cuGraph-Ops, cuGraph-DGL, cuGraph-PyG, and upstream DGL and PyG, +it will be easy to build GNN applications. + +## WholeMemory +WholeMemory can be regarded as a whole view of GPU memory. +WholeMemory exposes a handle of the memory instance no matter how the underlying data is stored across multiple GPUs. +WholeMemory assumes that separate process is used to control each GPU. + +### WholeMemory Basics +To define WholeMemory, we need to specify the following: + +#### 1. Specify the set of GPU to handle the Memory + +Since WholeMemory is owned by a set of GPUs, you must specify the set of GPUs. +This is done by creating [WholeMemory Communicator](#wholememory-communicator) and specifying the WholeMemory Communicator when creating WholeMemory. + +#### 2. Specify the location of the memory + +Although WholeMemory is owned by a set of GPUs, the memory itself can be located in host memory or in device memory. +The location of the memory need to be specified, two types of locations can be specified. + +- **Host memory**: will use pinned host memory as underlying storage. +- **Device memory**: will use GPU device memory as underlying storage. + +#### 3. Specify the address mapping mode of the memory + +As WholeMemory is owned by multiple GPUs, each GPU will access the whole memory space, so we need address mapping. +There are three types of address mapping modes (also known as WholeMemory types), they are: + +- **Continuous**: All memory from each GPU will be mapped into a single continuous memory address space for each GPU. + In this mode, each GPU can directly access the whole memory using a single pointer and offset, just like using normal + device memory. Software will see no difference. Hardware peer to peer access will handle the underlying communication. + +- **Chunked**: Memory from each GPU will be mapped into different memory chunks, one chunk for each GPU. + In this mode, direct access is also supported, but not using a single pointer. Software will see the chunked memory. + However, an abstract layer may help to hide this. + +- **Distributed**: Memory from other GPUs are not mapped into current GPU, so no direct access is supported. + To access memory of other GPU, explicit communication is needed. + +To learn more details about WholeMemory locations and WholeMemory types, please refer to +[WholeMemory Implementation Details](wholememory_implementation_details.md) + +### WholeMemory Communicator +WholeMemory Communicator has two main purpose: + +- **Defines a set of GPUs which works together on WholeMemory.** WholeMemory Communicator is created by all GPUs that + wants to work together. A WholeMemory Communicator can be reused as long as the GPU set needed is the same. +- **Provides underlying communication channel needed by WholeMemory.** WholeMemory may need commuincator between GPUs + during the WholeMemory creation and some OPs on some types of WholeMemory. + +To Create WholeMemory Communicator, a WholeMemory Unique ID needs to be created first, it is usually created by the first GPU in the set of GPUs, and then broadcasted to all GPUs that want to work together. Then all GPUs in this communicator +will call WholeMemory Communicator creation function using this WholeMemory Unique ID, and the rank of current GPU as +well as all GPU count. + +### WholeMemory Granularity +As underlying storage may be partitioned into multiple GPUs physically, this is usually not wanted inside one single +user data block. To help on this, when creating WholeMemory, the granularity of data can be specified. Then the +WholeMemory is considered as multiple block of the same granularity and will not get split inside the granularity. + +### WholeMemory Mapping +As WholeMemory provides a whole view of memory to GPU, to access WholeMemory, mapping is usually needed. +Different types of WholeMemory have different mapping methods supported as their names. +Some mappings supported include +- All the WholeMemory types support mapping the memory range that local GPU is responsible for. + That is, each rank can directly access "Local" memory in all types of WholeMemory. + Here "Local" memory doesn't have to be on current GPU's memory, it can be on host memory or even maybe on other GPU, + but it is guaranteed to be directly accessed by current GPU. +- Chunked and Continuous WholeMemory also support Chunked mapping. That is, memory of all GPUs can be mapped into + current GPU, one continuous chunk for one GPU. Each chunk can be directly accessed by current GPU. But the memory of + different chunks are not guaranteed to be continuous. +- Continuous WholeMemory can be mapped into continuous memory space. That is, memory of all GPUs are mapped into a + single range of virtual memory, accessing to different position of this memory will physically access to different + GPUs. This mapping will be handled by hardware (CPU pagetable or GPU pagetable). + +### Operations on WholeMemory +There are some operations that can be performed on WholeMemory. They are based on the mapping of WholeMemory. +#### Local Operation +As all WholeMemory supports mapping of local memory, so operation on local memory is supported. The operation can be +either read or write. Just use it as GPU memory of current device is OK. +#### Load and Store +To facilitate file operation, Load / Store WholeMemory from file or to file is supported. WholeMemory uses raw binary +file format for disk operation. For Load, the input file can be a single file or a list of files, if it is a list, they +will be logically concatenated together and then loaded. For store, each GPU stores its local memory to file, producing +a list of files. +#### Gather and Scatter +WholeMemory also supports Gather / Scatter operation, usually they operate on a +[WholeMemory Tensor](#wholememory-tensor). + +### WholeMemory Tensor +Compared to PyTorch, WholeMemory is like PyTorch Storage while a WholeMemory Tensor is like a PyTorch Tensor. +For now, WholeMemory supports only 1D and 2D tensors, or arrays and matrices. Only first dimension is partitioned. + +### WholeMemory Embedding +WholeMemory Embedding is just like a 2D WholeMemory Tensor, with two features added. They support cache and sparse +optimizers. +#### Cache Support +To create WholeMemory Embedding with a cache, WholeMemory CachePolicy needs to be be created first. WholeMemoryCachePolicy can be created with following fields: +- **WholeMemory Communicator**: WholeMemory CachePolicy also needs WholeMemory Communicator. + WholeMemory Communicator defines the set of GPUs that cache all the Embedding. + It can be the same as the WholeMemory Communicator used to create WholeMemory Embedding. +- **WholeMemory type**: WholeMemory CachePolicy uses WholeMemory type to specify the WholeMemory type of cache. +- **WholeMemory location**: WholeMemory CachePolicy use WholeMemory location to specify the location of the cache. +- **Access type**: Access type can be readonly or readwrite. +- **Cache ratio**: Specify how much memory the cache will use. This ratio is computed for each GPU set that caches the + whole embedding. + +The two most commonly used caches are: +- **Device cached host memory**: When the WholeMemory Communicator for Cache Policy is the same as the WholeMemory + Communicator used to create WholeMemory Embedding, it means that the cache has same GPU set as WholeMemory Embedding. + So each GPU just caches its own part of raw Embedding. + Most commonly, when raw WholeMemory Embedding is located on host memory, and the cache is on device + memory, each GPU just caches its own part of host memory. +- **Local cached global memory**: The WholeMemory Communicator of WholeMemory CachePolicy can also be a subset of the + WholeMemory Communicator of WholeMemory Embedding. In this case, the subset of GPUs together cache all the embeddings. + Normally, when raw WholeMemory Embedding is partitioned on different machine nodes, and we + want to cache some embeddings in local machine or local GPU, then the subset of GPU can be all the GPUs in the local + machine. For local cached global memory, only readonly is supported. + +#### WholeMemory Embedding Sparse Optimizer +Another feature of WholeMemory Embedding is that WholeMemory Embedding supports embedding training. +To efficiently train large embedding tables, a sparse optimizer is needed. +WholeMemory Embedding Sparse Optimizer can run on a cached or noncached WholeMemory Embedding. +Currently supported optimizers include SGD, Adam, RMSProp and AdaGrad. + +## Graph Structure +Graph structure in WholeGraph is also based on WholeMemory. +In WholeGraph, graph is stored in [CSR format](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format)). +Both ROW_INDEX (noted as `csr_row_ptr`) and COL_INDEX (notated as `csr_col_ind`) are stored in a +WholeMemory Tensor. So loading Graph Structure can use [WholeMemory Tensor Loading mechanism](#load-and-store). diff --git a/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md b/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md new file mode 100644 index 000000000..a5541109c --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md @@ -0,0 +1,58 @@ +# WholeMemory Implementation Details +As described in [WholeMemory Introduction](wholegraph_intro.md), there are two WholeMemory location and three +WholeMemory types. So there will be total six WholeMemory. + +| Type | CONTINUOUS | CONTINUOUS | CHUNKED | CHUNKED | DISTRIBUTED | DISTRIBUTED | +|:-------------:|:-----------:|:----------:|:---------:|:---------:|:-----------:|:-----------:| +| Location | DEVICE | HOST | DEVICE | HOST | DEVICE | HOST | +| Allocated by | EACH | FIRST | EACH | FIRST | EACH | EACH | +| Allocate API | Driver | Host | Runtime | Host | Runtime | Runtime | +| IPC Mapping | Unix fd | mmap | cudaIpc | mmap | No IPC map | No IPC map | + +For "Continuous" and "Chunked" types of WholeMemory, all memory is mapped to each GPU, +so these two types are all "Mapped" WholeMemory, in contrast to "Distributed" WholeMemory where all are not mapped. + +## WholeMemory Layout +Since the underlying memory of a single WholeMemory object may be on multiple GPU devices, the WholeGraph library will +partition data into these GPU devices. +The partition method guarantees that each GPU can access one continuous part of the entire memory. +Here "can access" means can directly access from CUDA kernels, but the memory doesn't have to be physically on that GPU. +For example,it can be on host memory or other GPU's device memory that can be access using P2P. +In that case the stored data has its own granularity that shouldn't be split. Data granularity can be specified while +creating WholeMemory. Then each data granularity can be considered as a block of data. + +The follow figure shows the layout of 15 data block over 4 GPUs. +![WholeMemory Layout](../imgs/general_wholememory.png) + +For WholeMemory Tensors, they can be 1D or 2D tensors. +For 1D tensor, data granularity is one element. For 2D tensor, data granularity is its 1D tensor. +The layout will be like this: +![WholeMemory Tensor Layout](../imgs/wholememory_tensor.png) + +## WholeMemory Allocation +As there are six types of WholeMemory, the allocation process of each type are as follows: + +### Device Continuous WholeMemory +For Device Continuous WholeMemory, first a range of virtual address space is reserved in each GPU, which covers the +entire memory range. Then a part of pyhsical memory is allocated in each GPU, as shown in the following figure. +![Device Continuous WholeMemory Allocation Step 1](../imgs/device_continuous_wholememory_step1.png) +After that, each GPU gathers all the memory handles from all GPUs, and maps them to the reserved address space. +![Device Continuous WholeMemory Allocation Step 2](../imgs/device_continuous_wholememory_step2.png) + +### Device Chunked WholeMemory +For Device Chunked WholeMemory, first each GPU allocates its own part of memory using CUDA runtime API, this will create +both a virtual address space and physical memory for its own memory. +![Device Chunked WholeMemory Allocation Step 1](../imgs/device_chunked_wholememory_step1.png) +Each GPU gathers the Ipc handle of memory from all other GPUs, and maps that into its own virtual address space. +![Device Chunked WholeMemory Allocation Step 2](../imgs/device_chunked_wholememory_step2.png) + +### Host Mapped WholeMemory +For Host, Continuous and Chunked are using the same method. First, rank and allocate the host physical and share that to all +ranks. +![Host Mapped WholeMemory Allocation Step 1](../imgs/host_mapped_wholememory_step1.png) +Then each rank registers that host memory to GPU address space. +![Host Mapped WholeMemory Allocation Step 2](../imgs/host_mapped_wholememory_step2.png) + +### Distributed WholeMemory +For Distributed WholeMemory, each GPU just malloc its own part of memory, no need to share to other GPUs. +![Distributed WholeMemory Allocation](../imgs/distributed_wholememory.png) diff --git a/docs/cugraph/source/wholegraph/basics/wholememory_intro.md b/docs/cugraph/source/wholegraph/basics/wholememory_intro.md new file mode 100644 index 000000000..7209da947 --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/wholememory_intro.md @@ -0,0 +1,123 @@ +## WholeMemory +WholeMemory can be regarded as a whole view of GPU memory. +WholeMemory exposes a handle to the memory instance no matter how the underlying data is stored across multiple GPUs. +WholeMemory assumes that a separate process is used to control each GPU. + +### WholeMemory Basics +To define WholeMemory, we need to specify the following: + +#### 1. Specify the set of GPU to handle the Memory + +As WholeMemory is owned by a set of GPUs, so the set of GPUs need to be specified. +This is done by creating [WholeMemory Communicator](#wholememory-communicator) and specify the WholeMemory Communicator +when creating WholeMemory. + +#### 2. Specify the location of the memory + +Although WholeMemory is owned by a set of GPUs, the memory itself can be located on host memory or on device memory. +So the location of the memory needs to be specified. Two types of location can be specified. + +- **Host memory**: will use pinned host memory as underlying storage. +- **Device memory**: will use GPU device memory as underlying storage. + +#### 3. Specify the address mapping mode of the memory + +As WholeMemory is owned by multiple GPUs, each GPU will access the whole memory space, so we need address mapping. +There are three types of address mapping modes (also known as WholeMemory types), they are: + +- **Continuous**: All memory from each GPU will be mapped into a single continuous memory address space for each GPU. + In this mode, each GPU can directly access the whole memory using a single pointer and offset, just like using normal + device memory. Software will see no difference. Hardware peer-to-peer access will handle the underlying communication. + +- **Chunked**: Memory from each GPU will be mapped into different memory chunks, one chunk for each GPU. + In this mode, direct access is also supported, but not using a single pointer. Software will see the chunked memory. + However, an abstract layer can hide this. + +- **Distributed**: Memory from other GPUs is not mapped into current GPU, so no direct access is supported. + To access memory of another GPU, explicit communication is needed. + +If you would like to know more details about WholeMemory locations and WholeMemory types, please refer to +[WholeMemory Implementation Details](wholememory_implementation_details.md) + +### WholeMemory Communicator +WholeMemory Communicator has two main purpose: + +- **Defines a set of GPUs which works together on WholeMemory.** WholeMemory Communicator is created by all GPUs that + wants to work together. A WholeMemory Communicator can be reused as long as the GPU set needed is the same. +- **Provides underlying communication channel needed by WholeMemory.** WholeMemory may need commuincator between GPUs + during the WholeMemory creation and some OPs on some types of WholeMemory. + +To Create WholeMemory Communicator, a WholeMemory Unique ID need to be created first, it is usually created by the first +GPU in the set of GPUs, and then broadcasted to all GPUs that want to work together. Then all GPUs in this communicator +will call WholeMemory Communicator creation function using this WholeMemory Unique ID, and the rank of current GPU as +well as all GPU count. + +### WholeMemory Granularity +As underlying storage may be physically partitioned into multiple GPUs, it is usually not wanted inside one single +user data block. To help with this, when creating WholeMemory, the granularity of data can be specified. Therefore +WholeMemory is considered as multiple blocks of the same granularity and will not get split inside the granularity. + +### WholeMemory Mapping +Since WholeMemory provides a whole view of memory to GPU, mapping is usually needed to access WholeMemory. +Different types of WholeMemory have different mapping methods supported as their names. +Some mappings supported include: +- All the WholeMemory types support mapping the memory range that local GPU is responsible for. + That is, each rank can directly access "Local" memory in all types of WholeMemory. + Here "Local" memory doesn't have to be on current GPU's memory, it can be on host memory or even maybe on other GPU, + but it is guaranteed to be directly accessed by current GPU. +- Chunked and Continuous WholeMemory also support Chunked mapping. That is, memory of all GPUs can be mapped into + current GPU, one continuous chunk for one GPU. Each chunk can be directly accessed by current GPU. But the memory of + different chunks are not guaranteed to be continuous. +- Continuous WholeMemory can be mapped into continuous memory space. That is, memory of all GPUs are mapped into a + single range of virtual memory, accessing different positions of this memory will physically access different + GPUs. This mapping will be handled by hardware (CPU pagetable or GPU pagetable). + +### Operations on WholeMemory +There are some operations that can be performed on WholeMemory. They are based on the mapping of WholeMemory. +#### Local Operation +As all WholeMemory supports mapping of local memory, so operation on local memory is supported. The operation can be +either read or write. Just use it as GPU memory of current device is OK. +#### Load / Store +To facilitate file operation, Load / Store WholeMemory from file or to file is supported. WholeMemory use raw binary +file format for disk operation. For Load, the input file can be single file or a list of files, if it is a list, they +will be logically concatenated together and then loaded. For store, each GPU stores its local memory to file, producing +a list of files. +#### Gather / Scatter +WholeMemory also supports Gather / Scatter operations, usually they operate on a +[WholeMemory Tensor](#wholememory-tensor). + +### WholeMemory Tensor +Compared to PyTorch, WholeMemory is like PyTorch Storage while WholeMemory Tensor is like PyTorch Tensor. +For now, WholeMemory supports only 1D and 2D tensor, or array and matrix. Only first dimension is partitioned. + +### WholeMemory Embedding +WholeMemory Embedding is just like 2D WholeMemory Tensor, with cache support and sparse optimizer support added. +#### Cache Support +WholeMemory Embedding supports cache. To create WholeMemory Embedding with cache, WholeMemory CachePolicy need first be +created. WholeMemoryCachePolicy can be created with following fields: +- **WholeMemory Communicator**: WholeMemory CachePolicy also need WholeMemory Communicator. + This WholeMemory Communicator defines the set of GPUs that cache the all the Embedding. + It can be the same as the WholeMemory Communicator used to create WholeMemory Embedding. +- **WholeMemory type**: WholeMemory CachePolicy uses WholeMemory type to specify the WholeMemory type of the cache. +- **WholeMemory location**: WholeMemory CachePolicy uses WholeMemory location to specify the location of the cache. +- **Access type**: Access type can be readonly or readwrite. +- **Cache ratio**: Specify how much memory the cache will use. This ratio is computed for each GPU set that caches the + whole embedding. + +There are two most commonly used caches. They are: +- **Device cached host memory**: When the WholeMemory Communicator for Cache Policy is the same as the WholeMemory + Communicator used to create WholeMemory Embedding, it means that cache has the same GPU set as WholeMemory Embedding. + So each GPU just cache its own part of raw Embedding. + Normally, when raw WholeMemory Embedding is located on host memory, and the cache is on device + memory, each GPU just caches its own part of host memory. +- **Local cached global memory**: The WholeMemory Communicator of WholeMemory CachePolicy can also be a subset of the + WholeMemory Communicator of WholeMemory Embedding. In this case, the subset of GPUs together cache all the embeddings. + Typically, raw WholeMemory Embedding is partitioned on different machine nodes, and we + want to cache some embeddings in local machine or local GPU, then the subset of GPUs can be all the GPUs on the local + machine. For local cached global memory supports just readonly. + +#### WholeMemory Embedding Sparse Optimizer +Another feature of WholeMemory Embedding is that WholeMemory Embedding supports embedding training. +To efficiently train large embedding tables, a sparse optimizer is needed. +The WholeMemory Embedding Sparse Optimizer can run on cached or non-cached WholeMemory Embedding. +Currently supported optimizers include SGD, Adam, RMSProp and AdaGrad. diff --git a/docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png b/docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png new file mode 100644 index 0000000000000000000000000000000000000000..b8a0447e6fb9dde5691c987aa734a6ba4ae24663 GIT binary patch literal 23136 zcmeFZ2UOErw=WuWyZJVv=$7UdL~tuei-3T18#a0g)exjgml7aA01K#~6p`K(AxP*o z5Mscs)X*eC5F{uN0tASJ5<)2NAC&#=citWEoIB2akM6JdGYBcRuKoL-Z`c1N z+x)Lzgr7F!V1Y-=YXoI)1U^F^@yoV9+}VI=@Gfv_6Os%{{*Z|_A6=27pH^7BY-p-V 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zFb{6PcMtyiKXCX>sQX`*Bz`9TXW|C_|C+j*>l=I57*q0IF@3|IY~GSpx{-NZ|HXd+ DoB#6Y literal 0 HcmV?d00001 diff --git a/docs/cugraph/source/wholegraph/index.rst b/docs/cugraph/source/wholegraph/index.rst new file mode 100644 index 000000000..2a69544b4 --- /dev/null +++ b/docs/cugraph/source/wholegraph/index.rst @@ -0,0 +1,14 @@ +WholeGraph +========== +RAPIDS WholeGraph has following package: + +* pylibwholegraph: shared memory-based GPU-accelerated GNN training + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + basics/index + installation/index + diff --git a/docs/cugraph/source/wholegraph/installation/container.md b/docs/cugraph/source/wholegraph/installation/container.md new file mode 100644 index 000000000..3a2c627c5 --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/container.md @@ -0,0 +1,29 @@ +# Build Container for WholeGraph +To run WholeGraph or build WholeGraph from source, set up the environment first. +We recommend using Docker images. +For example, to build the WholeGraph base image from the NGC pytorch 22.10 image, you can follow `Dockerfile`: +```dockerfile +FROM nvcr.io/nvidia/pytorch:22.10-py3 + +RUN apt update && DEBIAN_FRONTEND=noninteractive apt install -y lsb-core software-properties-common wget libspdlog-dev + +#RUN remove old cmake to update +RUN conda remove --force -y cmake +RUN rm -rf /usr/local/bin/cmake && rm -rf /usr/local/lib/cmake && rm -rf /usr/lib/cmake + +RUN apt-key adv --fetch-keys https://apt.kitware.com/keys/kitware-archive-latest.asc && \ + export LSB_CODENAME=$(lsb_release -cs) && \ + apt-add-repository -y "deb https://apt.kitware.com/ubuntu/ ${LSB_CODENAME} main" && \ + apt update && apt install -y cmake + +# update py for pytest +RUN pip3 install -U py +RUN pip3 install Cython setuputils3 scikit-build nanobind pytest-forked pytest +``` + +To run GNN applications, you may also need cuGraphOps, DGL and/or PyG libraries to run the GNN layers. +You may refer to [DGL](https://www.dgl.ai/pages/start.html) or [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) +For example, to install DGL, you may need to add: +```dockerfile +RUN pip3 install dgl -f https://data.dgl.ai/wheels/cu118/repo.html +``` diff --git a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md new file mode 100644 index 000000000..5b2072b05 --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md @@ -0,0 +1,48 @@ + +# Getting the WholeGraph Packages + +Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) +and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. + + +There are 4 ways to get WholeGraph packages: +1. [Quick start with Docker Repo](#docker) +2. [Conda Installation](#conda) +3. [Pip Installation](#pip) +4. [Build from Source](./source_build.md) + + +
+ +## Docker +The RAPIDS Docker containers (as of Release 23.10) contain all RAPIDS packages, including WholeGraph, as well as all required supporting packages. To download a container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries. + +
+ + +## Conda +It is easy to install WholeGraph using conda. You can get a minimal conda installation with [Miniconda](https://conda.io/miniconda.html) or get the full installation with [Anaconda](https://www.anaconda.com/download). + +WholeGraph conda packages + * libwholegraph + * pylibwholegraph + +Replace the package name in the example below to the one you want to install. + + +Install and update WholeGraph using the conda command: + +```bash +conda install -c rapidsai -c conda-forge -c nvidia wholegraph cudatoolkit=11.8 +``` + +
+ +## PIP +wholegraph, and all of RAPIDS, is available via pip. + +``` +pip install wholegraph-cu11 --extra-index-url=https://pypi.nvidia.com +``` + +
diff --git a/docs/cugraph/source/wholegraph/installation/index.rst b/docs/cugraph/source/wholegraph/installation/index.rst new file mode 100644 index 000000000..09f1cb44a --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/index.rst @@ -0,0 +1,9 @@ +Installation +============ + +.. toctree:: + :maxdepth: 2 + + getting_wholegraph + container + source_build diff --git a/docs/cugraph/source/wholegraph/installation/source_build.md b/docs/cugraph/source/wholegraph/installation/source_build.md new file mode 100644 index 000000000..c468048c3 --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/source_build.md @@ -0,0 +1,187 @@ +# Building from Source + +The following instructions are for users wishing to build wholegraph from source code. These instructions are tested on supported distributions of Linux,CUDA, +and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for a list of supported environments. +Other operating systems _might be_ compatible, but are not currently tested. + +The wholegraph package includes both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly. +The C/C++ CUDA library is `libwholegraph` and the python library is `pylibwholegraph`. + +## Prerequisites + +__Compiler__: +* `gcc` version 11.0+ +* `nvcc` version 11.0+ +* `cmake` version 3.26.4+ + +__CUDA__: +* CUDA 11.8+ +* NVIDIA driver 450.80.02+ +* Pascal architecture or better + +You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). + +__Other Packages__: +* ninja +* nccl +* cython +* setuputils3 +* scikit-learn +* scikit-build +* nanobind>=0.2.0 + +## Building wholegraph +To install wholegraph from source, ensure the dependencies are met. + +### Clone Repo and Configure Conda Environment +__GIT clone a version of the repository__ + + ```bash + # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME + export WHOLEGRAPH_HOME=$(pwd)/wholegraph + + # Download the wholegraph repo - if you have a forked version, use that path here instead + git clone https://github.com/rapidsai/wholegraph.git $WHOLEGRAPH_HOME + + cd $WHOLEGRAPH_HOME + ``` + +__Create the conda development environment__ + +```bash +# create the conda environment (assuming in base `wholegraph` directory) + +# for CUDA 11.x +conda env create --name wholegraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml + +# activate the environment +conda activate wholegraph_dev + +# to deactivate an environment +conda deactivate +``` + + - The environment can be updated as development includes/changes the dependencies. To do so, run: + + +```bash + +# Where XXX is the CUDA version +conda env update --name wholegraph_dev --file conda/environments/all_cuda-XXX_arch-x86_64.yaml + +conda activate wholegraph_dev +``` + + +### Build and Install Using the `build.sh` Script +Using the `build.sh` script make compiling and installing wholegraph a +breeze. To build and install, simply do: + +```bash +$ cd $WHOLEGRAPH_HOME +$ ./build.sh clean +$ ./build.sh libwholegraph +$ ./build.sh pylibwholegraph +``` + +There are several other options available on the build script for advanced users. +`build.sh` options: +```bash +build.sh [ ...] [ ...] + where is: + clean - remove all existing build artifacts and configuration (start over). + uninstall - uninstall libwholegraph and pylibwholegraph from a prior build/install (see also -n) + libwholegraph - build the libwholegraph C++ library. + pylibwholegraph - build the pylibwholegraph Python package. + tests - build the C++ (OPG) tests. + benchmarks - build benchmarks. + docs - build the docs + and is: + -v - verbose build mode + -g - build for debug + -n - no install step + --allgpuarch - build for all supported GPU architectures + --cmake-args=\\\"\\\" - add arbitrary CMake arguments to any cmake call + --compile-cmd - only output compile commands (invoke CMake without build) + --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) + -h | --h[elp] - print this text + + default action (no args) is to build and install 'libwholegraph' then 'pylibwholegraph' targets + +examples: +$ ./build.sh clean # remove prior build artifacts (start over) +$ ./build.sh + +# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4) +$ PARALLEL_LEVEL=4 ./build.sh libwholegraph + +Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. +``` + + +## Building each section independently +### Build and Install the C++/CUDA `libwholegraph` Library +CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. + +This project uses cmake for building the C/C++ library. To configure cmake, run: + + ```bash + # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME + export WHOLEGRAPH_HOME=$(pwd)/wholegraph + + cd $WHOLEGRAPH_HOME + cd cpp # enter cpp directory + mkdir build # create build directory + cd build # enter the build directory + cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX + + # now build the code + make -j # "-j" starts multiple threads + make install # install the libraries + ``` +The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/wholegraph` respectively. + +### Building and installing the Python package + +Build and Install the Python packages to your Python path: + +```bash +cd $WHOLEGRAPH_HOME +cd python +cd pylibwholegraph +python setup.py build_ext --inplace +python setup.py install # install pylibwholegraph +``` + +## Run tests + +Run either the C++ or the Python tests with datasets + + - **Python tests with datasets** + + ```bash + cd $WHOLEGRAPH_HOME + cd python + pytest + ``` + + - **C++ stand alone tests** + + From the build directory : + + ```bash + # Run the tests + cd $WHOLEGRAPH_HOME + cd cpp/build + gtests/PARALLEL_UTILS_TESTS # this is an executable file + ``` + + +Note: This conda installation only applies to Linux and Python versions 3.8/3.10. + +## Creating documentation + +Python API documentation can be generated from _./docs/wholegraph directory_. Or through using "./build.sh docs" + +## Attribution +Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md From 6ae2b724bd3f5f69b9ba915eaa29a2b84788fe4b Mon Sep 17 00:00:00 2001 From: Huiyu Xie Date: Tue, 21 Nov 2023 11:40:36 -0800 Subject: [PATCH 291/384] [DOC]: Fix invalid links and add materials to notebooks (#4002) I'm new to CuGraph repository and would like to contribute further in the future. So I start with minor tasks with docs to familiarize myself with the contribution process in this repository. Here is what I have done in this PR: - Fixed some invalid links in the documentation. - Corrected and added formulas related to centrality. - Added more references to the centrality to ensure consistency. Also, the images located at https://github.com/rapidsai/cugraph/tree/main/python/cugraph cannot be displayed because it is symbolic link to the repository's README file. But it's not a major issue. Authors: - Huiyu Xie (https://github.com/huiyuxie) - Ralph Liu (https://github.com/nv-rliu) - Naim (https://github.com/naimnv) - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4002 --- .../source/graph_support/algorithms/Centrality.md | 14 ++++++++------ .../cugraph/source/installation/getting_cugraph.md | 2 +- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/docs/cugraph/source/graph_support/algorithms/Centrality.md b/docs/cugraph/source/graph_support/algorithms/Centrality.md index f82a1fe12..8119e6552 100644 --- a/docs/cugraph/source/graph_support/algorithms/Centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/Centrality.md @@ -15,13 +15,15 @@ But which vertices are most important? The answer depends on which measure/algor |Algorithm |Notebooks Containing |Description | | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -|[Degree Centrality](./degree_centrality.html)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb) |Measure based on counting direct connections for each vertex| -|[Betweenness Centrality](./betweenness_centrality.html)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb) |Number of shortest paths through the vertex| -|[Eigenvector Centrality](./eigenvector_centrality.html)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| -|[Katz Centrality](./katz_centrality.html)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | -|Pagerank|[Centrality](./Centrality.ipynb), [Pagerank](../../link_analysis/Pagerank.ipynb) |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices. | +|[Degree Centrality](./degree_centrality.md)| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Degree](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Degree.ipynb) |Measure based on counting direct connections for each vertex| +|[Betweenness Centrality](./betweenness_centrality.md)| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Betweenness](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) |Number of shortest paths through the vertex| +|[Eigenvector Centrality](./eigenvector_centrality.md)|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Eigenvector](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| +|[Katz Centrality](./katz_centrality.md)|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | +|Pagerank|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices. | + +[System Requirements](https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md#requirements) + -[System Requirements](../../README.html#requirements) | Author Credit | Date | Update | cuGraph Version | Test Hardware | | --------------|------------|------------------|-----------------|----------------| diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index 509508c52..625f2c64c 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -9,7 +9,7 @@ There are 4 ways to get cuGraph packages: 1. [Quick start with Docker Repo](#docker) 2. [Conda Installation](#conda) 3. [Pip Installation](#pip) -4. [Build from Source](#SOURCE) +4. [Build from Source](./source_build.md)
From 93a8af24ee9ec33c39e96282c16044514babbcde Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 21 Nov 2023 20:44:54 -0600 Subject: [PATCH 292/384] nx-cugraph: add `eigenvector_centrality`, `katz_centrality`, `hits`, `pagerank` (#3968) Add `eigenvector_centrality`, `katz_centrality`, and `hits`. I may add pagerank next. Authors: - Erik Welch (https://github.com/eriknw) - Ralph Liu (https://github.com/nv-rliu) - Naim (https://github.com/naimnv) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3968 --- python/nx-cugraph/_nx_cugraph/__init__.py | 21 ++++ python/nx-cugraph/lint.yaml | 2 +- .../nx_cugraph/algorithms/__init__.py | 10 +- .../algorithms/centrality/__init__.py | 2 + .../algorithms/centrality/eigenvector.py | 64 ++++++++++ .../nx_cugraph/algorithms/centrality/katz.py | 100 ++++++++++++++++ .../algorithms/community/louvain.py | 34 ++++-- .../algorithms/link_analysis/__init__.py | 14 +++ .../algorithms/link_analysis/hits_alg.py | 81 +++++++++++++ .../algorithms/link_analysis/pagerank_alg.py | 112 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/classes/graph.py | 50 ++++---- python/nx-cugraph/nx_cugraph/interface.py | 21 +++- .../nx_cugraph/tests/test_ktruss.py | 2 +- python/nx-cugraph/nx_cugraph/utils/misc.py | 34 +++++- 14 files changed, 509 insertions(+), 38 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 910db1bc3..ef5f8f3fc 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -47,12 +47,14 @@ "diamond_graph", "dodecahedral_graph", "edge_betweenness_centrality", + "eigenvector_centrality", "empty_graph", "florentine_families_graph", "from_pandas_edgelist", "from_scipy_sparse_array", "frucht_graph", "heawood_graph", + "hits", "house_graph", "house_x_graph", "icosahedral_graph", @@ -62,6 +64,7 @@ "isolates", "k_truss", "karate_club_graph", + "katz_centrality", "krackhardt_kite_graph", "ladder_graph", "les_miserables_graph", @@ -75,6 +78,7 @@ "number_of_selfloops", "octahedral_graph", "out_degree_centrality", + "pagerank", "pappus_graph", "path_graph", "petersen_graph", @@ -96,19 +100,36 @@ # BEGIN: extra_docstrings "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", + "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.", "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", "k_truss": ( "Currently raises `NotImplementedError` for graphs with more than one connected\n" "component when k >= 3. We expect to fix this soon." ), + "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.", "louvain_communities": "`seed` parameter is currently ignored.", + "pagerank": "`dangling` parameter is not supported, but it is checked for validity.", # END: extra_docstrings }, "extra_parameters": { # BEGIN: extra_parameters + "eigenvector_centrality": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + }, + "hits": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + 'weight : string or None, optional (default="weight")': "The edge attribute to use as the edge weight.", + }, + "katz_centrality": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + }, "louvain_communities": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).", }, + "pagerank": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + }, # END: extra_parameters }, } diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 01a806e61..a94aa9f04 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -53,7 +53,7 @@ repos: rev: v0.1.3 hooks: - id: ruff - args: [--fix-only, --show-fixes] + args: [--fix-only, --show-fixes] # --unsafe-fixes] - repo: https://github.com/PyCQA/flake8 rev: 6.1.0 hooks: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 32cd6f31a..63841b15b 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,10 +10,18 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import bipartite, centrality, community, components, shortest_paths +from . import ( + bipartite, + centrality, + community, + components, + shortest_paths, + link_analysis, +) from .bipartite import complete_bipartite_graph from .centrality import * from .components import * from .core import * from .isolate import * from .shortest_paths import * +from .link_analysis import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py index af91f2278..496dc6aff 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py @@ -12,3 +12,5 @@ # limitations under the License. from .betweenness import * from .degree_alg import * +from .eigenvector import * +from .katz import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py new file mode 100644 index 000000000..c0f02a625 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py @@ -0,0 +1,64 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + networkx_algorithm, + not_implemented_for, +) + +__all__ = ["eigenvector_centrality"] + + +@not_implemented_for("multigraph") +@networkx_algorithm(extra_params=_dtype_param) +def eigenvector_centrality( + G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None +): + """`nstart` parameter is not used, but it is checked for validity.""" + G = _to_graph(G, weight, np.float32) + if len(G) == 0: + raise nx.NetworkXPointlessConcept( + "cannot compute centrality for the null graph" + ) + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + # Check if given nstart is valid even though we don't use it + nstart = G._dict_to_nodearray(nstart, dtype=dtype) + if (nstart == 0).all(): + raise nx.NetworkXError("initial vector cannot have all zero values") + if nstart.sum() == 0: + raise ZeroDivisionError + # nstart /= total # Uncomment (and assign total) when nstart is used below + try: + node_ids, values = plc.eigenvector_centrality( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(weight, 1, dtype, store_transposed=True), + epsilon=tol, + max_iterations=max_iter, + do_expensive_check=False, + ) + except RuntimeError as exc: + # Errors from PLC are sometimes a little scary and not very helpful + raise nx.PowerIterationFailedConvergence(max_iter) from exc + return G._nodearrays_to_dict(node_ids, values) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py new file mode 100644 index 000000000..b61b811b8 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py @@ -0,0 +1,100 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + networkx_algorithm, + not_implemented_for, +) + +__all__ = ["katz_centrality"] + + +@not_implemented_for("multigraph") +@networkx_algorithm(extra_params=_dtype_param) +def katz_centrality( + G, + alpha=0.1, + beta=1.0, + max_iter=1000, + tol=1.0e-6, + nstart=None, + normalized=True, + weight=None, + *, + dtype=None, +): + """`nstart` isn't used (but is checked), and `normalized=False` is not supported.""" + if not normalized: + # Redundant with the `_can_run` check below when being dispatched by NetworkX, + # but we raise here in case this funcion is called directly. + raise NotImplementedError("normalized=False is not supported.") + G = _to_graph(G, weight, np.float32) + if (N := len(G)) == 0: + return {} + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + # Check if given nstart is valid even though we don't use it + nstart = G._dict_to_nodearray(nstart, 0, dtype) + b = bs = None + try: + b = float(beta) + except (TypeError, ValueError) as exc: + try: + bs = G._dict_to_nodearray(beta, dtype=dtype) + b = 1.0 # float value must be given to PLC (and will be ignored) + except (KeyError, ValueError): + raise nx.NetworkXError( + "beta dictionary must have a value for every node" + ) from exc + try: + node_ids, values = plc.katz_centrality( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(weight, 1, dtype, store_transposed=True), + betas=bs, + alpha=alpha, + beta=b, + epsilon=N * tol, + max_iterations=max_iter, + do_expensive_check=False, + ) + except RuntimeError as exc: + # Errors from PLC are sometimes a little scary and not very helpful + raise nx.PowerIterationFailedConvergence(max_iter) from exc + return G._nodearrays_to_dict(node_ids, values) + + +@katz_centrality._can_run +def _( + G, + alpha=0.1, + beta=1.0, + max_iter=1000, + tol=1.0e-6, + nstart=None, + normalized=True, + weight=None, + *, + dtype=None, +): + return normalized diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index 45a3429d2..936d837da 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -16,6 +16,7 @@ from nx_cugraph.convert import _to_undirected_graph from nx_cugraph.utils import ( + _dtype_param, _groupby, _seed_to_int, networkx_algorithm, @@ -32,11 +33,19 @@ extra_params={ "max_level : int, optional": ( "Upper limit of the number of macro-iterations (max: 500)." - ) + ), + **_dtype_param, } ) def louvain_communities( - G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None + G, + weight="weight", + resolution=1, + threshold=0.0000001, + seed=None, + *, + max_level=None, + dtype=None, ): """`seed` parameter is currently ignored.""" # NetworkX allows both directed and undirected, but cugraph only allows undirected. @@ -54,20 +63,20 @@ def louvain_communities( stacklevel=2, ) max_level = 500 - vertices, clusters, modularity = plc.louvain( + node_ids, clusters, modularity = plc.louvain( resource_handle=plc.ResourceHandle(), - graph=G._get_plc_graph(), + graph=G._get_plc_graph(weight, 1, dtype), max_level=max_level, # TODO: add this parameter to NetworkX threshold=threshold, resolution=resolution, do_expensive_check=False, ) - groups = _groupby(clusters, vertices, groups_are_canonical=True) - rv = [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] - # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix + groups = _groupby(clusters, node_ids, groups_are_canonical=True) + rv = [set(G._nodearray_to_list(ids)) for ids in groups.values()] + # TODO: PLC doesn't handle isolated node_ids yet, so this is a temporary fix isolates = _isolates(G) if isolates.size > 0: - isolates = isolates[isolates > vertices.max()] + isolates = isolates[isolates > node_ids.max()] if isolates.size > 0: rv.extend({node} for node in G._nodearray_to_list(isolates)) return rv @@ -75,7 +84,14 @@ def louvain_communities( @louvain_communities._can_run def _( - G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None + G, + weight="weight", + resolution=1, + threshold=0.0000001, + seed=None, + *, + max_level=None, + dtype=None, ): # NetworkX allows both directed and undirected, but cugraph only allows undirected. return not G.is_directed() diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py new file mode 100644 index 000000000..a68d6940d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .hits_alg import * +from .pagerank_alg import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py new file mode 100644 index 000000000..1c8a47c24 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py @@ -0,0 +1,81 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + index_dtype, + networkx_algorithm, +) + +__all__ = ["hits"] + + +@networkx_algorithm( + extra_params={ + 'weight : string or None, optional (default="weight")': ( + "The edge attribute to use as the edge weight." + ), + **_dtype_param, + } +) +def hits( + G, + max_iter=100, + tol=1.0e-8, + nstart=None, + normalized=True, + *, + weight="weight", + dtype=None, +): + G = _to_graph(G, weight, np.float32) + if (N := len(G)) == 0: + return {}, {} + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + nstart = G._dict_to_nodearray(nstart, 0, dtype) + if max_iter <= 0: + if nx.__version__[:3] <= "3.2": + raise ValueError("`maxiter` must be a positive integer.") + raise nx.PowerIterationFailedConvergence(max_iter) + try: + node_ids, hubs, authorities = plc.hits( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(weight, 1, dtype, store_transposed=True), + tol=tol, + initial_hubs_guess_vertices=None + if nstart is None + else cp.arange(N, dtype=index_dtype), + initial_hubs_guess_values=nstart, + max_iter=max_iter, + normalized=normalized, + do_expensive_check=False, + ) + except RuntimeError as exc: + # Errors from PLC are sometimes a little scary and not very helpful + raise nx.PowerIterationFailedConvergence(max_iter) from exc + return ( + G._nodearrays_to_dict(node_ids, hubs), + G._nodearrays_to_dict(node_ids, authorities), + ) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py new file mode 100644 index 000000000..63f6e89c3 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py @@ -0,0 +1,112 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + index_dtype, + networkx_algorithm, +) + +__all__ = ["pagerank"] + + +@networkx_algorithm(extra_params=_dtype_param) +def pagerank( + G, + alpha=0.85, + personalization=None, + max_iter=100, + tol=1.0e-6, + nstart=None, + weight="weight", + dangling=None, + *, + dtype=None, +): + """`dangling` parameter is not supported, but it is checked for validity.""" + G = _to_graph(G, weight, 1, np.float32) + if (N := len(G)) == 0: + return {} + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + nstart = G._dict_to_nodearray(nstart, 0, dtype=dtype) + if (total := nstart.sum()) == 0: + raise ZeroDivisionError + nstart /= total + if personalization is not None: + personalization = G._dict_to_nodearray(personalization, 0, dtype=dtype) + if (total := personalization.sum()) == 0: + raise ZeroDivisionError + personalization /= total + if dangling is not None: + # Check if given dangling is valid even though we don't use it + dangling = G._dict_to_nodearray(dangling, 0) # Check validity + if dangling.sum() == 0: + raise ZeroDivisionError + if (G._out_degrees_array() == 0).any(): + raise NotImplementedError("custom dangling weights is not supported") + if max_iter <= 0: + raise nx.PowerIterationFailedConvergence(max_iter) + kwargs = { + "resource_handle": plc.ResourceHandle(), + "graph": G._get_plc_graph(weight, 1, dtype, store_transposed=True), + "precomputed_vertex_out_weight_vertices": None, + "precomputed_vertex_out_weight_sums": None, + "initial_guess_vertices": None + if nstart is None + else cp.arange(N, dtype=index_dtype), + "initial_guess_values": nstart, + "alpha": alpha, + "epsilon": N * tol, + "max_iterations": max_iter, + "do_expensive_check": False, + "fail_on_nonconvergence": False, + } + if personalization is None: + node_ids, values, is_converged = plc.pagerank(**kwargs) + else: + node_ids, values, is_converged = plc.personalized_pagerank( + personalization_vertices=cp.arange(N, dtype=index_dtype), # Why? + personalization_values=personalization, + **kwargs, + ) + if not is_converged: + raise nx.PowerIterationFailedConvergence(max_iter) + return G._nodearrays_to_dict(node_ids, values) + + +@pagerank._can_run +def pagerank( + G, + alpha=0.85, + personalization=None, + max_iter=100, + tol=1.0e-6, + nstart=None, + weight="weight", + dangling=None, + *, + dtype=None, +): + return dangling is None diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index fea318e03..e32f93d8b 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -79,7 +79,7 @@ class Graph: np.dtype(np.uint32): np.dtype(np.float64), np.dtype(np.uint64): np.dtype(np.float64), # raise if x > 2**53 # other - np.dtype(np.bool_): np.dtype(np.float16), + np.dtype(np.bool_): np.dtype(np.float32), np.dtype(np.float16): np.dtype(np.float32), } _plc_allowed_edge_types: ClassVar[set[np.dtype]] = { @@ -562,12 +562,13 @@ def _get_plc_graph( switch_indices: bool = False, edge_array: cp.ndarray[EdgeValue] | None = None, ): - if edge_array is not None: + if edge_array is not None or edge_attr is None: pass - elif edge_attr is None: - edge_array = None elif edge_attr not in self.edge_values: - raise KeyError("Graph has no edge attribute {edge_attr!r}") + if edge_default is None: + raise KeyError("Graph has no edge attribute {edge_attr!r}") + # If we were given a default edge value, then it's probably okay to + # use None for the edge_array if we don't have this edge attribute. elif edge_attr not in self.edge_masks: edge_array = self.edge_values[edge_attr] elif not self.edge_masks[edge_attr].all(): @@ -685,6 +686,9 @@ def _degrees_array(self): degrees += cp.bincount(self.dst_indices, minlength=self._N) return degrees + _in_degrees_array = _degrees_array + _out_degrees_array = _degrees_array + # Data conversions def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: """Convert an iterable of node IDs to an iterable of node keys.""" @@ -748,20 +752,22 @@ def _dict_to_nodearrays( values = cp.fromiter(d.values(), dtype) return node_ids, values - # def _dict_to_nodearray( - # self, - # d: dict[NodeKey, NodeValue] | cp.ndarray[NodeValue], - # default: NodeValue | None = None, - # dtype: Dtype | None = None, - # ) -> cp.ndarray[NodeValue]: - # if isinstance(d, cp.ndarray): - # if d.shape[0] != len(self): - # raise ValueError - # return d - # if default is None: - # val_iter = map(d.__getitem__, self) - # else: - # val_iter = (d.get(node, default) for node in self) - # if dtype is None: - # return cp.array(list(val_iter)) - # return cp.fromiter(val_iter, dtype) + def _dict_to_nodearray( + self, + d: dict[NodeKey, NodeValue] | cp.ndarray[NodeValue], + default: NodeValue | None = None, + dtype: Dtype | None = None, + ) -> cp.ndarray[NodeValue]: + if isinstance(d, cp.ndarray): + if d.shape[0] != len(self): + raise ValueError + if dtype is not None and d.dtype != dtype: + return d.astype(dtype) + return d + if default is None: + val_iter = map(d.__getitem__, self) + else: + val_iter = (d.get(node, default) for node in self) + if dtype is None: + return cp.array(list(val_iter)) + return cp.fromiter(val_iter, dtype) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 8903fdc54..be6b35960 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -72,12 +72,29 @@ def key(testpath): from packaging.version import parse nxver = parse(nx.__version__) - if nxver.major == 3 and nxver.minor in {0, 1}: + + if nxver.major == 3 and nxver.minor <= 2: + # Networkx versions prior to 3.2.1 have tests written to expect + # sp.sparse.linalg.ArpackNoConvergence exceptions raised on no + # convergence in HITS. Newer versions since the merge of + # https://github.com/networkx/networkx/pull/7084 expect + # nx.PowerIterationFailedConvergence, which is what nx_cugraph.hits + # raises, so we mark them as xfail for previous versions of NX. + xfail.update( + { + key( + "test_hits.py:TestHITS.test_hits_not_convergent" + ): "nx_cugraph.hits raises updated exceptions not caught in " + "these tests", + } + ) + + if nxver.major == 3 and nxver.minor <= 1: # MAINT: networkx 3.0, 3.1 # NetworkX 3.2 added the ability to "fallback to nx" if backend algorithms # raise NotImplementedError or `can_run` returns False. The tests below # exercise behavior we have not implemented yet, so we mark them as xfail - # for previous versions of NetworkX. + # for previous versions of NX. xfail.update( { key( diff --git a/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py index a3e4cee31..92fe23606 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py @@ -22,7 +22,7 @@ def test_k_truss(get_graph): Gnx = get_graph() Gcg = nxcg.from_networkx(Gnx, preserve_all_attrs=True) - for k in range(10): + for k in range(6): Hnx = nx.k_truss(Gnx, k) Hcg = nxcg.k_truss(Gcg, k) assert nx.utils.graphs_equal(Hnx, nxcg.to_networkx(Hcg)) diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 26f023bdc..e30337591 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -16,11 +16,14 @@ import operator as op import sys from random import Random -from typing import SupportsIndex +from typing import TYPE_CHECKING, SupportsIndex import cupy as cp import numpy as np +if TYPE_CHECKING: + from ..typing import Dtype + try: from itertools import pairwise # Python >=3.10 except ImportError: @@ -33,11 +36,26 @@ def pairwise(it): prev = cur -__all__ = ["index_dtype", "_groupby", "_seed_to_int", "_get_int_dtype"] +__all__ = [ + "index_dtype", + "_groupby", + "_seed_to_int", + "_get_int_dtype", + "_get_float_dtype", + "_dtype_param", +] # This may switch to np.uint32 at some point index_dtype = np.int32 +# To add to `extra_params=` of `networkx_algorithm` +_dtype_param = { + "dtype : dtype or None, optional": ( + "The data type (np.float32, np.float64, or None) to use for the edge weights " + "in the algorithm. If None, then dtype is determined by the edge values." + ), +} + def _groupby( groups: cp.ndarray, values: cp.ndarray, groups_are_canonical: bool = False @@ -144,3 +162,15 @@ def _get_int_dtype( return np.dtype(dtype_string) except TypeError as exc: raise ValueError("Value is too large to store as integer: {val}") from exc + + +def _get_float_dtype(dtype: Dtype): + """Promote dtype to float32 or float64 as appropriate.""" + if dtype is None: + return np.dtype(np.float32) + rv = np.promote_types(dtype, np.float32) + if np.float32 != rv != np.float64: + raise TypeError( + f"Dtype {dtype} cannot be safely promoted to float32 or float64" + ) + return rv From d82d31952a8ed32c5bb41d0b8f8637fbd294809e Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 22 Nov 2023 12:12:50 -0600 Subject: [PATCH 293/384] Adds missing connected_components algo to table. (#4019) --- python/nx-cugraph/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index 273a6112d..f6a9aac10 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -101,6 +101,7 @@ supported in nx-cugraph. | balanced_cut_clustering | ? | | betweenness_centrality | 23.10 | | bfs | ? | +| connected_components | 23.12 | | core_number | ? | | degree_centrality | 23.12 | | ecg | ? | From 6a55f6f2a452767c5fce43f9daff65d9e437ad85 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 29 Nov 2023 15:23:00 -0600 Subject: [PATCH 294/384] Removes unsupported `setup.py` calls, cleans up text (#4024) closes #4008 * Removes unsupported `setup.py` calls, which resulted in issue #4008 . The document now describes how to use `build.sh`, which is more consistent with docs for other RAPIDS libs. * Removes `build.sh` output from docs in favor of documenting how to use `--help` to eliminate the doc being out-of-date with the script. * Removes extra wording in places and cleans up various sections. Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4024 --- .../source/installation/source_build.md | 195 ++++++------------ 1 file changed, 65 insertions(+), 130 deletions(-) diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md index f5ee0741d..1a129d452 100644 --- a/docs/cugraph/source/installation/source_build.md +++ b/docs/cugraph/source/installation/source_build.md @@ -1,53 +1,46 @@ # Building from Source -The following instructions are for users wishing to build cuGraph from source code. These instructions are tested on supported distributions of Linux, CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for list of supported environments. Other operating systems _might be_ compatible, but are not currently tested. - -The cuGraph package include both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly. +These instructions are tested on supported versions/distributions of Linux, +CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) +for the list of supported environments. Other environments _might be_ +compatible, but are not currently tested. ## Prerequisites -__Compiler:__ +__Compilers:__ * `gcc` version 9.3+ -* `nvcc` version 11.0+ -* `cmake` version 3.20.1+ +* `nvcc` version 11.5+ __CUDA:__ -* CUDA 11.0+ +* CUDA 11.2+ * NVIDIA driver 450.80.02+ * Pascal architecture or better -You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). - -__Packages:__ -* `cmake` version 3.20.1+ -* `libcugraphops` (version matching source branch version, eg. `23.10`) - -You can obtain `libcugraphops` using `conda`/`mamba` from the `nvidia` channel, or using `pip` with the `--extra-index-url=https://pypi.nvidia.com` option. See the [RAPIDS docs](https://docs.rapids.ai/install#environment) for more details. - -## Building cuGraph -To install cuGraph from source, ensure the dependencies are met. +Further details and download links for these prerequisites are available on the +[RAPIDS System Requirements page](https://docs.rapids.ai/install#system-req). +## Setting up the development environment -### Clone Repo and Configure Conda Environment -__GIT clone a version of the repository__ - - ```bash - # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME - export CUGRAPH_HOME=$(pwd)/cugraph - - # Download the cuGraph repo - if you have a folked version, use that path here instead - git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME +### Clone the repository: +```bash +CUGRAPH_HOME=$(pwd)/cugraph +git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME +cd $CUGRAPH_HOME +``` - cd $CUGRAPH_HOME - ``` +### Create the conda environment -__Create the conda development environment__ +Using conda is the easiest way to install both the build and runtime +dependencies for cugraph. While it is possible to build and run cugraph without +conda, the required packages occasionally change, making it difficult to +document here. The best way to see the current dependencies needed for a build +and run environment is to examine the list of packages in the [conda +environment YAML +files](https://github.com/rapidsai/cugraph/blob/main/conda/environments). ```bash -# create the conda environment (assuming in base `cugraph` directory) - # for CUDA 11.x -conda env create --name cugraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml +conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml # activate the environment conda activate cugraph_dev @@ -56,101 +49,53 @@ conda activate cugraph_dev conda deactivate ``` - - The environment can be updated as development includes/changes the dependencies. To do so, run: - +The environment can be updated as cugraph adds/removes/updates its dependencies. To do so, run: ```bash - -# Where XXX is the CUDA 11 version -conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda11.XXX.yml - +# for CUDA 11.x +conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml conda activate cugraph_dev ``` +### Build and Install -### Build and Install Using the `build.sh` Script -Using the `build.sh` script make compiling and installing cuGraph a breeze. To build and install, simply do: +#### Build and install using `build.sh` +Using the `build.sh` script, located in the `$CUGRAPH_HOME` directory, is the +recommended way to build and install the cugraph libraries. By default, +`build.sh` will build and install a predefined set of targets +(packages/libraries), but can also accept a list of targets to build. -```bash -$ cd $CUGRAPH_HOME -$ ./build.sh clean -$ ./build.sh libcugraph -$ ./build.sh cugraph -``` +For example, to build only the cugraph C++ library (`libcugraph`) and the +high-level python library (`cugraph`) without building the C++ test binaries, +run this command: -There are several other options available on the build script for advanced users. -`build.sh` options: ```bash -build.sh [ ...] [ ...] - where is: - clean - remove all existing build artifacts and configuration (start over) - uninstall - uninstall libcugraph and cugraph from a prior build/install (see also -n) - libcugraph - build libcugraph.so and SG test binaries - libcugraph_etl - build libcugraph_etl.so and SG test binaries - pylibcugraph - build the pylibcugraph Python package - cugraph - build the cugraph Python package - nx-cugraph - build the nx-cugraph Python package - cugraph-service - build the cugraph-service_client and cugraph-service_server Python package - cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. - cugraph-dgl - build the cugraph-dgl extensions for DGL - cugraph-pyg - build the cugraph-dgl extensions for PyG - docs - build the docs - and is: - -v - verbose build mode - -g - build for debug - -n - do not install after a successful build - --pydevelop - use setup.py develop instead of install - --allgpuarch - build for all supported GPU architectures - --skip_cpp_tests - do not build the SG test binaries as part of the libcugraph and libcugraph_etl targets - --without_cugraphops - do not build algos that require cugraph-ops - --cmake_default_generator - use the default cmake generator instead of ninja - --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) - -h - print this text - - default action (no args) is to build and install 'libcugraph' then 'libcugraph_etl' then 'pylibcugraph' then 'cugraph' then 'cugraph-service' targets - -examples: -$ ./build.sh clean # remove prior build artifacts (start over) -$ ./build.sh libcugraph -v # compile and install libcugraph with verbose output -$ ./build.sh libcugraph -g # compile and install libcugraph for debug -$ ./build.sh libcugraph -n # compile libcugraph but do not install - -# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4) -$ PARALLEL_LEVEL=4 ./build.sh libcugraph - -Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. +$ cd $CUGRAPH_HOME +$ ./build.sh libcugraph pylibcugraph cugraph --skip_cpp_tests ``` +There are several other options available on the build script for advanced +users. Refer to the output of `--help` for details. -## Building each section independently -#### Build and Install the C++/CUDA `libcugraph` Library -CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. - -This project uses cmake for building the C/C++ library. To configure cmake, run: - - ```bash - # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME - export CUGRAPH_HOME=$(pwd)/cugraph - - cd $CUGRAPH_HOME - cd cpp # enter cpp directory - mkdir build # create build directory - cd build # enter the build directory - cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX - - # now build the code - make -j # "-j" starts multiple threads - make install # install the libraries - ``` -The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/cugraph` respectively. +Note that libraries will be installed to the location set in `$PREFIX` if set +(i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. #### Updating the RAFT branch -`libcugraph` uses the [RAFT](https://github.com/rapidsai/raft) library and there are times when it might be desirable to build against a different RAFT branch, such as when working on new features that might span both RAFT and cuGraph. +`libcugraph` uses the [RAFT](https://github.com/rapidsai/raft) library and +there are times when it might be desirable to build against a different RAFT +branch, such as when working on new features that might span both RAFT and +cuGraph. -For local development, the `CPM_raft_SOURCE=` option can be passed to the `cmake` command to enable `libcugraph` to use the local RAFT branch. +For local development, the `CPM_raft_SOURCE=` option can +be passed to the `cmake` command to enable `libcugraph` to use the local RAFT +branch. The `build.sh` script calls `cmake` to build the C/C++ targets, but +developers can call `cmake` directly in order to pass it options like those +described here. Refer to the `build.sh` script to see how to call `cmake` and +other commands directly. -To have CI test a `cugraph` pull request against a different RAFT branch, modify the bottom of the `cpp/cmake/thirdparty/get_raft.cmake` file as follows: +To have CI test a `cugraph` pull request against a different RAFT branch, +modify the bottom of the `cpp/cmake/thirdparty/get_raft.cmake` file as follows: ```cmake # Change pinned tag and fork here to test a commit in CI @@ -167,24 +112,10 @@ find_and_configure_raft(VERSION ${CUGRAPH_MIN_VERSION_raft} ) ``` -When the above change is pushed to a pull request, the continuous integration servers will use the specified RAFT branch to run the cuGraph tests. After the changes in the RAFT branch are merged to the release branch, remember to revert the `get_raft.cmake` file back to the original cuGraph branch. - -### Building and installing the Python package - -2) Install the Python packages to your Python path: - -```bash -cd $CUGRAPH_HOME -cd python -cd pylibcugraph -python setup.py build_ext --inplace -python setup.py install # install pylibcugraph -cd ../cugraph -python setup.py build_ext --inplace -python setup.py install # install cugraph python bindings - -``` - +When the above change is pushed to a pull request, the continuous integration +servers will use the specified RAFT branch to run the cuGraph tests. After the +changes in the RAFT branch are merged to the release branch, remember to revert +the `get_raft.cmake` file back to the original cuGraph branch. ## Run tests @@ -240,7 +171,10 @@ Note: This conda installation only applies to Linux and Python versions 3.8/3.10 ### (OPTIONAL) Set environment variable on activation -It is possible to configure the conda environment to set environmental variables on activation. Providing instructions to set PATH to include the CUDA toolkit bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be helpful. +It is possible to configure the conda environment to set environment variables +on activation. Providing instructions to set PATH to include the CUDA toolkit +bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be +helpful. ```bash cd ~/anaconda3/envs/cugraph_dev @@ -271,7 +205,8 @@ unset LD_LIBRARY_PATH ## Creating documentation -Python API documentation can be generated from _./docs/cugraph directory_. Or through using "./build.sh docs" +Python API documentation can be generated from _./docs/cugraph directory_. Or +through using "./build.sh docs" ## Attribution Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md From 5b0534f1d3e5868433fcc9bfe367c9147e7f90d2 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 12 Dec 2023 10:26:32 -0600 Subject: [PATCH 295/384] nx-cugraph: update usage of `nodes_or_number` for nx compat (#4028) These changes will be necessary when https://github.com/networkx/networkx/pull/7066 is merged. Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4028 --- python/nx-cugraph/lint.yaml | 8 +++--- .../nx_cugraph/algorithms/__init__.py | 4 +-- .../algorithms/bipartite/generators.py | 5 ++-- .../nx-cugraph/nx_cugraph/algorithms/core.py | 6 ++++- .../nx_cugraph/generators/classic.py | 26 +++++++------------ .../nx-cugraph/nx_cugraph/utils/decorators.py | 13 +++++++++- 6 files changed, 34 insertions(+), 28 deletions(-) diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index a94aa9f04..de6f20bc4 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -45,12 +45,12 @@ repos: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.10.1 + rev: 23.11.0 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.3 + rev: v0.1.7 hooks: - id: ruff args: [--fix-only, --show-fixes] # --unsafe-fixes] @@ -62,7 +62,7 @@ repos: additional_dependencies: &flake8_dependencies # These versions need updated manually - flake8==6.1.0 - - flake8-bugbear==23.9.16 + - flake8-bugbear==23.12.2 - flake8-simplify==0.21.0 - repo: https://github.com/asottile/yesqa rev: v1.5.0 @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.3 + rev: v0.1.7 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 63841b15b..e49474915 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -15,13 +15,13 @@ centrality, community, components, - shortest_paths, link_analysis, + shortest_paths, ) from .bipartite import complete_bipartite_graph from .centrality import * from .components import * from .core import * from .isolate import * -from .shortest_paths import * from .link_analysis import * +from .shortest_paths import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py index 1d3e762b4..25b9b3955 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py @@ -17,15 +17,14 @@ import numpy as np from nx_cugraph.generators._utils import _create_using_class, _number_and_nodes -from nx_cugraph.utils import index_dtype, networkx_algorithm, nodes_or_number +from nx_cugraph.utils import index_dtype, networkx_algorithm __all__ = [ "complete_bipartite_graph", ] -@nodes_or_number([0, 1]) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=[0, 1]) def complete_bipartite_graph(n1, n2, create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index 2219388bc..390598d07 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -31,7 +31,11 @@ def k_truss(G, k): if is_nx := isinstance(G, nx.Graph): G = nxcg.from_networkx(G, preserve_all_attrs=True) if nxcg.number_of_selfloops(G) > 0: - raise nx.NetworkXError( + if nx.__version__[:3] <= "3.2": + exc_class = nx.NetworkXError + else: + exc_class = nx.NetworkXNotImplemented + raise exc_class( "Input graph has self loops which is not permitted; " "Consider using G.remove_edges_from(nx.selfloop_edges(G))." ) diff --git a/python/nx-cugraph/nx_cugraph/generators/classic.py b/python/nx-cugraph/nx_cugraph/generators/classic.py index b196c2323..4213e6dd2 100644 --- a/python/nx-cugraph/nx_cugraph/generators/classic.py +++ b/python/nx-cugraph/nx_cugraph/generators/classic.py @@ -19,7 +19,7 @@ import nx_cugraph as nxcg -from ..utils import _get_int_dtype, index_dtype, networkx_algorithm, nodes_or_number +from ..utils import _get_int_dtype, index_dtype, networkx_algorithm from ._utils import ( _IS_NX32_OR_LESS, _common_small_graph, @@ -86,8 +86,7 @@ def circular_ladder_graph(n, create_using=None): return _ladder_graph(n, create_using, is_circular=True) -@nodes_or_number(0) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=0) def complete_graph(n, create_using=None): n, nodes = _number_and_nodes(n) if n < 3: @@ -143,8 +142,7 @@ def complete_multipartite_graph(*subset_sizes): ) -@nodes_or_number(0) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=0) def cycle_graph(n, create_using=None): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using) @@ -174,8 +172,7 @@ def cycle_graph(n, create_using=None): return G -@nodes_or_number(0) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=0) def empty_graph(n=0, create_using=None, default=nx.Graph): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using, default=default) @@ -242,8 +239,7 @@ def ladder_graph(n, create_using=None): return _ladder_graph(n, create_using) -@nodes_or_number([0, 1]) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=[0, 1]) def lollipop_graph(m, n, create_using=None): # Like complete_graph then path_graph orig_m, unused_nodes_m = m @@ -283,8 +279,7 @@ def null_graph(create_using=None): return _common_small_graph(0, None, create_using) -@nodes_or_number(0) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=0) def path_graph(n, create_using=None): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using) @@ -304,8 +299,7 @@ def path_graph(n, create_using=None): return G -@nodes_or_number(0) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=0) def star_graph(n, create_using=None): orig_n, orig_nodes = n n, nodes = _number_and_nodes(n) @@ -329,8 +323,7 @@ def star_graph(n, create_using=None): return G -@nodes_or_number([0, 1]) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=[0, 1]) def tadpole_graph(m, n, create_using=None): orig_m, unused_nodes_m = m orig_n, unused_nodes_n = n @@ -382,8 +375,7 @@ def turan_graph(n, r): return complete_multipartite_graph(*partitions) -@nodes_or_number(0) -@networkx_algorithm +@networkx_algorithm(nodes_or_number=0) def wheel_graph(n, create_using=None): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using) diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py index 0048aee51..a0dbfcec8 100644 --- a/python/nx-cugraph/nx_cugraph/utils/decorators.py +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -15,6 +15,7 @@ from functools import partial, update_wrapper from textwrap import dedent +import networkx as nx from networkx.utils.decorators import nodes_or_number, not_implemented_for from nx_cugraph.interface import BackendInterface @@ -47,10 +48,18 @@ def __new__( *, name: str | None = None, extra_params: dict[str, str] | str | None = None, + nodes_or_number: list[int] | int | None = None, ): if func is None: - return partial(networkx_algorithm, name=name, extra_params=extra_params) + return partial( + networkx_algorithm, + name=name, + extra_params=extra_params, + nodes_or_number=nodes_or_number, + ) instance = object.__new__(cls) + if nodes_or_number is not None and nx.__version__[:3] > "3.2": + func = nx.utils.decorators.nodes_or_number(nodes_or_number)(func) # update_wrapper sets __wrapped__, which will be used for the signature update_wrapper(instance, func) instance.__defaults__ = func.__defaults__ @@ -76,6 +85,8 @@ def __new__( setattr(BackendInterface, instance.name, instance) # Set methods so they are in __dict__ instance._can_run = instance._can_run + if nodes_or_number is not None and nx.__version__[:3] <= "3.2": + instance = nx.utils.decorators.nodes_or_number(nodes_or_number)(instance) return instance def _can_run(self, func): From 8f34b5381727e739398f3ad806be0430d3e89992 Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Thu, 14 Dec 2023 12:10:10 -0800 Subject: [PATCH 296/384] Switch to scikit-build-core (#4053) Contributes to rapidsai/build-planning#2 Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Bradley Dice (https://github.com/bdice) - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4053 --- .../wholegraph/installation/source_build.md | 2 +- python/nx-cugraph/setup.py | 18 ------------------ 2 files changed, 1 insertion(+), 19 deletions(-) delete mode 100644 python/nx-cugraph/setup.py diff --git a/docs/cugraph/source/wholegraph/installation/source_build.md b/docs/cugraph/source/wholegraph/installation/source_build.md index c468048c3..a7727ac40 100644 --- a/docs/cugraph/source/wholegraph/installation/source_build.md +++ b/docs/cugraph/source/wholegraph/installation/source_build.md @@ -27,7 +27,7 @@ __Other Packages__: * cython * setuputils3 * scikit-learn -* scikit-build +* scikit-build-core * nanobind>=0.2.0 ## Building wholegraph diff --git a/python/nx-cugraph/setup.py b/python/nx-cugraph/setup.py deleted file mode 100644 index c4ab53592..000000000 --- a/python/nx-cugraph/setup.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from setuptools import find_packages, setup - -packages = find_packages(include=["nx_cugraph*"]) -setup( - package_data={key: ["VERSION"] for key in packages}, -) From 77836ee3ec6f2e0ccce04a153be6097ebbd9957f Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 20 Dec 2023 19:18:38 -0600 Subject: [PATCH 297/384] nx-cugraph: add `ancestors` and `descendants` (#4029) Authors: - Erik Welch (https://github.com/eriknw) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4029 --- python/nx-cugraph/_nx_cugraph/__init__.py | 14 + .../nx_cugraph/algorithms/__init__.py | 3 + .../nx-cugraph/nx_cugraph/algorithms/core.py | 13 +- .../nx-cugraph/nx_cugraph/algorithms/dag.py | 55 ++++ .../algorithms/traversal/__init__.py | 13 + .../traversal/breadth_first_search.py | 250 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/classes/graph.py | 23 ++ python/nx-cugraph/nx_cugraph/convert.py | 79 +++--- .../nx-cugraph/nx_cugraph/convert_matrix.py | 1 + python/nx-cugraph/nx_cugraph/interface.py | 45 ++++ python/nx-cugraph/nx_cugraph/utils/misc.py | 52 +++- 11 files changed, 500 insertions(+), 48 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/dag.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/traversal/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 1fd436bb8..d02c9c3e9 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -29,8 +29,14 @@ # "description": "TODO", "functions": { # BEGIN: functions + "ancestors", "barbell_graph", "betweenness_centrality", + "bfs_edges", + "bfs_layers", + "bfs_predecessors", + "bfs_successors", + "bfs_tree", "bull_graph", "caveman_graph", "chvatal_graph", @@ -44,6 +50,8 @@ "davis_southern_women_graph", "degree_centrality", "desargues_graph", + "descendants", + "descendants_at_distance", "diamond_graph", "dodecahedral_graph", "edge_betweenness_centrality", @@ -53,6 +61,7 @@ "from_pandas_edgelist", "from_scipy_sparse_array", "frucht_graph", + "generic_bfs_edges", "heawood_graph", "hits", "house_graph", @@ -99,9 +108,14 @@ "extra_docstrings": { # BEGIN: extra_docstrings "betweenness_centrality": "`weight` parameter is not yet supported.", + "bfs_edges": "`sort_neighbors` parameter is not yet supported.", + "bfs_predecessors": "`sort_neighbors` parameter is not yet supported.", + "bfs_successors": "`sort_neighbors` parameter is not yet supported.", + "bfs_tree": "`sort_neighbors` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.", "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", + "generic_bfs_edges": "`neighbors` and `sort_neighbors` parameters are not yet supported.", "k_truss": ( "Currently raises `NotImplementedError` for graphs with more than one connected\n" "component when k >= 3. We expect to fix this soon." diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index e49474915..d28a629fe 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -17,11 +17,14 @@ components, link_analysis, shortest_paths, + traversal, ) from .bipartite import complete_bipartite_graph from .centrality import * from .components import * from .core import * +from .dag import * from .isolate import * from .link_analysis import * from .shortest_paths import * +from .traversal import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index 390598d07..c00df2d83 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -15,7 +15,12 @@ import pylibcugraph as plc import nx_cugraph as nxcg -from nx_cugraph.utils import _get_int_dtype, networkx_algorithm, not_implemented_for +from nx_cugraph.utils import ( + _get_int_dtype, + index_dtype, + networkx_algorithm, + not_implemented_for, +) __all__ = ["k_truss"] @@ -81,10 +86,8 @@ def k_truss(G, k): edge_values = {key: val[edge_indices] for key, val in G.edge_values.items()} edge_masks = {key: val[edge_indices] for key, val in G.edge_masks.items()} # Renumber step 2: edge indices - mapper = cp.zeros(len(G), src_indices.dtype) - mapper[node_indices] = cp.arange(node_indices.size, dtype=mapper.dtype) - src_indices = mapper[src_indices] - dst_indices = mapper[dst_indices] + src_indices = cp.searchsorted(node_indices, src_indices).astype(index_dtype) + dst_indices = cp.searchsorted(node_indices, dst_indices).astype(index_dtype) # Renumber step 3: node values node_values = {key: val[node_indices] for key, val in G.node_values.items()} node_masks = {key: val[node_indices] for key, val in G.node_masks.items()} diff --git a/python/nx-cugraph/nx_cugraph/algorithms/dag.py b/python/nx-cugraph/nx_cugraph/algorithms/dag.py new file mode 100644 index 000000000..067cfed91 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/dag.py @@ -0,0 +1,55 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import index_dtype, networkx_algorithm + +__all__ = [ + "descendants", + "ancestors", +] + + +def _ancestors_and_descendants(G, source, *, is_ancestors): + G = _to_graph(G) + if source not in G: + hash(source) # To raise TypeError if appropriate + raise nx.NetworkXError( + f"The node {source} is not in the {G.__class__.__name__.lower()}." + ) + src_index = source if G.key_to_id is None else G.key_to_id[source] + distances, predecessors, node_ids = plc.bfs( + handle=plc.ResourceHandle(), + graph=G._get_plc_graph(switch_indices=is_ancestors), + sources=cp.array([src_index], dtype=index_dtype), + direction_optimizing=False, + depth_limit=-1, + compute_predecessors=False, + do_expensive_check=False, + ) + mask = (distances != np.iinfo(distances.dtype).max) & (distances != 0) + return G._nodearray_to_set(node_ids[mask]) + + +@networkx_algorithm +def descendants(G, source): + return _ancestors_and_descendants(G, source, is_ancestors=False) + + +@networkx_algorithm +def ancestors(G, source): + return _ancestors_and_descendants(G, source, is_ancestors=True) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/traversal/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/traversal/__init__.py new file mode 100644 index 000000000..1751cd469 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/traversal/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .breadth_first_search import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py new file mode 100644 index 000000000..e2a7d46f4 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py @@ -0,0 +1,250 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from itertools import repeat + +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +import nx_cugraph as nxcg +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import _groupby, index_dtype, networkx_algorithm + +__all__ = [ + "bfs_edges", + "bfs_tree", + "bfs_predecessors", + "bfs_successors", + "descendants_at_distance", + "bfs_layers", + "generic_bfs_edges", +] + + +def _check_G_and_source(G, source): + G = _to_graph(G) + if source not in G: + hash(source) # To raise TypeError if appropriate + raise nx.NetworkXError( + f"The node {source} is not in the {G.__class__.__name__.lower()}." + ) + return G + + +def _bfs(G, source, *, depth_limit=None, reverse=False): + src_index = source if G.key_to_id is None else G.key_to_id[source] + distances, predecessors, node_ids = plc.bfs( + handle=plc.ResourceHandle(), + graph=G._get_plc_graph(switch_indices=reverse), + sources=cp.array([src_index], dtype=index_dtype), + direction_optimizing=False, + depth_limit=-1 if depth_limit is None else depth_limit, + compute_predecessors=True, + do_expensive_check=False, + ) + mask = predecessors >= 0 + return distances[mask], predecessors[mask], node_ids[mask] + + +@networkx_algorithm +def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbors=None): + """`neighbors` and `sort_neighbors` parameters are not yet supported.""" + return bfs_edges(source, depth_limit=depth_limit) + + +@generic_bfs_edges._can_run +def _(G, source, neighbors=None, depth_limit=None, sort_neighbors=None): + return neighbors is None and sort_neighbors is None + + +@networkx_algorithm +def bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None): + """`sort_neighbors` parameter is not yet supported.""" + G = _check_G_and_source(G, source) + if depth_limit is not None and depth_limit < 1: + return + distances, predecessors, node_ids = _bfs( + G, source, depth_limit=depth_limit, reverse=reverse + ) + # Using groupby like this is similar to bfs_predecessors + groups = _groupby([distances, predecessors], node_ids) + id_to_key = G.id_to_key + for key in sorted(groups): + children_ids = groups[key] + parent_id = key[1] + parent = id_to_key[parent_id] if id_to_key is not None else parent_id + yield from zip( + repeat(parent, children_ids.size), + G._nodeiter_to_iter(children_ids.tolist()), + ) + + +@bfs_edges._can_run +def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None): + return sort_neighbors is None + + +@networkx_algorithm +def bfs_tree(G, source, reverse=False, depth_limit=None, sort_neighbors=None): + """`sort_neighbors` parameter is not yet supported.""" + G = _check_G_and_source(G, source) + if depth_limit is not None and depth_limit < 1: + return nxcg.DiGraph.from_coo( + 1, + cp.array([], dtype=index_dtype), + cp.array([], dtype=index_dtype), + id_to_key=[source], + ) + + distances, predecessors, node_ids = _bfs( + G, + source, + depth_limit=depth_limit, + reverse=reverse, + ) + if predecessors.size == 0: + return nxcg.DiGraph.from_coo( + 1, + cp.array([], dtype=index_dtype), + cp.array([], dtype=index_dtype), + id_to_key=[source], + ) + # TODO: create renumbering helper function(s) + unique_node_ids = cp.unique(cp.hstack((predecessors, node_ids))) + # Renumber edges + src_indices = cp.searchsorted(unique_node_ids, predecessors).astype(index_dtype) + dst_indices = cp.searchsorted(unique_node_ids, node_ids).astype(index_dtype) + # Renumber nodes + if (id_to_key := G.id_to_key) is not None: + key_to_id = { + id_to_key[old_index]: new_index + for new_index, old_index in enumerate(unique_node_ids.tolist()) + } + else: + key_to_id = { + old_index: new_index + for new_index, old_index in enumerate(unique_node_ids.tolist()) + } + return nxcg.DiGraph.from_coo( + unique_node_ids.size, + src_indices, + dst_indices, + key_to_id=key_to_id, + ) + + +@bfs_tree._can_run +def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None): + return sort_neighbors is None + + +@networkx_algorithm +def bfs_successors(G, source, depth_limit=None, sort_neighbors=None): + """`sort_neighbors` parameter is not yet supported.""" + G = _check_G_and_source(G, source) + if depth_limit is not None and depth_limit < 1: + yield (source, []) + return + + distances, predecessors, node_ids = _bfs(G, source, depth_limit=depth_limit) + groups = _groupby([distances, predecessors], node_ids) + id_to_key = G.id_to_key + for key in sorted(groups): + children_ids = groups[key] + parent_id = key[1] + parent = id_to_key[parent_id] if id_to_key is not None else parent_id + children = G._nodearray_to_list(children_ids) + yield (parent, children) + + +@bfs_successors._can_run +def _(G, source, depth_limit=None, sort_neighbors=None): + return sort_neighbors is None + + +@networkx_algorithm +def bfs_layers(G, sources): + G = _to_graph(G) + if sources in G: + sources = [sources] + else: + sources = set(sources) + if not all(source in G for source in sources): + node = next(source for source in sources if source not in G) + raise nx.NetworkXError(f"The node {node} is not in the graph.") + sources = list(sources) + source_ids = G._list_to_nodearray(sources) + distances, predecessors, node_ids = plc.bfs( + handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + sources=source_ids, + direction_optimizing=False, + depth_limit=-1, + compute_predecessors=False, + do_expensive_check=False, + ) + mask = distances != np.iinfo(distances.dtype).max + distances = distances[mask] + node_ids = node_ids[mask] + groups = _groupby(distances, node_ids) + return (G._nodearray_to_list(groups[key]) for key in range(len(groups))) + + +@networkx_algorithm +def bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None): + """`sort_neighbors` parameter is not yet supported.""" + G = _check_G_and_source(G, source) + if depth_limit is not None and depth_limit < 1: + return + + distances, predecessors, node_ids = _bfs(G, source, depth_limit=depth_limit) + # We include `predecessors` in the groupby for "nicer" iteration order + groups = _groupby([distances, predecessors], node_ids) + id_to_key = G.id_to_key + for key in sorted(groups): + children_ids = groups[key] + parent_id = key[1] + parent = id_to_key[parent_id] if id_to_key is not None else parent_id + yield from zip( + G._nodeiter_to_iter(children_ids.tolist()), + repeat(parent, children_ids.size), + ) + + +@bfs_predecessors._can_run +def _(G, source, depth_limit=None, sort_neighbors=None): + return sort_neighbors is None + + +@networkx_algorithm +def descendants_at_distance(G, source, distance): + G = _check_G_and_source(G, source) + if distance is None or distance < 0: + return set() + if distance == 0: + return {source} + + src_index = source if G.key_to_id is None else G.key_to_id[source] + distances, predecessors, node_ids = plc.bfs( + handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + sources=cp.array([src_index], dtype=index_dtype), + direction_optimizing=False, + depth_limit=distance, + compute_predecessors=False, + do_expensive_check=False, + ) + mask = distances == distance + node_ids = node_ids[mask] + return G._nodearray_to_set(node_ids) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index e32f93d8b..cdd3f744f 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -458,6 +458,24 @@ def has_edge(self, u: NodeKey, v: NodeKey) -> bool: return False return bool(((self.src_indices == u) & (self.dst_indices == v)).any()) + def _neighbors(self, n: NodeKey) -> cp.ndarray[NodeValue]: + if n not in self: + hash(n) # To raise TypeError if appropriate + raise nx.NetworkXError( + f"The node {n} is not in the {self.__class__.__name__.lower()}." + ) + if self.key_to_id is not None: + n = self.key_to_id[n] + nbrs = self.dst_indices[self.src_indices == n] + if self.is_multigraph(): + nbrs = cp.unique(nbrs) + return nbrs + + @networkx_api + def neighbors(self, n: NodeKey) -> Iterator[NodeKey]: + nbrs = self._neighbors(n) + return iter(self._nodeiter_to_iter(nbrs.tolist())) + @networkx_api def has_node(self, n: NodeKey) -> bool: return n in self @@ -701,6 +719,11 @@ def _nodearray_to_list(self, node_ids: cp.ndarray[IndexValue]) -> list[NodeKey]: return node_ids.tolist() return list(self._nodeiter_to_iter(node_ids.tolist())) + def _list_to_nodearray(self, nodes: list[NodeKey]) -> cp.ndarray[IndexValue]: + if (key_to_id := self.key_to_id) is not None: + nodes = [key_to_id[node] for node in nodes] + return cp.array(nodes, dtype=index_dtype) + def _nodearray_to_set(self, node_ids: cp.ndarray[IndexValue]) -> set[NodeKey]: if self.key_to_id is None: return set(node_ids.tolist()) diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index 3c0814370..f265540a1 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -39,6 +39,24 @@ REQUIRED = ... +def _iterate_values(graph, adj, is_dicts, func): + # Using `dict.values` is faster and is the common case, but it doesn't always work + if is_dicts is not False: + it = concat(map(dict.values, adj.values())) + if graph is not None and graph.is_multigraph(): + it = concat(map(dict.values, it)) + try: + return func(it), True + except TypeError: + if is_dicts is True: + raise + # May not be regular dicts + it = concat(x.values() for x in adj.values()) + if graph is not None and graph.is_multigraph(): + it = concat(x.values() for x in it) + return func(it), False + + def from_networkx( graph: nx.Graph, edge_attrs: AttrKey | dict[AttrKey, EdgeValue | None] | None = None, @@ -152,6 +170,7 @@ def from_networkx( if isinstance(adj, nx.classes.coreviews.FilterAdjacency): adj = {k: dict(v) for k, v in adj.items()} + is_dicts = None N = len(adj) if ( not preserve_edge_attrs @@ -162,12 +181,9 @@ def from_networkx( # Either we weren't asked to preserve edge attributes, or there are no edges edge_attrs = None elif preserve_edge_attrs: - # Using comprehensions should be just as fast starting in Python 3.11 - it = concat(map(dict.values, adj.values())) - if graph.is_multigraph(): - it = concat(map(dict.values, it)) - # PERF: should we add `filter(None, ...)` to remove empty data dicts? - attr_sets = set(map(frozenset, it)) + attr_sets, is_dicts = _iterate_values( + graph, adj, is_dicts, lambda it: set(map(frozenset, it)) + ) attrs = frozenset.union(*attr_sets) edge_attrs = dict.fromkeys(attrs, REQUIRED) if len(attr_sets) > 1: @@ -207,10 +223,9 @@ def from_networkx( del edge_attrs[attr] # Else some edges have attribute (default already None) else: - it = concat(map(dict.values, adj.values())) - if graph.is_multigraph(): - it = concat(map(dict.values, it)) - attr_sets = set(map(required.intersection, it)) + attr_sets, is_dicts = _iterate_values( + graph, adj, is_dicts, lambda it: set(map(required.intersection, it)) + ) for attr in required - frozenset.union(*attr_sets): # No edges have these attributes del edge_attrs[attr] @@ -269,17 +284,19 @@ def from_networkx( dst_iter = map(key_to_id.__getitem__, dst_iter) if graph.is_multigraph(): dst_indices = np.fromiter(dst_iter, index_dtype) - num_multiedges = np.fromiter( - map(len, concat(map(dict.values, adj.values()))), index_dtype + num_multiedges, is_dicts = _iterate_values( + None, adj, is_dicts, lambda it: np.fromiter(map(len, it), index_dtype) ) # cp.repeat is slow to use here, so use numpy instead dst_indices = cp.array(np.repeat(dst_indices, num_multiedges)) # Determine edge keys and edge ids for multigraphs - edge_keys = list(concat(concat(map(dict.values, adj.values())))) - edge_indices = cp.fromiter( - concat(map(range, map(len, concat(map(dict.values, adj.values()))))), - index_dtype, - ) + if is_dicts: + edge_keys = list(concat(concat(map(dict.values, adj.values())))) + it = concat(map(dict.values, adj.values())) + else: + edge_keys = list(concat(concat(x.values() for x in adj.values()))) + it = concat(x.values() for x in adj.values()) + edge_indices = cp.fromiter(concat(map(range, map(len, it))), index_dtype) if edge_keys == edge_indices.tolist(): edge_keys = None # Prefer edge_indices else: @@ -323,19 +340,21 @@ def from_networkx( edge_masks[edge_attr] = cp.fromiter(iter_mask, bool) edge_values[edge_attr] = cp.array(vals, dtype) # if vals.ndim > 1: ... + elif edge_default is REQUIRED: + if dtype is None: + + def func(it, edge_attr=edge_attr): + return cp.array(list(map(op.itemgetter(edge_attr), it))) + + else: + + def func(it, edge_attr=edge_attr, dtype=dtype): + return cp.fromiter(map(op.itemgetter(edge_attr), it), dtype) + + edge_value, is_dicts = _iterate_values(graph, adj, is_dicts, func) + edge_values[edge_attr] = edge_value else: - if edge_default is REQUIRED: - # Using comprehensions should be fast starting in Python 3.11 - # iter_values = ( - # edgedata[edge_attr] - # for rowdata in adj.values() - # for edgedata in rowdata.values() - # ) - it = concat(map(dict.values, adj.values())) - if graph.is_multigraph(): - it = concat(map(dict.values, it)) - iter_values = map(op.itemgetter(edge_attr), it) - elif graph.is_multigraph(): + if graph.is_multigraph(): iter_values = ( edgedata.get(edge_attr, edge_default) for rowdata in adj.values() @@ -352,7 +371,7 @@ def from_networkx( edge_values[edge_attr] = cp.array(list(iter_values)) else: edge_values[edge_attr] = cp.fromiter(iter_values, dtype) - # if vals.ndim > 1: ... + # if vals.ndim > 1: ... # cp.repeat is slow to use here, so use numpy instead src_indices = np.repeat( diff --git a/python/nx-cugraph/nx_cugraph/convert_matrix.py b/python/nx-cugraph/nx_cugraph/convert_matrix.py index 6c8b8fb4a..80ca0c2fa 100644 --- a/python/nx-cugraph/nx_cugraph/convert_matrix.py +++ b/python/nx-cugraph/nx_cugraph/convert_matrix.py @@ -36,6 +36,7 @@ def from_pandas_edgelist( graph_class, inplace = _create_using_class(create_using) src_array = df[source].to_numpy() dst_array = df[target].to_numpy() + # TODO: create renumbering helper function(s) # Renumber step 0: node keys nodes = np.unique(np.concatenate([src_array, dst_array])) N = nodes.size diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index be6b35960..3f6449f57 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -12,6 +12,7 @@ # limitations under the License. from __future__ import annotations +import os import sys import networkx as nx @@ -246,7 +247,51 @@ def key(testpath): key("test_tree_isomorphism.py:test_positive"): too_slow, key("test_tree_isomorphism.py:test_negative"): too_slow, key("test_efficiency.py:TestEfficiency.test_using_ego_graph"): maybe_oom, + key("test_dag.py:TestDAG.test_antichains"): maybe_oom, + key( + "test_lowest_common_ancestors.py:" + "TestDAGLCA.test_all_pairs_lca_pairs_without_lca" + ): maybe_oom, + key( + "test_lowest_common_ancestors.py:" + "TestMultiDiGraph_DAGLCA.test_all_pairs_lca_pairs_without_lca" + ): maybe_oom, + # These repeatedly call `bfs_layers`, which converts the graph every call + key( + "test_vf2pp.py:TestGraphISOVF2pp.test_custom_graph2_different_labels" + ): too_slow, + key( + "test_vf2pp.py:TestGraphISOVF2pp.test_custom_graph3_same_labels" + ): too_slow, + key( + "test_vf2pp.py:TestGraphISOVF2pp.test_custom_graph3_different_labels" + ): too_slow, + key( + "test_vf2pp.py:TestGraphISOVF2pp.test_custom_graph4_same_labels" + ): too_slow, + key( + "test_vf2pp.py:TestGraphISOVF2pp." + "test_disconnected_graph_all_same_labels" + ): too_slow, + key( + "test_vf2pp.py:TestGraphISOVF2pp." + "test_disconnected_graph_all_different_labels" + ): too_slow, + key( + "test_vf2pp.py:TestGraphISOVF2pp." + "test_disconnected_graph_some_same_labels" + ): too_slow, + key( + "test_vf2pp.py:TestMultiGraphISOVF2pp." + "test_custom_multigraph3_same_labels" + ): too_slow, + key( + "test_vf2pp_helpers.py:TestNodeOrdering." + "test_matching_order_all_branches" + ): too_slow, } + if os.environ.get("PYTEST_NO_SKIP", False): + skip.clear() for item in items: kset = set(item.keywords) diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index e30337591..aa06d7fd2 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -58,16 +58,18 @@ def pairwise(it): def _groupby( - groups: cp.ndarray, values: cp.ndarray, groups_are_canonical: bool = False + groups: cp.ndarray | list[cp.ndarray], + values: cp.ndarray | list[cp.ndarray], + groups_are_canonical: bool = False, ) -> dict[int, cp.ndarray]: """Perform a groupby operation given an array of group IDs and array of values. Parameters ---------- - groups : cp.ndarray - Array that holds the group IDs. - values : cp.ndarray - Array of values to be grouped according to groups. + groups : cp.ndarray or list of cp.ndarray + Array or list of arrays that holds the group IDs. + values : cp.ndarray or list of cp.ndarray + Array or list of arrays of values to be grouped according to groups. Must be the same size as groups array. groups_are_canonical : bool, default False Whether the group IDs are consecutive integers beginning with 0. @@ -76,18 +78,42 @@ def _groupby( ------- dict with group IDs as keys and cp.ndarray as values. """ - if groups.size == 0: - return {} - sort_indices = cp.argsort(groups) - sorted_groups = groups[sort_indices] - sorted_values = values[sort_indices] - prepend = 1 if groups_are_canonical else sorted_groups[0] + 1 - left_bounds = cp.nonzero(cp.diff(sorted_groups, prepend=prepend))[0] - boundaries = pairwise(itertools.chain(left_bounds.tolist(), [groups.size])) + if isinstance(groups, list): + if groups_are_canonical: + raise ValueError( + "`groups_are_canonical=True` is not allowed when `groups` is a list." + ) + if len(groups) == 0 or (size := groups[0].size) == 0: + return {} + sort_indices = cp.lexsort(cp.vstack(groups[::-1])) + sorted_groups = cp.vstack([group[sort_indices] for group in groups]) + prepend = sorted_groups[:, 0].max() + 1 + changed = cp.abs(cp.diff(sorted_groups, prepend=prepend)).sum(axis=0) + changed[0] = 1 + left_bounds = cp.nonzero(changed)[0] + else: + if (size := groups.size) == 0: + return {} + sort_indices = cp.argsort(groups) + sorted_groups = groups[sort_indices] + prepend = 1 if groups_are_canonical else sorted_groups[0] + 1 + left_bounds = cp.nonzero(cp.diff(sorted_groups, prepend=prepend))[0] + if isinstance(values, list): + sorted_values = [vals[sort_indices] for vals in values] + else: + sorted_values = values[sort_indices] + boundaries = pairwise(itertools.chain(left_bounds.tolist(), [size])) if groups_are_canonical: it = enumerate(boundaries) + elif isinstance(groups, list): + it = zip(map(tuple, sorted_groups.T[left_bounds].tolist()), boundaries) else: it = zip(sorted_groups[left_bounds].tolist(), boundaries) + if isinstance(values, list): + return { + group: [sorted_vals[start:end] for sorted_vals in sorted_values] + for group, (start, end) in it + } return {group: sorted_values[start:end] for group, (start, end) in it} From 05e69ae5d4718f309df2034be300337246d3d17c Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 10 Jan 2024 11:55:13 -0600 Subject: [PATCH 298/384] Adds `nx-cugraph` benchmarks for 23.12 algos (SSSP, pagerank, hits, katz_centrality, degree_centrality, eigenvector_centrality) (#4065) closes rapidsai/graph_dl#404 * Adds benchmarks for algos added in the 23.12 release * SSSP * pagerank * hits * katz_centrality * degree_centrality * eigenvector_centrality * Refactors fixtures for easier usage and mainentance * uses `benchmark.pedantic` instead of `benchmark` to provide complete control over how to benchmark the algos, since `benchmark` will result in no fewer than 3 runs (calibrate timer, timed run, run to generate func return value) which can be too time consuming for slower runs. * Removes code to create `Dataset` objects for larger datasets and replaces with the equivalent objects that are now part of `cugraph.datasets` Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4065 --- .../nx-cugraph/pytest-based/bench_algos.py | 440 ++++++++++++++---- 1 file changed, 343 insertions(+), 97 deletions(-) diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py index 971c3ff10..a8ed18a20 100644 --- a/benchmarks/nx-cugraph/pytest-based/bench_algos.py +++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,29 +11,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +import random + import networkx as nx import pandas as pd import pytest from cugraph import datasets - -# FIXME: promote these to cugraph.datasets so the following steps aren't -# necessary -# -# These datasets can be downloaded using the script in the 'datasets' dir: -# -# cd /datasets -# ./get_test_data.sh --benchmark -# -# Then set the following env var so the dataset utils can find their location: -# -# export RAPIDS_DATASET_ROOT_DIR=/datasets -# -from cugraph_benchmarking.params import ( - hollywood, - europe_osm, - cit_patents, - soc_livejournal, -) +import nx_cugraph as nxcg # Attempt to import the NetworkX dispatching module, which is only needed when # testing with NX <3.2 in order to dynamically switch backends. NX >=3.2 allows @@ -45,22 +29,76 @@ ################################################################################ -# Fixtures and helpers -backend_params = ["cugraph", None] +# Fixtures and params + +# See https://pytest-benchmark.readthedocs.io/en/latest/glossary.html for how +# these variables are used. +rounds = 1 +iterations = 1 +warmup_rounds = 1 -dataset_params = [ +dataset_param_values = [ pytest.param(datasets.karate, marks=[pytest.mark.small, pytest.mark.undirected]), pytest.param(datasets.netscience, marks=[pytest.mark.small, pytest.mark.directed]), pytest.param( datasets.email_Eu_core, marks=[pytest.mark.small, pytest.mark.directed] ), - pytest.param(cit_patents, marks=[pytest.mark.medium, pytest.mark.directed]), - pytest.param(hollywood, marks=[pytest.mark.medium, pytest.mark.undirected]), - pytest.param(europe_osm, marks=[pytest.mark.medium, pytest.mark.undirected]), - pytest.param(soc_livejournal, marks=[pytest.mark.large, pytest.mark.directed]), + pytest.param( + datasets.cit_patents, marks=[pytest.mark.medium, pytest.mark.directed] + ), + pytest.param( + datasets.hollywood, marks=[pytest.mark.medium, pytest.mark.undirected] + ), + pytest.param( + datasets.soc_livejournal, marks=[pytest.mark.medium, pytest.mark.directed] + ), + pytest.param( + datasets.europe_osm, marks=[pytest.mark.large, pytest.mark.undirected] + ), ] +backend_param_values = ["cugraph", "cugraph-preconverted", None] + + +def setup_module(module): + """ + Trivial conversion call to force various one-time CUDA initialization + operations to happen outside of benchmarks. + """ + G = nx.karate_club_graph() + nxcg.from_networkx(G) + + +# Test IDs are generated using the lambda assigned to the ids arg to provide an +# easier-to-read name. This is especially helpful for Dataset objs (see +# https://docs.pytest.org/en/stable/reference/reference.html#pytest-fixture) +@pytest.fixture( + scope="module", params=dataset_param_values, ids=lambda ds: f"ds={str(ds)}" +) +def graph_obj(request): + """ + Returns a NX Graph or DiGraph obj from the dataset instance parameter. + """ + dataset = request.param + return nx_graph_from_dataset(dataset) + + +@pytest.fixture( + scope="module", + params=backend_param_values, + ids=lambda backend: f"backend={backend}", +) +def backend(request): + """ + Returns the backend name to use. This is done as a fixture for consistency + and simplicity when creating benchmarks (no need to mark the benchmark as + parametrized). + """ + return request.param + +################################################################################ +# Helpers def nx_graph_from_dataset(dataset_obj): """ Read the dataset specified by the dataset_obj and create and return a @@ -87,126 +125,334 @@ def nx_graph_from_dataset(dataset_obj): return G -# Test IDs are generated using the lambda assigned to the ids arg to provide an -# easier-to-read name from the Dataset obj string repr. -# See: https://docs.pytest.org/en/stable/reference/reference.html#pytest-fixture -@pytest.fixture(scope="module", params=dataset_params, ids=lambda ds: f"ds={str(ds)}") -def graph_obj(request): - """ - Returns a NX Graph or DiGraph obj from the dataset instance parameter. - """ - dataset = request.param - return nx_graph_from_dataset(dataset) - - -def get_legacy_backend_selector(backend_name): +def get_legacy_backend_wrapper(backend_name): """ Returns a callable that wraps an algo function with either the default - dispatch decorator, or the "testing" decorator which unconditionally - dispatches. + dispatcher (which dispatches based on input graph type), or the "testing" + dispatcher (which autoconverts and unconditionally dispatches). This is only supported for NetworkX <3.2 """ backends.plugin_name = "cugraph" orig_dispatch = backends._dispatch testing_dispatch = backends.test_override_dispatch - # Testing with the networkx <3.2 dispatch mechanism is based on decorating - # networkx APIs. The decorator is either one that only uses a backend if - # the input graph type is for that backend (the default decorator), or the - # "testing" decorator, which unconditionally converts a graph type to the - # type needed by the backend then calls the backend. If the cugraph backend - # is specified, create a callable that decorates the benchmarked function - # with the testing decorator. - # - # Because both the default and testing decorators assume they are only - # applied once and do bookkeeping to ensure algos are not registered - # multiple times, the callable also clears bookkeeping so the decorators - # can be reapplied multiple times. This is obviously a hack and networkx - # >=3.2 makes this use case properly supported. if backend_name == "cugraph": - - def wrapper(*args, **kwargs): - backends._registered_algorithms = {} - return testing_dispatch(*args, **kwargs) - + dispatch = testing_dispatch else: + dispatch = orig_dispatch + + def wrap_callable_for_dispatch(func, exhaust_returned_iterator=False): + # Networkx <3.2 registers functions when the dispatch decorator is + # applied (called) and errors if re-registered, so clear bookkeeping to + # allow it to be called repeatedly. + backends._registered_algorithms = {} + actual_func = dispatch(func) # returns the func the dispatcher picks def wrapper(*args, **kwargs): - backends._registered_algorithms = {} - return orig_dispatch(*args, **kwargs) + retval = actual_func(*args, **kwargs) + if exhaust_returned_iterator: + retval = list(retval) + return retval - return wrapper + return wrapper + + return wrap_callable_for_dispatch -def get_backend_selector(backend_name): +def get_backend_wrapper(backend_name): """ Returns a callable that wraps an algo function in order to set the "backend" kwarg on it. This is only supported for NetworkX >= 3.2 """ - def get_callable_for_func(func): + def wrap_callable_for_dispatch(func, exhaust_returned_iterator=False): def wrapper(*args, **kwargs): kwargs["backend"] = backend_name - return func(*args, **kwargs) + retval = func(*args, **kwargs) + if exhaust_returned_iterator: + retval = list(retval) + return retval return wrapper - return get_callable_for_func + return wrap_callable_for_dispatch @pytest.fixture( - scope="module", params=backend_params, ids=lambda backend: f"backend={backend}" + scope="module", + params=backend_param_values, + ids=lambda backend: f"backend={backend}", ) -def backend_selector(request): +def backend_wrapper(request): """ Returns a callable that takes a function algo and wraps it in another function that calls the algo using the appropriate backend. + + For example: if the backend to test is "cugraph", this will return a + function that calls nx.pagerank(..., backend='cugraph') """ backend_name = request.param + actual_backend_name = backend_name + + # Special case: cugraph-preconverted may be specified as a backend but this + # name is reserved to indicate a cugraph backend is to be used with a + # preconverted graph obj (rather than having the backend do the + # conversion). + if backend_name == "cugraph-preconverted": + actual_backend_name = "cugraph" + + # NX <3.2 does not support the backends= kwarg, so the backend must be + # enabled differently if backends is not None: - return get_legacy_backend_selector(backend_name) + wrapper = get_legacy_backend_wrapper(actual_backend_name) else: - return get_backend_selector(backend_name) + wrapper = get_backend_wrapper(actual_backend_name) + + wrapper.backend_name = backend_name + return wrapper + + +def get_graph_obj_for_benchmark(graph_obj, backend_wrapper): + """ + Given a Graph object and a backend name, return a converted Graph or the + original Graph object based on the backend to use. + + This is needed because some backend names are actually used as descriptions + for combinations of backends and converted/non-converted graphs. For + example, a benchmark may specify the "cugraph-preconverted" backend, which + is not an installed backend but instead refers to the "cugraph" backend + passed a NX Graph that has been converted to a nx-cugraph Graph object. + """ + G = graph_obj + if backend_wrapper.backend_name == "cugraph-preconverted": + G = nxcg.from_networkx(G) + return G ################################################################################ # Benchmarks -normalized_params = [True, False] -k_params = [10, 100] - - -@pytest.mark.parametrize("normalized", normalized_params, ids=lambda norm: f"{norm=}") -@pytest.mark.parametrize("k", k_params, ids=lambda k: f"{k=}") -def bench_betweenness_centrality(benchmark, graph_obj, backend_selector, normalized, k): - result = benchmark( - backend_selector(nx.betweenness_centrality), - graph_obj, - weight=None, - normalized=normalized, - k=k, +# normalized_param_values = [True, False] +# k_param_values = [10, 100] +normalized_param_values = [True] +k_param_values = [10] + + +@pytest.mark.parametrize( + "normalized", normalized_param_values, ids=lambda norm: f"{norm=}" +) +@pytest.mark.parametrize("k", k_param_values, ids=lambda k: f"{k=}") +def bench_betweenness_centrality(benchmark, graph_obj, backend_wrapper, normalized, k): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.betweenness_centrality), + args=(G,), + kwargs=dict( + weight=None, + normalized=normalized, + k=k, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, ) assert type(result) is dict -@pytest.mark.parametrize("normalized", normalized_params, ids=lambda norm: f"{norm=}") +@pytest.mark.parametrize( + "normalized", normalized_param_values, ids=lambda norm: f"{norm=}" +) +@pytest.mark.parametrize("k", k_param_values, ids=lambda k: f"{k=}") def bench_edge_betweenness_centrality( - benchmark, graph_obj, backend_selector, normalized + benchmark, graph_obj, backend_wrapper, normalized, k ): - result = benchmark( - backend_selector(nx.edge_betweenness_centrality), - graph_obj, - weight=None, - normalized=normalized, + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.edge_betweenness_centrality), + args=(G,), + kwargs=dict( + weight=None, + normalized=normalized, + k=k, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, ) assert type(result) is dict -def bench_louvain_communities(benchmark, graph_obj, backend_selector): +def bench_louvain_communities(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) # The cugraph backend for louvain_communities only supports undirected graphs - if isinstance(graph_obj, nx.DiGraph): - G = graph_obj.to_undirected() - else: - G = graph_obj - result = benchmark(backend_selector(nx.community.louvain_communities), G) + if G.is_directed(): + G = G.to_undirected() + result = benchmark.pedantic( + target=backend_wrapper(nx.community.louvain_communities), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list + + +def bench_degree_centrality(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.degree_centrality), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_eigenvector_centrality(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.eigenvector_centrality), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +@pytest.mark.parametrize( + "normalized", normalized_param_values, ids=lambda norm: f"{norm=}" +) +def bench_hits(benchmark, graph_obj, backend_wrapper, normalized): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.hits), + args=(G,), + kwargs=dict( + normalized=normalized, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is tuple + assert len(result) == 2 + assert type(result[0]) is dict + assert type(result[1]) is dict + + +def bench_in_degree_centrality(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.in_degree_centrality), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +@pytest.mark.parametrize( + "normalized", normalized_param_values, ids=lambda norm: f"{norm=}" +) +def bench_katz_centrality(benchmark, graph_obj, backend_wrapper, normalized): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.katz_centrality), + args=(G,), + kwargs=dict( + normalized=normalized, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_k_truss(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + # DiGraphs are not supported + if G.is_directed(): + G = G.to_undirected() + result = benchmark.pedantic( + target=backend_wrapper(nx.k_truss), + args=(G,), + kwargs=dict( + k=2, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + # Check that this at least appears to be some kind of NX-like Graph + assert hasattr(result, "has_node") + + +def bench_out_degree_centrality(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.out_degree_centrality), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_pagerank(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.pagerank), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_single_source_shortest_path_length(benchmark, graph_obj, backend_wrapper): + # Use the node with the highest degree + degrees = graph_obj.degree() # list of tuples of (node, degree) + node = max(degrees, key=lambda t: t[1])[0] + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + + result = benchmark.pedantic( + target=backend_wrapper(nx.single_source_shortest_path_length), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_single_target_shortest_path_length(benchmark, graph_obj, backend_wrapper): + # Use the node with the highest degree + degrees = graph_obj.degree() # list of tuples of (node, degree) + node = max(degrees, key=lambda t: t[1])[0] + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + + result = benchmark.pedantic( + target=backend_wrapper( + nx.single_target_shortest_path_length, exhaust_returned_iterator=True + ), + args=(G,), + kwargs=dict( + target=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + # exhaust_returned_iterator=True forces the result to a list, but is not + # needed for this algo in NX 3.3+ which returns a dict instead of an + # iterator. Forcing to a list does not change the benchmark timing. assert type(result) is list From 43fb580bea476eb1f9672bd57df31530feca6348 Mon Sep 17 00:00:00 2001 From: James Lamb Date: Thu, 11 Jan 2024 11:23:40 -0600 Subject: [PATCH 299/384] refactor CUDA versions in dependencies.yaml (#4084) Contributes to https://github.com/rapidsai/build-planning/issues/7. Proposes splitting the `cuda-version` dependency in `dependencies.yaml` out to its own thing, separate from the bits of the CUDA Toolkit this project needs. ### Benefits of this change * prevents accidental inclusion of multiple `cuda-version` version in environments * reduces update effort (via enabling more use of globs like `"12.*"`) * improves the chance that errors like "`conda` recipe is missing a dependency" are caught in CI Authors: - James Lamb (https://github.com/jameslamb) - Bradley Dice (https://github.com/bdice) Approvers: - Vyas Ramasubramani (https://github.com/vyasr) - Bradley Dice (https://github.com/bdice) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/4084 --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bab39557c..188ea1a26 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -52,7 +52,7 @@ repos: pass_filenames: false additional_dependencies: [gitpython] - repo: https://github.com/rapidsai/dependency-file-generator - rev: v1.5.1 + rev: v1.8.0 hooks: - id: rapids-dependency-file-generator args: ["--clean"] From d671c82f43199a782cb377064b3a088eb74bb48e Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Thu, 11 Jan 2024 15:11:00 -0600 Subject: [PATCH 300/384] nx-cugraph: indicate which plc algorithms are used and version_added (#4069) Pretty simple PR. I would like for us to use this metadata when creating tables of supported algorithms. Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4069 --- .../algorithms/bipartite/generators.py | 4 +- .../algorithms/centrality/betweenness.py | 20 +- .../algorithms/centrality/degree_alg.py | 8 +- .../algorithms/centrality/eigenvector.py | 9 +- .../nx_cugraph/algorithms/centrality/katz.py | 9 +- .../algorithms/community/louvain.py | 10 +- .../algorithms/components/connected.py | 10 +- .../nx-cugraph/nx_cugraph/algorithms/core.py | 4 +- .../nx-cugraph/nx_cugraph/algorithms/dag.py | 6 +- .../nx_cugraph/algorithms/isolate.py | 8 +- .../algorithms/link_analysis/hits_alg.py | 6 +- .../algorithms/link_analysis/pagerank_alg.py | 11 +- .../algorithms/shortest_paths/unweighted.py | 6 +- .../traversal/breadth_first_search.py | 16 +- .../nx-cugraph/nx_cugraph/classes/function.py | 4 +- .../nx-cugraph/nx_cugraph/convert_matrix.py | 9 +- .../nx_cugraph/generators/classic.py | 32 +-- .../nx_cugraph/generators/community.py | 4 +- .../nx-cugraph/nx_cugraph/generators/small.py | 44 ++-- .../nx_cugraph/generators/social.py | 10 +- .../nx-cugraph/nx_cugraph/scripts/__init__.py | 12 + .../nx-cugraph/nx_cugraph/scripts/__main__.py | 38 +++ .../nx_cugraph/scripts/print_table.py | 78 ++++++ .../nx_cugraph/scripts/print_tree.py | 241 ++++++++++++++++++ .../nx-cugraph/nx_cugraph/utils/decorators.py | 31 ++- python/nx-cugraph/pyproject.toml | 8 + 26 files changed, 538 insertions(+), 100 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/scripts/__init__.py create mode 100755 python/nx-cugraph/nx_cugraph/scripts/__main__.py create mode 100755 python/nx-cugraph/nx_cugraph/scripts/print_table.py create mode 100755 python/nx-cugraph/nx_cugraph/scripts/print_tree.py diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py index 25b9b3955..5a0c970c9 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -24,7 +24,7 @@ ] -@networkx_algorithm(nodes_or_number=[0, 1]) +@networkx_algorithm(nodes_or_number=[0, 1], version_added="23.12") def complete_bipartite_graph(n1, n2, create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py index 210e1f0a2..ba2b3d9c8 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,11 +18,16 @@ __all__ = ["betweenness_centrality", "edge_betweenness_centrality"] -@networkx_algorithm +@networkx_algorithm( + is_incomplete=True, # weight not supported + is_different=True, # RNG with seed is different + plc="betweenness_centrality", + version_added="23.10", +) def betweenness_centrality( G, k=None, normalized=True, weight=None, endpoints=False, seed=None ): - """`weight` parameter is not yet supported.""" + """`weight` parameter is not yet supported, and RNG with seed may be different.""" if weight is not None: raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" @@ -46,9 +51,14 @@ def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None): return weight is None -@networkx_algorithm +@networkx_algorithm( + is_incomplete=True, # weight not supported + is_different=True, # RNG with seed is different + plc="edge_betweenness_centrality", + version_added="23.10", +) def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None): - """`weight` parameter is not yet supported.""" + """`weight` parameter is not yet supported, and RNG with seed may be different.""" if weight is not None: raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py index 0b2fd24af..a319eb3a1 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,7 +16,7 @@ __all__ = ["degree_centrality", "in_degree_centrality", "out_degree_centrality"] -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def degree_centrality(G): G = _to_graph(G) if len(G) <= 1: @@ -27,7 +27,7 @@ def degree_centrality(G): @not_implemented_for("undirected") -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def in_degree_centrality(G): G = _to_directed_graph(G) if len(G) <= 1: @@ -38,7 +38,7 @@ def in_degree_centrality(G): @not_implemented_for("undirected") -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def out_degree_centrality(G): G = _to_directed_graph(G) if len(G) <= 1: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py index c0f02a625..9e615955a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -26,7 +26,12 @@ @not_implemented_for("multigraph") -@networkx_algorithm(extra_params=_dtype_param) +@networkx_algorithm( + extra_params=_dtype_param, + is_incomplete=True, # nstart not supported + plc="eigenvector_centrality", + version_added="23.12", +) def eigenvector_centrality( G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None ): diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py index b61b811b8..a2fb950c1 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -26,7 +26,12 @@ @not_implemented_for("multigraph") -@networkx_algorithm(extra_params=_dtype_param) +@networkx_algorithm( + extra_params=_dtype_param, + is_incomplete=True, # nstart and normalized=False not supported + plc="katz_centrality", + version_added="23.12", +) def katz_centrality( G, alpha=0.1, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index 936d837da..d023bab1a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -35,7 +35,11 @@ "Upper limit of the number of macro-iterations (max: 500)." ), **_dtype_param, - } + }, + is_incomplete=True, # seed not supported; self-loops not supported + is_different=True, # RNG different + plc="louvain", + version_added="23.10", ) def louvain_communities( G, @@ -47,7 +51,7 @@ def louvain_communities( max_level=None, dtype=None, ): - """`seed` parameter is currently ignored.""" + """`seed` parameter is currently ignored, and self-loops are not yet supported.""" # NetworkX allows both directed and undirected, but cugraph only allows undirected. seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py index 41f3457d5..cb12aed1d 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -30,7 +30,7 @@ @not_implemented_for("directed") -@networkx_algorithm +@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def number_connected_components(G): return sum(1 for _ in connected_components(G)) # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL @@ -57,7 +57,7 @@ def _(G): @not_implemented_for("directed") -@networkx_algorithm +@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def connected_components(G): G = _to_undirected_graph(G) if G.src_indices.size == 0: @@ -86,7 +86,7 @@ def connected_components(G): @not_implemented_for("directed") -@networkx_algorithm +@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def is_connected(G): G = _to_undirected_graph(G) if len(G) == 0: @@ -110,7 +110,7 @@ def is_connected(G): @not_implemented_for("directed") -@networkx_algorithm +@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def node_connected_component(G, n): # We could also do plain BFS from n G = _to_undirected_graph(G) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index c00df2d83..e4520c271 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -27,7 +27,7 @@ @not_implemented_for("directed") @not_implemented_for("multigraph") -@networkx_algorithm +@networkx_algorithm(is_incomplete=True, plc="k_truss_subgraph", version_added="23.12") def k_truss(G, k): """ Currently raises `NotImplementedError` for graphs with more than one connected diff --git a/python/nx-cugraph/nx_cugraph/algorithms/dag.py b/python/nx-cugraph/nx_cugraph/algorithms/dag.py index 067cfed91..ad5b7594a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/dag.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/dag.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -45,11 +45,11 @@ def _ancestors_and_descendants(G, source, *, is_ancestors): return G._nodearray_to_set(node_ids[mask]) -@networkx_algorithm +@networkx_algorithm(plc="bfs", version_added="24.02") def descendants(G, source): return _ancestors_and_descendants(G, source, is_ancestors=False) -@networkx_algorithm +@networkx_algorithm(plc="bfs", version_added="24.02") def ancestors(G, source): return _ancestors_and_descendants(G, source, is_ancestors=True) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py index d32223fb3..c7e5d7113 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -25,7 +25,7 @@ __all__ = ["is_isolate", "isolates", "number_of_isolates"] -@networkx_algorithm +@networkx_algorithm(version_added="23.10") def is_isolate(G, n): G = _to_graph(G) index = n if G.key_to_id is None else G.key_to_id[n] @@ -51,13 +51,13 @@ def _isolates(G) -> cp.ndarray[IndexValue]: return cp.nonzero(_mark_isolates(G))[0] -@networkx_algorithm +@networkx_algorithm(version_added="23.10") def isolates(G): G = _to_graph(G) return G._nodeiter_to_iter(iter(_isolates(G).tolist())) -@networkx_algorithm +@networkx_algorithm(version_added="23.10") def number_of_isolates(G): G = _to_graph(G) return _mark_isolates(G).sum().tolist() diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py index 1c8a47c24..caa01327a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -32,7 +32,9 @@ "The edge attribute to use as the edge weight." ), **_dtype_param, - } + }, + plc="hits", + version_added="23.12", ) def hits( G, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py index 63f6e89c3..d45d019c1 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -26,7 +26,12 @@ __all__ = ["pagerank"] -@networkx_algorithm(extra_params=_dtype_param) +@networkx_algorithm( + extra_params=_dtype_param, + is_incomplete=True, # dangling not supported + plc={"pagerank", "personalized_pagerank"}, + version_added="23.12", +) def pagerank( G, alpha=0.85, @@ -97,7 +102,7 @@ def pagerank( @pagerank._can_run -def pagerank( +def _( G, alpha=0.85, personalization=None, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py index 3413a637b..b1032a823 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -21,12 +21,12 @@ __all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"] -@networkx_algorithm +@networkx_algorithm(plc="bfs", version_added="23.12") def single_source_shortest_path_length(G, source, cutoff=None): return _single_shortest_path_length(G, source, cutoff, "Source") -@networkx_algorithm +@networkx_algorithm(plc="bfs", version_added="23.12") def single_target_shortest_path_length(G, target, cutoff=None): return _single_shortest_path_length(G, target, cutoff, "Target") diff --git a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py index e2a7d46f4..aa671bbb7 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -57,7 +57,7 @@ def _bfs(G, source, *, depth_limit=None, reverse=False): return distances[mask], predecessors[mask], node_ids[mask] -@networkx_algorithm +@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbors=None): """`neighbors` and `sort_neighbors` parameters are not yet supported.""" return bfs_edges(source, depth_limit=depth_limit) @@ -68,7 +68,7 @@ def _(G, source, neighbors=None, depth_limit=None, sort_neighbors=None): return neighbors is None and sort_neighbors is None -@networkx_algorithm +@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") def bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" G = _check_G_and_source(G, source) @@ -95,7 +95,7 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm +@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") def bfs_tree(G, source, reverse=False, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" G = _check_G_and_source(G, source) @@ -149,7 +149,7 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm +@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") def bfs_successors(G, source, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" G = _check_G_and_source(G, source) @@ -173,7 +173,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm +@networkx_algorithm(plc="bfs", version_added="24.02") def bfs_layers(G, sources): G = _to_graph(G) if sources in G: @@ -201,7 +201,7 @@ def bfs_layers(G, sources): return (G._nodearray_to_list(groups[key]) for key in range(len(groups))) -@networkx_algorithm +@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") def bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" G = _check_G_and_source(G, source) @@ -227,7 +227,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm +@networkx_algorithm(plc="bfs", version_added="24.02") def descendants_at_distance(G, source, distance): G = _check_G_and_source(G, source) if distance is None or distance < 0: diff --git a/python/nx-cugraph/nx_cugraph/classes/function.py b/python/nx-cugraph/nx_cugraph/classes/function.py index 633e4abd7..435dfe372 100644 --- a/python/nx-cugraph/nx_cugraph/classes/function.py +++ b/python/nx-cugraph/nx_cugraph/classes/function.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -16,7 +16,7 @@ __all__ = ["number_of_selfloops"] -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def number_of_selfloops(G): G = _to_graph(G) is_selfloop = G.src_indices == G.dst_indices diff --git a/python/nx-cugraph/nx_cugraph/convert_matrix.py b/python/nx-cugraph/nx_cugraph/convert_matrix.py index 80ca0c2fa..1a2ecde9b 100644 --- a/python/nx-cugraph/nx_cugraph/convert_matrix.py +++ b/python/nx-cugraph/nx_cugraph/convert_matrix.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -23,7 +23,8 @@ ] -@networkx_algorithm +# Value columns with string dtype is not supported +@networkx_algorithm(is_incomplete=True, version_added="23.12") def from_pandas_edgelist( df, source="source", @@ -32,7 +33,7 @@ def from_pandas_edgelist( create_using=None, edge_key=None, ): - """cudf.DataFrame inputs also supported.""" + """cudf.DataFrame inputs also supported; value columns with str is unsuppported.""" graph_class, inplace = _create_using_class(create_using) src_array = df[source].to_numpy() dst_array = df[target].to_numpy() @@ -120,7 +121,7 @@ def from_pandas_edgelist( return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def from_scipy_sparse_array( A, parallel_edges=False, create_using=None, edge_attribute="weight" ): diff --git a/python/nx-cugraph/nx_cugraph/generators/classic.py b/python/nx-cugraph/nx_cugraph/generators/classic.py index 4213e6dd2..a548beea3 100644 --- a/python/nx-cugraph/nx_cugraph/generators/classic.py +++ b/python/nx-cugraph/nx_cugraph/generators/classic.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -51,7 +51,7 @@ concat = itertools.chain.from_iterable -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def barbell_graph(m1, m2, create_using=None): # Like two complete graphs and a path_graph m1 = _ensure_nonnegative_int(m1) @@ -81,12 +81,12 @@ def barbell_graph(m1, m2, create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def circular_ladder_graph(n, create_using=None): return _ladder_graph(n, create_using, is_circular=True) -@networkx_algorithm(nodes_or_number=0) +@networkx_algorithm(nodes_or_number=0, version_added="23.12") def complete_graph(n, create_using=None): n, nodes = _number_and_nodes(n) if n < 3: @@ -99,7 +99,7 @@ def complete_graph(n, create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def complete_multipartite_graph(*subset_sizes): if not subset_sizes: return nxcg.Graph() @@ -142,7 +142,7 @@ def complete_multipartite_graph(*subset_sizes): ) -@networkx_algorithm(nodes_or_number=0) +@networkx_algorithm(nodes_or_number=0, version_added="23.12") def cycle_graph(n, create_using=None): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using) @@ -172,7 +172,7 @@ def cycle_graph(n, create_using=None): return G -@networkx_algorithm(nodes_or_number=0) +@networkx_algorithm(nodes_or_number=0, version_added="23.12") def empty_graph(n=0, create_using=None, default=nx.Graph): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using, default=default) @@ -234,12 +234,12 @@ def _ladder_graph(n, create_using, *, is_circular=False): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def ladder_graph(n, create_using=None): return _ladder_graph(n, create_using) -@networkx_algorithm(nodes_or_number=[0, 1]) +@networkx_algorithm(nodes_or_number=[0, 1], version_added="23.12") def lollipop_graph(m, n, create_using=None): # Like complete_graph then path_graph orig_m, unused_nodes_m = m @@ -274,12 +274,12 @@ def lollipop_graph(m, n, create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def null_graph(create_using=None): return _common_small_graph(0, None, create_using) -@networkx_algorithm(nodes_or_number=0) +@networkx_algorithm(nodes_or_number=0, version_added="23.12") def path_graph(n, create_using=None): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using) @@ -299,7 +299,7 @@ def path_graph(n, create_using=None): return G -@networkx_algorithm(nodes_or_number=0) +@networkx_algorithm(nodes_or_number=0, version_added="23.12") def star_graph(n, create_using=None): orig_n, orig_nodes = n n, nodes = _number_and_nodes(n) @@ -323,7 +323,7 @@ def star_graph(n, create_using=None): return G -@networkx_algorithm(nodes_or_number=[0, 1]) +@networkx_algorithm(nodes_or_number=[0, 1], version_added="23.12") def tadpole_graph(m, n, create_using=None): orig_m, unused_nodes_m = m orig_n, unused_nodes_n = n @@ -361,12 +361,12 @@ def tadpole_graph(m, n, create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def trivial_graph(create_using=None): return _common_small_graph(1, None, create_using) -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def turan_graph(n, r): if not 1 <= r <= n: raise nx.NetworkXError("Must satisfy 1 <= r <= n") @@ -375,7 +375,7 @@ def turan_graph(n, r): return complete_multipartite_graph(*partitions) -@networkx_algorithm(nodes_or_number=0) +@networkx_algorithm(nodes_or_number=0, version_added="23.12") def wheel_graph(n, create_using=None): n, nodes = _number_and_nodes(n) graph_class, inplace = _create_using_class(create_using) diff --git a/python/nx-cugraph/nx_cugraph/generators/community.py b/python/nx-cugraph/nx_cugraph/generators/community.py index e5cb03e8c..9b0e0848d 100644 --- a/python/nx-cugraph/nx_cugraph/generators/community.py +++ b/python/nx-cugraph/nx_cugraph/generators/community.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -27,7 +27,7 @@ ] -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def caveman_graph(l, k): # noqa: E741 l = _ensure_int(l) # noqa: E741 k = _ensure_int(k) diff --git a/python/nx-cugraph/nx_cugraph/generators/small.py b/python/nx-cugraph/nx_cugraph/generators/small.py index b9a189c31..45487571c 100644 --- a/python/nx-cugraph/nx_cugraph/generators/small.py +++ b/python/nx-cugraph/nx_cugraph/generators/small.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -43,7 +43,7 @@ ] -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def bull_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -56,7 +56,7 @@ def bull_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def chvatal_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -85,7 +85,7 @@ def chvatal_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def cubical_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -105,7 +105,7 @@ def cubical_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def desargues_graph(create_using=None): # This can also be defined w.r.t. LCF_graph graph_class, inplace = _create_using_class(create_using) @@ -146,7 +146,7 @@ def desargues_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def diamond_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -159,7 +159,7 @@ def diamond_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def dodecahedral_graph(create_using=None): # This can also be defined w.r.t. LCF_graph graph_class, inplace = _create_using_class(create_using) @@ -200,7 +200,7 @@ def dodecahedral_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def frucht_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -235,7 +235,7 @@ def frucht_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def heawood_graph(create_using=None): # This can also be defined w.r.t. LCF_graph graph_class, inplace = _create_using_class(create_using) @@ -274,7 +274,7 @@ def heawood_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def house_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -287,7 +287,7 @@ def house_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def house_x_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -306,7 +306,7 @@ def house_x_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def icosahedral_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -337,7 +337,7 @@ def icosahedral_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def krackhardt_kite_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -366,7 +366,7 @@ def krackhardt_kite_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def moebius_kantor_graph(create_using=None): # This can also be defined w.r.t. LCF_graph graph_class, inplace = _create_using_class(create_using) @@ -407,7 +407,7 @@ def moebius_kantor_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def octahedral_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -428,7 +428,7 @@ def octahedral_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def pappus_graph(): # This can also be defined w.r.t. LCF_graph # fmt: off @@ -452,7 +452,7 @@ def pappus_graph(): return nxcg.Graph.from_coo(18, src_indices, dst_indices, name="Pappus Graph") -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def petersen_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -479,7 +479,7 @@ def petersen_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def sedgewick_maze_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -500,7 +500,7 @@ def sedgewick_maze_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def tetrahedral_graph(create_using=None): # This can also be defined w.r.t. complete_graph graph_class, inplace = _create_using_class(create_using) @@ -517,7 +517,7 @@ def tetrahedral_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def truncated_cube_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -548,7 +548,7 @@ def truncated_cube_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def truncated_tetrahedron_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): @@ -583,7 +583,7 @@ def truncated_tetrahedron_graph(create_using=None): return G -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def tutte_graph(create_using=None): graph_class, inplace = _create_using_class(create_using) if graph_class.is_directed(): diff --git a/python/nx-cugraph/nx_cugraph/generators/social.py b/python/nx-cugraph/nx_cugraph/generators/social.py index 3c936d07a..07e82c63f 100644 --- a/python/nx-cugraph/nx_cugraph/generators/social.py +++ b/python/nx-cugraph/nx_cugraph/generators/social.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -25,7 +25,7 @@ ] -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def davis_southern_women_graph(): # fmt: off src_indices = cp.array( @@ -88,7 +88,7 @@ def davis_southern_women_graph(): ) -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def florentine_families_graph(): # fmt: off src_indices = cp.array( @@ -114,7 +114,7 @@ def florentine_families_graph(): return nxcg.Graph.from_coo(15, src_indices, dst_indices, id_to_key=nodes) -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def karate_club_graph(): # fmt: off src_indices = cp.array( @@ -175,7 +175,7 @@ def karate_club_graph(): ) -@networkx_algorithm +@networkx_algorithm(version_added="23.12") def les_miserables_graph(): # fmt: off src_indices = cp.array( diff --git a/python/nx-cugraph/nx_cugraph/scripts/__init__.py b/python/nx-cugraph/nx_cugraph/scripts/__init__.py new file mode 100644 index 000000000..aeae60781 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/scripts/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/python/nx-cugraph/nx_cugraph/scripts/__main__.py b/python/nx-cugraph/nx_cugraph/scripts/__main__.py new file mode 100755 index 000000000..c0963e64c --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/scripts/__main__.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +if __name__ == "__main__": + import argparse + + from nx_cugraph.scripts import print_table, print_tree + + parser = argparse.ArgumentParser( + parents=[ + print_table.get_argumentparser(add_help=False), + print_tree.get_argumentparser(add_help=False), + ], + description="Print info about functions implemented by nx-cugraph", + ) + parser.add_argument("action", choices=["print_table", "print_tree"]) + args = parser.parse_args() + if args.action == "print_table": + print_table.main() + else: + print_tree.main( + by=args.by, + networkx_path=args.networkx_path, + dispatch_name=args.dispatch_name or args.dispatch_name_always, + version_added=args.version_added, + plc=args.plc, + dispatch_name_if_different=not args.dispatch_name_always, + ) diff --git a/python/nx-cugraph/nx_cugraph/scripts/print_table.py b/python/nx-cugraph/nx_cugraph/scripts/print_table.py new file mode 100755 index 000000000..7e69de63d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/scripts/print_table.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import sys +from collections import namedtuple + +from networkx.utils.backends import _registered_algorithms as algos + +from _nx_cugraph import get_info +from nx_cugraph.interface import BackendInterface + + +def get_funcpath(func): + return f"{func.__module__}.{func.__name__}" + + +def get_path_to_name(): + return { + get_funcpath(algos[funcname]): funcname + for funcname in get_info()["functions"].keys() & algos.keys() + } + + +Info = namedtuple( + "Info", + "networkx_path, dispatch_name, version_added, plc, is_incomplete, is_different", +) + + +def get_path_to_info(path_to_name=None, version_added_sep=".", plc_sep="/"): + if path_to_name is None: + path_to_name = get_path_to_name() + rv = {} + for funcpath in sorted(path_to_name): + funcname = path_to_name[funcpath] + cufunc = getattr(BackendInterface, funcname) + plc = plc_sep.join(sorted(cufunc._plc_names)) if cufunc._plc_names else "" + version_added = cufunc.version_added.replace(".", version_added_sep) + is_incomplete = cufunc.is_incomplete + is_different = cufunc.is_different + rv[funcpath] = Info( + funcpath, funcname, version_added, plc, is_incomplete, is_different + ) + return rv + + +def main(path_to_info=None, *, file=sys.stdout): + if path_to_info is None: + path_to_info = get_path_to_info(version_added_sep=".") + lines = ["networkx_path,dispatch_name,version_added,plc,is_incomplete,is_different"] + lines.extend(",".join(info) for info in path_to_info.values()) + text = "\n".join(lines) + print(text, file=file) + return text + + +def get_argumentparser(add_help=True): + return argparse.ArgumentParser( + description="Print info about functions implemented by nx-cugraph as CSV", + add_help=add_help, + ) + + +if __name__ == "__main__": + parser = get_argumentparser() + args = parser.parse_args() + main() diff --git a/python/nx-cugraph/nx_cugraph/scripts/print_tree.py b/python/nx-cugraph/nx_cugraph/scripts/print_tree.py new file mode 100755 index 000000000..bb75d735c --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/scripts/print_tree.py @@ -0,0 +1,241 @@ +#!/usr/bin/env python +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import re +import sys + +import networkx as nx + +from nx_cugraph.scripts.print_table import get_path_to_info + + +def add_branch(G, path, extra="", *, skip=0): + branch = path.split(".") + prev = ".".join(branch[: skip + 1]) + for i in range(skip + 2, len(branch)): + cur = ".".join(branch[:i]) + G.add_edge(prev, cur) + prev = cur + if extra: + if not isinstance(extra, str): + extra = ", ".join(extra) + path += f" ({extra})" + G.add_edge(prev, path) + + +def get_extra( + info, + *, + networkx_path=False, + dispatch_name=False, + version_added=False, + plc=False, + dispatch_name_if_different=False, + incomplete=False, + different=False, +): + extra = [] + if networkx_path: + extra.append(info.networkx_path) + if dispatch_name and ( + not dispatch_name_if_different + or info.dispatch_name != info.networkx_path.rsplit(".", 1)[-1] + ): + extra.append(info.dispatch_name) + if version_added: + v = info.version_added + if len(v) != 5: + raise ValueError(f"Is there something wrong with version: {v!r}?") + extra.append(v[:2] + "." + v[-2:]) + if plc and info.plc: + extra.append(info.plc) + if incomplete and info.is_incomplete: + extra.append("is-incomplete") + if different and info.is_different: + extra.append("is-different") + return extra + + +def create_tree( + path_to_info=None, + *, + by="networkx_path", + skip=0, + networkx_path=False, + dispatch_name=False, + version_added=False, + plc=False, + dispatch_name_if_different=False, + incomplete=False, + different=False, + prefix="", +): + if path_to_info is None: + path_to_info = get_path_to_info() + if isinstance(by, str): + by = [by] + G = nx.DiGraph() + for info in sorted( + path_to_info.values(), + key=lambda x: (*(getattr(x, b) for b in by), x.networkx_path), + ): + if not all(getattr(info, b) for b in by): + continue + path = prefix + ".".join(getattr(info, b) for b in by) + extra = get_extra( + info, + networkx_path=networkx_path, + dispatch_name=dispatch_name, + version_added=version_added, + plc=plc, + dispatch_name_if_different=dispatch_name_if_different, + incomplete=incomplete, + different=different, + ) + add_branch(G, path, extra=extra, skip=skip) + return G + + +def main( + path_to_info=None, + *, + by="networkx_path", + networkx_path=False, + dispatch_name=False, + version_added=False, + plc=False, + dispatch_name_if_different=True, + incomplete=False, + different=False, + file=sys.stdout, +): + if path_to_info is None: + path_to_info = get_path_to_info(version_added_sep="-") + kwargs = { + "networkx_path": networkx_path, + "dispatch_name": dispatch_name, + "version_added": version_added, + "plc": plc, + "dispatch_name_if_different": dispatch_name_if_different, + "incomplete": incomplete, + "different": different, + } + if by == "networkx_path": + G = create_tree(path_to_info, by="networkx_path", **kwargs) + text = re.sub(r"[A-Za-z_\./]+\.", "", ("\n".join(nx.generate_network_text(G)))) + elif by == "plc": + G = create_tree( + path_to_info, by=["plc", "networkx_path"], prefix="plc-", **kwargs + ) + text = re.sub( + "plc-", + "plc.", + re.sub( + r" plc-[A-Za-z_\./]*\.", + " ", + "\n".join(nx.generate_network_text(G)), + ), + ) + elif by == "version_added": + G = create_tree( + path_to_info, + by=["version_added", "networkx_path"], + prefix="version_added-", + **kwargs, + ) + text = re.sub( + "version_added-", + "version: ", + re.sub( + r" version_added-[-0-9A-Za-z_\./]*\.", + " ", + "\n".join(nx.generate_network_text(G)), + ), + ).replace("-", ".") + else: + raise ValueError( + "`by` argument should be one of {'networkx_path', 'plc', 'version_added' " + f"got: {by}" + ) + print(text, file=file) + return text + + +def get_argumentparser(add_help=True): + parser = argparse.ArgumentParser( + "Print a tree showing NetworkX functions implemented by nx-cugraph", + add_help=add_help, + ) + parser.add_argument( + "--by", + choices=["networkx_path", "plc", "version_added"], + default="networkx_path", + help="How to group functions", + ) + parser.add_argument( + "--dispatch-name", + "--dispatch_name", + action="store_true", + help="Show the dispatch name in parentheses if different from NetworkX name", + ) + parser.add_argument( + "--dispatch-name-always", + "--dispatch_name_always", + action="store_true", + help="Always show the dispatch name in parentheses", + ) + parser.add_argument( + "--plc", + "--pylibcugraph", + action="store_true", + help="Show the used pylibcugraph function in parentheses", + ) + parser.add_argument( + "--version-added", + "--version_added", + action="store_true", + help="Show the version added in parentheses", + ) + parser.add_argument( + "--networkx-path", + "--networkx_path", + action="store_true", + help="Show the full networkx path in parentheses", + ) + parser.add_argument( + "--incomplete", + action="store_true", + help="Show which functions are incomplete", + ) + parser.add_argument( + "--different", + action="store_true", + help="Show which functions are different", + ) + return parser + + +if __name__ == "__main__": + parser = get_argumentparser() + args = parser.parse_args() + main( + by=args.by, + networkx_path=args.networkx_path, + dispatch_name=args.dispatch_name or args.dispatch_name_always, + version_added=args.version_added, + plc=args.plc, + dispatch_name_if_different=not args.dispatch_name_always, + incomplete=args.incomplete, + different=args.different, + ) diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py index a0dbfcec8..d09a9e961 100644 --- a/python/nx-cugraph/nx_cugraph/utils/decorators.py +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -41,14 +41,25 @@ class networkx_algorithm: name: str extra_doc: str | None extra_params: dict[str, str] | None + version_added: str + is_incomplete: bool + is_different: bool + _plc_names: set[str] | None def __new__( cls, func=None, *, name: str | None = None, + # Extra parameter info that is added to NetworkX docstring extra_params: dict[str, str] | str | None = None, + # Applies `nodes_or_number` decorator compatibly across versions (3.3 changed) nodes_or_number: list[int] | int | None = None, + # Metadata (for introspection only) + version_added: str, # Required + is_incomplete: bool = False, # See self.extra_doc for details if True + is_different: bool = False, # See self.extra_doc for details if True + plc: str | set[str] | None = None, # Hidden from user, may be removed someday ): if func is None: return partial( @@ -56,6 +67,10 @@ def __new__( name=name, extra_params=extra_params, nodes_or_number=nodes_or_number, + plc=plc, + version_added=version_added, + is_incomplete=is_incomplete, + is_different=is_different, ) instance = object.__new__(cls) if nodes_or_number is not None and nx.__version__[:3] > "3.2": @@ -74,6 +89,15 @@ def __new__( f"extra_params must be dict, str, or None; got {type(extra_params)}" ) instance.extra_params = extra_params + if plc is None or isinstance(plc, set): + instance._plc_names = plc + elif isinstance(plc, str): + instance._plc_names = {plc} + else: + raise TypeError(f"plc argument must be str, set, or None; got {type(plc)}") + instance.version_added = version_added + instance.is_incomplete = is_incomplete + instance.is_different = is_different # The docstring on our function is added to the NetworkX docstring. instance.extra_doc = ( dedent(func.__doc__.lstrip("\n").rstrip()) if func.__doc__ else None @@ -91,6 +115,11 @@ def __new__( def _can_run(self, func): """Set the `can_run` attribute to the decorated function.""" + if not func.__name__.startswith("_"): + raise ValueError( + "The name of the function used by `_can_run` must begin with '_'; " + f"got: {func.__name__!r}" + ) self.can_run = func def __call__(self, /, *args, **kwargs): diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index b29578b03..63ac11591 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -52,12 +52,20 @@ test = [ Homepage = "https://github.com/rapidsai/cugraph" Documentation = "https://docs.rapids.ai/api/cugraph/stable/" +# "plugin" used in nx version < 3.2 [project.entry-points."networkx.plugins"] cugraph = "nx_cugraph.interface:BackendInterface" [project.entry-points."networkx.plugin_info"] cugraph = "_nx_cugraph:get_info" +# "backend" used in nx version >= 3.2 +[project.entry-points."networkx.backends"] +cugraph = "nx_cugraph.interface:BackendInterface" + +[project.entry-points."networkx.backend_info"] +cugraph = "_nx_cugraph:get_info" + [tool.setuptools] license-files = ["LICENSE"] From 06f8970523913639fe1156d8d635754f199693eb Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Fri, 12 Jan 2024 12:06:46 -0500 Subject: [PATCH 301/384] Fix OOB error, BFS C API should validate that the source vertex is a valid vertex (#4077) * Added error check to be sure that the source vertex is a valid vertex in the graph. * Updated `nx_cugraph.Graph` class to create PLC graphs using `vertices_array` in order to include isolated vertices. This is now needed since the error check added in this PR prevents NetworkX tests from passing if isolated vertices are treated as invalid, so this change prevents that. * This resolves the problem that required the test workarounds done [here](https://github.com/rapidsai/cugraph/pull/4029#discussion_r1433332010) in [4029](https://github.com/rapidsai/cugraph/pull/4029), so those workarounds have been removed in this PR. Closes #4067 Authors: - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Ray Douglass (https://github.com/raydouglass) - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4077 --- python/nx-cugraph/nx_cugraph/classes/graph.py | 17 ++++++++++++++++- python/nx-cugraph/nx_cugraph/interface.py | 13 +------------ 2 files changed, 17 insertions(+), 13 deletions(-) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index cdd3f744f..cb6b4e7ae 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -65,6 +65,7 @@ class Graph: key_to_id: dict[NodeKey, IndexValue] | None _id_to_key: list[NodeKey] | None _N: int + _node_ids: cp.ndarray[IndexValue] | None # holds plc.SGGraph.vertices_array data # Used by graph._get_plc_graph _plc_type_map: ClassVar[dict[np.dtype, np.dtype]] = { @@ -116,6 +117,7 @@ def from_coo( new_graph.key_to_id = None if key_to_id is None else dict(key_to_id) new_graph._id_to_key = None if id_to_key is None else list(id_to_key) new_graph._N = op.index(N) # Ensure N is integral + new_graph._node_ids = None new_graph.graph = new_graph.graph_attr_dict_factory() new_graph.graph.update(attr) size = new_graph.src_indices.size @@ -157,6 +159,16 @@ def from_coo( f"(got {new_graph.dst_indices.dtype.name})." ) new_graph.dst_indices = dst_indices + + # If the graph contains isolates, plc.SGGraph() must be passed a value + # for vertices_array that contains every vertex ID, since the + # src/dst_indices arrays will not contain IDs for isolates. Create this + # only if needed. Like src/dst_indices, the _node_ids array must be + # maintained for the lifetime of the plc.SGGraph + isolates = nxcg.algorithms.isolate._isolates(new_graph) + if len(isolates) > 0: + new_graph._node_ids = cp.arange(new_graph._N, dtype=index_dtype) + return new_graph @classmethod @@ -405,6 +417,7 @@ def clear(self) -> None: self.src_indices = cp.empty(0, self.src_indices.dtype) self.dst_indices = cp.empty(0, self.dst_indices.dtype) self._N = 0 + self._node_ids = None self.key_to_id = None self._id_to_key = None @@ -637,6 +650,7 @@ def _get_plc_graph( dst_indices = self.dst_indices if switch_indices: src_indices, dst_indices = dst_indices, src_indices + return plc.SGGraph( resource_handle=plc.ResourceHandle(), graph_properties=plc.GraphProperties( @@ -649,6 +663,7 @@ def _get_plc_graph( store_transposed=store_transposed, renumber=False, do_expensive_check=False, + vertices_array=self._node_ids, ) def _sort_edge_indices(self, primary="src"): diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 3f6449f57..34eb59698 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -242,20 +242,9 @@ def key(testpath): ) too_slow = "Too slow to run" - maybe_oom = "out of memory in CI" skip = { key("test_tree_isomorphism.py:test_positive"): too_slow, key("test_tree_isomorphism.py:test_negative"): too_slow, - key("test_efficiency.py:TestEfficiency.test_using_ego_graph"): maybe_oom, - key("test_dag.py:TestDAG.test_antichains"): maybe_oom, - key( - "test_lowest_common_ancestors.py:" - "TestDAGLCA.test_all_pairs_lca_pairs_without_lca" - ): maybe_oom, - key( - "test_lowest_common_ancestors.py:" - "TestMultiDiGraph_DAGLCA.test_all_pairs_lca_pairs_without_lca" - ): maybe_oom, # These repeatedly call `bfs_layers`, which converts the graph every call key( "test_vf2pp.py:TestGraphISOVF2pp.test_custom_graph2_different_labels" From 775fc5ab6ea7164e6496fac0aa7dbf4496a8a684 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 16 Jan 2024 09:39:31 -0600 Subject: [PATCH 302/384] nx-cugraph: PLC now handles isolated nodes; clean up our workarounds (#4092) Hooray for removing and cleaning code! Tests also added (we already tested isolated nodes for Louvain). nx-cugraph was updated to handle isolated nodes by passing the node set to PLC in #4077 Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4092 --- python/nx-cugraph/_nx_cugraph/__init__.py | 10 +-- python/nx-cugraph/nx_cugraph/_version.py | 6 +- .../algorithms/community/louvain.py | 12 +--- .../algorithms/components/connected.py | 64 +++++++------------ .../nx-cugraph/nx_cugraph/tests/__init__.py | 3 +- .../nx_cugraph/tests/test_connected.py | 30 +++++++++ 6 files changed, 61 insertions(+), 64 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_connected.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index d02c9c3e9..4e869c76b 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -107,21 +107,21 @@ }, "extra_docstrings": { # BEGIN: extra_docstrings - "betweenness_centrality": "`weight` parameter is not yet supported.", + "betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.", "bfs_edges": "`sort_neighbors` parameter is not yet supported.", "bfs_predecessors": "`sort_neighbors` parameter is not yet supported.", "bfs_successors": "`sort_neighbors` parameter is not yet supported.", "bfs_tree": "`sort_neighbors` parameter is not yet supported.", - "edge_betweenness_centrality": "`weight` parameter is not yet supported.", + "edge_betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.", "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.", - "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", + "from_pandas_edgelist": "cudf.DataFrame inputs also supported; value columns with str is unsuppported.", "generic_bfs_edges": "`neighbors` and `sort_neighbors` parameters are not yet supported.", "k_truss": ( "Currently raises `NotImplementedError` for graphs with more than one connected\n" "component when k >= 3. We expect to fix this soon." ), "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.", - "louvain_communities": "`seed` parameter is currently ignored.", + "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.", "pagerank": "`dangling` parameter is not supported, but it is checked for validity.", # END: extra_docstrings }, diff --git a/python/nx-cugraph/nx_cugraph/_version.py b/python/nx-cugraph/nx_cugraph/_version.py index 868a2e194..a528a3bfe 100644 --- a/python/nx-cugraph/nx_cugraph/_version.py +++ b/python/nx-cugraph/nx_cugraph/_version.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,12 +11,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# - import importlib.resources # Read VERSION file from the module that is symlinked to VERSION file -# in the root of the repo at build time or copied to the moudle at +# in the root of the repo at build time or copied to the module at # installation. VERSION is a separate file that allows CI build-time scripts # to update version info (including commit hashes) without modifying # source files. diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index d023bab1a..413ff9ca5 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -23,8 +23,6 @@ not_implemented_for, ) -from ..isolate import _isolates - __all__ = ["louvain_communities"] @@ -56,7 +54,6 @@ def louvain_communities( seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) if G.src_indices.size == 0: - # TODO: PLC doesn't handle empty graphs gracefully! return [{key} for key in G._nodeiter_to_iter(range(len(G)))] if max_level is None: max_level = 500 @@ -76,14 +73,7 @@ def louvain_communities( do_expensive_check=False, ) groups = _groupby(clusters, node_ids, groups_are_canonical=True) - rv = [set(G._nodearray_to_list(ids)) for ids in groups.values()] - # TODO: PLC doesn't handle isolated node_ids yet, so this is a temporary fix - isolates = _isolates(G) - if isolates.size > 0: - isolates = isolates[isolates > node_ids.max()] - if isolates.size > 0: - rv.extend({node} for node in G._nodearray_to_list(isolates)) - return rv + return [set(G._nodearray_to_list(ids)) for ids in groups.values()] @louvain_communities._can_run diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py index cb12aed1d..95cc907a8 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py @@ -10,8 +10,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import itertools - import cupy as cp import networkx as nx import pylibcugraph as plc @@ -19,8 +17,6 @@ from nx_cugraph.convert import _to_undirected_graph from nx_cugraph.utils import _groupby, networkx_algorithm, not_implemented_for -from ..isolate import _isolates - __all__ = [ "number_connected_components", "connected_components", @@ -32,19 +28,17 @@ @not_implemented_for("directed") @networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def number_connected_components(G): - return sum(1 for _ in connected_components(G)) - # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL - # G = _to_undirected_graph(G) - # unused_node_ids, labels = plc.weakly_connected_components( - # resource_handle=plc.ResourceHandle(), - # graph=G._get_plc_graph(), - # offsets=None, - # indices=None, - # weights=None, - # labels=None, - # do_expensive_check=False, - # ) - # return cp.unique(labels).size + G = _to_undirected_graph(G) + unused_node_ids, labels = plc.weakly_connected_components( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + offsets=None, + indices=None, + weights=None, + labels=None, + do_expensive_check=False, + ) + return cp.unique(labels).size @number_connected_components._can_run @@ -61,7 +55,6 @@ def _(G): def connected_components(G): G = _to_undirected_graph(G) if G.src_indices.size == 0: - # TODO: PLC doesn't handle empty graphs (or isolated nodes) gracefully! return [{key} for key in G._nodeiter_to_iter(range(len(G)))] node_ids, labels = plc.weakly_connected_components( resource_handle=plc.ResourceHandle(), @@ -73,16 +66,7 @@ def connected_components(G): do_expensive_check=False, ) groups = _groupby(labels, node_ids) - it = (G._nodearray_to_set(connected_ids) for connected_ids in groups.values()) - # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix - isolates = _isolates(G) - if isolates.size > 0: - isolates = isolates[isolates > node_ids.max()] - if isolates.size > 0: - it = itertools.chain( - it, ({node} for node in G._nodearray_to_list(isolates)) - ) - return it + return (G._nodearray_to_set(connected_ids) for connected_ids in groups.values()) @not_implemented_for("directed") @@ -93,20 +77,16 @@ def is_connected(G): raise nx.NetworkXPointlessConcept( "Connectivity is undefined for the null graph." ) - for community in connected_components(G): - return len(community) == len(G) - raise RuntimeError # pragma: no cover - # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL - # unused_node_ids, labels = plc.weakly_connected_components( - # resource_handle=plc.ResourceHandle(), - # graph=G._get_plc_graph(), - # offsets=None, - # indices=None, - # weights=None, - # labels=None, - # do_expensive_check=False, - # ) - # return labels.size == len(G) and cp.unique(labels).size == 1 + unused_node_ids, labels = plc.weakly_connected_components( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + offsets=None, + indices=None, + weights=None, + labels=None, + do_expensive_check=False, + ) + return bool((labels == labels[0]).all()) @not_implemented_for("directed") diff --git a/python/nx-cugraph/nx_cugraph/tests/__init__.py b/python/nx-cugraph/nx_cugraph/tests/__init__.py index ce94db52f..c2002fd3f 100644 --- a/python/nx-cugraph/nx_cugraph/tests/__init__.py +++ b/python/nx-cugraph/nx_cugraph/tests/__init__.py @@ -1,5 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. -# +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/python/nx-cugraph/nx_cugraph/tests/test_connected.py b/python/nx-cugraph/nx_cugraph/tests/test_connected.py new file mode 100644 index 000000000..fa9f283ab --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_connected.py @@ -0,0 +1,30 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx + +import nx_cugraph as nxcg + + +def test_connected_isolated_nodes(): + G = nx.complete_graph(4) + G.add_node(max(G) + 1) + assert nx.is_connected(G) is False + assert nxcg.is_connected(G) is False + assert nx.number_connected_components(G) == 2 + assert nxcg.number_connected_components(G) == 2 + assert sorted(nx.connected_components(G)) == [{0, 1, 2, 3}, {4}] + assert sorted(nxcg.connected_components(G)) == [{0, 1, 2, 3}, {4}] + assert nx.node_connected_component(G, 0) == {0, 1, 2, 3} + assert nxcg.node_connected_component(G, 0) == {0, 1, 2, 3} + assert nx.node_connected_component(G, 4) == {4} + assert nxcg.node_connected_component(G, 4) == {4} From 203784c34362f3869e346afe9f11be85925a5a9b Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 16 Jan 2024 19:39:48 -0600 Subject: [PATCH 303/384] nx-cugraph: add weakly connected components (#4071) This doesn't currently work, because `plc.weakly_connected_components` only works on symmetric graphs (so it's not actually performing wcc now is it?): > RuntimeError: non-success value returned from cugraph_weakly_connected_components: CUGRAPH_UNKNOWN_ERROR cuGraph failure at file=[...]/cugraph/cpp/src/components/weakly_connected_components_impl.cuh line=283: Invalid input argument: input graph should be symmetric for weakly connected components. _These are high-priority algorithms for `nx-cugraph`, because they are widely used by networkx dependents._ Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4071 --- python/nx-cugraph/_nx_cugraph/__init__.py | 6 ++ .../algorithms/components/__init__.py | 4 +- .../algorithms/components/connected.py | 22 ++++- .../components/strongly_connected.py | 85 +++++++++++++++++++ .../algorithms/components/weakly_connected.py | 47 ++++++++++ .../nx_cugraph/algorithms/isolate.py | 24 ++++-- python/nx-cugraph/nx_cugraph/classes/graph.py | 23 ++++- python/nx-cugraph/nx_cugraph/interface.py | 7 +- 8 files changed, 204 insertions(+), 14 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 4e869c76b..d9b997411 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -70,6 +70,8 @@ "in_degree_centrality", "is_connected", "is_isolate", + "is_strongly_connected", + "is_weakly_connected", "isolates", "k_truss", "karate_club_graph", @@ -85,6 +87,8 @@ "number_connected_components", "number_of_isolates", "number_of_selfloops", + "number_strongly_connected_components", + "number_weakly_connected_components", "octahedral_graph", "out_degree_centrality", "pagerank", @@ -95,6 +99,7 @@ "single_source_shortest_path_length", "single_target_shortest_path_length", "star_graph", + "strongly_connected_components", "tadpole_graph", "tetrahedral_graph", "trivial_graph", @@ -102,6 +107,7 @@ "truncated_tetrahedron_graph", "turan_graph", "tutte_graph", + "weakly_connected_components", "wheel_graph", # END: functions }, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py index 26816ef36..12a09b535 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,3 +11,5 @@ # See the License for the specific language governing permissions and # limitations under the License. from .connected import * +from .strongly_connected import * +from .weakly_connected import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py index 95cc907a8..cdb9f54f6 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py @@ -29,9 +29,15 @@ @networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def number_connected_components(G): G = _to_undirected_graph(G) + return _number_connected_components(G) + + +def _number_connected_components(G, symmetrize=None): + if G.src_indices.size == 0: + return len(G) unused_node_ids, labels = plc.weakly_connected_components( resource_handle=plc.ResourceHandle(), - graph=G._get_plc_graph(), + graph=G._get_plc_graph(symmetrize=symmetrize), offsets=None, indices=None, weights=None, @@ -54,11 +60,15 @@ def _(G): @networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def connected_components(G): G = _to_undirected_graph(G) + return _connected_components(G) + + +def _connected_components(G, symmetrize=None): if G.src_indices.size == 0: return [{key} for key in G._nodeiter_to_iter(range(len(G)))] node_ids, labels = plc.weakly_connected_components( resource_handle=plc.ResourceHandle(), - graph=G._get_plc_graph(), + graph=G._get_plc_graph(symmetrize=symmetrize), offsets=None, indices=None, weights=None, @@ -73,13 +83,19 @@ def connected_components(G): @networkx_algorithm(plc="weakly_connected_components", version_added="23.12") def is_connected(G): G = _to_undirected_graph(G) + return _is_connected(G) + + +def _is_connected(G, symmetrize=None): if len(G) == 0: raise nx.NetworkXPointlessConcept( "Connectivity is undefined for the null graph." ) + if G.src_indices.size == 0: + return len(G) == 1 unused_node_ids, labels = plc.weakly_connected_components( resource_handle=plc.ResourceHandle(), - graph=G._get_plc_graph(), + graph=G._get_plc_graph(symmetrize=symmetrize), offsets=None, indices=None, weights=None, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py new file mode 100644 index 000000000..8fdf99ed5 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py @@ -0,0 +1,85 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import pylibcugraph as plc + +from nx_cugraph.convert import _to_directed_graph +from nx_cugraph.utils import ( + _groupby, + index_dtype, + networkx_algorithm, + not_implemented_for, +) + +__all__ = [ + "number_strongly_connected_components", + "strongly_connected_components", + "is_strongly_connected", +] + + +def _strongly_connected_components(G): + # TODO: create utility function to convert just the indices to CSR + # TODO: this uses a legacy PLC function (strongly_connected_components) + N = len(G) + indices = cp.lexsort(cp.vstack((G.dst_indices, G.src_indices))) + dst_indices = G.dst_indices[indices] + offsets = cp.searchsorted( + G.src_indices, cp.arange(N + 1, dtype=index_dtype), sorter=indices + ).astype(index_dtype) + labels = cp.zeros(N, dtype=index_dtype) + plc.strongly_connected_components( + offsets=offsets, + indices=dst_indices, + weights=None, + num_verts=N, + num_edges=dst_indices.size, + labels=labels, + ) + return labels + + +@not_implemented_for("undirected") +@networkx_algorithm(version_added="24.02", plc="strongly_connected_components") +def strongly_connected_components(G): + G = _to_directed_graph(G) + if G.src_indices.size == 0: + return [{key} for key in G._nodeiter_to_iter(range(len(G)))] + labels = _strongly_connected_components(G) + groups = _groupby(labels, cp.arange(len(G), dtype=index_dtype)) + return (G._nodearray_to_set(connected_ids) for connected_ids in groups.values()) + + +@not_implemented_for("undirected") +@networkx_algorithm(version_added="24.02", plc="strongly_connected_components") +def number_strongly_connected_components(G): + G = _to_directed_graph(G) + if G.src_indices.size == 0: + return len(G) + labels = _strongly_connected_components(G) + return cp.unique(labels).size + + +@not_implemented_for("undirected") +@networkx_algorithm(version_added="24.02", plc="strongly_connected_components") +def is_strongly_connected(G): + G = _to_directed_graph(G) + if len(G) == 0: + raise nx.NetworkXPointlessConcept( + "Connectivity is undefined for the null graph." + ) + if G.src_indices.size == 0: + return len(G) == 1 + labels = _strongly_connected_components(G) + return bool((labels == labels[0]).all()) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py new file mode 100644 index 000000000..5b797b391 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py @@ -0,0 +1,47 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nx_cugraph.convert import _to_directed_graph +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +from .connected import ( + _connected_components, + _is_connected, + _number_connected_components, +) + +__all__ = [ + "number_weakly_connected_components", + "weakly_connected_components", + "is_weakly_connected", +] + + +@not_implemented_for("undirected") +@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +def weakly_connected_components(G): + G = _to_directed_graph(G) + return _connected_components(G, symmetrize="union") + + +@not_implemented_for("undirected") +@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +def number_weakly_connected_components(G): + G = _to_directed_graph(G) + return _number_connected_components(G, symmetrize="union") + + +@not_implemented_for("undirected") +@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +def is_weakly_connected(G): + G = _to_directed_graph(G) + return _is_connected(G, symmetrize="union") diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py index c7e5d7113..62b47a9b3 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py @@ -15,9 +15,10 @@ from typing import TYPE_CHECKING import cupy as cp +import numpy as np from nx_cugraph.convert import _to_graph -from nx_cugraph.utils import networkx_algorithm +from nx_cugraph.utils import index_dtype, networkx_algorithm if TYPE_CHECKING: # pragma: no cover from nx_cugraph.typing import IndexValue @@ -36,19 +37,28 @@ def is_isolate(G, n): ) -def _mark_isolates(G) -> cp.ndarray[bool]: +def _mark_isolates(G, symmetrize=None) -> cp.ndarray[bool]: """Return a boolean mask array indicating indices of isolated nodes.""" mark_isolates = cp.ones(len(G), bool) - mark_isolates[G.src_indices] = False - if G.is_directed(): - mark_isolates[G.dst_indices] = False + if G.is_directed() and symmetrize == "intersection": + N = G._N + # Upcast to int64 so indices don't overflow + src_dst = N * G.src_indices.astype(np.int64) + G.dst_indices + src_dst_T = G.src_indices + N * G.dst_indices.astype(np.int64) + src_dst_new = cp.intersect1d(src_dst, src_dst_T) + new_indices = cp.floor_divide(src_dst_new, N, dtype=index_dtype) + mark_isolates[new_indices] = False + else: + mark_isolates[G.src_indices] = False + if G.is_directed(): + mark_isolates[G.dst_indices] = False return mark_isolates -def _isolates(G) -> cp.ndarray[IndexValue]: +def _isolates(G, symmetrize=None) -> cp.ndarray[IndexValue]: """Like isolates, but return an array of indices instead of an iterator of nodes.""" G = _to_graph(G) - return cp.nonzero(_mark_isolates(G))[0] + return cp.nonzero(_mark_isolates(G, symmetrize=symmetrize))[0] @networkx_algorithm(version_added="23.10") diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index cb6b4e7ae..251e92b70 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -592,6 +592,7 @@ def _get_plc_graph( store_transposed: bool = False, switch_indices: bool = False, edge_array: cp.ndarray[EdgeValue] | None = None, + symmetrize: str | None = None, ): if edge_array is not None or edge_attr is None: pass @@ -650,12 +651,30 @@ def _get_plc_graph( dst_indices = self.dst_indices if switch_indices: src_indices, dst_indices = dst_indices, src_indices + if symmetrize is not None: + if edge_array is not None: + raise NotImplementedError( + "edge_array must be None when symmetrizing the graph" + ) + N = self._N + # Upcast to int64 so indices don't overflow + src_dst = N * src_indices.astype(np.int64) + dst_indices + src_dst_T = src_indices + N * dst_indices.astype(np.int64) + if symmetrize == "union": + src_dst_new = cp.union1d(src_dst, src_dst_T) + elif symmetrize == "intersection": + src_dst_new = cp.intersect1d(src_dst, src_dst_T) + else: + raise ValueError( + f'symmetrize must be "union" or "intersection"; got "{symmetrize}"' + ) + src_indices, dst_indices = cp.divmod(src_dst_new, N, dtype=index_dtype) return plc.SGGraph( resource_handle=plc.ResourceHandle(), graph_properties=plc.GraphProperties( - is_multigraph=self.is_multigraph(), - is_symmetric=not self.is_directed(), + is_multigraph=self.is_multigraph() and symmetrize is None, + is_symmetric=not self.is_directed() or symmetrize is not None, ), src_or_offset_array=src_indices, dst_or_index_array=dst_indices, diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 34eb59698..04591c0e9 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -68,7 +68,12 @@ def key(testpath): louvain_different = "Louvain may be different due to RNG" no_string_dtype = "string edge values not currently supported" - xfail = {} + xfail = { + key( + "test_strongly_connected.py:" + "TestStronglyConnected.test_condensation_mapping_and_members" + ): "Strongly connected groups in different iteration order", + } from packaging.version import parse From bdd29fe713862ea595068463576208f5d0b563d3 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Thu, 18 Jan 2024 05:22:11 -0600 Subject: [PATCH 304/384] `nx-cugraph`: add `to_undirected` method; add reciprocity algorithms (#4063) Getting `G.to_undirected` to work was more involved than I expected, but at least we got two algorithms "for free" out of the effort! We raise `NotImplementedError` for `multidigraph.to_undirected()` for now. I would say that understanding the reciprocity algorithms is the first step to understanding `to_undirected`. Authors: - Erik Welch (https://github.com/eriknw) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4063 --- python/nx-cugraph/_nx_cugraph/__init__.py | 2 + python/nx-cugraph/lint.yaml | 14 +-- .../nx_cugraph/algorithms/__init__.py | 3 +- .../nx_cugraph/algorithms/reciprocity.py | 93 ++++++++++++++ .../nx-cugraph/nx_cugraph/classes/digraph.py | 114 +++++++++++++++++- python/nx-cugraph/nx_cugraph/classes/graph.py | 7 +- .../nx_cugraph/classes/multidigraph.py | 10 +- .../nx_cugraph/classes/multigraph.py | 4 +- .../nx_cugraph/tests/test_generators.py | 27 +---- .../nx_cugraph/tests/test_graph_methods.py | 67 ++++++++++ .../nx_cugraph/tests/testing_utils.py | 38 ++++++ 11 files changed, 342 insertions(+), 37 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/reciprocity.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_graph_methods.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/testing_utils.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index d9b997411..69320e6b5 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -91,10 +91,12 @@ "number_weakly_connected_components", "octahedral_graph", "out_degree_centrality", + "overall_reciprocity", "pagerank", "pappus_graph", "path_graph", "petersen_graph", + "reciprocity", "sedgewick_maze_graph", "single_source_shortest_path_length", "single_target_shortest_path_length", diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index de6f20bc4..0d4f0b594 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # https://pre-commit.com/ # @@ -36,7 +36,7 @@ repos: - id: autoflake args: [--in-place] - repo: https://github.com/pycqa/isort - rev: 5.12.0 + rev: 5.13.2 hooks: - id: isort - repo: https://github.com/asottile/pyupgrade @@ -45,23 +45,23 @@ repos: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.11.0 + rev: 23.12.1 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.7 + rev: v0.1.13 hooks: - id: ruff args: [--fix-only, --show-fixes] # --unsafe-fixes] - repo: https://github.com/PyCQA/flake8 - rev: 6.1.0 + rev: 7.0.0 hooks: - id: flake8 args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=SIM105'] # Why is this necessary? additional_dependencies: &flake8_dependencies # These versions need updated manually - - flake8==6.1.0 + - flake8==7.0.0 - flake8-bugbear==23.12.2 - flake8-simplify==0.21.0 - repo: https://github.com/asottile/yesqa @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.7 + rev: v0.1.13 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index d28a629fe..65700838f 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -26,5 +26,6 @@ from .dag import * from .isolate import * from .link_analysis import * +from .reciprocity import * from .shortest_paths import * from .traversal import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/reciprocity.py b/python/nx-cugraph/nx_cugraph/algorithms/reciprocity.py new file mode 100644 index 000000000..c87abdf9f --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/reciprocity.py @@ -0,0 +1,93 @@ +# Copyright (c) 2023-2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np + +from nx_cugraph.convert import _to_directed_graph +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +__all__ = ["reciprocity", "overall_reciprocity"] + + +@not_implemented_for("undirected", "multigraph") +@networkx_algorithm(version_added="24.02") +def reciprocity(G, nodes=None): + if nodes is None: + return overall_reciprocity(G) + G = _to_directed_graph(G) + N = G._N + # 'nodes' can also be a single node identifier + if nodes in G: + index = nodes if G.key_to_id is None else G.key_to_id[nodes] + mask = (G.src_indices == index) | (G.dst_indices == index) + src_indices = G.src_indices[mask] + if src_indices.size == 0: + raise nx.NetworkXError("Not defined for isolated nodes.") + dst_indices = G.dst_indices[mask] + # Create two lists of edge identifiers, one for each direction. + # Edge identifiers can be created from a pair of node + # identifiers. Simply adding src IDs to dst IDs is not adequate, so + # make one set of values (either src or dst depending on direction) + # unique by multiplying values by N. + # Upcast to int64 so indices don't overflow. + edges_a_b = N * src_indices.astype(np.int64) + dst_indices + edges_b_a = src_indices + N * dst_indices.astype(np.int64) + # Find the matching edge identifiers in each list. The edge identifier + # generation ensures the ID for A->B == the ID for B->A + recip_indices = cp.intersect1d( + edges_a_b, + edges_b_a, + # assume_unique=True, # cupy <= 12.2.0 also assumes sorted + ) + num_selfloops = (src_indices == dst_indices).sum().tolist() + return (recip_indices.size - num_selfloops) / edges_a_b.size + + # Don't include self-loops + mask = G.src_indices != G.dst_indices + src_indices = G.src_indices[mask] + dst_indices = G.dst_indices[mask] + # Create two lists of edges, one for each direction, and find the matching + # IDs in each list (see description above). + edges_a_b = N * src_indices.astype(np.int64) + dst_indices + edges_b_a = src_indices + N * dst_indices.astype(np.int64) + recip_indices = cp.intersect1d( + edges_a_b, + edges_b_a, + # assume_unique=True, # cupy <= 12.2.0 also assumes sorted + ) + numer = cp.bincount(recip_indices // N, minlength=N) + denom = cp.bincount(src_indices, minlength=N) + denom += cp.bincount(dst_indices, minlength=N) + recip = 2 * numer / denom + node_ids = G._nodekeys_to_nodearray(nodes) + return G._nodearrays_to_dict(node_ids, recip[node_ids]) + + +@not_implemented_for("undirected", "multigraph") +@networkx_algorithm(version_added="24.02") +def overall_reciprocity(G): + G = _to_directed_graph(G) + if G.number_of_edges() == 0: + raise nx.NetworkXError("Not defined for empty graphs") + # Create two lists of edges, one for each direction, and find the matching + # IDs in each list (see description in reciprocity()). + edges_a_b = G._N * G.src_indices.astype(np.int64) + G.dst_indices + edges_b_a = G.src_indices + G._N * G.dst_indices.astype(np.int64) + recip_indices = cp.intersect1d( + edges_a_b, + edges_b_a, + # assume_unique=True, # cupy <= 12.2.0 also assumes sorted + ) + num_selfloops = (G.src_indices == G.dst_indices).sum().tolist() + return (recip_indices.size - num_selfloops) / edges_a_b.size diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index 52ea2334c..3392f3362 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,13 +12,16 @@ # limitations under the License. from __future__ import annotations +from copy import deepcopy from typing import TYPE_CHECKING import cupy as cp import networkx as nx +import numpy as np import nx_cugraph as nxcg +from ..utils import index_dtype from .graph import Graph if TYPE_CHECKING: # pragma: no cover @@ -59,6 +62,115 @@ def number_of_edges( def reverse(self, copy: bool = True) -> DiGraph: return self._copy(not copy, self.__class__, reverse=True) + @networkx_api + def to_undirected(self, reciprocal=False, as_view=False): + N = self._N + # Upcast to int64 so indices don't overflow + src_dst_indices_old = N * self.src_indices.astype(np.int64) + self.dst_indices + if reciprocal: + src_dst_indices_new = cp.intersect1d( + src_dst_indices_old, + self.src_indices + N * self.dst_indices.astype(np.int64), + # assume_unique=True, # cupy <= 12.2.0 also assumes sorted + ) + if self.edge_values: + sorter = cp.argsort(src_dst_indices_old) + idx = cp.searchsorted( + src_dst_indices_old, src_dst_indices_new, sorter=sorter + ) + indices = sorter[idx] + src_indices = self.src_indices[indices].copy() + dst_indices = self.dst_indices[indices].copy() + edge_values = { + key: val[indices].copy() for key, val in self.edge_values.items() + } + edge_masks = { + key: val[indices].copy() for key, val in self.edge_masks.items() + } + else: + src_indices, dst_indices = cp.divmod( + src_dst_indices_new, N, dtype=index_dtype + ) + else: + src_dst_indices_old_T = self.src_indices + N * self.dst_indices.astype( + np.int64 + ) + if self.edge_values: + src_dst_extra = cp.setdiff1d( + src_dst_indices_old_T, src_dst_indices_old, assume_unique=True + ) + sorter = cp.argsort(src_dst_indices_old_T) + idx = cp.searchsorted( + src_dst_indices_old_T, src_dst_extra, sorter=sorter + ) + indices = sorter[idx] + src_indices = cp.hstack((self.src_indices, self.dst_indices[indices])) + dst_indices = cp.hstack((self.dst_indices, self.src_indices[indices])) + edge_values = { + key: cp.hstack((val, val[indices])) + for key, val in self.edge_values.items() + } + edge_masks = { + key: cp.hstack((val, val[indices])) + for key, val in self.edge_masks.items() + } + else: + src_dst_indices_new = cp.union1d( + src_dst_indices_old, src_dst_indices_old_T + ) + src_indices, dst_indices = cp.divmod( + src_dst_indices_new, N, dtype=index_dtype + ) + + if self.edge_values: + recip_indices = cp.lexsort(cp.vstack((src_indices, dst_indices))) + for key, mask in edge_masks.items(): + # Make sure we choose a value that isn't masked out + val = edge_values[key] + rmask = mask[recip_indices] + recip_only = rmask & ~mask + val[recip_only] = val[recip_indices[recip_only]] + only = mask & ~rmask + val[recip_indices[only]] = val[only] + mask |= mask[recip_indices] + # Arbitrarily choose to use value from (j > i) edge + mask = src_indices < dst_indices + left_idx = cp.nonzero(mask)[0] + right_idx = recip_indices[mask] + for val in edge_values.values(): + val[left_idx] = val[right_idx] + else: + edge_values = {} + edge_masks = {} + + node_values = self.node_values + node_masks = self.node_masks + key_to_id = self.key_to_id + id_to_key = None if key_to_id is None else self._id_to_key + if not as_view: + node_values = {key: val.copy() for key, val in node_values.items()} + node_masks = {key: val.copy() for key, val in node_masks.items()} + if key_to_id is not None: + key_to_id = key_to_id.copy() + if id_to_key is not None: + id_to_key = id_to_key.copy() + rv = self.to_undirected_class().from_coo( + N, + src_indices, + dst_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + ) + if as_view: + rv.graph = self.graph + else: + rv.graph.update(deepcopy(self.graph)) + return rv + # Many more methods to implement... ################### diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 251e92b70..45f81ad11 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -531,7 +531,7 @@ def to_directed(self, as_view: bool = False) -> nxcg.DiGraph: @networkx_api def to_undirected(self, as_view: bool = False) -> Graph: # Does deep copy in networkx - return self.copy(as_view) + return self._copy(as_view, self.to_undirected_class()) # Not implemented... # adj, adjacency, add_edge, add_edges_from, add_node, @@ -742,6 +742,11 @@ def _degrees_array(self): _out_degrees_array = _degrees_array # Data conversions + def _nodekeys_to_nodearray(self, nodes: Iterable[NodeKey]) -> cp.array[IndexValue]: + if self.key_to_id is None: + return cp.fromiter(nodes, dtype=index_dtype) + return cp.fromiter(map(self.key_to_id.__getitem__, nodes), dtype=index_dtype) + def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: """Convert an iterable of node IDs to an iterable of node keys.""" if (id_to_key := self.id_to_key) is not None: diff --git a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py index 2c7bfc007..2e7a55a9e 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -33,3 +33,11 @@ def is_directed(cls) -> bool: @classmethod def to_networkx_class(cls) -> type[nx.MultiDiGraph]: return nx.MultiDiGraph + + ########################## + # NetworkX graph methods # + ########################## + + @networkx_api + def to_undirected(self, reciprocal=False, as_view=False): + raise NotImplementedError diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py index 23466dc7d..fb787369e 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -399,7 +399,7 @@ def to_directed(self, as_view: bool = False) -> nxcg.MultiDiGraph: @networkx_api def to_undirected(self, as_view: bool = False) -> MultiGraph: # Does deep copy in networkx - return self.copy(as_view) + return self._copy(as_view, self.to_undirected_class()) ################### # Private methods # diff --git a/python/nx-cugraph/nx_cugraph/tests/test_generators.py b/python/nx-cugraph/nx_cugraph/tests/test_generators.py index 511f8dcd8..c751b0fe2 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_generators.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_generators.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -17,30 +17,9 @@ import nx_cugraph as nxcg -nxver = parse(nx.__version__) - +from .testing_utils import assert_graphs_equal -def assert_graphs_equal(Gnx, Gcg): - assert isinstance(Gnx, nx.Graph) - assert isinstance(Gcg, nxcg.Graph) - assert Gnx.number_of_nodes() == Gcg.number_of_nodes() - assert Gnx.number_of_edges() == Gcg.number_of_edges() - assert Gnx.is_directed() == Gcg.is_directed() - assert Gnx.is_multigraph() == Gcg.is_multigraph() - G = nxcg.to_networkx(Gcg) - rv = nx.utils.graphs_equal(G, Gnx) - if not rv: - print("GRAPHS ARE NOT EQUAL!") - assert sorted(G) == sorted(Gnx) - assert sorted(G._adj) == sorted(Gnx._adj) - assert sorted(G._node) == sorted(Gnx._node) - for k in sorted(G._adj): - print(k, sorted(G._adj[k]), sorted(Gnx._adj[k])) - print(nx.to_scipy_sparse_array(G).todense()) - print(nx.to_scipy_sparse_array(Gnx).todense()) - print(G.graph) - print(Gnx.graph) - assert rv +nxver = parse(nx.__version__) if nxver.major == 3 and nxver.minor < 2: diff --git a/python/nx-cugraph/nx_cugraph/tests/test_graph_methods.py b/python/nx-cugraph/nx_cugraph/tests/test_graph_methods.py new file mode 100644 index 000000000..3120995a2 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_graph_methods.py @@ -0,0 +1,67 @@ +# Copyright (c) 2023-2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest + +import nx_cugraph as nxcg + +from .testing_utils import assert_graphs_equal + + +def _create_Gs(): + rv = [] + rv.append(nx.DiGraph()) + G = nx.DiGraph() + G.add_edge(0, 1) + G.add_edge(1, 0) + rv.append(G) + G = G.copy() + G.add_edge(0, 2) + rv.append(G) + G = G.copy() + G.add_edge(1, 1) + rv.append(G) + G = nx.DiGraph() + G.add_edge(0, 1, x=1, y=2) + G.add_edge(1, 0, x=10, z=3) + rv.append(G) + G = G.copy() + G.add_edge(0, 2, a=42) + rv.append(G) + G = G.copy() + G.add_edge(1, 1, a=4) + rv.append(G) + return rv + + +@pytest.mark.parametrize("Gnx", _create_Gs()) +@pytest.mark.parametrize("reciprocal", [False, True]) +def test_to_undirected_directed(Gnx, reciprocal): + Gcg = nxcg.DiGraph(Gnx) + assert_graphs_equal(Gnx, Gcg) + Hnx1 = Gnx.to_undirected(reciprocal=reciprocal) + Hcg1 = Gcg.to_undirected(reciprocal=reciprocal) + assert_graphs_equal(Hnx1, Hcg1) + Hnx2 = Hnx1.to_directed() + Hcg2 = Hcg1.to_directed() + assert_graphs_equal(Hnx2, Hcg2) + + +def test_multidigraph_to_undirected(): + Gnx = nx.MultiDiGraph() + Gnx.add_edge(0, 1) + Gnx.add_edge(0, 1) + Gnx.add_edge(1, 0) + Gcg = nxcg.MultiDiGraph(Gnx) + with pytest.raises(NotImplementedError): + Gcg.to_undirected() diff --git a/python/nx-cugraph/nx_cugraph/tests/testing_utils.py b/python/nx-cugraph/nx_cugraph/tests/testing_utils.py new file mode 100644 index 000000000..6d4741c9c --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/testing_utils.py @@ -0,0 +1,38 @@ +# Copyright (c) 2023-2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx + +import nx_cugraph as nxcg + + +def assert_graphs_equal(Gnx, Gcg): + assert isinstance(Gnx, nx.Graph) + assert isinstance(Gcg, nxcg.Graph) + assert Gnx.number_of_nodes() == Gcg.number_of_nodes() + assert Gnx.number_of_edges() == Gcg.number_of_edges() + assert Gnx.is_directed() == Gcg.is_directed() + assert Gnx.is_multigraph() == Gcg.is_multigraph() + G = nxcg.to_networkx(Gcg) + rv = nx.utils.graphs_equal(G, Gnx) + if not rv: + print("GRAPHS ARE NOT EQUAL!") + assert sorted(G) == sorted(Gnx) + assert sorted(G._adj) == sorted(Gnx._adj) + assert sorted(G._node) == sorted(Gnx._node) + for k in sorted(G._adj): + print(k, sorted(G._adj[k]), sorted(Gnx._adj[k])) + print(nx.to_scipy_sparse_array(G).todense()) + print(nx.to_scipy_sparse_array(Gnx).todense()) + print(G.graph) + print(Gnx.graph) + assert rv From 76cb77ff60d0d8a80feffb2cf11b33ad4fc24665 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Thu, 18 Jan 2024 14:59:02 -0500 Subject: [PATCH 305/384] DOC v24.04 Updates [skip ci] --- VERSION | 2 +- docs/cugraph/source/conf.py | 4 ++-- python/nx-cugraph/_nx_cugraph/__init__.py | 4 ++-- python/nx-cugraph/pyproject.toml | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/VERSION b/VERSION index 3c6c5e2b7..4a2fe8aa5 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -24.02.00 +24.04.00 diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index cef06a584..141e14a04 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -77,9 +77,9 @@ # built documents. # # The short X.Y version. -version = '24.02' +version = '24.04' # The full version, including alpha/beta/rc tags. -release = '24.02.00' +release = '24.04.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 69320e6b5..3e30bb451 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -24,7 +24,7 @@ "backend_name": "cugraph", "project": "nx-cugraph", "package": "nx_cugraph", - "url": "https://github.com/rapidsai/cugraph/tree/branch-24.02/python/nx-cugraph", + "url": "https://github.com/rapidsai/cugraph/tree/branch-24.04/python/nx-cugraph", "short_summary": "GPU-accelerated backend.", # "description": "TODO", "functions": { @@ -182,7 +182,7 @@ def get_info(): # FIXME: can this use the standard VERSION file and update mechanism? -__version__ = "24.02.00" +__version__ = "24.04.00" if __name__ == "__main__": from pathlib import Path diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 63ac11591..a7525530a 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -33,7 +33,7 @@ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", "numpy>=1.21", - "pylibcugraph==24.2.*", + "pylibcugraph==24.4.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. [project.optional-dependencies] From 7557a948e47a70196e71fa676d7265ffcb2fc487 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Fri, 19 Jan 2024 09:15:36 -0600 Subject: [PATCH 306/384] nx-cugraph: add triangles and clustering algorithms (#4093) NetworkX tests are somewhat underspecified regarding how to handle self-loops for these algorithms. Also, I'm not sure if transitivity is supposed to work on directed graphs. Once #4071 is merged, it should be easy to add `is_bipartite` function (and maybe others?). Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4093 --- python/nx-cugraph/_nx_cugraph/__init__.py | 8 ++ .../nx_cugraph/algorithms/__init__.py | 4 +- .../algorithms/bipartite/__init__.py | 3 +- .../nx_cugraph/algorithms/bipartite/basic.py | 31 ++++ .../nx_cugraph/algorithms/cluster.py | 136 ++++++++++++++++++ .../nx-cugraph/nx_cugraph/classes/digraph.py | 18 ++- python/nx-cugraph/nx_cugraph/classes/graph.py | 13 +- .../nx_cugraph/tests/test_cluster.py | 48 +++++++ 8 files changed, 251 insertions(+), 10 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/cluster.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_cluster.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 69320e6b5..5bfbf082c 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -30,6 +30,7 @@ "functions": { # BEGIN: functions "ancestors", + "average_clustering", "barbell_graph", "betweenness_centrality", "bfs_edges", @@ -41,6 +42,7 @@ "caveman_graph", "chvatal_graph", "circular_ladder_graph", + "clustering", "complete_bipartite_graph", "complete_graph", "complete_multipartite_graph", @@ -68,6 +70,7 @@ "house_x_graph", "icosahedral_graph", "in_degree_centrality", + "is_bipartite", "is_connected", "is_isolate", "is_strongly_connected", @@ -104,6 +107,8 @@ "strongly_connected_components", "tadpole_graph", "tetrahedral_graph", + "transitivity", + "triangles", "trivial_graph", "truncated_cube_graph", "truncated_tetrahedron_graph", @@ -115,11 +120,13 @@ }, "extra_docstrings": { # BEGIN: extra_docstrings + "average_clustering": "Directed graphs and `weight` parameter are not yet supported.", "betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.", "bfs_edges": "`sort_neighbors` parameter is not yet supported.", "bfs_predecessors": "`sort_neighbors` parameter is not yet supported.", "bfs_successors": "`sort_neighbors` parameter is not yet supported.", "bfs_tree": "`sort_neighbors` parameter is not yet supported.", + "clustering": "Directed graphs and `weight` parameter are not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.", "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.", "from_pandas_edgelist": "cudf.DataFrame inputs also supported; value columns with str is unsuppported.", @@ -131,6 +138,7 @@ "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.", "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.", "pagerank": "`dangling` parameter is not supported, but it is checked for validity.", + "transitivity": "Directed graphs are not yet supported.", # END: extra_docstrings }, "extra_parameters": { diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 65700838f..de4e9466b 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -13,14 +13,16 @@ from . import ( bipartite, centrality, + cluster, community, components, link_analysis, shortest_paths, traversal, ) -from .bipartite import complete_bipartite_graph +from .bipartite import complete_bipartite_graph, is_bipartite from .centrality import * +from .cluster import * from .components import * from .core import * from .dag import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py index 062be973d..e028299c6 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,4 +10,5 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from .basic import * from .generators import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py new file mode 100644 index 000000000..d0e9a5c7f --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py @@ -0,0 +1,31 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp + +from nx_cugraph.algorithms.cluster import _triangles +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import networkx_algorithm + +__all__ = [ + "is_bipartite", +] + + +@networkx_algorithm(plc="triangle_count", version_added="24.02") +def is_bipartite(G): + G = _to_graph(G) + # Counting triangles may not be the fastest way to do this, but it is simple. + node_ids, triangles, is_single_node = _triangles( + G, None, symmetrize="union" if G.is_directed() else None + ) + return int(cp.count_nonzero(triangles)) == 0 diff --git a/python/nx-cugraph/nx_cugraph/algorithms/cluster.py b/python/nx-cugraph/nx_cugraph/algorithms/cluster.py new file mode 100644 index 000000000..951c358ff --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/cluster.py @@ -0,0 +1,136 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import pylibcugraph as plc + +from nx_cugraph.convert import _to_undirected_graph +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +__all__ = [ + "triangles", + "average_clustering", + "clustering", + "transitivity", +] + + +def _triangles(G, nodes, symmetrize=None): + if nodes is not None: + if is_single_node := (nodes in G): + nodes = [nodes if G.key_to_id is None else G.key_to_id[nodes]] + else: + nodes = list(nodes) + nodes = G._list_to_nodearray(nodes) + else: + is_single_node = False + if len(G) == 0: + return None, None, is_single_node + node_ids, triangles = plc.triangle_count( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(symmetrize=symmetrize), + start_list=nodes, + do_expensive_check=False, + ) + return node_ids, triangles, is_single_node + + +@not_implemented_for("directed") +@networkx_algorithm(plc="triangle_count", version_added="24.02") +def triangles(G, nodes=None): + G = _to_undirected_graph(G) + node_ids, triangles, is_single_node = _triangles(G, nodes) + if len(G) == 0: + return {} + if is_single_node: + return int(triangles[0]) + return G._nodearrays_to_dict(node_ids, triangles) + + +@not_implemented_for("directed") +@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") +def clustering(G, nodes=None, weight=None): + """Directed graphs and `weight` parameter are not yet supported.""" + G = _to_undirected_graph(G) + node_ids, triangles, is_single_node = _triangles(G, nodes) + if len(G) == 0: + return {} + if is_single_node: + numer = int(triangles[0]) + if numer == 0: + return 0 + degree = int((G.src_indices == nodes).sum()) + return 2 * numer / (degree * (degree - 1)) + degrees = G._degrees_array(ignore_selfloops=True)[node_ids] + denom = degrees * (degrees - 1) + results = 2 * triangles / denom + results = cp.where(denom, results, 0) # 0 where we divided by 0 + return G._nodearrays_to_dict(node_ids, results) + + +@clustering._can_run +def _(G, nodes=None, weight=None): + return weight is None and not G.is_directed() + + +@not_implemented_for("directed") +@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") +def average_clustering(G, nodes=None, weight=None, count_zeros=True): + """Directed graphs and `weight` parameter are not yet supported.""" + G = _to_undirected_graph(G) + node_ids, triangles, is_single_node = _triangles(G, nodes) + if len(G) == 0: + raise ZeroDivisionError + degrees = G._degrees_array(ignore_selfloops=True)[node_ids] + if not count_zeros: + mask = triangles != 0 + triangles = triangles[mask] + if triangles.size == 0: + raise ZeroDivisionError + degrees = degrees[mask] + denom = degrees * (degrees - 1) + results = 2 * triangles / denom + if count_zeros: + results = cp.where(denom, results, 0) # 0 where we divided by 0 + return float(results.mean()) + + +@average_clustering._can_run +def _(G, nodes=None, weight=None, count_zeros=True): + return weight is None and not G.is_directed() + + +@not_implemented_for("directed") +@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") +def transitivity(G): + """Directed graphs are not yet supported.""" + G = _to_undirected_graph(G) + if len(G) == 0: + return 0 + node_ids, triangles = plc.triangle_count( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + start_list=None, + do_expensive_check=False, + ) + numer = int(triangles.sum()) + if numer == 0: + return 0 + degrees = G._degrees_array(ignore_selfloops=True)[node_ids] + denom = int((degrees * (degrees - 1)).sum()) + return 2 * numer / denom + + +@transitivity._can_run +def _(G): + # Is transitivity supposed to work on directed graphs? + return not G.is_directed() diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index 3392f3362..f8217a2c7 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -177,8 +177,16 @@ def to_undirected(self, reciprocal=False, as_view=False): # Private methods # ################### - def _in_degrees_array(self): - return cp.bincount(self.dst_indices, minlength=self._N) - - def _out_degrees_array(self): - return cp.bincount(self.src_indices, minlength=self._N) + def _in_degrees_array(self, *, ignore_selfloops=False): + dst_indices = self.dst_indices + if ignore_selfloops: + not_selfloops = self.src_indices != dst_indices + dst_indices = dst_indices[not_selfloops] + return cp.bincount(dst_indices, minlength=self._N) + + def _out_degrees_array(self, *, ignore_selfloops=False): + src_indices = self.src_indices + if ignore_selfloops: + not_selfloops = src_indices != self.dst_indices + src_indices = src_indices[not_selfloops] + return cp.bincount(src_indices, minlength=self._N) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 45f81ad11..4aa2de153 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -732,10 +732,17 @@ def _become(self, other: Graph): self.graph = graph return self - def _degrees_array(self): - degrees = cp.bincount(self.src_indices, minlength=self._N) + def _degrees_array(self, *, ignore_selfloops=False): + src_indices = self.src_indices + dst_indices = self.dst_indices + if ignore_selfloops: + not_selfloops = src_indices != dst_indices + src_indices = src_indices[not_selfloops] + if self.is_directed(): + dst_indices = dst_indices[not_selfloops] + degrees = cp.bincount(src_indices, minlength=self._N) if self.is_directed(): - degrees += cp.bincount(self.dst_indices, minlength=self._N) + degrees += cp.bincount(dst_indices, minlength=self._N) return degrees _in_degrees_array = _degrees_array diff --git a/python/nx-cugraph/nx_cugraph/tests/test_cluster.py b/python/nx-cugraph/nx_cugraph/tests/test_cluster.py new file mode 100644 index 000000000..ad4770f1a --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_cluster.py @@ -0,0 +1,48 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest +from packaging.version import parse + +nxver = parse(nx.__version__) + +if nxver.major == 3 and nxver.minor < 2: + pytest.skip("Need NetworkX >=3.2 to test clustering", allow_module_level=True) + + +def test_selfloops(): + G = nx.complete_graph(5) + H = nx.complete_graph(5) + H.add_edge(0, 0) + H.add_edge(1, 1) + H.add_edge(2, 2) + # triangles + expected = nx.triangles(G) + assert expected == nx.triangles(H) + assert expected == nx.triangles(G, backend="cugraph") + assert expected == nx.triangles(H, backend="cugraph") + # average_clustering + expected = nx.average_clustering(G) + assert expected == nx.average_clustering(H) + assert expected == nx.average_clustering(G, backend="cugraph") + assert expected == nx.average_clustering(H, backend="cugraph") + # clustering + expected = nx.clustering(G) + assert expected == nx.clustering(H) + assert expected == nx.clustering(G, backend="cugraph") + assert expected == nx.clustering(H, backend="cugraph") + # transitivity + expected = nx.transitivity(G) + assert expected == nx.transitivity(H) + assert expected == nx.transitivity(G, backend="cugraph") + assert expected == nx.transitivity(H, backend="cugraph") From a176b44ffc5ee811f7d67501b0b2aa8b79121354 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Fri, 19 Jan 2024 12:46:12 -0600 Subject: [PATCH 307/384] nx-cugraph: add `core_number` (undirected graphs only) (#4100) Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4100 --- python/nx-cugraph/_nx_cugraph/__init__.py | 2 ++ .../nx-cugraph/nx_cugraph/algorithms/core.py | 30 ++++++++++++++++++- 2 files changed, 31 insertions(+), 1 deletion(-) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 5bfbf082c..8deac55f4 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -47,6 +47,7 @@ "complete_graph", "complete_multipartite_graph", "connected_components", + "core_number", "cubical_graph", "cycle_graph", "davis_southern_women_graph", @@ -127,6 +128,7 @@ "bfs_successors": "`sort_neighbors` parameter is not yet supported.", "bfs_tree": "`sort_neighbors` parameter is not yet supported.", "clustering": "Directed graphs and `weight` parameter are not yet supported.", + "core_number": "Directed graphs are not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.", "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.", "from_pandas_edgelist": "cudf.DataFrame inputs also supported; value columns with str is unsuppported.", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index e4520c271..f323cdf60 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -15,6 +15,7 @@ import pylibcugraph as plc import nx_cugraph as nxcg +from nx_cugraph.convert import _to_undirected_graph from nx_cugraph.utils import ( _get_int_dtype, index_dtype, @@ -22,7 +23,34 @@ not_implemented_for, ) -__all__ = ["k_truss"] +__all__ = ["core_number", "k_truss"] + + +@not_implemented_for("directed") +@not_implemented_for("multigraph") +@networkx_algorithm(is_incomplete=True, plc="core_number", version_added="24.02") +def core_number(G): + """Directed graphs are not yet supported.""" + G = _to_undirected_graph(G) + if len(G) == 0: + return {} + if nxcg.number_of_selfloops(G) > 0: + raise nx.NetworkXNotImplemented( + "Input graph has self loops which is not permitted; " + "Consider using G.remove_edges_from(nx.selfloop_edges(G))." + ) + node_ids, core_numbers = plc.core_number( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + degree_type="bidirectional", + do_expensive_check=False, + ) + return G._nodearrays_to_dict(node_ids, core_numbers) + + +@core_number._can_run +def _(G): + return not G.is_directed() @not_implemented_for("directed") From 1252999bbb190683b17b6c095aac2c15ae51d237 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 22 Jan 2024 10:31:48 -0600 Subject: [PATCH 308/384] nx-cugraph: add `is_tree`, etc. (#4097) These are now possible b/c we have `connected_components` and `weakly_connected_components` (and their `is_*` equivalents). Authors: - Erik Welch (https://github.com/eriknw) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4097 --- python/nx-cugraph/_nx_cugraph/__init__.py | 4 + .../nx_cugraph/algorithms/__init__.py | 2 + .../nx_cugraph/algorithms/isolate.py | 2 +- .../nx_cugraph/algorithms/tree/__init__.py | 13 ++++ .../nx_cugraph/algorithms/tree/recognition.py | 74 +++++++++++++++++++ .../nx-cugraph/nx_cugraph/classes/digraph.py | 12 +-- .../nx-cugraph/nx_cugraph/classes/function.py | 4 +- python/nx-cugraph/nx_cugraph/classes/graph.py | 4 +- 8 files changed, 107 insertions(+), 8 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/tree/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 8deac55f4..9bca031a2 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -71,10 +71,14 @@ "house_x_graph", "icosahedral_graph", "in_degree_centrality", + "is_arborescence", "is_bipartite", + "is_branching", "is_connected", + "is_forest", "is_isolate", "is_strongly_connected", + "is_tree", "is_weakly_connected", "isolates", "k_truss", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index de4e9466b..08658ad94 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -19,6 +19,7 @@ link_analysis, shortest_paths, traversal, + tree, ) from .bipartite import complete_bipartite_graph, is_bipartite from .centrality import * @@ -31,3 +32,4 @@ from .reciprocity import * from .shortest_paths import * from .traversal import * +from .tree.recognition import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py index 62b47a9b3..9621fbeaa 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py @@ -70,4 +70,4 @@ def isolates(G): @networkx_algorithm(version_added="23.10") def number_of_isolates(G): G = _to_graph(G) - return _mark_isolates(G).sum().tolist() + return int(cp.count_nonzero(_mark_isolates(G))) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/tree/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/tree/__init__.py new file mode 100644 index 000000000..91bf72417 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/tree/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .recognition import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py b/python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py new file mode 100644 index 000000000..0b82f079d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py @@ -0,0 +1,74 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx + +import nx_cugraph as nxcg +from nx_cugraph.convert import _to_directed_graph, _to_graph +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +__all__ = ["is_arborescence", "is_branching", "is_forest", "is_tree"] + + +@not_implemented_for("undirected") +@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +def is_arborescence(G): + G = _to_directed_graph(G) + return is_tree(G) and int(G._in_degrees_array().max()) <= 1 + + +@not_implemented_for("undirected") +@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +def is_branching(G): + G = _to_directed_graph(G) + return is_forest(G) and int(G._in_degrees_array().max()) <= 1 + + +@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +def is_forest(G): + G = _to_graph(G) + if len(G) == 0: + raise nx.NetworkXPointlessConcept("G has no nodes.") + if is_directed := G.is_directed(): + connected_components = nxcg.weakly_connected_components + else: + connected_components = nxcg.connected_components + for components in connected_components(G): + node_ids = G._list_to_nodearray(list(components)) + # TODO: create utilities for creating subgraphs + mask = cp.isin(G.src_indices, node_ids) & cp.isin(G.dst_indices, node_ids) + # A tree must have an edge count equal to the number of nodes minus the + # tree's root node. + if is_directed: + if int(cp.count_nonzero(mask)) != len(components) - 1: + return False + else: + src_indices = G.src_indices[mask] + dst_indices = G.dst_indices[mask] + if int(cp.count_nonzero(src_indices <= dst_indices)) != len(components) - 1: + return False + return True + + +@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +def is_tree(G): + G = _to_graph(G) + if len(G) == 0: + raise nx.NetworkXPointlessConcept("G has no nodes.") + if G.is_directed(): + is_connected = nxcg.is_weakly_connected + else: + is_connected = nxcg.is_connected + # A tree must have an edge count equal to the number of nodes minus the + # tree's root node. + return len(G) - 1 == G.number_of_edges() and is_connected(G) diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index f8217a2c7..169815eb0 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -25,7 +25,7 @@ from .graph import Graph if TYPE_CHECKING: # pragma: no cover - from nx_cugraph.typing import NodeKey + from nx_cugraph.typing import AttrKey __all__ = ["DiGraph"] @@ -47,10 +47,8 @@ def to_networkx_class(cls) -> type[nx.DiGraph]: return nx.DiGraph @networkx_api - def number_of_edges( - self, u: NodeKey | None = None, v: NodeKey | None = None - ) -> int: - if u is not None or v is not None: + def size(self, weight: AttrKey | None = None) -> int: + if weight is not None: raise NotImplementedError return self.src_indices.size @@ -182,6 +180,8 @@ def _in_degrees_array(self, *, ignore_selfloops=False): if ignore_selfloops: not_selfloops = self.src_indices != dst_indices dst_indices = dst_indices[not_selfloops] + if dst_indices.size == 0: + return cp.zeros(self._N, dtype=np.int64) return cp.bincount(dst_indices, minlength=self._N) def _out_degrees_array(self, *, ignore_selfloops=False): @@ -189,4 +189,6 @@ def _out_degrees_array(self, *, ignore_selfloops=False): if ignore_selfloops: not_selfloops = src_indices != self.dst_indices src_indices = src_indices[not_selfloops] + if src_indices.size == 0: + return cp.zeros(self._N, dtype=np.int64) return cp.bincount(src_indices, minlength=self._N) diff --git a/python/nx-cugraph/nx_cugraph/classes/function.py b/python/nx-cugraph/nx_cugraph/classes/function.py index 435dfe372..7212a4d2d 100644 --- a/python/nx-cugraph/nx_cugraph/classes/function.py +++ b/python/nx-cugraph/nx_cugraph/classes/function.py @@ -10,6 +10,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import cupy as cp + from nx_cugraph.convert import _to_graph from nx_cugraph.utils import networkx_algorithm @@ -20,4 +22,4 @@ def number_of_selfloops(G): G = _to_graph(G) is_selfloop = G.src_indices == G.dst_indices - return is_selfloop.sum().tolist() + return int(cp.count_nonzero(is_selfloop)) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 4aa2de153..f69766875 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -522,7 +522,7 @@ def size(self, weight: AttrKey | None = None) -> int: if weight is not None: raise NotImplementedError # If no self-edges, then `self.src_indices.size // 2` - return int((self.src_indices <= self.dst_indices).sum()) + return int(cp.count_nonzero(self.src_indices <= self.dst_indices)) @networkx_api def to_directed(self, as_view: bool = False) -> nxcg.DiGraph: @@ -740,6 +740,8 @@ def _degrees_array(self, *, ignore_selfloops=False): src_indices = src_indices[not_selfloops] if self.is_directed(): dst_indices = dst_indices[not_selfloops] + if src_indices.size == 0: + return cp.zeros(self._N, dtype=np.int64) degrees = cp.bincount(src_indices, minlength=self._N) if self.is_directed(): degrees += cp.bincount(dst_indices, minlength=self._N) From 14d04e37f6845a3b2ab9dc3b24e922ffeb3c6236 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 24 Jan 2024 16:54:30 -0600 Subject: [PATCH 309/384] nx-cugraph: rename `plc=` to `_plc=` (#4106) As discussed here: https://github.com/rapidsai/cugraph/pull/4093#discussion_r1458012713 Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4106 --- python/nx-cugraph/lint.yaml | 6 +-- .../nx_cugraph/algorithms/bipartite/basic.py | 2 +- .../algorithms/centrality/betweenness.py | 4 +- .../algorithms/centrality/eigenvector.py | 2 +- .../nx_cugraph/algorithms/centrality/katz.py | 2 +- .../nx_cugraph/algorithms/cluster.py | 16 ++++++-- .../algorithms/community/louvain.py | 2 +- .../algorithms/components/connected.py | 13 +++---- .../components/strongly_connected.py | 6 +-- .../algorithms/components/weakly_connected.py | 6 +-- .../nx-cugraph/nx_cugraph/algorithms/core.py | 4 +- .../nx-cugraph/nx_cugraph/algorithms/dag.py | 4 +- .../algorithms/link_analysis/hits_alg.py | 2 +- .../algorithms/link_analysis/pagerank_alg.py | 2 +- .../algorithms/shortest_paths/unweighted.py | 4 +- .../traversal/breadth_first_search.py | 38 +++++++++++++++---- .../nx_cugraph/algorithms/tree/recognition.py | 8 ++-- .../nx-cugraph/nx_cugraph/utils/decorators.py | 16 ++++---- 18 files changed, 84 insertions(+), 53 deletions(-) diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 0d4f0b594..5a4773168 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -50,7 +50,7 @@ repos: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.13 + rev: v0.1.14 hooks: - id: ruff args: [--fix-only, --show-fixes] # --unsafe-fixes] @@ -62,7 +62,7 @@ repos: additional_dependencies: &flake8_dependencies # These versions need updated manually - flake8==7.0.0 - - flake8-bugbear==23.12.2 + - flake8-bugbear==24.1.17 - flake8-simplify==0.21.0 - repo: https://github.com/asottile/yesqa rev: v1.5.0 @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.13 + rev: v0.1.14 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py index d0e9a5c7f..46c6b5407 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py @@ -21,7 +21,7 @@ ] -@networkx_algorithm(plc="triangle_count", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="triangle_count") def is_bipartite(G): G = _to_graph(G) # Counting triangles may not be the fastest way to do this, but it is simple. diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py index ba2b3d9c8..f6bb142cd 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py @@ -21,8 +21,8 @@ @networkx_algorithm( is_incomplete=True, # weight not supported is_different=True, # RNG with seed is different - plc="betweenness_centrality", version_added="23.10", + _plc="betweenness_centrality", ) def betweenness_centrality( G, k=None, normalized=True, weight=None, endpoints=False, seed=None @@ -54,8 +54,8 @@ def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None): @networkx_algorithm( is_incomplete=True, # weight not supported is_different=True, # RNG with seed is different - plc="edge_betweenness_centrality", version_added="23.10", + _plc="edge_betweenness_centrality", ) def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None): """`weight` parameter is not yet supported, and RNG with seed may be different.""" diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py index 9e615955a..65a863366 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py @@ -29,8 +29,8 @@ @networkx_algorithm( extra_params=_dtype_param, is_incomplete=True, # nstart not supported - plc="eigenvector_centrality", version_added="23.12", + _plc="eigenvector_centrality", ) def eigenvector_centrality( G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py index a2fb950c1..4a0684f72 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py @@ -29,8 +29,8 @@ @networkx_algorithm( extra_params=_dtype_param, is_incomplete=True, # nstart and normalized=False not supported - plc="katz_centrality", version_added="23.12", + _plc="katz_centrality", ) def katz_centrality( G, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/cluster.py b/python/nx-cugraph/nx_cugraph/algorithms/cluster.py index 951c358ff..a458e6c04 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/cluster.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/cluster.py @@ -45,7 +45,7 @@ def _triangles(G, nodes, symmetrize=None): @not_implemented_for("directed") -@networkx_algorithm(plc="triangle_count", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="triangle_count") def triangles(G, nodes=None): G = _to_undirected_graph(G) node_ids, triangles, is_single_node = _triangles(G, nodes) @@ -57,9 +57,13 @@ def triangles(G, nodes=None): @not_implemented_for("directed") -@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count") def clustering(G, nodes=None, weight=None): """Directed graphs and `weight` parameter are not yet supported.""" + if weight is not None: + raise NotImplementedError( + "Weighted implementation of clustering not currently supported" + ) G = _to_undirected_graph(G) node_ids, triangles, is_single_node = _triangles(G, nodes) if len(G) == 0: @@ -83,9 +87,13 @@ def _(G, nodes=None, weight=None): @not_implemented_for("directed") -@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count") def average_clustering(G, nodes=None, weight=None, count_zeros=True): """Directed graphs and `weight` parameter are not yet supported.""" + if weight is not None: + raise NotImplementedError( + "Weighted implementation of average_clustering not currently supported" + ) G = _to_undirected_graph(G) node_ids, triangles, is_single_node = _triangles(G, nodes) if len(G) == 0: @@ -110,7 +118,7 @@ def _(G, nodes=None, weight=None, count_zeros=True): @not_implemented_for("directed") -@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count") def transitivity(G): """Directed graphs are not yet supported.""" G = _to_undirected_graph(G) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index 413ff9ca5..f58f1000f 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -36,8 +36,8 @@ }, is_incomplete=True, # seed not supported; self-loops not supported is_different=True, # RNG different - plc="louvain", version_added="23.10", + _plc="louvain", ) def louvain_communities( G, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py index cdb9f54f6..24955e3ea 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py @@ -26,7 +26,7 @@ @not_implemented_for("directed") -@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") +@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components") def number_connected_components(G): G = _to_undirected_graph(G) return _number_connected_components(G) @@ -50,14 +50,11 @@ def _number_connected_components(G, symmetrize=None): @number_connected_components._can_run def _(G): # NetworkX <= 3.2.1 does not check directedness for us - try: - return not G.is_directed() - except Exception: - return False + return not G.is_directed() @not_implemented_for("directed") -@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") +@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components") def connected_components(G): G = _to_undirected_graph(G) return _connected_components(G) @@ -80,7 +77,7 @@ def _connected_components(G, symmetrize=None): @not_implemented_for("directed") -@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") +@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components") def is_connected(G): G = _to_undirected_graph(G) return _is_connected(G) @@ -106,7 +103,7 @@ def _is_connected(G, symmetrize=None): @not_implemented_for("directed") -@networkx_algorithm(plc="weakly_connected_components", version_added="23.12") +@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components") def node_connected_component(G, n): # We could also do plain BFS from n G = _to_undirected_graph(G) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py index 8fdf99ed5..d17131297 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py @@ -51,7 +51,7 @@ def _strongly_connected_components(G): @not_implemented_for("undirected") -@networkx_algorithm(version_added="24.02", plc="strongly_connected_components") +@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") def strongly_connected_components(G): G = _to_directed_graph(G) if G.src_indices.size == 0: @@ -62,7 +62,7 @@ def strongly_connected_components(G): @not_implemented_for("undirected") -@networkx_algorithm(version_added="24.02", plc="strongly_connected_components") +@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") def number_strongly_connected_components(G): G = _to_directed_graph(G) if G.src_indices.size == 0: @@ -72,7 +72,7 @@ def number_strongly_connected_components(G): @not_implemented_for("undirected") -@networkx_algorithm(version_added="24.02", plc="strongly_connected_components") +@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") def is_strongly_connected(G): G = _to_directed_graph(G) if len(G) == 0: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py index 5b797b391..e42acdd3d 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py @@ -27,21 +27,21 @@ @not_implemented_for("undirected") -@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components") def weakly_connected_components(G): G = _to_directed_graph(G) return _connected_components(G, symmetrize="union") @not_implemented_for("undirected") -@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components") def number_weakly_connected_components(G): G = _to_directed_graph(G) return _number_connected_components(G, symmetrize="union") @not_implemented_for("undirected") -@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components") def is_weakly_connected(G): G = _to_directed_graph(G) return _is_connected(G, symmetrize="union") diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index f323cdf60..71f61abf4 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -28,7 +28,7 @@ @not_implemented_for("directed") @not_implemented_for("multigraph") -@networkx_algorithm(is_incomplete=True, plc="core_number", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="core_number") def core_number(G): """Directed graphs are not yet supported.""" G = _to_undirected_graph(G) @@ -55,7 +55,7 @@ def _(G): @not_implemented_for("directed") @not_implemented_for("multigraph") -@networkx_algorithm(is_incomplete=True, plc="k_truss_subgraph", version_added="23.12") +@networkx_algorithm(is_incomplete=True, version_added="23.12", _plc="k_truss_subgraph") def k_truss(G, k): """ Currently raises `NotImplementedError` for graphs with more than one connected diff --git a/python/nx-cugraph/nx_cugraph/algorithms/dag.py b/python/nx-cugraph/nx_cugraph/algorithms/dag.py index ad5b7594a..64be0a581 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/dag.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/dag.py @@ -45,11 +45,11 @@ def _ancestors_and_descendants(G, source, *, is_ancestors): return G._nodearray_to_set(node_ids[mask]) -@networkx_algorithm(plc="bfs", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="bfs") def descendants(G, source): return _ancestors_and_descendants(G, source, is_ancestors=False) -@networkx_algorithm(plc="bfs", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="bfs") def ancestors(G, source): return _ancestors_and_descendants(G, source, is_ancestors=True) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py index caa01327a..9e723624a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py @@ -33,8 +33,8 @@ ), **_dtype_param, }, - plc="hits", version_added="23.12", + _plc="hits", ) def hits( G, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py index d45d019c1..55fcc3e52 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py @@ -29,8 +29,8 @@ @networkx_algorithm( extra_params=_dtype_param, is_incomplete=True, # dangling not supported - plc={"pagerank", "personalized_pagerank"}, version_added="23.12", + _plc={"pagerank", "personalized_pagerank"}, ) def pagerank( G, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py index b1032a823..201249595 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py @@ -21,12 +21,12 @@ __all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"] -@networkx_algorithm(plc="bfs", version_added="23.12") +@networkx_algorithm(version_added="23.12", _plc="bfs") def single_source_shortest_path_length(G, source, cutoff=None): return _single_shortest_path_length(G, source, cutoff, "Source") -@networkx_algorithm(plc="bfs", version_added="23.12") +@networkx_algorithm(version_added="23.12", _plc="bfs") def single_target_shortest_path_length(G, target, cutoff=None): return _single_shortest_path_length(G, target, cutoff, "Target") diff --git a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py index aa671bbb7..ef1c01136 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py @@ -57,9 +57,17 @@ def _bfs(G, source, *, depth_limit=None, reverse=False): return distances[mask], predecessors[mask], node_ids[mask] -@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs") def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbors=None): """`neighbors` and `sort_neighbors` parameters are not yet supported.""" + if neighbors is not None: + raise NotImplementedError( + "neighbors argument in generic_bfs_edges is not currently supported" + ) + if sort_neighbors is not None: + raise NotImplementedError( + "sort_neighbors argument in generic_bfs_edges is not currently supported" + ) return bfs_edges(source, depth_limit=depth_limit) @@ -68,9 +76,13 @@ def _(G, source, neighbors=None, depth_limit=None, sort_neighbors=None): return neighbors is None and sort_neighbors is None -@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs") def bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" + if sort_neighbors is not None: + raise NotImplementedError( + "sort_neighbors argument in bfs_edges is not currently supported" + ) G = _check_G_and_source(G, source) if depth_limit is not None and depth_limit < 1: return @@ -95,9 +107,13 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs") def bfs_tree(G, source, reverse=False, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" + if sort_neighbors is not None: + raise NotImplementedError( + "sort_neighbors argument in bfs_tree is not currently supported" + ) G = _check_G_and_source(G, source) if depth_limit is not None and depth_limit < 1: return nxcg.DiGraph.from_coo( @@ -149,9 +165,13 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs") def bfs_successors(G, source, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" + if sort_neighbors is not None: + raise NotImplementedError( + "sort_neighbors argument in bfs_successors is not currently supported" + ) G = _check_G_and_source(G, source) if depth_limit is not None and depth_limit < 1: yield (source, []) @@ -173,7 +193,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm(plc="bfs", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="bfs") def bfs_layers(G, sources): G = _to_graph(G) if sources in G: @@ -201,9 +221,13 @@ def bfs_layers(G, sources): return (G._nodearray_to_list(groups[key]) for key in range(len(groups))) -@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02") +@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs") def bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None): """`sort_neighbors` parameter is not yet supported.""" + if sort_neighbors is not None: + raise NotImplementedError( + "sort_neighbors argument in bfs_predecessors is not currently supported" + ) G = _check_G_and_source(G, source) if depth_limit is not None and depth_limit < 1: return @@ -227,7 +251,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None): return sort_neighbors is None -@networkx_algorithm(plc="bfs", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="bfs") def descendants_at_distance(G, source, distance): G = _check_G_and_source(G, source) if distance is None or distance < 0: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py b/python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py index 0b82f079d..74f57b5ea 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/tree/recognition.py @@ -21,20 +21,20 @@ @not_implemented_for("undirected") -@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components") def is_arborescence(G): G = _to_directed_graph(G) return is_tree(G) and int(G._in_degrees_array().max()) <= 1 @not_implemented_for("undirected") -@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components") def is_branching(G): G = _to_directed_graph(G) return is_forest(G) and int(G._in_degrees_array().max()) <= 1 -@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components") def is_forest(G): G = _to_graph(G) if len(G) == 0: @@ -60,7 +60,7 @@ def is_forest(G): return True -@networkx_algorithm(plc="weakly_connected_components", version_added="24.02") +@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components") def is_tree(G): G = _to_graph(G) if len(G) == 0: diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py index d09a9e961..011ebfd6e 100644 --- a/python/nx-cugraph/nx_cugraph/utils/decorators.py +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -59,7 +59,7 @@ def __new__( version_added: str, # Required is_incomplete: bool = False, # See self.extra_doc for details if True is_different: bool = False, # See self.extra_doc for details if True - plc: str | set[str] | None = None, # Hidden from user, may be removed someday + _plc: str | set[str] | None = None, # Hidden from user, may be removed someday ): if func is None: return partial( @@ -67,10 +67,10 @@ def __new__( name=name, extra_params=extra_params, nodes_or_number=nodes_or_number, - plc=plc, version_added=version_added, is_incomplete=is_incomplete, is_different=is_different, + _plc=_plc, ) instance = object.__new__(cls) if nodes_or_number is not None and nx.__version__[:3] > "3.2": @@ -89,12 +89,14 @@ def __new__( f"extra_params must be dict, str, or None; got {type(extra_params)}" ) instance.extra_params = extra_params - if plc is None or isinstance(plc, set): - instance._plc_names = plc - elif isinstance(plc, str): - instance._plc_names = {plc} + if _plc is None or isinstance(_plc, set): + instance._plc_names = _plc + elif isinstance(_plc, str): + instance._plc_names = {_plc} else: - raise TypeError(f"plc argument must be str, set, or None; got {type(plc)}") + raise TypeError( + f"_plc argument must be str, set, or None; got {type(_plc)}" + ) instance.version_added = version_added instance.is_incomplete = is_incomplete instance.is_different = is_different From bb68892338a2bc54b03af76d12c97f70554b8307 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Thu, 25 Jan 2024 14:05:45 -0600 Subject: [PATCH 310/384] nx-cugraph: add `complement` and `reverse` (#4103) We apparently already had `G.reverse()`, which made that function extra easy :) Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4103 --- python/nx-cugraph/_nx_cugraph/__init__.py | 2 + .../nx_cugraph/algorithms/__init__.py | 2 + .../algorithms/operators/__init__.py | 13 +++++ .../nx_cugraph/algorithms/operators/unary.py | 55 +++++++++++++++++++ 4 files changed, 72 insertions(+) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/operators/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 9bca031a2..2f283aa15 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -43,6 +43,7 @@ "chvatal_graph", "circular_ladder_graph", "clustering", + "complement", "complete_bipartite_graph", "complete_graph", "complete_multipartite_graph", @@ -105,6 +106,7 @@ "path_graph", "petersen_graph", "reciprocity", + "reverse", "sedgewick_maze_graph", "single_source_shortest_path_length", "single_target_shortest_path_length", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 08658ad94..7aafa85f5 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -17,6 +17,7 @@ community, components, link_analysis, + operators, shortest_paths, traversal, tree, @@ -29,6 +30,7 @@ from .dag import * from .isolate import * from .link_analysis import * +from .operators import * from .reciprocity import * from .shortest_paths import * from .traversal import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/operators/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/operators/__init__.py new file mode 100644 index 000000000..32fd45f57 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/operators/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .unary import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py b/python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py new file mode 100644 index 000000000..08abc9f28 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py @@ -0,0 +1,55 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np + +import nx_cugraph as nxcg +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import index_dtype, networkx_algorithm + +__all__ = ["complement", "reverse"] + + +@networkx_algorithm(version_added="24.02") +def complement(G): + G = _to_graph(G) + N = G._N + # Upcast to int64 so indices don't overflow. + edges_a_b = N * G.src_indices.astype(np.int64) + G.dst_indices + # Now compute flattened indices for all edges except self-loops + # Alt (slower): + # edges_full = np.arange(N * N) + # edges_full = edges_full[(edges_full % (N + 1)).astype(bool)] + edges_full = cp.arange(1, N * (N - 1) + 1) + cp.repeat(cp.arange(N - 1), N) + edges_comp = cp.setdiff1d( + edges_full, + edges_a_b, + assume_unique=not G.is_multigraph(), + ) + src_indices, dst_indices = cp.divmod(edges_comp, N) + return G.__class__.from_coo( + N, + src_indices.astype(index_dtype), + dst_indices.astype(index_dtype), + key_to_id=G.key_to_id, + ) + + +@networkx_algorithm(version_added="24.02") +def reverse(G, copy=True): + if not G.is_directed(): + raise nx.NetworkXError("Cannot reverse an undirected graph.") + if isinstance(G, nx.Graph): + G = nxcg.from_networkx(G, preserve_all_attrs=True) + return G.reverse(copy=copy) From 0aa3579a6b8794ef1253ea839a7ed9ab06ffc989 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Thu, 1 Feb 2024 14:14:49 -0600 Subject: [PATCH 311/384] Adds option to rapids_cpm_find for raft to disable hnswlib feature, adds updates for pytest 8 compat, temporarily skips IO intensive test in CI (#4121) * Adds option to rapids_cpm_find for raft to disable hnswlib feature, which is not needed by cugraph. This resolves cmake failures such as the following: ``` CMake Error at /home/coder/cugraph/cpp/build/release/_deps/raft-build/raft-targets.cmake:56 (set_target_properties): The link interface of target "raft::raft" contains: hnswlib::hnswlib but the target was not found. ``` * TEMPORARILY skips bulk sampling IO tests which were hanging on CI runners. These tests pass locally, so we believe this is related to IO issues on CI runners, but will investigate further in 24.04 * Adds updates for pytest >=8 * Adds option to pytest to append source paths to PYTHONPATH instead of prepend (the default). The prepended PYTHONPATH caused the dask `LocalCUDACluster` to import cugraph modules from the source tree instead of the install location, which resulted in `ImportError`s. This appears to be a behavior change in pytest 8. * Adds an `xfail` for an [NX test known to be incompatible with pytest>=8](https://github.com/networkx/networkx/pull/7203) Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Divye Gala (https://github.com/divyegala) - Chuck Hastings (https://github.com/ChuckHastings) - Ray Douglass (https://github.com/raydouglass) - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4121 --- python/nx-cugraph/nx_cugraph/interface.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 04591c0e9..a57074aab 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -80,18 +80,26 @@ def key(testpath): nxver = parse(nx.__version__) if nxver.major == 3 and nxver.minor <= 2: - # Networkx versions prior to 3.2.1 have tests written to expect - # sp.sparse.linalg.ArpackNoConvergence exceptions raised on no - # convergence in HITS. Newer versions since the merge of - # https://github.com/networkx/networkx/pull/7084 expect - # nx.PowerIterationFailedConvergence, which is what nx_cugraph.hits - # raises, so we mark them as xfail for previous versions of NX. xfail.update( { + # NetworkX versions prior to 3.2.1 have tests written to + # expect sp.sparse.linalg.ArpackNoConvergence exceptions + # raised on no convergence in HITS. Newer versions since + # the merge of + # https://github.com/networkx/networkx/pull/7084 expect + # nx.PowerIterationFailedConvergence, which is what + # nx_cugraph.hits raises, so we mark them as xfail for + # previous versions of NX. key( "test_hits.py:TestHITS.test_hits_not_convergent" ): "nx_cugraph.hits raises updated exceptions not caught in " "these tests", + # NetworkX versions 3.2 and older contain tests that fail + # with pytest>=8. Assume pytest>=8 and mark xfail. + key( + "test_strongly_connected.py:" + "TestStronglyConnected.test_connected_raise" + ): "test is incompatible with pytest>=8", } ) From 1b897fae06213b00737d6fe493be978884309402 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Thu, 1 Feb 2024 17:57:24 -0600 Subject: [PATCH 312/384] Adds benchmarks for additional nx-cugraph 24.02 algos (#4112) * Adds 23 benchmarks for BFS, connected components, triangles, and other related algos * Adds a shell script to run the algo benchmarks individually for easier comparison to NX * Fixes a bug in `nx_cugraph.generic_bfs_edges()` uncovered by these benchmarks Authors: - Rick Ratzel (https://github.com/rlratzel) - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) Approvers: - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4112 --- .../nx-cugraph/pytest-based/bench_algos.py | 366 +++++++++++++++++- .../nx-cugraph/pytest-based/run-2402.sh | 46 +++ .../traversal/breadth_first_search.py | 2 +- .../nx-cugraph/nx_cugraph/classes/digraph.py | 12 +- python/nx-cugraph/nx_cugraph/classes/graph.py | 4 +- 5 files changed, 413 insertions(+), 17 deletions(-) create mode 100755 benchmarks/nx-cugraph/pytest-based/run-2402.sh diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py index a8ed18a20..97eb32e2a 100644 --- a/benchmarks/nx-cugraph/pytest-based/bench_algos.py +++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py @@ -38,20 +38,27 @@ warmup_rounds = 1 dataset_param_values = [ + # name: karate, nodes: 34, edges: 156 pytest.param(datasets.karate, marks=[pytest.mark.small, pytest.mark.undirected]), + # name: netscience, nodes: 1461, edges: 5484 pytest.param(datasets.netscience, marks=[pytest.mark.small, pytest.mark.directed]), + # name: email-Eu-core, nodes: 1005, edges: 25571 pytest.param( datasets.email_Eu_core, marks=[pytest.mark.small, pytest.mark.directed] ), + # name: cit-Patents, nodes: 3774768, edges: 16518948 pytest.param( datasets.cit_patents, marks=[pytest.mark.medium, pytest.mark.directed] ), + # name: hollywood, nodes: 1139905, edges: 57515616 pytest.param( datasets.hollywood, marks=[pytest.mark.medium, pytest.mark.undirected] ), + # name: soc-LiveJournal1, nodes: 4847571, edges: 68993773 pytest.param( datasets.soc_livejournal, marks=[pytest.mark.medium, pytest.mark.directed] ), + # name: europe_osm, nodes: 50912018, edges: 54054660 pytest.param( datasets.europe_osm, marks=[pytest.mark.large, pytest.mark.undirected] ), @@ -226,12 +233,21 @@ def get_graph_obj_for_benchmark(graph_obj, backend_wrapper): """ G = graph_obj if backend_wrapper.backend_name == "cugraph-preconverted": - G = nxcg.from_networkx(G) + G = nxcg.from_networkx(G, preserve_all_attrs=True) return G +def get_highest_degree_node(graph_obj): + degrees = graph_obj.degree() # list of tuples of (node, degree) + return max(degrees, key=lambda t: t[1])[0] + + ################################################################################ # Benchmarks +def bench_from_networkx(benchmark, graph_obj): + benchmark(nxcg.from_networkx, graph_obj) + + # normalized_param_values = [True, False] # k_param_values = [10, 100] normalized_param_values = [True] @@ -284,7 +300,7 @@ def bench_edge_betweenness_centrality( def bench_louvain_communities(benchmark, graph_obj, backend_wrapper): G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) - # The cugraph backend for louvain_communities only supports undirected graphs + # DiGraphs are not supported if G.is_directed(): G = G.to_undirected() result = benchmark.pedantic( @@ -416,10 +432,8 @@ def bench_pagerank(benchmark, graph_obj, backend_wrapper): def bench_single_source_shortest_path_length(benchmark, graph_obj, backend_wrapper): - # Use the node with the highest degree - degrees = graph_obj.degree() # list of tuples of (node, degree) - node = max(degrees, key=lambda t: t[1])[0] G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) result = benchmark.pedantic( target=backend_wrapper(nx.single_source_shortest_path_length), @@ -435,11 +449,8 @@ def bench_single_source_shortest_path_length(benchmark, graph_obj, backend_wrapp def bench_single_target_shortest_path_length(benchmark, graph_obj, backend_wrapper): - # Use the node with the highest degree - degrees = graph_obj.degree() # list of tuples of (node, degree) - node = max(degrees, key=lambda t: t[1])[0] G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) - + node = get_highest_degree_node(graph_obj) result = benchmark.pedantic( target=backend_wrapper( nx.single_target_shortest_path_length, exhaust_returned_iterator=True @@ -456,3 +467,340 @@ def bench_single_target_shortest_path_length(benchmark, graph_obj, backend_wrapp # needed for this algo in NX 3.3+ which returns a dict instead of an # iterator. Forcing to a list does not change the benchmark timing. assert type(result) is list + + +def bench_ancestors(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.ancestors), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is set + + +def bench_average_clustering(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + # DiGraphs are not supported by nx-cugraph + if G.is_directed(): + G = G.to_undirected() + result = benchmark.pedantic( + target=backend_wrapper(nx.average_clustering), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is float + + +def bench_generic_bfs_edges(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.generic_bfs_edges, exhaust_returned_iterator=True), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list + + +def bench_bfs_edges(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.bfs_edges, exhaust_returned_iterator=True), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list + + +def bench_bfs_layers(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.bfs_layers, exhaust_returned_iterator=True), + args=(G,), + kwargs=dict( + sources=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list + + +def bench_bfs_predecessors(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.bfs_predecessors, exhaust_returned_iterator=True), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list + + +def bench_bfs_successors(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.bfs_successors, exhaust_returned_iterator=True), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list + + +def bench_bfs_tree(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.bfs_tree), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + # Check that this at least appears to be some kind of NX-like Graph + assert hasattr(result, "has_node") + + +def bench_clustering(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + # DiGraphs are not supported by nx-cugraph + if G.is_directed(): + G = G.to_undirected() + result = benchmark.pedantic( + target=backend_wrapper(nx.clustering), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_core_number(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + # DiGraphs are not supported by nx-cugraph + if G.is_directed(): + G = G.to_undirected() + result = benchmark.pedantic( + target=backend_wrapper(nx.core_number), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_descendants(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.descendants), + args=(G,), + kwargs=dict( + source=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is set + + +def bench_descendants_at_distance(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.descendants_at_distance), + args=(G,), + kwargs=dict( + source=node, + distance=1, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is set + + +def bench_is_bipartite(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.is_bipartite), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is bool + + +def bench_is_strongly_connected(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.is_strongly_connected), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is bool + + +def bench_is_weakly_connected(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.is_weakly_connected), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is bool + + +def bench_number_strongly_connected_components(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.number_strongly_connected_components), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is int + + +def bench_number_weakly_connected_components(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.number_weakly_connected_components), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is int + + +def bench_overall_reciprocity(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper(nx.overall_reciprocity), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is float + + +def bench_reciprocity(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + node = get_highest_degree_node(graph_obj) + result = benchmark.pedantic( + target=backend_wrapper(nx.reciprocity), + args=(G,), + kwargs=dict( + nodes=node, + ), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is float + + +def bench_strongly_connected_components(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper( + nx.strongly_connected_components, exhaust_returned_iterator=True + ), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list + + +def bench_transitivity(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + # DiGraphs are not supported by nx-cugraph + if G.is_directed(): + G = G.to_undirected() + result = benchmark.pedantic( + target=backend_wrapper(nx.transitivity), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is float + + +def bench_triangles(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + # DiGraphs are not supported + if G.is_directed(): + G = G.to_undirected() + result = benchmark.pedantic( + target=backend_wrapper(nx.triangles), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + +def bench_weakly_connected_components(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + result = benchmark.pedantic( + target=backend_wrapper( + nx.weakly_connected_components, exhaust_returned_iterator=True + ), + args=(G,), + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is list diff --git a/benchmarks/nx-cugraph/pytest-based/run-2402.sh b/benchmarks/nx-cugraph/pytest-based/run-2402.sh new file mode 100755 index 000000000..44ed0bda4 --- /dev/null +++ b/benchmarks/nx-cugraph/pytest-based/run-2402.sh @@ -0,0 +1,46 @@ +#!/bin/bash +# +# Copyright (c) 2024, NVIDIA CORPORATION. +# +# Runs benchmarks for the 24.02 algos. +# Pass either a or b or both. This is useful for separating batches of runs on different GPUs: +# CUDA_VISIBLE_DEVICES=1 run-2402.sh b + +mkdir -p logs + +# benches="$benches ..." pattern is easy to comment out individual runs +benches= + +while [[ $1 != "" ]]; do + if [[ $1 == "a" ]]; then + benches="$benches bench_ancestors" + benches="$benches bench_average_clustering" + benches="$benches bench_generic_bfs_edges" + benches="$benches bench_bfs_edges" + benches="$benches bench_bfs_layers" + benches="$benches bench_bfs_predecessors" + benches="$benches bench_bfs_successors" + benches="$benches bench_bfs_tree" + benches="$benches bench_clustering" + benches="$benches bench_core_number" + benches="$benches bench_descendants" + elif [[ $1 == "b" ]]; then + benches="$benches bench_descendants_at_distance" + benches="$benches bench_is_bipartite" + benches="$benches bench_is_strongly_connected" + benches="$benches bench_is_weakly_connected" + benches="$benches bench_number_strongly_connected_components" + benches="$benches bench_number_weakly_connected_components" + benches="$benches bench_overall_reciprocity" + benches="$benches bench_reciprocity" + benches="$benches bench_strongly_connected_components" + benches="$benches bench_transitivity" + benches="$benches bench_triangles" + benches="$benches bench_weakly_connected_components" + fi + shift +done + +for bench in $benches; do + pytest -sv -k "soc-livejournal1" "bench_algos.py::$bench" 2>&1 | tee "logs/${bench}.log" +done diff --git a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py index ef1c01136..f5d5e2a99 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py @@ -68,7 +68,7 @@ def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbor raise NotImplementedError( "sort_neighbors argument in generic_bfs_edges is not currently supported" ) - return bfs_edges(source, depth_limit=depth_limit) + return bfs_edges(G, source, depth_limit=depth_limit) @generic_bfs_edges._can_run diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index 169815eb0..e5cfb8f68 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -86,9 +86,9 @@ def to_undirected(self, reciprocal=False, as_view=False): key: val[indices].copy() for key, val in self.edge_masks.items() } else: - src_indices, dst_indices = cp.divmod( - src_dst_indices_new, N, dtype=index_dtype - ) + src_indices, dst_indices = cp.divmod(src_dst_indices_new, N) + src_indices = src_indices.astype(index_dtype) + dst_indices = dst_indices.astype(index_dtype) else: src_dst_indices_old_T = self.src_indices + N * self.dst_indices.astype( np.int64 @@ -116,9 +116,9 @@ def to_undirected(self, reciprocal=False, as_view=False): src_dst_indices_new = cp.union1d( src_dst_indices_old, src_dst_indices_old_T ) - src_indices, dst_indices = cp.divmod( - src_dst_indices_new, N, dtype=index_dtype - ) + src_indices, dst_indices = cp.divmod(src_dst_indices_new, N) + src_indices = src_indices.astype(index_dtype) + dst_indices = dst_indices.astype(index_dtype) if self.edge_values: recip_indices = cp.lexsort(cp.vstack((src_indices, dst_indices))) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index f69766875..0951ee6b1 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -668,7 +668,9 @@ def _get_plc_graph( raise ValueError( f'symmetrize must be "union" or "intersection"; got "{symmetrize}"' ) - src_indices, dst_indices = cp.divmod(src_dst_new, N, dtype=index_dtype) + src_indices, dst_indices = cp.divmod(src_dst_new, N) + src_indices = src_indices.astype(index_dtype) + dst_indices = dst_indices.astype(index_dtype) return plc.SGGraph( resource_handle=plc.ResourceHandle(), From 3cad19e9f5c6c73f32158ac0c3736ee4ff9fff4e Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Thu, 1 Feb 2024 17:59:58 -0600 Subject: [PATCH 313/384] nx-cugraph: use coverage to ensure all algorithms were run (#4108) Heh, there is probably a "better" way to do this, but this way was fast and easy enough to do, and I hope is "good enough". Authors: - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/4108 --- python/nx-cugraph/lint.yaml | 2 +- .../nx_cugraph/scripts/print_table.py | 2 +- .../nx_cugraph/tests/ensure_algos_covered.py | 84 +++++++++++++++++++ .../nx-cugraph/nx_cugraph/tests/test_bfs.py | 33 ++++++++ python/nx-cugraph/run_nx_tests.sh | 14 +++- 5 files changed, 129 insertions(+), 6 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/tests/ensure_algos_covered.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_bfs.py diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 5a4773168..8e87fc235 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -26,7 +26,7 @@ repos: - id: mixed-line-ending - id: trailing-whitespace - repo: https://github.com/abravalheri/validate-pyproject - rev: v0.15 + rev: v0.16 hooks: - id: validate-pyproject name: Validate pyproject.toml diff --git a/python/nx-cugraph/nx_cugraph/scripts/print_table.py b/python/nx-cugraph/nx_cugraph/scripts/print_table.py index 7e69de63d..117a1444f 100755 --- a/python/nx-cugraph/nx_cugraph/scripts/print_table.py +++ b/python/nx-cugraph/nx_cugraph/scripts/print_table.py @@ -59,7 +59,7 @@ def main(path_to_info=None, *, file=sys.stdout): if path_to_info is None: path_to_info = get_path_to_info(version_added_sep=".") lines = ["networkx_path,dispatch_name,version_added,plc,is_incomplete,is_different"] - lines.extend(",".join(info) for info in path_to_info.values()) + lines.extend(",".join(map(str, info)) for info in path_to_info.values()) text = "\n".join(lines) print(text, file=file) return text diff --git a/python/nx-cugraph/nx_cugraph/tests/ensure_algos_covered.py b/python/nx-cugraph/nx_cugraph/tests/ensure_algos_covered.py new file mode 100644 index 000000000..7047f0eea --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/ensure_algos_covered.py @@ -0,0 +1,84 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Ensure that all functions wrapped by @networkx_algorithm were called. + +This file is run by CI and should not normally be run manually. +""" +import inspect +import json +from pathlib import Path + +from nx_cugraph.interface import BackendInterface +from nx_cugraph.utils import networkx_algorithm + +with Path("coverage.json").open() as f: + coverage = json.load(f) + +filenames_to_executed_lines = { + "nx_cugraph/" + + filename.rsplit("nx_cugraph/", 1)[-1]: set(coverage_info["executed_lines"]) + for filename, coverage_info in coverage["files"].items() +} + + +def unwrap(func): + while hasattr(func, "__wrapped__"): + func = func.__wrapped__ + return func + + +def get_func_filename(func): + return "nx_cugraph" + inspect.getfile(unwrap(func)).rsplit("nx_cugraph", 1)[-1] + + +def get_func_linenos(func): + lines, lineno = inspect.getsourcelines(unwrap(func)) + for i, line in enumerate(lines, lineno): + if ":\n" in line: + return set(range(i + 1, lineno + len(lines))) + raise RuntimeError(f"Could not determine line numbers for function {func}") + + +def has_any_coverage(func): + return bool( + filenames_to_executed_lines[get_func_filename(func)] & get_func_linenos(func) + ) + + +def main(): + no_coverage = set() + for attr, func in vars(BackendInterface).items(): + if not isinstance(func, networkx_algorithm): + continue + if not has_any_coverage(func): + no_coverage.add(attr) + if no_coverage: + msg = "The following algorithms have no coverage: " + ", ".join( + sorted(no_coverage) + ) + # Create a border of "!" + msg = ( + "\n\n" + + "!" * (len(msg) + 6) + + "\n!! " + + msg + + " !!\n" + + "!" * (len(msg) + 6) + + "\n" + ) + raise AssertionError(msg) + print("\nSuccess: coverage determined all algorithms were called!\n") + + +if __name__ == "__main__": + main() diff --git a/python/nx-cugraph/nx_cugraph/tests/test_bfs.py b/python/nx-cugraph/nx_cugraph/tests/test_bfs.py new file mode 100644 index 000000000..c2b22e989 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_bfs.py @@ -0,0 +1,33 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest +from packaging.version import parse + +nxver = parse(nx.__version__) + +if nxver.major == 3 and nxver.minor < 2: + pytest.skip("Need NetworkX >=3.2 to test clustering", allow_module_level=True) + + +def test_generic_bfs_edges(): + # generic_bfs_edges currently isn't exercised by networkx tests + Gnx = nx.karate_club_graph() + Gcg = nx.karate_club_graph(backend="cugraph") + for depth_limit in (0, 1, 2): + for source in Gnx: + # Some ordering is arbitrary, so I think there's a chance + # this test may fail if networkx or nx-cugraph changes. + nx_result = nx.generic_bfs_edges(Gnx, source, depth_limit=depth_limit) + cg_result = nx.generic_bfs_edges(Gcg, source, depth_limit=depth_limit) + assert sorted(nx_result) == sorted(cg_result), (source, depth_limit) diff --git a/python/nx-cugraph/run_nx_tests.sh b/python/nx-cugraph/run_nx_tests.sh index 07c97cdf9..da7a2014c 100755 --- a/python/nx-cugraph/run_nx_tests.sh +++ b/python/nx-cugraph/run_nx_tests.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash # -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # # NETWORKX_GRAPH_CONVERT=cugraph # Used by networkx versions 3.0 and 3.1 @@ -30,7 +30,13 @@ NETWORKX_TEST_BACKEND=cugraph \ NETWORKX_FALLBACK_TO_NX=True \ pytest \ --pyargs networkx \ - --cov=nx_cugraph.algorithms \ - --cov-report term-missing \ - --no-cov-on-fail \ + --config-file=$(dirname $0)/pyproject.toml \ + --cov-config=$(dirname $0)/pyproject.toml \ + --cov=nx_cugraph \ + --cov-report= \ "$@" +coverage report \ + --include="*/nx_cugraph/algorithms/*" \ + --omit=__init__.py \ + --show-missing \ + --rcfile=$(dirname $0)/pyproject.toml From c06dd95b7881c37fdc5c118ef5275b9ac7cb8831 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Thu, 1 Feb 2024 18:46:22 -0600 Subject: [PATCH 314/384] Removes the `networkx_algorithm` decorator to all SCC functions to disable dispatching to them (#4120) The current cugraph `strongly_connected_components` is a legacy implementation with known issues, and in most cases should not be used until the cugraph team can provide an update. This PR removes the `networkx_algorithm` decorator from all SCC functions to disable dispatching. Users can still run the SCC functions here by accessing them directly from `nx_cugraph`: ```python >>> import nx_cugraph as nxcg >>> nxcg.strongly_connected_components(...) ``` Tested by running the `nx_cugraph` tests (`pytest nx_cugraph/tests`) and the NetworkX tests (`run_nx_tests.sh`) _Note: using the "non-breaking" label since this API was only present in nightlies and never released._ Authors: - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) Approvers: - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4120 --- python/nx-cugraph/_nx_cugraph/__init__.py | 9 +++---- .../components/strongly_connected.py | 24 ++++++++++++------- python/nx-cugraph/nx_cugraph/interface.py | 12 ++++++---- 3 files changed, 28 insertions(+), 17 deletions(-) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 2f283aa15..8b5c87a63 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -12,7 +12,11 @@ # limitations under the License. """Tell NetworkX about the cugraph backend. This file can update itself: -$ make plugin-info # Recommended method for development +$ make plugin-info + +or + +$ make all # Recommended - runs 'plugin-info' followed by 'lint' or @@ -78,7 +82,6 @@ "is_connected", "is_forest", "is_isolate", - "is_strongly_connected", "is_tree", "is_weakly_connected", "isolates", @@ -96,7 +99,6 @@ "number_connected_components", "number_of_isolates", "number_of_selfloops", - "number_strongly_connected_components", "number_weakly_connected_components", "octahedral_graph", "out_degree_centrality", @@ -111,7 +113,6 @@ "single_source_shortest_path_length", "single_target_shortest_path_length", "star_graph", - "strongly_connected_components", "tadpole_graph", "tetrahedral_graph", "transitivity", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py index d17131297..a63b3237d 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py @@ -15,12 +15,7 @@ import pylibcugraph as plc from nx_cugraph.convert import _to_directed_graph -from nx_cugraph.utils import ( - _groupby, - index_dtype, - networkx_algorithm, - not_implemented_for, -) +from nx_cugraph.utils import _groupby, index_dtype, not_implemented_for __all__ = [ "number_strongly_connected_components", @@ -50,8 +45,19 @@ def _strongly_connected_components(G): return labels +# The networkx_algorithm decorator is (temporarily) removed to disable +# dispatching for this function. The current cugraph +# strongly_connected_components is a legacy implementation with known issues, +# and in most cases should not be used until the cugraph team can provide an +# update. +# +# Users can still call this via the nx_cugraph module directly: +# >>> import nx_cugraph as nxcg +# >>> nxcg.strongly_connected_components(...) + + @not_implemented_for("undirected") -@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") +# @networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") def strongly_connected_components(G): G = _to_directed_graph(G) if G.src_indices.size == 0: @@ -62,7 +68,7 @@ def strongly_connected_components(G): @not_implemented_for("undirected") -@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") +# @networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") def number_strongly_connected_components(G): G = _to_directed_graph(G) if G.src_indices.size == 0: @@ -72,7 +78,7 @@ def number_strongly_connected_components(G): @not_implemented_for("undirected") -@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") +# @networkx_algorithm(version_added="24.02", _plc="strongly_connected_components") def is_strongly_connected(G): G = _to_directed_graph(G) if len(G) == 0: diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index a57074aab..46ea5831b 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -69,10 +69,14 @@ def key(testpath): no_string_dtype = "string edge values not currently supported" xfail = { - key( - "test_strongly_connected.py:" - "TestStronglyConnected.test_condensation_mapping_and_members" - ): "Strongly connected groups in different iteration order", + # This is removed while strongly_connected_components() is not + # dispatchable. See algorithms/components/strongly_connected.py for + # details. + # + # key( + # "test_strongly_connected.py:" + # "TestStronglyConnected.test_condensation_mapping_and_members" + # ): "Strongly connected groups in different iteration order", } from packaging.version import parse From 4e05a47b5193dca0ccb7589877e026379ad3d848 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Thu, 1 Feb 2024 19:58:16 -0500 Subject: [PATCH 315/384] corrected links in C API and added groups for support functions (#4131) Fixes broken links in C API docs in Traversal, Sampling and Community algorithm sections resolves issue #4116 Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4131 --- .../cugraph/source/api_docs/cugraph_c/community.rst | 10 ++-------- docs/cugraph/source/api_docs/cugraph_c/labeling.rst | 4 ++-- docs/cugraph/source/api_docs/cugraph_c/sampling.rst | 13 +++++-------- .../source/api_docs/cugraph_c/similarity.rst | 4 ++-- .../cugraph/source/api_docs/cugraph_c/traversal.rst | 4 ++-- 5 files changed, 13 insertions(+), 22 deletions(-) diff --git a/docs/cugraph/source/api_docs/cugraph_c/community.rst b/docs/cugraph/source/api_docs/cugraph_c/community.rst index 0bbfe365c..d55325720 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/community.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/community.rst @@ -1,12 +1,6 @@ Community ========= -.. role:: py(code) - :language: c - :class: highlight - -``#include `` - Triangle Counting ----------------- .. doxygenfunction:: cugraph_triangle_count @@ -45,8 +39,8 @@ Spectral Clustering - Modularity Maximization .. doxygenfunction:: cugraph_analyze_clustering_modularity :project: libcugraph -Spectral Clusteriong - Edge Cut -------------------------------- +Spectral Clustering - Edge Cut +------------------------------ .. doxygenfunction:: cugraph_analyze_clustering_edge_cut :project: libcugraph diff --git a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst index af105ee8f..4ca598c0a 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst @@ -12,8 +12,8 @@ Strongly Connected Components .. doxygenfunction:: cugraph_strongly_connected_components :project: libcugraph -Support -------- +Labeling Support Functions +-------------------------- .. doxygengroup:: labeling :project: libcugraph :members: diff --git a/docs/cugraph/source/api_docs/cugraph_c/sampling.rst b/docs/cugraph/source/api_docs/cugraph_c/sampling.rst index 21b837daf..3d5af713c 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/sampling.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/sampling.rst @@ -7,7 +7,7 @@ Uniform Random Walks :project: libcugraph Biased Random Walks --------------------- +------------------- .. doxygenfunction:: cugraph_biased_random_walks :project: libcugraph @@ -21,16 +21,13 @@ Node2Vec .. doxygenfunction:: cugraph_node2vec :project: libcugraph -Uniform Neighborhood Sampling ------------------------------ -.. doxygenfunction:: cugraph_uniform_neighbor_sample_with_edge_properties - :project: libcugraph - +Uniform Neighbor Sampling +------------------------- .. doxygenfunction:: cugraph_uniform_neighbor_sample :project: libcugraph -Support -------- +Sampling Support Functions +-------------------------- .. doxygengroup:: samplingC :project: libcugraph :members: diff --git a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst index fba07ad20..200ba6957 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst @@ -17,8 +17,8 @@ Overlap .. doxygenfunction:: cugraph_overlap_coefficients :project: libcugraph -Support -------- +Similarty Support Functions +--------------------------- .. doxygengroup:: similarity :project: libcugraph :members: diff --git a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst index c90760e9e..1578951e0 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst @@ -22,8 +22,8 @@ Extract Max Path Length .. doxygenfunction:: cugraph_extract_paths_result_get_max_path_length :project: libcugraph -Support -------- +Traversal Support Functions +--------------------------- .. doxygengroup:: traversal :project: libcugraph :members: From 4b3a8bc6334abf3f4299841b29ff99a19d9c3296 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Fri, 2 Feb 2024 00:00:18 -0600 Subject: [PATCH 316/384] Updates nx-cugraph README.md with latest algos (#4135) closes #4079 * Updates nx-cugraph `README.md` with latest algos * Adds script to auto-update `README.md` from `nx_cugraph` metadata directly Authors: - Rick Ratzel (https://github.com/rlratzel) - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4135 --- python/nx-cugraph/README.md | 182 ++++++++++++++---- .../nx_cugraph/scripts/print_tree.py | 4 +- 2 files changed, 143 insertions(+), 43 deletions(-) diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index f6a9aac10..5d0554734 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -89,48 +89,146 @@ interface to its CUDA-based graph analytics library) and [CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's familiar and easy-to-use API. -Below is the list of algorithms (many listed using pylibcugraph names), -available today in pylibcugraph or implemented using CuPy, that are or will be -supported in nx-cugraph. - -| feature/algo | release/target version | -| ----- | ----- | -| analyze_clustering_edge_cut | ? | -| analyze_clustering_modularity | ? | -| analyze_clustering_ratio_cut | ? | -| balanced_cut_clustering | ? | -| betweenness_centrality | 23.10 | -| bfs | ? | -| connected_components | 23.12 | -| core_number | ? | -| degree_centrality | 23.12 | -| ecg | ? | -| edge_betweenness_centrality | 23.10 | -| ego_graph | ? | -| eigenvector_centrality | 23.12 | -| get_two_hop_neighbors | ? | -| hits | 23.12 | -| in_degree_centrality | 23.12 | -| induced_subgraph | ? | -| jaccard_coefficients | ? | -| katz_centrality | 23.12 | -| k_core | ? | -| k_truss_subgraph | 23.12 | -| leiden | ? | -| louvain | 23.10 | -| node2vec | ? | -| out_degree_centrality | 23.12 | -| overlap_coefficients | ? | -| pagerank | 23.12 | -| personalized_pagerank | ? | -| sorensen_coefficients | ? | -| spectral_modularity_maximization | ? | -| sssp | 23.12 | -| strongly_connected_components | ? | -| triangle_count | ? | -| uniform_neighbor_sample | ? | -| uniform_random_walks | ? | -| weakly_connected_components | ? | +Below is the list of algorithms that are currently supported in nx-cugraph. + +### Algorithms + +``` +bipartite + ├─ basic + │ └─ is_bipartite + └─ generators + └─ complete_bipartite_graph +centrality + ├─ betweenness + │ ├─ betweenness_centrality + │ └─ edge_betweenness_centrality + ├─ degree_alg + │ ├─ degree_centrality + │ ├─ in_degree_centrality + │ └─ out_degree_centrality + ├─ eigenvector + │ └─ eigenvector_centrality + └─ katz + └─ katz_centrality +cluster + ├─ average_clustering + ├─ clustering + ├─ transitivity + └─ triangles +community + └─ louvain + └─ louvain_communities +components + ├─ connected + │ ├─ connected_components + │ ├─ is_connected + │ ├─ node_connected_component + │ └─ number_connected_components + └─ weakly_connected + ├─ is_weakly_connected + ├─ number_weakly_connected_components + └─ weakly_connected_components +core + ├─ core_number + └─ k_truss +dag + ├─ ancestors + └─ descendants +isolate + ├─ is_isolate + ├─ isolates + └─ number_of_isolates +link_analysis + ├─ hits_alg + │ └─ hits + └─ pagerank_alg + └─ pagerank +operators + └─ unary + ├─ complement + └─ reverse +reciprocity + ├─ overall_reciprocity + └─ reciprocity +shortest_paths + └─ unweighted + ├─ single_source_shortest_path_length + └─ single_target_shortest_path_length +traversal + └─ breadth_first_search + ├─ bfs_edges + ├─ bfs_layers + ├─ bfs_predecessors + ├─ bfs_successors + ├─ bfs_tree + ├─ descendants_at_distance + └─ generic_bfs_edges +tree + └─ recognition + ├─ is_arborescence + ├─ is_branching + ├─ is_forest + └─ is_tree +``` + +### Generators + +``` +classic + ├─ barbell_graph + ├─ circular_ladder_graph + ├─ complete_graph + ├─ complete_multipartite_graph + ├─ cycle_graph + ├─ empty_graph + ├─ ladder_graph + ├─ lollipop_graph + ├─ null_graph + ├─ path_graph + ├─ star_graph + ├─ tadpole_graph + ├─ trivial_graph + ├─ turan_graph + └─ wheel_graph +community + └─ caveman_graph +small + ├─ bull_graph + ├─ chvatal_graph + ├─ cubical_graph + ├─ desargues_graph + ├─ diamond_graph + ├─ dodecahedral_graph + ├─ frucht_graph + ├─ heawood_graph + ├─ house_graph + ├─ house_x_graph + ├─ icosahedral_graph + ├─ krackhardt_kite_graph + ├─ moebius_kantor_graph + ├─ octahedral_graph + ├─ pappus_graph + ├─ petersen_graph + ├─ sedgewick_maze_graph + ├─ tetrahedral_graph + ├─ truncated_cube_graph + ├─ truncated_tetrahedron_graph + └─ tutte_graph +social + ├─ davis_southern_women_graph + ├─ florentine_families_graph + ├─ karate_club_graph + └─ les_miserables_graph +``` + +### Other + +``` +convert_matrix + ├─ from_pandas_edgelist + └─ from_scipy_sparse_array +``` To request nx-cugraph backend support for a NetworkX API that is not listed above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph). diff --git a/python/nx-cugraph/nx_cugraph/scripts/print_tree.py b/python/nx-cugraph/nx_cugraph/scripts/print_tree.py index bb75d735c..485873a44 100755 --- a/python/nx-cugraph/nx_cugraph/scripts/print_tree.py +++ b/python/nx-cugraph/nx_cugraph/scripts/print_tree.py @@ -133,7 +133,9 @@ def main( } if by == "networkx_path": G = create_tree(path_to_info, by="networkx_path", **kwargs) - text = re.sub(r"[A-Za-z_\./]+\.", "", ("\n".join(nx.generate_network_text(G)))) + text = re.sub( + r" [A-Za-z_\./]+\.", " ", ("\n".join(nx.generate_network_text(G))) + ) elif by == "plc": G = create_tree( path_to_info, by=["plc", "networkx_path"], prefix="plc-", **kwargs From 0823cf3a4eb2d36a4f687ff9f8c185fc4a34690f Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Wed, 7 Feb 2024 05:52:06 -0800 Subject: [PATCH 317/384] Exclude tests from builds (#4147) --- python/nx-cugraph/pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 63ac11591..d8adeec0b 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -79,6 +79,7 @@ include = [ "_nx_cugraph*", "_nx_cugraph.*", ] +exclude = ["*tests*"] [tool.black] line-length = 88 From 8c6a19e9174df421851a57563263b81341b1a009 Mon Sep 17 00:00:00 2001 From: Ray Douglass <3107146+raydouglass@users.noreply.github.com> Date: Thu, 8 Feb 2024 17:15:59 -0500 Subject: [PATCH 318/384] Revert "Exclude tests from builds (#4147)" (#4157) This reverts commit 0823cf3a4eb2d36a4f687ff9f8c185fc4a34690f. --- python/nx-cugraph/pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index d8adeec0b..63ac11591 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -79,7 +79,6 @@ include = [ "_nx_cugraph*", "_nx_cugraph.*", ] -exclude = ["*tests*"] [tool.black] line-length = 88 From c000f4bdbdc91d6339d736244bac9ca8657f2fab Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Tue, 13 Feb 2024 18:24:54 +0100 Subject: [PATCH 319/384] Add a new notebook for SNMG benchmark runs (#4091) This PR adds new notebook for SNMG benchmark runs. It - [x] simplifies bookkeeping and data presentation. - [x] update each cugraph function wrappers to handle both SG and MG calls depending on a flag. The object is to have a common set of function wrappers for SG, SNMG and MNMG and put them in a common module to avoid duplication. Authors: - Naim (https://github.com/naimnv) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) - Joseph Nke (https://github.com/jnke2016) URL: https://github.com/rapidsai/cugraph/pull/4091 --- .../synth_release_single_node_multi_gpu.ipynb | 950 ++++++++++++++++++ 1 file changed, 950 insertions(+) create mode 100644 notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb diff --git a/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb b/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb new file mode 100644 index 000000000..c44f475c4 --- /dev/null +++ b/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb @@ -0,0 +1,950 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Skip notebook test\n", + "-----\n", + "\n", + "#### NOTE: This notebook will take hours to run.\n", + "-----\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Comparing NetworkX vs cuGraph using synthetic data on various algorithms on single node multi GPU (SNMG) cluster\n", + "\n", + "\n", + "This notebook compares the execution times of many of the cuGraph and NetworkX algorithms when run against identical synthetic data at multiple scales.\n", + "\n", + "This notebook uses the RMAT data generator which allows the creation of graphs at various scales. The notebook, by default, runs on a set of selected sizes but users are free to change or add to that list." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notebook Credits\n", + "\n", + " \n", + "| Author | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|---------------------|-----------------|------------------------|\n", + "| Don Acosta | 1/12/2023 | Created | 23.02 nightly | RTX A6000, CUDA 11.7 |\n", + "| Brad Rees | 1/27/2023 | Modified | 23.02 nightly | RTX A6000, CUDA 11.7 |\n", + "| Naim, Md | 2/08/2024 | Modified for SNMG | 24.04 nightly | RTX A6000, CUDA 12.0 |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Timing " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When looking at the overall workflow, NetworkX and cuGraph do things differently. For example, NetworkX spends a lot of time creating the graph data structure. cuGraph on the other hand does a lazy creation of the data structure when an algorithm is called. To further complicate the comparison problem, NetworkX does not always return the answer. In some cases, it returns a generator that is then called to produce the data. \n", + "\n", + "This benchmark produces two performance metrics:\n", + " - (1)\tJust the algorithm run time \n", + " - (2)\tThe algorithm plus graph creation time\n", + "\n", + "Since GPU memory is a precious resource, having a lot of temporary data laying around is avoided. So once a graph is created, the raw data is dropped. \n", + " \n", + "__What is not timed__: Generating the data with R-MAT

\n", + "__What is timed__: (1) creating a Graph, (2) running the algorithm (3) run any generators\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "| Algorithm | Type | Undirected Graph | Directed Graph | Notes\n", + "| ------------------------|---------------|------ | ------- |-------------\n", + "| Katz | Centrality | X | | \n", + "| Betweenness Centrality | Centrality | X | | Estimated, k = 100\n", + "| Louvain | Community | X | | Uses python-louvain for comparison\n", + "| Triangle Counting | Community | X | |\n", + "| Core Number | Core | X | |\n", + "| PageRank | Link Analysis | | X |\n", + "| Jaccard | Similarity | X | |\n", + "| BFS | Traversal | X | | No depth limit\n", + "| SSSP | Traversal | X | | \n", + "\n", + "\n", + "### Test Data\n", + "Data is generated using a Recursive MATrix (R-MAT) graph generation algorithm. \n", + "The generator specifics are documented [here](https://docs.rapids.ai/api/cugraph/stable/api_docs/generator.html)\n", + "\n", + "\n", + "\n", + "### Notes\n", + "* Running Betweenness Centrality on the full graph is prohibitive using NetworkX. Anything over k=100 can explode runtime to days\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Import Modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# system and other\n", + "import gc\n", + "import os\n", + "from time import perf_counter\n", + "import pandas as pd\n", + "from collections import defaultdict\n", + "\n", + "# rapids\n", + "import cugraph\n", + "\n", + "# liblibraries to setup dask cluster and client\n", + "from dask.distributed import Client\n", + "from dask_cuda import LocalCUDACluster\n", + "from cugraph.dask.comms import comms as Comms\n", + "\n", + "# NetworkX libraries\n", + "import networkx as nx\n", + "\n", + "# RMAT data generator\n", + "from cugraph.generators import rmat\n", + "from cugraph.structure import NumberMap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try: \n", + " import community\n", + "except ModuleNotFoundError:\n", + " os.system('pip install python-louvain')\n", + " import community" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine the scale of the test data\n", + "RMAT generates graph where the number of vertices is a power of 2 and the number of edges is based on an edge factor times the number vertices.\n", + "\n", + "Since RMAT tends to generate about 50% isolated vertices, those vertices are dropped from the graph data. Hence the number of vertices is closer to (2 ** scale) / 2\n", + "\n", + "\n", + "| Scale | Vertices (est) | Edges |\n", + "| ------|----------------|--------|\n", + "| 10 | 512 | 16,384 | \n", + "| 11 | 1,024 | 32,768| \n", + "| 12 | 2,048 | 65,536| \n", + "| 13 | 4,096 | 131,072| \n", + "| 14 | 8,192 | 262,144| \n", + "| 15 | 16,384 | 524,288 | \n", + "| 16 | 32,768 | 1,048,576 | \n", + "| 17 | 65,536 | 2,097,152 | \n", + "| 18 | 131,072 | 4,194,304 | \n", + "| 19 | 262,144 | 8,388,608 | \n", + "| 20 | 524,288 | 16,777,216 | \n", + "| 21 | 1,048,576 | 33,554,432 | \n", + "| 22 | 2,097,152 | 67,108,864 | \n", + "| 23 | 4,194,304 | 134,217,728 | \n", + "| 24 | 8,388,608 | 268,435,456 | \n", + "| 25 | 16,777,216 | 536,870,912 | \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test Data Sizes\n", + "# Here you can create an array of test data sizes. Then set the \"data\" variable to the array you want\n", + "# the dictionary format is 'name' : scale\n", + "\n", + "\n", + "# These scales are used by R-MAT to determine the number of vertices/edges in the synthetic data graph.\n", + "data_full = {\n", + " 'data_scale_10' : 10,\n", + " 'data_scale_12' : 12,\n", + " 'data_scale_14' : 14,\n", + " 'data_scale_16' : 16,\n", + " 'data_scale_18' : 18,\n", + " 'data_scale_20' : 20,\n", + "}\n", + "\n", + "# for quick testing\n", + "data_quick = {\n", + " 'data_scale_9' : 9,\n", + " 'data_scale_10' : 10,\n", + " 'data_scale_11' : 11,\n", + "}\n", + "\n", + "\n", + "# Which dataset is to be used\n", + "data = data_quick\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate data\n", + "The data is generated once for each size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data generator \n", + "# The result is an edgelist of the size determined by the scale and edge factor\n", + "def generate_data(scale, edgefactor=16, mg=False):\n", + " _gdf = rmat(\n", + " scale,\n", + " (2 ** scale) * edgefactor,\n", + " 0.57,\n", + " 0.19,\n", + " 0.19,\n", + " 42,\n", + " clip_and_flip=False,\n", + " scramble_vertex_ids=True,\n", + " create_using=None, # return edgelist instead of Graph instance\n", + " mg=mg # determines whether generated data will be used on one or multiple GPUs\n", + " )\n", + "\n", + " clean_coo = NumberMap.renumber(_gdf, src_col_names=\"src\", dst_col_names=\"dst\")[0]\n", + " if mg:\n", + " clean_coo.rename(columns={\"renumbered_src\": \"src\", \"renumbered_dst\": \"dst\"})\n", + " else:\n", + " clean_coo.rename(columns={\"renumbered_src\": \"src\", \"renumbered_dst\": \"dst\"}, inplace=True)\n", + "\n", + " print(f'Generated a dataframe of type {type(clean_coo)}, with {len(clean_coo)} edges')\n", + " \n", + " return clean_coo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Graph functions\n", + "There are two types of graphs created:\n", + "* Directed Graphs - calls to create_nx_digraph, create_cu_directed_graph.\n", + "* Undirected Graphs - calls to create_xx_ugraph <- fully symmeterized" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NetworkX\n", + "def create_nx_graph(_df , directed=False):\n", + " t1 = perf_counter()\n", + " if directed:\n", + " g_type = nx.DiGraph\n", + " else:\n", + " g_type = nx.Graph\n", + " \n", + " _gnx = nx.from_pandas_edgelist(_df,\n", + " source='src',\n", + " target='dst',\n", + " edge_attr=None,\n", + " create_using=g_type)\n", + " t2 = perf_counter() - t1\n", + "\n", + " return _gnx, t2\n", + "\n", + "\n", + "\n", + "# cuGraph\n", + "def create_cu_graph(_df, transpose=False, directed=False, mg=False):\n", + " t1 = perf_counter()\n", + " _g = cugraph.Graph(directed=directed)\n", + "\n", + " if mg:\n", + " _g.from_dask_cudf_edgelist(_df, source=\"src\", destination=\"dst\", edge_attr=None)\n", + " else:\n", + " _g.from_cudf_edgelist(_df,\n", + " source='src',\n", + " destination='dst',\n", + " edge_attr=None,\n", + " renumber=False,\n", + " store_transposed=transpose)\n", + " t2 = perf_counter() - t1\n", + "\n", + " return _g, t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Algorithm Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Katz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_katz(_G, alpha):\n", + " t1 = perf_counter()\n", + " _ = nx.katz_centrality(_G, alpha)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_katz(_G, alpha, mg=False):\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _ = cugraph.dask.katz_centrality(_G, alpha)\n", + " else:\n", + "\n", + " _ = cugraph.katz_centrality(_G, alpha)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Betweenness Centrality" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_bc(_G, _k):\n", + " t1 = perf_counter()\n", + " _ = nx.betweenness_centrality(_G, k=_k)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_bc(_G, _k, mg=False):\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _ = cugraph.dask.betweenness_centrality(_G, k=_k)\n", + " else: \n", + " _ = cugraph.betweenness_centrality(_G, k=_k)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Louvain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_louvain(_G):\n", + " t1 = perf_counter()\n", + " parts = community.best_partition(_G)\n", + " \n", + " # Calculating modularity scores for comparison\n", + " _ = community.modularity(parts, _G)\n", + " \n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_louvain(_G, mg=False):\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _, modularity = cugraph.dask.louvain(_G)\n", + " print (f'modularity: {modularity}')\n", + " else:\n", + " _,_ = cugraph.louvain(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Triangle Counting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_tc(_G):\n", + " t1 = perf_counter()\n", + " nx_count = nx.triangles(_G)\n", + "\n", + " # To get the number of triangles, we would need to loop through the array and add up each count\n", + " count = 0\n", + " for key, value in nx_count.items():\n", + " count = count + value\n", + " \n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_tc(_G, mg=False):\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _ = cugraph.dask.triangle_count(_G)\n", + " else:\n", + " _ = cugraph.triangle_count(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Core Number" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_core_num(_G):\n", + " t1 = perf_counter()\n", + " _G.remove_edges_from(nx.selfloop_edges(_G))\n", + " nx_count = nx.core_number(_G)\n", + " \n", + " count = 0\n", + " for key, value in nx_count.items():\n", + " count = count + value\n", + " \n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_core_num(_G, mg=False):\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _ = cugraph.dask.core_number(_G)\n", + " else:\n", + " _ = cugraph.core_number(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PageRank" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_pagerank(_G):\n", + " t1 = perf_counter()\n", + " _ = nx.pagerank(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2 \n", + "\n", + "def cu_pagerank(_G, mg=False):\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _ = cugraph.dask.pagerank(_G)\n", + " else:\n", + " _ = cugraph.pagerank(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Jaccard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_jaccard(_G):\n", + " t1 = perf_counter()\n", + " nj = nx.jaccard_coefficient(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_jaccard(_G, mg=False):\n", + " t1 = perf_counter()\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _ = cugraph.dask.jaccard(_G)\n", + " else:\n", + " _ = cugraph.jaccard_coefficient(_G)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BFS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_bfs(_G, seed):\n", + " t1 = perf_counter()\n", + " nb = nx.bfs_edges(_G, seed)\n", + " nb_list = list(nb) # gen -> list\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_bfs(_G, seed=0, mg=False):\n", + " t1 = perf_counter()\n", + " if mg:\n", + " _ = cugraph.dask.bfs(_G, seed)\n", + " else:\n", + " _ = cugraph.bfs(_G, seed)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SSSP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nx_sssp(_G, seed):\n", + " t1 = perf_counter()\n", + " _ = nx.shortest_path(_G, seed)\n", + " t2 = perf_counter() - t1\n", + " return t2\n", + "\n", + "def cu_sssp(_G, seed = 0, mg=False):\n", + " \n", + " t1 = perf_counter()\n", + " # SSSP requires weighted graph\n", + " if mg:\n", + " _ = cugraph.dask.bfs(_G, seed)\n", + " else:\n", + " _ = cugraph.bfs(_G, seed)\n", + " t2 = perf_counter() - t1\n", + " return t2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MG Benchmark" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize multi-GPU environment\n", + "Before we get started, we need to set up a dask (local) cluster of workers to execute our work, and a client to coordinate and schedule work for that cluster.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup a local dask cluster of workers, and a client\n", + "cluster = LocalCUDACluster()\n", + "client = Client(cluster)\n", + "Comms.initialize(p2p=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Placeholders to collect execution run statistics " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "nx_algo_run_times = defaultdict(defaultdict)\n", + "cugraph_algo_run_times = defaultdict(defaultdict)\n", + "perf_algos = defaultdict(defaultdict)\n", + "perf = defaultdict(defaultdict)\n", + "cugraph_graph_creation_times = defaultdict()\n", + "nx_graph_creation_times = defaultdict()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run NX and cuGraph algorithms for all datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "for dataset, scale in data.items():\n", + " \n", + " # generate data\n", + " print(\"------------------------------\")\n", + " print(f'Creating Graph of Scale = {scale}')\n", + " \n", + " gdf = generate_data(scale, edgefactor=16, mg=True)\n", + " gdf = gdf.repartition(gdf.npartitions * 3)\n", + "\n", + " # Copy data to host to create NX graph\n", + " pdf = pd.DataFrame(columns=['src', 'dst'])\n", + " for part_idx in range(gdf.npartitions):\n", + " computed_df = gdf.partitions[part_idx].compute().to_pandas()\n", + " pdf = pd.concat([pdf, computed_df], ignore_index=True, sort=False)\n", + "\n", + " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", + " \n", + " # create cuGraph and NX graphs\n", + " g_cu, tcu = create_cu_graph(gdf, mg=True)\n", + " g_nx, tnx = create_nx_graph(pdf)\n", + " cugraph_graph_creation_times[dataset] = tcu\n", + " nx_graph_creation_times[dataset] = tnx\n", + " del gdf, pdf\n", + "\n", + " # prep\n", + " deg = g_cu.degree()\n", + " deg_max = deg['degree'].max().compute()\n", + " alpha = 1 / deg_max\n", + " num_nodes = g_cu.number_of_vertices()\n", + " del deg\n", + " gc.collect()\n", + "\n", + " #-- Katz \n", + " algorithm = \"Katz\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " print(\"n.\", end='')\n", + " tx = nx_katz(g_nx, alpha)\n", + " print(\"c.\", end='')\n", + " tc = cu_katz(g_cu, alpha, mg=True)\n", + " print(\"\")\n", + " \n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " #-- BC\n", + " algorithm = \"BC\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " k = 100\n", + " if k > num_nodes:\n", + " k = int(num_nodes)\n", + " print(\"n.\", end='')\n", + " tx = nx_bc(g_nx, k)\n", + " print(\"c.\", end='')\n", + " tc = cu_bc(g_cu, k, mg=True)\n", + " print(\" \")\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " #-- Louvain\n", + " algorithm = \"Louvain\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " print(\"n.\", end='')\n", + " tx = nx_louvain(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_louvain(g_cu, mg=True)\n", + " print(\" \")\n", + "\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " #-- TC\n", + " algorithm = \"TC\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " print(\"n.\", end='')\n", + " tx = nx_tc(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_tc(g_cu, mg=True)\n", + " print(\" \")\n", + " \n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " #-- Core Number\n", + " algorithm = \"Core Number\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " print(\"n.\", end='')\n", + " tx = nx_core_num(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_core_num(g_cu, mg=True)\n", + " print(\" \")\n", + "\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " #-- PageRank\n", + " algorithm = \"PageRank\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " print(\"n.\", end='')\n", + " tx = nx_pagerank(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_pagerank(g_cu, mg=True)\n", + " print(\" \")\n", + "\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " #-- Jaccard\n", + " algorithm = \"Jaccard\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + "\n", + " print(\"n.\", end='')\n", + " tx = nx_jaccard(g_nx)\n", + " print(\"c.\", end='')\n", + " tc = cu_jaccard(g_cu, mg=True)\n", + " print(\" \")\n", + "\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " # Seed for BFS and SSSP\n", + " nx_seed = list(g_nx.nodes)[0]\n", + " cu_seed = g_cu.nodes().compute().to_pandas().iloc[0]\n", + "\n", + " #-- BFS\n", + " algorithm = \"BFS\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " print(\"n.\", end='')\n", + " tx = nx_bfs(g_nx, nx_seed)\n", + " print(\"c.\", end='')\n", + " tc = cu_bfs(g_cu, seed = cu_seed, mg=True)\n", + " print(\" \")\n", + "\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " #-- SSSP\n", + " algorithm = \"SSSP\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", + " print(\"n.\", end='')\n", + " tx = nx_sssp(g_nx, nx_seed)\n", + "\n", + " print(\"c.\", end='')\n", + " tc = cu_sssp(g_cu, seed = cu_seed, mg=True)\n", + " print(\" \")\n", + "\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", + "\n", + " del g_cu, g_nx\n", + " gc.collect()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### cuGraph speedup of different algorithms w.r.t. NX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX)------\\n\")\n", + "print(pd.DataFrame(perf))\n", + "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX, excluding graph creation time)------\\n\")\n", + "print(pd.DataFrame(perf_algos))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Nx and cuGraph execution times for different algorithms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "nx_and_cugraph_run_times = pd.DataFrame()\n", + "for dataset in cugraph_algo_run_times.keys():\n", + " temp_df = pd.DataFrame({'NX': nx_algo_run_times[dataset], 'cuGraph': cugraph_algo_run_times[dataset]})\n", + " columns = [(dataset, 'cuGraph'), (dataset, 'NX')]\n", + " temp_df.columns = pd.MultiIndex.from_tuples(columns)\n", + " nx_and_cugraph_run_times = pd.concat([temp_df, nx_and_cugraph_run_times], axis=1)\n", + "\n", + "print(\"\\n\\t------Nx and cuGraph execution times for different algorithms-----\\n\")\n", + "print(nx_and_cugraph_run_times)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean up multi-GPU environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Comms.destroy()\n", + "client.close()\n", + "cluster.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", + "___" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cudfdev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.1.0" + }, + "vscode": { + "interpreter": { + "hash": "587ff963ecd34554a9da41c94362e2baa062d9a57502e220f049e10816826984" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 108c36ebdc352cdae3ef4d5d8ec547f72a357322 Mon Sep 17 00:00:00 2001 From: James Lamb Date: Thu, 15 Feb 2024 05:30:26 -0600 Subject: [PATCH 320/384] Support CUDA 12.2 (#4088) * switches to CUDA 12.2.2 for building conda packages and wheels * adds new tests running against CUDA 12.2.2 ### Notes for Reviewers This is part of ongoing work to build and test packages against CUDA 12.2.2 across all of RAPIDS. For more details see: * https://github.com/rapidsai/build-planning/issues/7 * https://github.com/rapidsai/shared-workflows/pull/166 Planning a second round of PRs to revert these references back to a proper `branch-24.{nn}` release branch of `shared-workflows` once https://github.com/rapidsai/shared-workflows/pull/166 is merged. *(created with `rapids-reviser`)* Authors: - James Lamb (https://github.com/jameslamb) - Bradley Dice (https://github.com/bdice) - Vyas Ramasubramani (https://github.com/vyasr) - https://github.com/jakirkham Approvers: - Jake Awe (https://github.com/AyodeAwe) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/4088 --- docs/cugraph/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 141e14a04..719f94d48 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018-2023, NVIDIA CORPORATION. +# Copyright (c) 2018-2024, NVIDIA CORPORATION. # # pygdf documentation build configuration file, created by # sphinx-quickstart on Wed May 3 10:59:22 2017. From 62a2e24461b975de9ae55ae247ab232d07bd62a8 Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Thu, 15 Feb 2024 14:47:30 +0100 Subject: [PATCH 321/384] Update SG notebook (#4169) Update SG notebook to handle a handle a couple of cases where the current notebook would fail for unweighted graph and if the graph doesn't have node id 0. **Changes for SG notebook:** - For graphs without weights, run bfs instead of sssp. - For the input graph doesn't have node with id 0, peek an existing node id as seed for BFS and SSP - simplify bookkeeping for run statistics **Changes for MG notebook:** - Call SSSP or BFS based on weighted attribute Authors: - Naim (https://github.com/naimnv) Approvers: - Brad Rees (https://github.com/BradReesWork) - Don Acosta (https://github.com/acostadon) URL: https://github.com/rapidsai/cugraph/pull/4169 --- ...e.ipynb => synth_release_single_gpu.ipynb} | 276 ++++++++---------- .../synth_release_single_node_multi_gpu.ipynb | 43 +-- 2 files changed, 154 insertions(+), 165 deletions(-) rename notebooks/cugraph_benchmarks/{synth_release.ipynb => synth_release_single_gpu.ipynb} (77%) diff --git a/notebooks/cugraph_benchmarks/synth_release.ipynb b/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb similarity index 77% rename from notebooks/cugraph_benchmarks/synth_release.ipynb rename to notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb index 18979f3ec..1acef5d55 100644 --- a/notebooks/cugraph_benchmarks/synth_release.ipynb +++ b/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb @@ -40,7 +40,8 @@ "| Author | Date | Update | cuGraph Version | Test Hardware |\n", "| --------------|------------|---------------------|-----------------|------------------------|\n", "| Don Acosta | 1/12/2023 | Created | 23.02 nightly | RTX A6000, CUDA 11.7 |\n", - "| Brad Rees | 1/27/2023 | Modified | 23.02 nightly | RTX A6000, CUDA 11.7 |\n" + "| Brad Rees | 1/27/2023 | Modified | 23.02 nightly | RTX A6000, CUDA 11.7 |\n", + "| Naim, Md | 2/12/2024 | Modified | 24.04 nightly | RTX A6000, CUDA 12.0 |\n" ] }, { @@ -124,12 +125,11 @@ "import gc\n", "import os\n", "from time import perf_counter\n", - "import numpy as np\n", - "import math\n", + "import pandas as pd\n", + "from collections import defaultdict\n", "\n", "# rapids\n", "import cugraph\n", - "import cudf\n", "\n", "# NetworkX libraries\n", "import networkx as nx\n", @@ -212,7 +212,7 @@ "\n", "\n", "# Which dataset is to be used\n", - "data = data_full\n" + "data = data_quick\n" ] }, { @@ -518,16 +518,13 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_bfs(_G):\n", - " seed = 0\n", + "def nx_bfs(_G, seed):\n", " t1 = perf_counter()\n", - " nb = nx.bfs_edges(_G, seed)\n", - " nb_list = list(nb) # gen -> list\n", + " _ = nx.bfs_edges(_G, seed)\n", " t2 = perf_counter() - t1\n", " return t2\n", "\n", - "def cu_bfs(_G):\n", - " seed = 0\n", + "def cu_bfs(_G, seed):\n", " t1 = perf_counter()\n", " _ = cugraph.bfs(_G, seed)\n", " t2 = perf_counter() - t1\n", @@ -547,17 +544,21 @@ "metadata": {}, "outputs": [], "source": [ - "def nx_sssp(_G):\n", - " seed = 0\n", + "def nx_sssp(_G, seed):\n", " t1 = perf_counter()\n", - " _ = nx.shortest_path(_G, seed)\n", + " if nx.is_weighted(_G):\n", + " _ = nx.shortest_path(_G, seed)\n", + " else:\n", + " _ = nx.bfs_edges(_G, seed)\n", " t2 = perf_counter() - t1\n", " return t2\n", "\n", - "def cu_sssp(_G):\n", - " seed = 0\n", + "def cu_sssp(_G, seed):\n", " t1 = perf_counter()\n", - " _ = cugraph.sssp(_G, seed)\n", + " if _G.weighted:\n", + " _ = cugraph.sssp(_G, seed)\n", + " else:\n", + " _ = cugraph.bfs(_G, seed)\n", " t2 = perf_counter() - t1\n", " return t2\n" ] @@ -571,6 +572,27 @@ "# Benchmark" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Placeholders to collect algorithm run statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cugraph_algo_run_times = defaultdict(defaultdict)\n", + "nx_algo_run_times = defaultdict(defaultdict)\n", + "cugraph_graph_creation_times = defaultdict()\n", + "nx_graph_creation_times = defaultdict()\n", + "perf_algos = defaultdict(defaultdict)\n", + "perf = defaultdict(defaultdict)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -587,79 +609,50 @@ "metadata": {}, "outputs": [], "source": [ - "# arrays to capture performance gains\n", - "names = []\n", - "algos = []\n", - "graph_create_cu = []\n", - "graph_create_nx = []\n", - "\n", - "# Two dimension data [file, perf]\n", - "time_algo_nx = [] # NetworkX\n", - "time_algo_cu = [] # cuGraph\n", - "perf = []\n", - "perf_algo = []\n", "\n", - "algos.append(\" \")\n", - "\n", - "i = 0\n", - "for k,v in data.items():\n", - " # init all the 2-d arrays\n", - " time_algo_nx.append([])\n", - " time_algo_cu.append([])\n", - " perf.append([])\n", - " perf_algo.append([])\n", - "\n", - " # Saved the file Name\n", - " names.append(k)\n", + "for dataset, scale in data.items():\n", "\n", " # generate data\n", " print(\"------------------------------\")\n", - " print(f'Creating Graph of Scale = {v}')\n", - "\n", - " gdf = generate_data(v)\n", + " print(f'Creating Graph of Scale = {scale}')\n", + " \n", + " gdf = generate_data(scale)\n", " pdf = gdf.to_pandas()\n", - " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", "\n", - " # create the graphs\n", + " print(f\"\\tdata in gdf {len(gdf)} and data in pandas {len(pdf)}\")\n", + " \n", + " # create cuGraph and NX graphs\n", " g_cu, tcu = create_cu_graph(gdf)\n", " g_nx, tnx = create_nx_graph(pdf)\n", - " graph_create_cu.append(tcu)\n", - " graph_create_nx.append(tnx)\n", + " cugraph_graph_creation_times[dataset] = tcu\n", + " nx_graph_creation_times[dataset] = tnx\n", " del gdf, pdf\n", "\n", " # prep\n", " deg = g_cu.degree()\n", " deg_max = deg['degree'].max()\n", - "\n", " alpha = 1 / deg_max\n", " num_nodes = g_cu.number_of_vertices()\n", - "\n", " del deg\n", " gc.collect()\n", "\n", - " #----- Algorithm order is same as defined at top ----\n", - "\n", " #-- Katz \n", - " print(\"\\tKatz \", end = '')\n", - " if i == 0: \n", - " algos.append(\"Katz\")\n", - "\n", + " algorithm = \"Katz\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", " tx = nx_katz(g_nx, alpha)\n", " print(\"c.\", end='')\n", " tc = cu_katz(g_cu, alpha)\n", " print(\"\")\n", - "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " \n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", " #-- BC\n", - " print(\"\\tBC k=100 \", end='')\n", - " if i == 0:\n", - " algos.append(\"BC Estimate fixed\")\n", - "\n", + " algorithm = \"BC\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " k = 100\n", " if k > num_nodes:\n", " k = int(num_nodes)\n", @@ -668,80 +661,70 @@ " print(\"c.\", end='')\n", " tc = cu_bc(g_cu, k)\n", " print(\" \")\n", - "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", " #-- Louvain\n", - " print(\"\\tLouvain \", end='')\n", - " if i == 0:\n", - " algos.append(\"Louvain\")\n", - "\n", + " algorithm = \"Louvain\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", " tx = nx_louvain(g_nx)\n", " print(\"c.\", end='')\n", " tc = cu_louvain(g_cu)\n", " print(\" \")\n", "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", " #-- TC\n", - " print(\"\\tTC \", end='')\n", - " if i == 0:\n", - " algos.append(\"TC\")\n", - "\n", + " algorithm = \"TC\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", " tx = nx_tc(g_nx)\n", " print(\"c.\", end='')\n", " tc = cu_tc(g_cu)\n", " print(\" \")\n", - "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " \n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", " #-- Core Number\n", - " print(\"\\tCore Number \", end='')\n", - " if i == 0:\n", - " algos.append(\"Core Number\")\n", - "\n", + " algorithm = \"Core Number\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", " tx = nx_core_num(g_nx)\n", " print(\"c.\", end='')\n", " tc = cu_core_num(g_cu)\n", " print(\" \")\n", "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", " #-- PageRank\n", - " print(\"\\tPageRank \", end='')\n", - " if i == 0:\n", - " algos.append(\"PageRank\")\n", - "\n", + " algorithm = \"PageRank\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", " tx = nx_pagerank(g_nx)\n", " print(\"c.\", end='')\n", " tc = cu_pagerank(g_cu)\n", " print(\" \")\n", "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", " #-- Jaccard\n", - " print(\"\\tJaccard \", end='')\n", - " if i == 0:\n", - " algos.append(\"Jaccard\")\n", + " algorithm = \"Jaccard\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", "\n", " print(\"n.\", end='')\n", " tx = nx_jaccard(g_nx)\n", @@ -749,46 +732,44 @@ " tc = cu_jaccard(g_cu)\n", " print(\" \")\n", "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", - " #-- BFS\n", - " print(\"\\tBFS \", end='')\n", - " if i == 0:\n", - " algos.append(\"BFS\")\n", + " # Seed for BFS and SSSP\n", + " nx_seed = list(g_nx.nodes)[0]\n", + " cu_seed = g_cu.nodes().to_pandas().iloc[0]\n", "\n", + " #-- BFS\n", + " algorithm = \"BFS\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", - " tx = nx_bfs(g_nx)\n", + " tx = nx_bfs(g_nx, seed=nx_seed)\n", " print(\"c.\", end='')\n", - " tc = cu_bfs(g_cu)\n", + " tc = cu_bfs(g_cu, seed=cu_seed)\n", " print(\" \")\n", "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", " #-- SSSP\n", - " print(\"\\tSSSP \", end='')\n", - " if i == 0:\n", - " algos.append(\"SSP\")\n", - "\n", + " algorithm = \"SSSP\"\n", + " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", - " tx = nx_sssp(g_nx)\n", + " tx = nx_sssp(g_nx, seed=nx_seed)\n", + "\n", " print(\"c.\", end='')\n", - " tc = cu_sssp(g_cu)\n", + " tc = cu_sssp(g_cu, seed=cu_seed)\n", " print(\" \")\n", "\n", - " time_algo_nx[i].append(tx)\n", - " time_algo_cu[i].append(tc)\n", - " perf_algo[i].append ( (tx/tc) )\n", - " perf[i].append( (tx + tnx) / (tc + tcu) )\n", + " nx_algo_run_times[dataset][algorithm] = tx\n", + " cugraph_algo_run_times[dataset][algorithm] = tc\n", + " perf_algos[dataset][algorithm] = tx/tc \n", + " perf[dataset][algorithm] = (tx + tnx) / (tc + tcu)\n", "\n", - " # increament count\n", - " i = i + 1\n", - " \n", " del g_cu, g_nx\n", " gc.collect()\n" ] @@ -799,13 +780,11 @@ "metadata": {}, "outputs": [], "source": [ - "#Print results\n", - "print(algos)\n", - "\n", - "for i in range(num_datasets):\n", - " print(f\"{names[i]}\")\n", - " print(f\"{perf[i]}\")\n", - " print(f\"{perf_algo[i]}\")" + "# Speedup\n", + "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX)------\\n\")\n", + "print(pd.DataFrame(perf))\n", + "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX, excluding graph creation time)------\\n\")\n", + "print(pd.DataFrame(perf_algos))" ] }, { @@ -814,15 +793,16 @@ "metadata": {}, "outputs": [], "source": [ - "#Print results\n", - "print(\"\\n------------------------------\")\n", - "print(\"\\tAlgorithm Run times (NX then cuGraph)\\n\")\n", + "# Nx and cuGraph execution times for different algorithms\n", + "nx_and_cugraph_run_times = pd.DataFrame()\n", + "for dataset in cugraph_algo_run_times.keys():\n", + " temp_df = pd.DataFrame({'NX': nx_algo_run_times[dataset], 'cuGraph': cugraph_algo_run_times[dataset]})\n", + " columns = [(dataset, 'cuGraph'), (dataset, 'NX')]\n", + " temp_df.columns = pd.MultiIndex.from_tuples(columns)\n", + " nx_and_cugraph_run_times = pd.concat([temp_df, nx_and_cugraph_run_times], axis=1)\n", "\n", - "print(algos)\n", - "for i in range(num_datasets):\n", - " print(f\"{names[i]}\")\n", - " print(f\"{time_algo_nx[i]}\")\n", - " print(f\"{time_algo_cu[i]}\")" + "print(\"\\n\\t------cuGraph and NX execution times for different algorithms-----\\n\")\n", + "print(nx_and_cugraph_run_times)" ] }, { @@ -855,7 +835,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 15:55:03) \n[GCC 10.4.0]" + "version": "3.1.0" }, "vscode": { "interpreter": { diff --git a/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb b/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb index c44f475c4..dd0983016 100644 --- a/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb +++ b/notebooks/cugraph_benchmarks/synth_release_single_node_multi_gpu.ipynb @@ -39,9 +39,9 @@ " \n", "| Author | Date | Update | cuGraph Version | Test Hardware |\n", "| --------------|------------|---------------------|-----------------|------------------------|\n", - "| Don Acosta | 1/12/2023 | Created | 23.02 nightly | RTX A6000, CUDA 11.7 |\n", - "| Brad Rees | 1/27/2023 | Modified | 23.02 nightly | RTX A6000, CUDA 11.7 |\n", - "| Naim, Md | 2/08/2024 | Modified for SNMG | 24.04 nightly | RTX A6000, CUDA 12.0 |\n" + "| Don Acosta | 1/12/2023 | Created | 23.02 nightly | 2xRTX A6000, CUDA 11.7 |\n", + "| Brad Rees | 1/27/2023 | Modified | 23.02 nightly | 2xRTX A6000, CUDA 11.7 |\n", + "| Naim, Md | 2/08/2024 | Modified for SNMG | 24.04 nightly | 2xRTX A6000, CUDA 12.0 |\n" ] }, { @@ -590,7 +590,10 @@ "source": [ "def nx_sssp(_G, seed):\n", " t1 = perf_counter()\n", - " _ = nx.shortest_path(_G, seed)\n", + " if nx.is_weighted(_G):\n", + " _ = nx.shortest_path(_G, seed)\n", + " else:\n", + " _ = nx.bfs_edges(_G, seed)\n", " t2 = perf_counter() - t1\n", " return t2\n", "\n", @@ -599,9 +602,17 @@ " t1 = perf_counter()\n", " # SSSP requires weighted graph\n", " if mg:\n", - " _ = cugraph.dask.bfs(_G, seed)\n", + " if _G.weighted: \n", + " _ = cugraph.dask.sssp(_G, seed)\n", + " else:\n", + " _ = cugraph.dask.bfs(_G, seed)\n", + "\n", " else:\n", - " _ = cugraph.bfs(_G, seed)\n", + " if _G.weighted:\n", + " _ = cugraph.ssp(_G, seed)\n", + " else:\n", + " _ = cugraph.bfs(_G, seed)\n", + "\n", " t2 = perf_counter() - t1\n", " return t2\n" ] @@ -646,14 +657,12 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", - "nx_algo_run_times = defaultdict(defaultdict)\n", "cugraph_algo_run_times = defaultdict(defaultdict)\n", - "perf_algos = defaultdict(defaultdict)\n", - "perf = defaultdict(defaultdict)\n", + "nx_algo_run_times = defaultdict(defaultdict)\n", "cugraph_graph_creation_times = defaultdict()\n", - "nx_graph_creation_times = defaultdict()\n" + "nx_graph_creation_times = defaultdict()\n", + "perf_algos = defaultdict(defaultdict)\n", + "perf = defaultdict(defaultdict)" ] }, { @@ -811,9 +820,9 @@ " algorithm = \"BFS\"\n", " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", - " tx = nx_bfs(g_nx, nx_seed)\n", + " tx = nx_bfs(g_nx, seed=nx_seed)\n", " print(\"c.\", end='')\n", - " tc = cu_bfs(g_cu, seed = cu_seed, mg=True)\n", + " tc = cu_bfs(g_cu, seed=cu_seed, mg=True)\n", " print(\" \")\n", "\n", " nx_algo_run_times[dataset][algorithm] = tx\n", @@ -825,10 +834,10 @@ " algorithm = \"SSSP\"\n", " print(f\"\\t{algorithm} \", end = '')\n", " print(\"n.\", end='')\n", - " tx = nx_sssp(g_nx, nx_seed)\n", + " tx = nx_sssp(g_nx, seed=nx_seed)\n", "\n", " print(\"c.\", end='')\n", - " tc = cu_sssp(g_cu, seed = cu_seed, mg=True)\n", + " tc = cu_sssp(g_cu, seed=cu_seed, mg=True)\n", " print(\" \")\n", "\n", " nx_algo_run_times[dataset][algorithm] = tx\n", @@ -856,7 +865,7 @@ "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX)------\\n\")\n", "print(pd.DataFrame(perf))\n", "print(\"\\n\\t------Speedup (cuGraph w.r.t. NX, excluding graph creation time)------\\n\")\n", - "print(pd.DataFrame(perf_algos))\n" + "print(pd.DataFrame(perf_algos))" ] }, { From dfdca8130b4eea35b567f1e5b6f5715926f280ef Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Sun, 25 Feb 2024 18:06:16 -0600 Subject: [PATCH 322/384] Update `louvain_communities` to match NetworkX 3.3 (added max_level) (#4177) We already supported `max_level=`, and this was just upstreamed to networkx here: https://github.com/networkx/networkx/pull/6909 Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4177 --- python/nx-cugraph/_nx_cugraph/__init__.py | 1 - .../algorithms/community/louvain.py | 19 ++++++++++++++----- python/nx-cugraph/nx_cugraph/interface.py | 6 ++++++ .../nx_cugraph/tests/test_match_api.py | 10 +++++++++- 4 files changed, 29 insertions(+), 7 deletions(-) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index a57c43944..64614fa39 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -164,7 +164,6 @@ }, "louvain_communities": { "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", - "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).", }, "pagerank": { "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index f58f1000f..f7638f47a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -12,6 +12,7 @@ # limitations under the License. import warnings +import networkx as nx import pylibcugraph as plc from nx_cugraph.convert import _to_undirected_graph @@ -25,13 +26,21 @@ __all__ = ["louvain_communities"] +# max_level argument was added to NetworkX 3.3 +if nx.__version__[:3] <= "3.2": + _max_level_param = { + "max_level : int, optional": ( + "Upper limit of the number of macro-iterations (max: 500)." + ) + } +else: + _max_level_param = {} + @not_implemented_for("directed") @networkx_algorithm( extra_params={ - "max_level : int, optional": ( - "Upper limit of the number of macro-iterations (max: 500)." - ), + **_max_level_param, **_dtype_param, }, is_incomplete=True, # seed not supported; self-loops not supported @@ -44,9 +53,9 @@ def louvain_communities( weight="weight", resolution=1, threshold=0.0000001, + max_level=None, seed=None, *, - max_level=None, dtype=None, ): """`seed` parameter is currently ignored, and self-loops are not yet supported.""" @@ -82,9 +91,9 @@ def _( weight="weight", resolution=1, threshold=0.0000001, + max_level=None, seed=None, *, - max_level=None, dtype=None, ): # NetworkX allows both directed and undirected, but cugraph only allows undirected. diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 46ea5831b..3c62fc362 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -257,6 +257,12 @@ def key(testpath): ): different_iteration_order, } ) + elif nxver.minor >= 3: + xfail.update( + { + key("test_louvain.py:test_max_level"): louvain_different, + } + ) too_slow = "Too slow to run" skip = { diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py index a654ff343..595f7819a 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -14,10 +14,13 @@ import inspect import networkx as nx +from packaging.version import parse import nx_cugraph as nxcg from nx_cugraph.utils import networkx_algorithm +nxver = parse(nx.__version__) + def test_match_signature_and_names(): """Simple test to ensure our signatures and basic module layout match networkx.""" @@ -41,6 +44,11 @@ def test_match_signature_and_names(): else: orig_func = dispatchable_func.orig_func + if nxver.major == 3 and nxver.minor <= 2 and name == "louvain_communities": + # The signature of louvain_communities changed in NetworkX 3.3, and + # we updated to match, so we skip this check in older versions. + continue + # Matching signatures? orig_sig = inspect.signature(orig_func) func_sig = inspect.signature(func) From 4f4f761ec4aa0fa48b9b964c25e2f2c8b4914a8a Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 26 Feb 2024 14:43:25 -0600 Subject: [PATCH 323/384] nx-cugraph: update `get_info` function keys to NetworkX 3.3 (#4192) Updating to match changes from https://github.com/networkx/networkx/pull/7219 Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4192 --- python/nx-cugraph/_nx_cugraph/__init__.py | 32 +++++++++++++---------- python/nx-cugraph/_nx_cugraph/core.py | 24 ++++++++--------- 2 files changed, 30 insertions(+), 26 deletions(-) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 64614fa39..c6c43110a 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -126,8 +126,8 @@ "wheel_graph", # END: functions }, - "extra_docstrings": { - # BEGIN: extra_docstrings + "additional_docs": { + # BEGIN: additional_docs "average_clustering": "Directed graphs and `weight` parameter are not yet supported.", "betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.", "bfs_edges": "`sort_neighbors` parameter is not yet supported.", @@ -148,10 +148,10 @@ "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.", "pagerank": "`dangling` parameter is not supported, but it is checked for validity.", "transitivity": "Directed graphs are not yet supported.", - # END: extra_docstrings + # END: additional_docs }, - "extra_parameters": { - # BEGIN: extra_parameters + "additional_parameters": { + # BEGIN: additional_parameters "eigenvector_centrality": { "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", }, @@ -168,7 +168,7 @@ "pagerank": { "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", }, - # END: extra_parameters + # END: additional_parameters }, } @@ -178,20 +178,24 @@ def get_info(): This tells NetworkX about the cugraph backend without importing nx_cugraph. """ - # Convert to e.g. `{"functions": {"myfunc": {"extra_docstring": ...}}}` + # Convert to e.g. `{"functions": {"myfunc": {"additional_docs": ...}}}` d = _info.copy() - info_keys = { - "extra_docstrings": "extra_docstring", - "extra_parameters": "extra_parameters", - } + info_keys = {"additional_docs", "additional_parameters"} d["functions"] = { func: { - new_key: vals[func] - for old_key, new_key in info_keys.items() - if func in (vals := d[old_key]) + info_key: vals[func] + for info_key in info_keys + if func in (vals := d[info_key]) } for func in d["functions"] } + # Add keys for Networkx <3.3 + for func_info in d["functions"].values(): + if "additional_docs" in func_info: + func_info["extra_docstring"] = func_info["additional_docs"] + if "additional_parameters" in func_info: + func_info["extra_parameters"] = func_info["additional_parameters"] + for key in info_keys: del d[key] return d diff --git a/python/nx-cugraph/_nx_cugraph/core.py b/python/nx-cugraph/_nx_cugraph/core.py index 72f920389..c4de197f3 100644 --- a/python/nx-cugraph/_nx_cugraph/core.py +++ b/python/nx-cugraph/_nx_cugraph/core.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -24,13 +24,13 @@ def get_functions(): } -def get_extra_docstrings(functions=None): +def get_additional_docs(functions=None): if functions is None: functions = get_functions() return {key: val.extra_doc for key, val in functions.items() if val.extra_doc} -def get_extra_parameters(functions=None): +def get_additional_parameters(functions=None): if functions is None: functions = get_functions() return {key: val.extra_params for key, val in functions.items() if val.extra_params} @@ -73,18 +73,18 @@ def main(filepath): to_add = [f'"{name}",' for name in sorted(functions)] text = update_text(text, to_add, "functions") - # Update extra_docstrings - extra_docstrings = get_extra_docstrings(functions) - to_add = list(dict_to_lines(extra_docstrings)) - text = update_text(text, to_add, "extra_docstrings") + # Update additional_docs + additional_docs = get_additional_docs(functions) + to_add = list(dict_to_lines(additional_docs)) + text = update_text(text, to_add, "additional_docs") - # Update extra_parameters - extra_parameters = get_extra_parameters(functions) + # Update additional_parameters + additional_parameters = get_additional_parameters(functions) to_add = [] - for name in sorted(extra_parameters): - params = extra_parameters[name] + for name in sorted(additional_parameters): + params = additional_parameters[name] to_add.append(f"{name!r}: {{") to_add.extend(dict_to_lines(params, indent=" " * 4)) to_add.append("},") - text = update_text(text, to_add, "extra_parameters") + text = update_text(text, to_add, "additional_parameters") return text From ec8ddd09edb1ea15729d74e82e6b5b73429f268d Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 27 Feb 2024 13:53:26 -0600 Subject: [PATCH 324/384] nx-cugraph: automatically generate trees in README.md (#4156) This updates how we create trees. Also, CI now tests that auto-generated files are up-to-date (not updating these has gotten me a couple of times). Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Jake Awe (https://github.com/AyodeAwe) URL: https://github.com/rapidsai/cugraph/pull/4156 --- .gitignore | 2 + .pre-commit-config.yaml | 3 +- python/nx-cugraph/Makefile | 13 +- python/nx-cugraph/README.md | 270 +++++++++--------- python/nx-cugraph/lint.yaml | 12 +- .../algorithms/link_analysis/hits_alg.py | 6 +- .../algorithms/link_analysis/pagerank_alg.py | 6 +- python/nx-cugraph/nx_cugraph/classes/graph.py | 3 +- .../nx_cugraph/classes/multigraph.py | 4 +- python/nx-cugraph/nx_cugraph/interface.py | 6 +- .../nx_cugraph/scripts/print_table.py | 3 +- .../nx_cugraph/scripts/print_tree.py | 122 +++++--- python/nx-cugraph/pyproject.toml | 13 +- python/nx-cugraph/scripts/update_readme.py | 203 +++++++++++++ 14 files changed, 453 insertions(+), 213 deletions(-) create mode 100644 python/nx-cugraph/scripts/update_readme.py diff --git a/.gitignore b/.gitignore index 358650cfc..2fea10229 100644 --- a/.gitignore +++ b/.gitignore @@ -78,6 +78,8 @@ datasets/* !datasets/karate-disjoint.csv !datasets/netscience.csv +# nx-cugraph side effects +python/nx-cugraph/objects.inv .pydevproject diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 188ea1a26..6b7ff1441 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,8 +15,9 @@ repos: hooks: - id: black language_version: python3 - args: [--target-version=py38] + args: [--target-version=py39] files: ^(python/.*|benchmarks/.*)$ + exclude: ^python/nx-cugraph/ - repo: https://github.com/PyCQA/flake8 rev: 6.0.0 hooks: diff --git a/python/nx-cugraph/Makefile b/python/nx-cugraph/Makefile index 6e1b98ee6..6500d834e 100644 --- a/python/nx-cugraph/Makefile +++ b/python/nx-cugraph/Makefile @@ -1,12 +1,12 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. +# Copyright (c) 2023-2024, NVIDIA CORPORATION. SHELL= /bin/bash .PHONY: all -all: plugin-info lint +all: plugin-info lint readme .PHONY: lint lint: - git ls-files | xargs pre-commit run --config lint.yaml --files + git ls-files | xargs pre-commit run --config lint.yaml --files || true .PHONY: lint-update lint-update: @@ -15,3 +15,10 @@ lint-update: .PHONY: plugin-info plugin-info: python _nx_cugraph/__init__.py + +objects.inv: + wget https://networkx.org/documentation/stable/objects.inv + +.PHONY: readme +readme: objects.inv + python scripts/update_readme.py README.md objects.inv diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index 5d0554734..8201dc34e 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -91,144 +91,144 @@ familiar and easy-to-use API. Below is the list of algorithms that are currently supported in nx-cugraph. -### Algorithms - -``` -bipartite - ├─ basic - │ └─ is_bipartite - └─ generators - └─ complete_bipartite_graph -centrality - ├─ betweenness - │ ├─ betweenness_centrality - │ └─ edge_betweenness_centrality - ├─ degree_alg - │ ├─ degree_centrality - │ ├─ in_degree_centrality - │ └─ out_degree_centrality - ├─ eigenvector - │ └─ eigenvector_centrality - └─ katz - └─ katz_centrality -cluster - ├─ average_clustering - ├─ clustering - ├─ transitivity - └─ triangles -community - └─ louvain - └─ louvain_communities -components - ├─ connected - │ ├─ connected_components - │ ├─ is_connected - │ ├─ node_connected_component - │ └─ number_connected_components - └─ weakly_connected - ├─ is_weakly_connected - ├─ number_weakly_connected_components - └─ weakly_connected_components -core - ├─ core_number - └─ k_truss -dag - ├─ ancestors - └─ descendants -isolate - ├─ is_isolate - ├─ isolates - └─ number_of_isolates -link_analysis - ├─ hits_alg - │ └─ hits - └─ pagerank_alg - └─ pagerank -operators - └─ unary - ├─ complement - └─ reverse -reciprocity - ├─ overall_reciprocity - └─ reciprocity -shortest_paths - └─ unweighted - ├─ single_source_shortest_path_length - └─ single_target_shortest_path_length -traversal - └─ breadth_first_search - ├─ bfs_edges - ├─ bfs_layers - ├─ bfs_predecessors - ├─ bfs_successors - ├─ bfs_tree - ├─ descendants_at_distance - └─ generic_bfs_edges -tree - └─ recognition - ├─ is_arborescence - ├─ is_branching - ├─ is_forest - └─ is_tree -``` - -### Generators - -``` -classic - ├─ barbell_graph - ├─ circular_ladder_graph - ├─ complete_graph - ├─ complete_multipartite_graph - ├─ cycle_graph - ├─ empty_graph - ├─ ladder_graph - ├─ lollipop_graph - ├─ null_graph - ├─ path_graph - ├─ star_graph - ├─ tadpole_graph - ├─ trivial_graph - ├─ turan_graph - └─ wheel_graph -community - └─ caveman_graph -small - ├─ bull_graph - ├─ chvatal_graph - ├─ cubical_graph - ├─ desargues_graph - ├─ diamond_graph - ├─ dodecahedral_graph - ├─ frucht_graph - ├─ heawood_graph - ├─ house_graph - ├─ house_x_graph - ├─ icosahedral_graph - ├─ krackhardt_kite_graph - ├─ moebius_kantor_graph - ├─ octahedral_graph - ├─ pappus_graph - ├─ petersen_graph - ├─ sedgewick_maze_graph - ├─ tetrahedral_graph - ├─ truncated_cube_graph - ├─ truncated_tetrahedron_graph - └─ tutte_graph -social - ├─ davis_southern_women_graph - ├─ florentine_families_graph - ├─ karate_club_graph - └─ les_miserables_graph -``` +### [Algorithms](https://networkx.org/documentation/latest/reference/algorithms/index.html) + + + +### [Generators](https://networkx.org/documentation/latest/reference/generators.html) + +
+classic
+ ├─ barbell_graph
+ ├─ circular_ladder_graph
+ ├─ complete_graph
+ ├─ complete_multipartite_graph
+ ├─ cycle_graph
+ ├─ empty_graph
+ ├─ ladder_graph
+ ├─ lollipop_graph
+ ├─ null_graph
+ ├─ path_graph
+ ├─ star_graph
+ ├─ tadpole_graph
+ ├─ trivial_graph
+ ├─ turan_graph
+ └─ wheel_graph
+community
+ └─ caveman_graph
+small
+ ├─ bull_graph
+ ├─ chvatal_graph
+ ├─ cubical_graph
+ ├─ desargues_graph
+ ├─ diamond_graph
+ ├─ dodecahedral_graph
+ ├─ frucht_graph
+ ├─ heawood_graph
+ ├─ house_graph
+ ├─ house_x_graph
+ ├─ icosahedral_graph
+ ├─ krackhardt_kite_graph
+ ├─ moebius_kantor_graph
+ ├─ octahedral_graph
+ ├─ pappus_graph
+ ├─ petersen_graph
+ ├─ sedgewick_maze_graph
+ ├─ tetrahedral_graph
+ ├─ truncated_cube_graph
+ ├─ truncated_tetrahedron_graph
+ └─ tutte_graph
+social
+ ├─ davis_southern_women_graph
+ ├─ florentine_families_graph
+ ├─ karate_club_graph
+ └─ les_miserables_graph
+
### Other -``` -convert_matrix - ├─ from_pandas_edgelist - └─ from_scipy_sparse_array -``` +
+convert_matrix
+ ├─ from_pandas_edgelist
+ └─ from_scipy_sparse_array
+
To request nx-cugraph backend support for a NetworkX API that is not listed above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph). diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 8e87fc235..fdd24861d 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -31,7 +31,7 @@ repos: - id: validate-pyproject name: Validate pyproject.toml - repo: https://github.com/PyCQA/autoflake - rev: v2.2.1 + rev: v2.3.0 hooks: - id: autoflake args: [--in-place] @@ -40,17 +40,17 @@ repos: hooks: - id: isort - repo: https://github.com/asottile/pyupgrade - rev: v3.15.0 + rev: v3.15.1 hooks: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.12.1 + rev: 24.2.0 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.14 + rev: v0.2.2 hooks: - id: ruff args: [--fix-only, --show-fixes] # --unsafe-fixes] @@ -62,7 +62,7 @@ repos: additional_dependencies: &flake8_dependencies # These versions need updated manually - flake8==7.0.0 - - flake8-bugbear==24.1.17 + - flake8-bugbear==24.2.6 - flake8-simplify==0.21.0 - repo: https://github.com/asottile/yesqa rev: v1.5.0 @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.14 + rev: v0.2.2 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py index 9e723624a..e61a931c0 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py @@ -66,9 +66,9 @@ def hits( resource_handle=plc.ResourceHandle(), graph=G._get_plc_graph(weight, 1, dtype, store_transposed=True), tol=tol, - initial_hubs_guess_vertices=None - if nstart is None - else cp.arange(N, dtype=index_dtype), + initial_hubs_guess_vertices=( + None if nstart is None else cp.arange(N, dtype=index_dtype) + ), initial_hubs_guess_values=nstart, max_iter=max_iter, normalized=normalized, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py index 55fcc3e52..40224e91d 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py @@ -78,9 +78,9 @@ def pagerank( "graph": G._get_plc_graph(weight, 1, dtype, store_transposed=True), "precomputed_vertex_out_weight_vertices": None, "precomputed_vertex_out_weight_sums": None, - "initial_guess_vertices": None - if nstart is None - else cp.arange(N, dtype=index_dtype), + "initial_guess_vertices": ( + None if nstart is None else cp.arange(N, dtype=index_dtype) + ), "initial_guess_values": nstart, "alpha": alpha, "epsilon": N * tol, diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 0951ee6b1..5132e6a54 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -634,8 +634,7 @@ def _get_plc_graph( "pylibcugraph only supports float16 and float32 dtypes." ) elif ( - edge_array.dtype == np.uint64 - and edge_array.max().tolist() > 2**53 + edge_array.dtype == np.uint64 and edge_array.max().tolist() > 2**53 ): raise ValueError( f"Integer value of value is too large (> 2**53): {val}; " diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py index fb787369e..de58474de 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -360,9 +360,7 @@ def get_edge_data( if k not in self.edge_masks or self.edge_masks[k][index] } return { - edge_keys[index] - if edge_keys is not None - else index: { + edge_keys[index] if edge_keys is not None else index: { k: v[index].tolist() for k, v in self.edge_values.items() if k not in self.edge_masks or self.edge_masks[k][index] diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 3c62fc362..d044ba696 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -220,9 +220,9 @@ def key(testpath): ) if sys.version_info[:2] == (3, 9): # This test is sensitive to RNG, which depends on Python version - xfail[ - key("test_louvain.py:test_threshold") - ] = "Louvain does not support seed parameter" + xfail[key("test_louvain.py:test_threshold")] = ( + "Louvain does not support seed parameter" + ) if nxver.major == 3 and nxver.minor >= 2: xfail.update( { diff --git a/python/nx-cugraph/nx_cugraph/scripts/print_table.py b/python/nx-cugraph/nx_cugraph/scripts/print_table.py index 117a1444f..7c9028124 100755 --- a/python/nx-cugraph/nx_cugraph/scripts/print_table.py +++ b/python/nx-cugraph/nx_cugraph/scripts/print_table.py @@ -61,7 +61,8 @@ def main(path_to_info=None, *, file=sys.stdout): lines = ["networkx_path,dispatch_name,version_added,plc,is_incomplete,is_different"] lines.extend(",".join(map(str, info)) for info in path_to_info.values()) text = "\n".join(lines) - print(text, file=file) + if file is not None: + print(text, file=file) return text diff --git a/python/nx-cugraph/nx_cugraph/scripts/print_tree.py b/python/nx-cugraph/nx_cugraph/scripts/print_tree.py index 485873a44..fbb1c3dd0 100755 --- a/python/nx-cugraph/nx_cugraph/scripts/print_tree.py +++ b/python/nx-cugraph/nx_cugraph/scripts/print_tree.py @@ -12,29 +12,58 @@ # See the License for the specific language governing permissions and # limitations under the License. import argparse -import re import sys -import networkx as nx +from nx_cugraph.scripts.print_table import Info, get_path_to_info -from nx_cugraph.scripts.print_table import get_path_to_info +def assoc_in(d, keys, value): + """Like Clojure's assoc-in, but modifies d in-place.""" + inner = d + keys = iter(keys) + key = next(keys) + for next_key in keys: + if key not in inner: + inner[key] = {} + inner = inner[key] + key = next_key + inner[key] = value + return d -def add_branch(G, path, extra="", *, skip=0): - branch = path.split(".") - prev = ".".join(branch[: skip + 1]) - for i in range(skip + 2, len(branch)): - cur = ".".join(branch[:i]) - G.add_edge(prev, cur) - prev = cur - if extra: - if not isinstance(extra, str): - extra = ", ".join(extra) - path += f" ({extra})" - G.add_edge(prev, path) + +def default_get_payload_internal(keys): + return keys[-1] + + +def tree_lines( + tree, + parents=(), + are_levels_closing=(), + get_payload_internal=default_get_payload_internal, +): + pre = "".join( + " " if is_level_closing else " │ " + for is_level_closing in are_levels_closing + ) + c = "├" + are_levels_closing += (False,) + for i, (key, val) in enumerate(tree.items(), 1): + if i == len(tree): # Last item + c = "└" + are_levels_closing = are_levels_closing[:-1] + (True,) + if isinstance(val, str): + yield pre + f" {c}─ " + val + else: + yield pre + f" {c}─ " + get_payload_internal((*parents, key)) + yield from tree_lines( + val, + (*parents, key), + are_levels_closing, + get_payload_internal=get_payload_internal, + ) -def get_extra( +def get_payload( info, *, networkx_path=False, @@ -64,7 +93,10 @@ def get_extra( extra.append("is-incomplete") if different and info.is_different: extra.append("is-different") - return extra + extra = ", ".join(extra) + if extra: + extra = f" ({extra})" + return info.networkx_path.rsplit(".", 1)[-1] + extra def create_tree( @@ -80,12 +112,20 @@ def create_tree( incomplete=False, different=False, prefix="", + strip_networkx=True, + get_payload=get_payload, ): if path_to_info is None: path_to_info = get_path_to_info() + if strip_networkx: + path_to_info = { + key: Info(info.networkx_path.replace("networkx.", "", 1), *info[1:]) + for key, info in path_to_info.items() + } if isinstance(by, str): by = [by] - G = nx.DiGraph() + # We rely on the fact that dicts maintain order + tree = {} for info in sorted( path_to_info.values(), key=lambda x: (*(getattr(x, b) for b in by), x.networkx_path), @@ -93,7 +133,7 @@ def create_tree( if not all(getattr(info, b) for b in by): continue path = prefix + ".".join(getattr(info, b) for b in by) - extra = get_extra( + payload = get_payload( info, networkx_path=networkx_path, dispatch_name=dispatch_name, @@ -103,8 +143,8 @@ def create_tree( incomplete=incomplete, different=different, ) - add_branch(G, path, extra=extra, skip=skip) - return G + assoc_in(tree, path.split("."), payload) + return tree def main( @@ -132,45 +172,33 @@ def main( "different": different, } if by == "networkx_path": - G = create_tree(path_to_info, by="networkx_path", **kwargs) - text = re.sub( - r" [A-Za-z_\./]+\.", " ", ("\n".join(nx.generate_network_text(G))) - ) + tree = create_tree(path_to_info, by="networkx_path", **kwargs) + text = "\n".join(tree_lines(tree)) elif by == "plc": - G = create_tree( - path_to_info, by=["plc", "networkx_path"], prefix="plc-", **kwargs - ) - text = re.sub( - "plc-", - "plc.", - re.sub( - r" plc-[A-Za-z_\./]*\.", - " ", - "\n".join(nx.generate_network_text(G)), - ), + tree = create_tree( + path_to_info, + by=["plc", "networkx_path"], + prefix="plc-", + **kwargs, ) + text = "\n".join(tree_lines(tree)).replace("plc-", "plc.") elif by == "version_added": - G = create_tree( + tree = create_tree( path_to_info, by=["version_added", "networkx_path"], prefix="version_added-", **kwargs, ) - text = re.sub( - "version_added-", - "version: ", - re.sub( - r" version_added-[-0-9A-Za-z_\./]*\.", - " ", - "\n".join(nx.generate_network_text(G)), - ), - ).replace("-", ".") + text = "\n".join(tree_lines(tree)).replace("version_added-", "version: ") + for digit in "0123456789": + text = text.replace(f"2{digit}-", f"2{digit}.") else: raise ValueError( "`by` argument should be one of {'networkx_path', 'plc', 'version_added' " f"got: {by}" ) - print(text, file=file) + if file is not None: + print(text, file=file) return text diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index a7525530a..60a4b5589 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -149,8 +149,10 @@ exclude_lines = [ # https://github.com/charliermarsh/ruff/ line-length = 88 target-version = "py39" +[tool.ruff.lint] unfixable = [ "F841", # unused-variable (Note: can leave useless expression) + "B905", # zip-without-explicit-strict (Note: prefer `zip(x, y, strict=True)`) ] select = [ "ALL", @@ -172,7 +174,6 @@ ignore = [ # "SIM401", # Use dict.get ... instead of if-else-block (Note: if-else better for coverage and sometimes clearer) # "TRY004", # Prefer `TypeError` exception for invalid type (Note: good advice, but not worth the nuisance) "B904", # Bare `raise` inside exception clause (like TRY200; sometimes okay) - "TRY200", # Use `raise from` to specify exception cause (Note: sometimes okay to raise original exception) # Intentionally ignored "A003", # Class attribute ... is shadowing a python builtin @@ -224,22 +225,22 @@ ignore = [ "PD", # pandas-vet (Intended for scripts that use pandas, not libraries) ] -[tool.ruff.per-file-ignores] +[tool.ruff.lint.per-file-ignores] "__init__.py" = ["F401"] # Allow unused imports (w/o defining `__all__`) # Allow assert, print, RNG, and no docstring "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"] "_nx_cugraph/__init__.py" = ["E501"] "nx_cugraph/algorithms/**/*py" = ["D205", "D401"] # Allow flexible docstrings for algorithms -[tool.ruff.flake8-annotations] +[tool.ruff.lint.flake8-annotations] mypy-init-return = true -[tool.ruff.flake8-builtins] +[tool.ruff.lint.flake8-builtins] builtins-ignorelist = ["copyright"] -[tool.ruff.flake8-pytest-style] +[tool.ruff.lint.flake8-pytest-style] fixture-parentheses = false mark-parentheses = false -[tool.ruff.pydocstyle] +[tool.ruff.lint.pydocstyle] convention = "numpy" diff --git a/python/nx-cugraph/scripts/update_readme.py b/python/nx-cugraph/scripts/update_readme.py new file mode 100644 index 000000000..1ab5a76c4 --- /dev/null +++ b/python/nx-cugraph/scripts/update_readme.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python +# Copyright (c) 2024, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import re +import zlib +from collections import namedtuple +from pathlib import Path +from warnings import warn + +from nx_cugraph.scripts.print_tree import create_tree, tree_lines + +# See: https://sphobjinv.readthedocs.io/en/stable/syntax.html +DocObject = namedtuple( + "DocObject", + "name, domain, role, priority, uri, displayname", +) + + +def parse_docobject(line): + left, right = line.split(":") + name, domain = left.rsplit(" ", 1) + role, priority, uri, displayname = right.split(" ", 3) + if displayname == "-": + displayname = name + if uri.endswith("$"): + uri = uri[:-1] + name + return DocObject(name, domain, role, priority, uri, displayname) + + +def replace_body(text, match, new_body): + start, stop = match.span("body") + return text[:start] + new_body + text[stop:] + + +# NetworkX isn't perfectly intersphinx-compatible, so manually specify some urls. +# See: https://github.com/networkx/networkx/issues/7278 +MANUAL_OBJECT_URLS = { + "networkx.algorithms.centrality.betweenness": ( + "https://networkx.org/documentation/stable/reference/" + "algorithms/centrality.html#shortest-path-betweenness" + ), + "networkx.algorithms.centrality.degree_alg": ( + "https://networkx.org/documentation/stable/reference/" + "algorithms/centrality.html#degree" + ), + "networkx.algorithms.centrality.eigenvector": ( + "https://networkx.org/documentation/stable/reference/" + "algorithms/centrality.html#eigenvector" + ), + "networkx.algorithms.centrality.katz": ( + "https://networkx.org/documentation/stable/reference/" + "algorithms/centrality.html#eigenvector" + ), + "networkx.algorithms.components.connected": ( + "https://networkx.org/documentation/stable/reference/" + "algorithms/component.html#connectivity" + ), + "networkx.algorithms.components.weakly_connected": ( + "https://networkx.org/documentation/stable/reference/" + "algorithms/component.html#weak-connectivity" + ), +} + + +def main(readme_file, objects_filename): + """``readme_file`` must be readable and writable, so use mode ``"a+"``""" + # Use the `objects.inv` file to determine URLs. For details about this file, see: + # https://sphobjinv.readthedocs.io/en/stable/syntax.html + # We might be better off using a library like that, but roll our own for now. + with Path(objects_filename).open("rb") as objects_file: + line = objects_file.readline() + if line != b"# Sphinx inventory version 2\n": + raise RuntimeError(f"Bad line in objects.inv:\n\n{line}") + line = objects_file.readline() + if line != b"# Project: NetworkX\n": + raise RuntimeError(f"Bad line in objects.inv:\n\n{line}") + line = objects_file.readline() + if not line.startswith(b"# Version: "): + raise RuntimeError(f"Bad line in objects.inv:\n\n{line}") + line = objects_file.readline() + if line != b"# The remainder of this file is compressed using zlib.\n": + raise RuntimeError(f"Bad line in objects.inv:\n\n{line}") + zlib_data = objects_file.read() + objects_text = zlib.decompress(zlib_data).decode().strip() + objects_list = [parse_docobject(line) for line in objects_text.split("\n")] + doc_urls = { + obj.name: "https://networkx.org/documentation/stable/" + obj.uri + for obj in objects_list + } + if len(objects_list) != len(doc_urls): + raise RuntimeError("Oops; duplicate names found in objects.inv") + + def get_payload(info, **kwargs): + path = "networkx." + info.networkx_path + subpath, name = path.rsplit(".", 1) + # Many objects are referred to in modules above where they are defined. + while subpath: + path = f"{subpath}.{name}" + if path in doc_urls: + return f'{name}' + subpath = subpath.rsplit(".", 1)[0] + warn(f"Unable to find URL for {name!r}: {path}", stacklevel=0) + return name + + def get_payload_internal(keys): + path = "networkx." + ".".join(keys) + name = keys[-1] + if path in doc_urls: + return f'{name}' + path2 = "reference/" + "/".join(keys) + if path2 in doc_urls: + return f'{name}' + if path in MANUAL_OBJECT_URLS: + return f'{name}' + warn(f"Unable to find URL for {name!r}: {path}", stacklevel=0) + return name + + readme_file.seek(0) + text = readme_file.read() + tree = create_tree(get_payload=get_payload) + # Algorithms + match = re.search( + r"### .Algorithms(?P.*?)
\n(?P.*?)\n
", + text, + re.DOTALL, + ) + if not match: + raise RuntimeError("Algorithms section not found!") + lines = [] + for key, val in tree["algorithms"].items(): + lines.append(get_payload_internal(("algorithms", key))) + lines.extend( + tree_lines( + val, + parents=("algorithms", key), + get_payload_internal=get_payload_internal, + ) + ) + text = replace_body(text, match, "\n".join(lines)) + # Generators + match = re.search( + r"### .Generators(?P.*?)
\n(?P.*?)\n
", + text, + re.DOTALL, + ) + if not match: + raise RuntimeError("Generators section not found!") + lines = [] + for key, val in tree["generators"].items(): + lines.append(get_payload_internal(("generators", key))) + lines.extend( + tree_lines( + val, + parents=("generators", key), + get_payload_internal=get_payload_internal, + ) + ) + text = replace_body(text, match, "\n".join(lines)) + # Other + match = re.search( + r"### Other\n(?P.*?)
\n(?P.*?)\n
", + text, + re.DOTALL, + ) + if not match: + raise RuntimeError("Other section not found!") + lines = [] + for key, val in tree.items(): + if key in {"algorithms", "generators"}: + continue + lines.append(get_payload_internal((key,))) + lines.extend( + tree_lines(val, parents=(key,), get_payload_internal=get_payload_internal) + ) + text = replace_body(text, match, "\n".join(lines)) + # Now overwrite README.md + readme_file.truncate(0) + readme_file.write(text) + return text + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + "Update README.md to show NetworkX functions implemented by nx-cugraph" + ) + parser.add_argument("readme_filename", help="Path to the README.md file") + parser.add_argument( + "networkx_objects", help="Path to the objects.inv file from networkx docs" + ) + args = parser.parse_args() + with Path(args.readme_filename).open("a+") as readme_file: + main(readme_file, args.networkx_objects) From 5d712daed932a0f08b6c66e386c67d03bb778e18 Mon Sep 17 00:00:00 2001 From: James Lamb Date: Thu, 29 Feb 2024 13:10:42 -0600 Subject: [PATCH 325/384] Add support for Python 3.11, require NumPy 1.23+ (#4195) Contributes to https://github.com/rapidsai/build-planning/issues/3 This PR adds support for Python 3.11. It also bumps uses of `NumPy` to `numpy>=1.23`, see https://github.com/rapidsai/build-planning/issues/3#issuecomment-1967952280. ## Notes for Reviewers This is part of ongoing work to add Python 3.11 support across RAPIDS. The Python 3.11 CI workflows introduced in https://github.com/rapidsai/shared-workflows/pull/176 are *optional*... they are not yet required to run successfully for PRs to be merged. This PR can be merged once all jobs are running successfully (including the non-required jobs for Python 3.11). The CI logs should be verified that the jobs are building and testing with Python 3.11. See https://github.com/rapidsai/shared-workflows/pull/176 for more details. Authors: - James Lamb (https://github.com/jameslamb) Approvers: - Jake Awe (https://github.com/AyodeAwe) - Kyle Edwards (https://github.com/KyleFromNVIDIA) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4195 --- python/nx-cugraph/pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 60a4b5589..07ec0eab2 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -25,6 +25,7 @@ classifiers = [ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3 :: Only", "Intended Audience :: Developers", "Topic :: Software Development :: Libraries :: Python Modules", @@ -32,7 +33,7 @@ classifiers = [ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", - "numpy>=1.21", + "numpy>=1.23", "pylibcugraph==24.4.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. From 5f467ef7deb52b7b57c750bc36a613c45c5b252a Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Mon, 4 Mar 2024 14:29:15 -0800 Subject: [PATCH 326/384] Update installation docs for CUDA 12. (#4207) Closes #3909. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4207 --- .../source/installation/getting_cugraph.md | 20 ++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index 625f2c64c..d9d3fa55c 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -15,7 +15,7 @@ There are 4 ways to get cuGraph packages:
## Docker -The RAPIDS Docker containers contain all RAPIDS packages, including all from cuGraph, as well as all required supporting packages. To download a container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph. +The RAPIDS Docker containers contain all RAPIDS packages, including all from cuGraph, as well as all required supporting packages. To download a RAPIDS container, please see the [Docker Hub page for rapidsai/base](https://hub.docker.com/r/rapidsai/base), choosing a tag based on the NVIDIA CUDA version you're running. Also, the [rapidsai/notebooks](https://hub.docker.com/r/rapidsai/notebooks) container provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph.
@@ -31,6 +31,8 @@ cuGraph Conda packages * cugraph-service-server * cugraph-dgl * cugraph-pyg + * cugraph-equivariant + * nx-cugraph Replace the package name in the example below to the one you want to install. @@ -38,10 +40,12 @@ Replace the package name in the example below to the one you want to install. Install and update cuGraph using the conda command: ```bash -conda install -c rapidsai -c conda-forge -c nvidia cugraph cudatoolkit=11.8 +conda install -c rapidsai -c conda-forge -c nvidia cugraph cuda-version=12.0 ``` -Note: This conda installation only applies to Linux and Python versions 3.8/3.10. +Alternatively, use `cuda-version=11.8` for packages supporting CUDA 11. + +Note: This conda installation only applies to Linux and Python versions 3.9/3.10.
@@ -49,10 +53,16 @@ Note: This conda installation only applies to Linux and Python versions 3.8/3.10 cuGraph, and all of RAPIDS, is available via pip. ``` -pip install cugraph-cu11 --extra-index-url=https://pypi.ngc.nvidia.com +pip install cugraph-cu12 --extra-index-url=https://pypi.ngc.nvidia.com ``` -pip packages for other packages are being worked and should be available in early 2023 +Replace `-cu12` with `-cu11` for packages supporting CUDA 11. + +Also available: + * cugraph-dgl-cu12 + * cugraph-pyg-cu12 + * cugraph-equivariant-cu12 + * nx-cugraph-cu12
From be60042519f7e67c1f8bdf28074b135e5117025e Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Wed, 6 Mar 2024 09:08:53 -0500 Subject: [PATCH 327/384] Fix in Docs to address question asked in issue 4182 (#4204) Added compute comment and changed DiGraph to Graph(directed=True) Tested in a notebook. Addresses question in #4182 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4204 --- docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst index 772d3abc4..a2d497890 100644 --- a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst +++ b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst @@ -60,12 +60,15 @@ Example ) # create graph from input data - G = cugraph.DiGraph() + G = cugraph.Graph(directed=True) G.from_dask_cudf_edgelist(e_list, source='src', destination='dst') # run PageRank pr_df = dask_cugraph.pagerank(G, tol=1e-4) + # need to call compute to generate results + pr_df.compute() + # cluster clean up Comms.destroy() client.close() From 00e2d55b7cc1d7b62c036501454bc3c4df2f534c Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 6 Mar 2024 18:33:06 -0600 Subject: [PATCH 328/384] Fix `louvain_communities`, `PropertyGraph`, cudf `column.full`, dgl 2.1.0 CI failures (#4215) * Updates cudf usage in hypergraph to use `cudf.core.column.as_column` instead of the deprecated `cudf.core.column.full` API * Adds code to call louvain using a signature that's compatible with both pre and post NX 3.3 * Adds an upper bound to the DGL version pin to ensure a compatible version is used * Updates a PropertyGraph test to handle minor ordering changes in the result DataFrame * Test matrices starting 24.04 have changed, resulting in an "empty matrix" error when attempting to use the supported combination used for PyG (PyG wheel tests need pytorch with CUDA 12.2 on arm64), so the matrix was simplified to remove the CUDA 11.8 requirement. cc @tingyu66 Authors: - Erik Welch (https://github.com/eriknw) - Naim (https://github.com/naimnv) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/4215 --- python/nx-cugraph/_nx_cugraph/__init__.py | 1 + .../algorithms/community/louvain.py | 93 ++++++++++++++----- .../nx_cugraph/tests/test_match_api.py | 5 - 3 files changed, 69 insertions(+), 30 deletions(-) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index c6c43110a..b2f13d25f 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -164,6 +164,7 @@ }, "louvain_communities": { "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).", }, "pagerank": { "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index f7638f47a..ea1318060 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -37,18 +37,23 @@ _max_level_param = {} -@not_implemented_for("directed") -@networkx_algorithm( - extra_params={ - **_max_level_param, - **_dtype_param, - }, - is_incomplete=True, # seed not supported; self-loops not supported - is_different=True, # RNG different - version_added="23.10", - _plc="louvain", -) -def louvain_communities( +def _louvain_communities_nx32( + G, + weight="weight", + resolution=1, + threshold=0.0000001, + seed=None, + *, + max_level=None, + dtype=None, +): + """`seed` parameter is currently ignored, and self-loops are not yet supported.""" + return _louvain_communities( + G, weight, resolution, threshold, max_level, seed, dtype=dtype + ) + + +def _louvain_communities( G, weight="weight", resolution=1, @@ -85,16 +90,54 @@ def louvain_communities( return [set(G._nodearray_to_list(ids)) for ids in groups.values()] -@louvain_communities._can_run -def _( - G, - weight="weight", - resolution=1, - threshold=0.0000001, - max_level=None, - seed=None, - *, - dtype=None, -): - # NetworkX allows both directed and undirected, but cugraph only allows undirected. - return not G.is_directed() +_louvain_decorator = networkx_algorithm( + extra_params={ + **_max_level_param, + **_dtype_param, + }, + is_incomplete=True, # seed not supported; self-loops not supported + is_different=True, # RNG different + version_added="23.10", + _plc="louvain", + name="louvain_communities", +) + +if _max_level_param: # networkx <= 3.2 + _louvain_communities_nx32.__name__ = "louvain_communities" + louvain_communities = not_implemented_for("directed")( + _louvain_decorator(_louvain_communities_nx32) + ) + + @louvain_communities._can_run + def _( + G, + weight="weight", + resolution=1, + threshold=0.0000001, + seed=None, + *, + max_level=None, + dtype=None, + ): + # NetworkX allows both directed and undirected, but cugraph only undirected. + return not G.is_directed() + +else: # networkx >= 3.3 + _louvain_communities.__name__ = "louvain_communities" + louvain_communities = not_implemented_for("directed")( + _louvain_decorator(_louvain_communities) + ) + + @louvain_communities._can_run + def _( + G, + weight="weight", + resolution=1, + threshold=0.0000001, + max_level=None, + seed=None, + *, + dtype=None, + ): + # NetworkX allows both directed and undirected, but cugraph only undirected. + return not G.is_directed() diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py index 595f7819a..bd6f20e84 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py @@ -44,11 +44,6 @@ def test_match_signature_and_names(): else: orig_func = dispatchable_func.orig_func - if nxver.major == 3 and nxver.minor <= 2 and name == "louvain_communities": - # The signature of louvain_communities changed in NetworkX 3.3, and - # we updated to match, so we skip this check in older versions. - continue - # Matching signatures? orig_sig = inspect.signature(orig_func) func_sig = inspect.signature(func) From f802eb32be6e8daacfd4265b3824455c4a593cf5 Mon Sep 17 00:00:00 2001 From: jakirkham Date: Wed, 6 Mar 2024 21:03:08 -0800 Subject: [PATCH 329/384] Add Python 3.11 to Conda docs (#4213) xref: https://github.com/rapidsai/cugraph/pull/4207#discussion_r1511881073 Authors: - https://github.com/jakirkham Approvers: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4213 --- docs/cugraph/source/installation/getting_cugraph.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index d9d3fa55c..0695c690a 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -45,7 +45,7 @@ conda install -c rapidsai -c conda-forge -c nvidia cugraph cuda-version=12.0 Alternatively, use `cuda-version=11.8` for packages supporting CUDA 11. -Note: This conda installation only applies to Linux and Python versions 3.9/3.10. +Note: This conda installation only applies to Linux and Python versions 3.9/3.10/3.11.
From 0f17760e01aac8d5774bb1675def4c9a2d00d4af Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Thu, 7 Mar 2024 21:03:36 -0600 Subject: [PATCH 330/384] Trim trailing whitespace (#4206) This PR trims trailing whitespace and normalizes file endings in all files, and adds pre-commit hooks to enforce this for future contributions. These pre-commit hooks already exist in most other RAPIDS repositories, so it would be good to standardize. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Brad Rees (https://github.com/BradReesWork) - Jake Awe (https://github.com/AyodeAwe) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/4206 --- .pre-commit-config.yaml | 4 +- docs/cugraph/Makefile | 2 +- docs/cugraph/README.md | 2 +- docs/cugraph/source/_static/references.css | 2 +- .../api_docs/cugraph-ops/c_cpp/index.rst | 1 - .../source/api_docs/cugraph-ops/index.rst | 2 +- .../api_docs/cugraph-ops/python/index.rst | 2 +- .../api_docs/cugraph-pyg/cugraph_pyg.rst | 1 - .../source/api_docs/cugraph/components.rst | 1 - .../source/api_docs/cugraph/dask-cugraph.rst | 4 +- .../source/api_docs/cugraph/generators.rst | 1 - .../api_docs/cugraph/graph_implementation.rst | 2 +- .../api_docs/cugraph/helper_functions.rst | 1 - .../source/api_docs/cugraph/layout.rst | 1 - .../source/api_docs/cugraph/link_analysis.rst | 1 - docs/cugraph/source/api_docs/cugraph/tree.rst | 1 - .../source/api_docs/cugraph_c/centrality.rst | 1 - .../source/api_docs/cugraph_c/community.rst | 1 - .../source/api_docs/cugraph_c/labeling.rst | 2 +- .../source/api_docs/cugraph_c/similarity.rst | 2 +- .../source/api_docs/cugraph_c/traversal.rst | 2 +- docs/cugraph/source/api_docs/index.rst | 2 - .../cugraph/source/api_docs/service/index.rst | 2 +- .../source/basics/cugraph_cascading.md | 8 +- docs/cugraph/source/basics/cugraph_intro.md | 81 +++++++++---------- docs/cugraph/source/basics/nx_transition.rst | 27 +++---- docs/cugraph/source/dev_resources/API.rst | 2 +- docs/cugraph/source/dev_resources/index.rst | 2 +- .../source/graph_support/DGL_support.md | 12 +-- .../source/graph_support/PyG_support.md | 2 +- .../source/graph_support/algorithms.md | 2 +- .../graph_support/algorithms/Similarity.md | 6 +- .../algorithms/betweenness_centrality.md | 2 +- .../algorithms/degree_centrality.md | 4 +- .../algorithms/eigenvector_centrality.md | 5 +- .../algorithms/jaccard_similarity.md | 4 +- .../algorithms/katz_centrality.md | 2 +- .../algorithms/overlap_similarity.md | 4 +- .../algorithms/sorensen_coefficient.md | 4 +- .../source/graph_support/compatibility.rst | 2 +- .../source/graph_support/cugraph_service.rst | 4 +- .../graph_support/cugraphops_support.rst | 2 +- .../source/graph_support/datastores.rst | 1 - .../source/graph_support/feature_stores.md | 2 +- .../source/graph_support/gnn_support.rst | 4 +- .../source/graph_support/graph_algorithms.rst | 4 +- docs/cugraph/source/graph_support/index.rst | 2 +- .../source/graph_support/knowledge_stores.md | 2 +- .../source/graph_support/property_graph.md | 22 ++--- .../graph_support/wholegraph_support.md | 2 +- docs/cugraph/source/index.rst | 4 +- .../source/installation/getting_cugraph.md | 3 +- docs/cugraph/source/installation/index.rst | 2 +- docs/cugraph/source/references/cugraph_ref.md | 1 - docs/cugraph/source/references/datasets.md | 14 ++-- docs/cugraph/source/references/index.rst | 2 +- docs/cugraph/source/references/licenses.md | 2 +- docs/cugraph/source/releases/index.rst | 2 +- .../source/tutorials/community_resources.md | 2 +- .../source/tutorials/cugraph_blogs.rst | 2 +- .../source/tutorials/cugraph_notebooks.md | 4 +- .../cugraph/source/tutorials/how_to_guides.md | 16 ++-- .../wholememory_implementation_details.md | 2 +- docs/cugraph/source/wholegraph/index.rst | 1 - .../installation/getting_wholegraph.md | 2 +- print_env.sh | 15 ++-- 66 files changed, 154 insertions(+), 174 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6b7ff1441..3d893e0e5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,11 +5,13 @@ exclude: '^thirdparty' repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: check-added-large-files - id: debug-statements + - id: end-of-file-fixer - id: mixed-line-ending + - id: trailing-whitespace - repo: https://github.com/psf/black rev: 22.10.0 hooks: diff --git a/docs/cugraph/Makefile b/docs/cugraph/Makefile index f92d0be69..ac16367ef 100644 --- a/docs/cugraph/Makefile +++ b/docs/cugraph/Makefile @@ -23,4 +23,4 @@ clean: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/cugraph/README.md b/docs/cugraph/README.md index 734867af9..970a03a04 100644 --- a/docs/cugraph/README.md +++ b/docs/cugraph/README.md @@ -5,7 +5,7 @@ All prerequisite for building docs are in the cugraph development conda environm ## Steps to follow: -In order to build the docs, we need the conda dev environment from cugraph and we need to build cugraph from source. +In order to build the docs, we need the conda dev environment from cugraph and we need to build cugraph from source. 1. Create a conda env and build cugraph from source. The dependencies to build rapids from source are installed in that conda environment, and then rapids is built and installed into the same environment. diff --git a/docs/cugraph/source/_static/references.css b/docs/cugraph/source/_static/references.css index 225cf13ba..d1f647233 100644 --- a/docs/cugraph/source/_static/references.css +++ b/docs/cugraph/source/_static/references.css @@ -20,4 +20,4 @@ dl.citation > dt.label > span::before { /* Add closing bracket */ dl.citation > dt.label > span::after { content: "]"; -} \ No newline at end of file +} diff --git a/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst index 5545bebe9..39dae955e 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst @@ -1,3 +1,2 @@ cugraph-ops C++ API Reference ============================= - diff --git a/docs/cugraph/source/api_docs/cugraph-ops/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/index.rst index fdfd5baab..0f6a6c937 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/index.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/index.rst @@ -8,4 +8,4 @@ This page provides a list of all publicly accessible modules, methods and classe :caption: API Documentation python/index - c_cpp/index \ No newline at end of file + c_cpp/index diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst index 082c7741f..fb25f2fa0 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst @@ -1,5 +1,5 @@ cugraph-ops Python API reference -================================ +================================ This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace. diff --git a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst index f7d7f5f22..a150d4db9 100644 --- a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst +++ b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst @@ -11,4 +11,3 @@ cugraph-pyg .. cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore .. cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler - diff --git a/docs/cugraph/source/api_docs/cugraph/components.rst b/docs/cugraph/source/api_docs/cugraph/components.rst index 560aa1f8c..e61291fcc 100644 --- a/docs/cugraph/source/api_docs/cugraph/components.rst +++ b/docs/cugraph/source/api_docs/cugraph/components.rst @@ -20,4 +20,3 @@ Connected Components (MG) :toctree: ../api/cugraph/ cugraph.dask.components.connectivity.weakly_connected_components - diff --git a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst index a2d497890..d9ba3f3a7 100644 --- a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst +++ b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst @@ -48,12 +48,12 @@ Example ) input_data.to_csv(input_data_path, index=False) - # helper function to set the reader chunk size to automatically get one partition per GPU + # helper function to set the reader chunk size to automatically get one partition per GPU chunksize = dask_cugraph.get_chunksize(input_data_path) # multi-GPU CSV reader e_list = dask_cudf.read_csv( - input_data_path, + input_data_path, chunksize=chunksize, names=['src', 'dst'], dtype=['int32', 'int32'], diff --git a/docs/cugraph/source/api_docs/cugraph/generators.rst b/docs/cugraph/source/api_docs/cugraph/generators.rst index 4f93d943e..f5180a172 100644 --- a/docs/cugraph/source/api_docs/cugraph/generators.rst +++ b/docs/cugraph/source/api_docs/cugraph/generators.rst @@ -11,4 +11,3 @@ RMAT :toctree: ../api/cugraph/ cugraph.generators.rmat - diff --git a/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst b/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst index 91c16c242..ae14306ce 100644 --- a/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst +++ b/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst @@ -31,4 +31,4 @@ Graph Implementation edges nodes neighbors - vertex_column_size \ No newline at end of file + vertex_column_size diff --git a/docs/cugraph/source/api_docs/cugraph/helper_functions.rst b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst index 02cb599ae..e7091c50c 100644 --- a/docs/cugraph/source/api_docs/cugraph/helper_functions.rst +++ b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst @@ -20,4 +20,3 @@ Methods cugraph.dask.comms.comms.get_handle cugraph.dask.comms.comms.get_worker_id cugraph.dask.common.read_utils.get_chunksize - diff --git a/docs/cugraph/source/api_docs/cugraph/layout.rst b/docs/cugraph/source/api_docs/cugraph/layout.rst index ed97caf54..d416676a6 100644 --- a/docs/cugraph/source/api_docs/cugraph/layout.rst +++ b/docs/cugraph/source/api_docs/cugraph/layout.rst @@ -10,4 +10,3 @@ Force Atlas 2 :toctree: ../api/cugraph/ cugraph.force_atlas2 - diff --git a/docs/cugraph/source/api_docs/cugraph/link_analysis.rst b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst index 48b5ec117..698880c1b 100644 --- a/docs/cugraph/source/api_docs/cugraph/link_analysis.rst +++ b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst @@ -32,4 +32,3 @@ Pagerank (MG) :toctree: ../api/cugraph/ cugraph.dask.link_analysis.pagerank.pagerank - diff --git a/docs/cugraph/source/api_docs/cugraph/tree.rst b/docs/cugraph/source/api_docs/cugraph/tree.rst index 5ba2242eb..d8a89046a 100644 --- a/docs/cugraph/source/api_docs/cugraph/tree.rst +++ b/docs/cugraph/source/api_docs/cugraph/tree.rst @@ -18,4 +18,3 @@ Maximum Spanning Tree :toctree: ../api/cugraph/ cugraph.tree.minimum_spanning_tree.maximum_spanning_tree - diff --git a/docs/cugraph/source/api_docs/cugraph_c/centrality.rst b/docs/cugraph/source/api_docs/cugraph_c/centrality.rst index f34e26ad7..3bea608fd 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/centrality.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/centrality.rst @@ -48,4 +48,3 @@ Centrality Support Functions :project: libcugraph :members: :content-only: - diff --git a/docs/cugraph/source/api_docs/cugraph_c/community.rst b/docs/cugraph/source/api_docs/cugraph_c/community.rst index d55325720..6b500a972 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/community.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/community.rst @@ -54,4 +54,3 @@ Community Support Functions :project: libcugraph :members: :content-only: - diff --git a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst index 4ca598c0a..2b709ebd3 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst @@ -17,4 +17,4 @@ Labeling Support Functions .. doxygengroup:: labeling :project: libcugraph :members: - :content-only: \ No newline at end of file + :content-only: diff --git a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst index 200ba6957..75735925e 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst @@ -22,4 +22,4 @@ Similarty Support Functions .. doxygengroup:: similarity :project: libcugraph :members: - :content-only: \ No newline at end of file + :content-only: diff --git a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst index 1578951e0..bde30f4fa 100644 --- a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst +++ b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst @@ -27,4 +27,4 @@ Traversal Support Functions .. doxygengroup:: traversal :project: libcugraph :members: - :content-only: \ No newline at end of file + :content-only: diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 74ca98bb9..1b907165a 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -35,5 +35,3 @@ Additional Graph Packages API Documentation :caption: Additional Graph Packages API Documentation service/index.rst - - diff --git a/docs/cugraph/source/api_docs/service/index.rst b/docs/cugraph/source/api_docs/service/index.rst index a58cf2074..ca251e475 100644 --- a/docs/cugraph/source/api_docs/service/index.rst +++ b/docs/cugraph/source/api_docs/service/index.rst @@ -7,4 +7,4 @@ cugraph-service API Reference :caption: cugraph-service API Documentation cugraph_service_client - cugraph_service_server \ No newline at end of file + cugraph_service_server diff --git a/docs/cugraph/source/basics/cugraph_cascading.md b/docs/cugraph/source/basics/cugraph_cascading.md index 7795e9e00..bad3d7fa6 100644 --- a/docs/cugraph/source/basics/cugraph_cascading.md +++ b/docs/cugraph/source/basics/cugraph_cascading.md @@ -3,7 +3,7 @@ BLUF: cuGraph does not support method cascading -[Method Cascading](https://en.wikipedia.org/wiki/Method_cascading) is a popular, and useful, functional programming concept and is a great way to make code more readable. Python supports method cascading ... _for the most part_. There are a number of Python built-in classes that do not support cascading. +[Method Cascading](https://en.wikipedia.org/wiki/Method_cascading) is a popular, and useful, functional programming concept and is a great way to make code more readable. Python supports method cascading ... _for the most part_. There are a number of Python built-in classes that do not support cascading. An example, from cuDF, is a sequence of method calls for loading data and then finding the largest values from a subset of the data (yes there are other ways this could be done): @@ -11,13 +11,13 @@ An example, from cuDF, is a sequence of method calls for loading data and then gdf = cudf.from_pandas(df).query(‘val > 200’).nlargest(‘va’3) ``` -cuGraph does not support method cascading for two main reasons: (1) the object-oriented nature of the Graph data object leverages in-place methods, and (2) the fact that algorithms operate on graphs rather than graphs running algorithms. +cuGraph does not support method cascading for two main reasons: (1) the object-oriented nature of the Graph data object leverages in-place methods, and (2) the fact that algorithms operate on graphs rather than graphs running algorithms. ## Graph Data Objects -cuGraph follows an object-oriented design for the Graph objects. Users create a Graph and can then add data to object, but every add method call returns `None`. +cuGraph follows an object-oriented design for the Graph objects. Users create a Graph and can then add data to object, but every add method call returns `None`. _Why Inplace methods?_
-cuGraph focuses on the big graph problems where there are 10s of millions to trillions of edges (Giga bytes to Terabytes of data). At that scale, creating a copy of the data becomes memory inefficient. +cuGraph focuses on the big graph problems where there are 10s of millions to trillions of edges (Giga bytes to Terabytes of data). At that scale, creating a copy of the data becomes memory inefficient. _Why not return `self` rather than `None`?_
It would be simple to modify the methods to return `self` rather than `None`, however it opens the methods to misinterpretation. Consider the following code: diff --git a/docs/cugraph/source/basics/cugraph_intro.md b/docs/cugraph/source/basics/cugraph_intro.md index 10d14f8a0..7ad282560 100644 --- a/docs/cugraph/source/basics/cugraph_intro.md +++ b/docs/cugraph/source/basics/cugraph_intro.md @@ -1,51 +1,51 @@ # cuGraph Introduction -The Data Scientist has a collection of techniques within their -proverbial toolbox. Data engineering, statistical analysis, and -machine learning are among the most commonly known. However, there -are numerous cases where the focus of the analysis is on the -relationship between data elements. In those cases, the data is best -represented as a graph. Graph analysis, also called network analysis, -is a collection of algorithms for answering questions posed against +The Data Scientist has a collection of techniques within their +proverbial toolbox. Data engineering, statistical analysis, and +machine learning are among the most commonly known. However, there +are numerous cases where the focus of the analysis is on the +relationship between data elements. In those cases, the data is best +represented as a graph. Graph analysis, also called network analysis, +is a collection of algorithms for answering questions posed against graph data. Graph analysis is not new. -The first graph problem was posed by Euler in 1736, the [Seven Bridges of -Konigsberg](https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg), -and laid the foundation for the mathematical field of graph theory. -The application of graph analysis covers a wide variety of fields, including +The first graph problem was posed by Euler in 1736, the [Seven Bridges of +Konigsberg](https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg), +and laid the foundation for the mathematical field of graph theory. +The application of graph analysis covers a wide variety of fields, including marketing, biology, physics, computer science, sociology, and cyber to name a few. -RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates -into the RAPIDS data science ecosystem and allows the data scientist to easily -call graph algorithms using data stored in a GPU DataFrame, NetworkX Graphs, or even -CuPy or SciPy sparse Matrix. +RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates +into the RAPIDS data science ecosystem and allows the data scientist to easily +call graph algorithms using data stored in a GPU DataFrame, NetworkX Graphs, or even +CuPy or SciPy sparse Matrix. ## Vision -The vision of RAPIDS cuGraph is to ___make graph analysis ubiquitous to the -point that users just think in terms of analysis and not technologies or -frameworks___. This is a goal that many of us on the cuGraph team have been -working on for almost twenty years. Many of the early attempts focused on -solving one problem or using one technique. Those early attempts worked for -the initial goal but tended to break as the scope changed (e.g., shifting -to solving a dynamic graph problem with a static graph solution). The limiting -factors usually came down to compute power, ease-of-use, or choosing a data -structure that was not suited for all problems. NVIDIA GPUs, CUDA, and RAPIDS -have totally changed the paradigm and the goal of an accelerated unified graph +The vision of RAPIDS cuGraph is to ___make graph analysis ubiquitous to the +point that users just think in terms of analysis and not technologies or +frameworks___. This is a goal that many of us on the cuGraph team have been +working on for almost twenty years. Many of the early attempts focused on +solving one problem or using one technique. Those early attempts worked for +the initial goal but tended to break as the scope changed (e.g., shifting +to solving a dynamic graph problem with a static graph solution). The limiting +factors usually came down to compute power, ease-of-use, or choosing a data +structure that was not suited for all problems. NVIDIA GPUs, CUDA, and RAPIDS +have totally changed the paradigm and the goal of an accelerated unified graph analytic library is now possible. -The compute power of the latest NVIDIA GPUs (RAPIDS supports Pascal and later -GPU architectures) make graph analytics 1000x faster on average over NetworkX. -Moreover, the internal memory speed within a GPU allows cuGraph to rapidly -switch the data structure to best suit the needs of the analytic rather than -being restricted to a single data structure. cuGraph is working with several -frameworks for both static and dynamic graph data structures so that we always -have a solution to any graph problem. Since Python has emerged as the de facto -language for data science, allowing interactivity and the ability to run graph -analytics in Python makes cuGraph familiar and approachable. RAPIDS wraps all -the graph analytic goodness mentioned above with the ability to perform -high-speed ETL, statistics, and machine learning. To make things even better, -RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support +The compute power of the latest NVIDIA GPUs (RAPIDS supports Pascal and later +GPU architectures) make graph analytics 1000x faster on average over NetworkX. +Moreover, the internal memory speed within a GPU allows cuGraph to rapidly +switch the data structure to best suit the needs of the analytic rather than +being restricted to a single data structure. cuGraph is working with several +frameworks for both static and dynamic graph data structures so that we always +have a solution to any graph problem. Since Python has emerged as the de facto +language for data science, allowing interactivity and the ability to run graph +analytics in Python makes cuGraph familiar and approachable. RAPIDS wraps all +the graph analytic goodness mentioned above with the ability to perform +high-speed ETL, statistics, and machine learning. To make things even better, +RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support multi-billion edge graphs. @@ -65,9 +65,4 @@ possible. However, each field has its own argot (jargon) for describing the graph (or network). In our documentation, we try to be consistent. In Python documentation we will mostly use the terms __Node__ and __Edge__ to better match NetworkX preferred term use, as well as other Python-based tools. At -the CUDA/C layer, we favor the mathematical terms of __Vertex__ and __Edge__. - - - - - \ No newline at end of file +the CUDA/C layer, we favor the mathematical terms of __Vertex__ and __Edge__. diff --git a/docs/cugraph/source/basics/nx_transition.rst b/docs/cugraph/source/basics/nx_transition.rst index 3d116162c..984986581 100644 --- a/docs/cugraph/source/basics/nx_transition.rst +++ b/docs/cugraph/source/basics/nx_transition.rst @@ -2,7 +2,7 @@ NetworkX Compatibility and Transition ************************************** -*Note: this is a work in progress and will be updatred and changed as we better flesh out +*Note: this is a work in progress and will be updatred and changed as we better flesh out compatibility issues* One of the goals of RAPIDS cuGraph is to mimic the NetworkX API to simplify @@ -13,7 +13,7 @@ and then the construction of a graph object; that is all before the execution of a graph algorithm. RAPIDS and cuGraph allow a portion or the complete analytic workflow to be accelerated. To achieve the maximum amount of acceleration, we encourage fully replacing existing code with cuGraph. -But sometimes it is easier to replace just a portion. +But sometimes it is easier to replace just a portion. Last Update ########### @@ -23,7 +23,7 @@ Release: 0.16 Information on `NetworkX `_ -This transition guide in an expansion of the Medium Blog on `NetworkX Compatibility +This transition guide in an expansion of the Medium Blog on `NetworkX Compatibility `_ @@ -33,7 +33,7 @@ Easy Path – Use NetworkX Graph Objects, Accelerated Algorithms Rather than updating all of your existing code, simply update the calls to graph algorithms by replacing the module name. This allows all the complicated ETL code to be unchanged while still seeing significate performance -improvements. +improvements. In the following example, the cuGraph module is being imported as “cnx”. While module can be assigned any name can be used, we picked cnx to reduce @@ -44,10 +44,10 @@ changes. :width: 600 It is that easy. All algorithms in cuGraph support a NetworkX graph object as -input and match the NetworkX API list of arguments. +input and match the NetworkX API list of arguments. Currently, cuGraph accepts both NetworkX Graph and DiGraph objects. We will be -adding support for Bipartite graph and Multigraph over the next few releases. +adding support for Bipartite graph and Multigraph over the next few releases. | @@ -58,7 +58,7 @@ Differences in Algorithms Since cuGraph currently does not support attribute rich graphs, those algorithms that return simple scores (centrality, clustering, etc.) best match the NetworkX process. Algorithms that return a subgraph will do so without -any additional attributes on the nodes or edges. +any additional attributes on the nodes or edges. Algorithms that exactly match ***************************** @@ -142,8 +142,8 @@ Algorithms where the results are different ****************************************** -For example, the NetworkX traversal algorithms typically return a generator -rather than a dictionary. +For example, the NetworkX traversal algorithms typically return a generator +rather than a dictionary. +----------------------------+-------------------------------------------------+ @@ -154,7 +154,7 @@ rather than a dictionary. | | (on roadmap to update) | +----------------------------+-------------------------------------------------+ | Jaccard coefficient | Currently we only do a 1-hop computation rather | -| | than an all-pairs. Fix is on roadmap | +| | than an all-pairs. Fix is on roadmap | +----------------------------+-------------------------------------------------+ | Breadth First Search (BFS) | Returns a Pandas DataFrame with: | | | [vertex][distance][predecessor] | @@ -185,14 +185,13 @@ code for building a NetworkX Graph:: ) -The code block is perfectly fine for NetworkX. However, the process of iterating over the dataframe and adding one node at a time is problematic for GPUs and something that we try and avoid. cuGraph stores data in columns (i.e. arrays). Resizing an array requires allocating a new array one element larger, copying the data, and adding the new value. That is not very efficient. +The code block is perfectly fine for NetworkX. However, the process of iterating over the dataframe and adding one node at a time is problematic for GPUs and something that we try and avoid. cuGraph stores data in columns (i.e. arrays). Resizing an array requires allocating a new array one element larger, copying the data, and adding the new value. That is not very efficient. -If your code follows the above model of inserting one element at a time, the we suggest either rewriting that code or using it as is within NetworkX and just accelerating the algorithms with cuGraph. +If your code follows the above model of inserting one element at a time, the we suggest either rewriting that code or using it as is within NetworkX and just accelerating the algorithms with cuGraph. Now, if your code bulk loads the data from Pandas, then RAPIDS can accelerate that process by orders of magnitude. .. image:: ../images/Nx_Cg_2.png :width: 600 -The above cuGraph code will create cuGraph.Graph object and not a NetworkX.Graph object. - +The above cuGraph code will create cuGraph.Graph object and not a NetworkX.Graph object. diff --git a/docs/cugraph/source/dev_resources/API.rst b/docs/cugraph/source/dev_resources/API.rst index 409e307fd..e32315d2f 100644 --- a/docs/cugraph/source/dev_resources/API.rst +++ b/docs/cugraph/source/dev_resources/API.rst @@ -2,4 +2,4 @@ API === -https://docs.rapids.ai/api/cugraph/nightly/api_docs/index.html \ No newline at end of file +https://docs.rapids.ai/api/cugraph/nightly/api_docs/index.html diff --git a/docs/cugraph/source/dev_resources/index.rst b/docs/cugraph/source/dev_resources/index.rst index 8568772b3..fc2c4f478 100644 --- a/docs/cugraph/source/dev_resources/index.rst +++ b/docs/cugraph/source/dev_resources/index.rst @@ -7,4 +7,4 @@ Developer Resources :maxdepth: 3 https://docs.rapids.ai/maintainers - API.rst \ No newline at end of file + API.rst diff --git a/docs/cugraph/source/graph_support/DGL_support.md b/docs/cugraph/source/graph_support/DGL_support.md index fc1f1b45b..dc4f66180 100644 --- a/docs/cugraph/source/graph_support/DGL_support.md +++ b/docs/cugraph/source/graph_support/DGL_support.md @@ -2,7 +2,7 @@ ## Description -[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.DGLGraph) class, which uses cugraph for storing graph structure and node/edge feature data. Using cugraph as the backend allows DGL users to access a collection of GPU accelerated algorithms for graph analytics, such as centrality computation and community detection. +[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.DGLGraph) class, which uses cugraph for storing graph structure and node/edge feature data. Using cugraph as the backend allows DGL users to access a collection of GPU accelerated algorithms for graph analytics, such as centrality computation and community detection. ## Conda @@ -22,7 +22,7 @@ mamba env create -n cugraph_dgl_dev --file conda/cugraph_dgl_dev_11.6.yml ### Install in editable mode ``` -pip install -e . +pip install -e . ``` ### Run tests @@ -43,12 +43,12 @@ sampler = dgl.dataloading.NeighborSampler( train_dataloader = dgl.dataloading.DataLoader( cugraph_g, -train_idx, -sampler, -device=device, +train_idx, +sampler, +device=device, batch_size=1024, shuffle=True, -drop_last=False, +drop_last=False, num_workers=0) ``` diff --git a/docs/cugraph/source/graph_support/PyG_support.md b/docs/cugraph/source/graph_support/PyG_support.md index 42d4d1c55..b57ce7fcc 100644 --- a/docs/cugraph/source/graph_support/PyG_support.md +++ b/docs/cugraph/source/graph_support/PyG_support.md @@ -1,3 +1,3 @@ # cugraph_pyg -[RAPIDS](https://rapids.ai) cugraph_pyg enables the ability to use cugraph graph storage and sampling with PyTorch Geometric (PyG). PyG users will have access to cuGraph through the PyG GraphStore, FeatureStore, and Sampler interfaces. \ No newline at end of file +[RAPIDS](https://rapids.ai) cugraph_pyg enables the ability to use cugraph graph storage and sampling with PyTorch Geometric (PyG). PyG users will have access to cuGraph through the PyG GraphStore, FeatureStore, and Sampler interfaces. diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md index a1b80e927..8a5158f2f 100644 --- a/docs/cugraph/source/graph_support/algorithms.md +++ b/docs/cugraph/source/graph_support/algorithms.md @@ -92,4 +92,4 @@ Copyright (c) 2019 - 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/algorithms/Similarity.md b/docs/cugraph/source/graph_support/algorithms/Similarity.md index 18c0a94d5..96adc25ea 100644 --- a/docs/cugraph/source/graph_support/algorithms/Similarity.md +++ b/docs/cugraph/source/graph_support/algorithms/Similarity.md @@ -5,8 +5,8 @@ The RAPIDS cuGraph Similarity folder contain a collection of Jupyter Notebooks t Results of Similarity algorithms are often used to answer questions like: * Could two vertices be duplicates or aliases of the same actor? * Can we predict missing edges based of the similarity between two nodes? -* Are multiple similar communities within the graph? -* Can I create recommendations based on the similarity between vertices in the graph. +* Are multiple similar communities within the graph? +* Can I create recommendations based on the similarity between vertices in the graph. Manipulation of the data before or after the graph analytic is not covered here. Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended @@ -35,4 +35,4 @@ Copyright (c) 2019 - 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md b/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md index 7dc692684..89e5e0bdb 100644 --- a/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md @@ -39,4 +39,4 @@ Copyright (c) 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/algorithms/degree_centrality.md b/docs/cugraph/source/graph_support/algorithms/degree_centrality.md index 64fe694e4..9253c665f 100644 --- a/docs/cugraph/source/graph_support/algorithms/degree_centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/degree_centrality.md @@ -11,7 +11,7 @@ Degree centrality of a vertex 𝑣 is the sum of the edges incident on that node * When you need a really quick identifcation of important nodes on very simply structured data. * In cases like collaboration networks where all links have equal importance. * In many biologic and transportation networks, shear number of connections is important to itentify critical nodes whether they be proteins or airports. -* In huge graphs, Degree centrality is a the quickest +* In huge graphs, Degree centrality is a the quickest ## When not to use Degree Centrality * When weights, edge direction or edge types matter @@ -29,4 +29,4 @@ Copyright (c) 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md b/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md index 5217dc497..8a9c7c7c7 100644 --- a/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md @@ -18,11 +18,11 @@ Where M(v) is the adjacency list for the set of vertices(v) and λ is a constant ## When to use Eigenvector Centrality * When the quality and quantity of edges matters, in other words, connections to other high-degree nodes is important -* To calculate influence in nuanced networks like social and financial networks. +* To calculate influence in nuanced networks like social and financial networks. ## When not to use Eigenvector Centrality * in graphs with many disconnected groups -* in graphs containing many distinct and different communities +* in graphs containing many distinct and different communities * in networks with negative weights * in huge networks eigenvector centrality can become computationally infeasible in single threaded systems. @@ -39,4 +39,3 @@ Licensed under the Apache License, Version 2.0 (the "License"); you may not use Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ___ - diff --git a/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md b/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md index c9ffc76d1..dde98d71e 100644 --- a/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md +++ b/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md @@ -1,6 +1,6 @@ # Jaccard Similarity -The Jaccard similarity between two sets is defined as the ratio of the volume of their intersection divided by the volume of their union. +The Jaccard similarity between two sets is defined as the ratio of the volume of their intersection divided by the volume of their union. The Jaccard Similarity can then be defined as @@ -31,4 +31,4 @@ Copyright (c) 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/algorithms/katz_centrality.md b/docs/cugraph/source/graph_support/algorithms/katz_centrality.md index 3bfe4d40c..69b5d6b27 100644 --- a/docs/cugraph/source/graph_support/algorithms/katz_centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/katz_centrality.md @@ -25,4 +25,4 @@ Copyright (c) 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md b/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md index 026d713fb..d9f9f681e 100644 --- a/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md +++ b/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md @@ -15,7 +15,7 @@ $overlap(A,B) = \frac{|A \cap B|}{min(|A|,|B|)}$ ## When not to use Overlap Similarity * You are trying to compare graphs of extremely different sizes * In overly sparse or dense graph can overlap similarity can miss relationships or give fals positives respectively. -* In directed graphs, there are better algorithms to use. +* In directed graphs, there are better algorithms to use. ## How computationally expensive is it? @@ -29,4 +29,4 @@ Copyright (c) 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md b/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md index 01b6709de..67d981815 100644 --- a/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md +++ b/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md @@ -23,7 +23,7 @@ In graphs, the sets refer to the set of connected nodes or neighborhood of nodes ## How computationally expensive is it? While cuGraph's parallelism mitigates run cost, [Big O notation](https://en.wikipedia.org/wiki/Big_O_notation) is still the standard to compare algorithm execution time. -The cost to run O(n * m) where n is the number of nodes in the graph and m is the number of groups to test. +The cost to run O(n * m) where n is the number of nodes in the graph and m is the number of groups to test. ___ Copyright (c) 2023, NVIDIA CORPORATION. @@ -31,4 +31,4 @@ Copyright (c) 2023, NVIDIA CORPORATION. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -___ \ No newline at end of file +___ diff --git a/docs/cugraph/source/graph_support/compatibility.rst b/docs/cugraph/source/graph_support/compatibility.rst index be0cc21c2..ddb3f1d5f 100644 --- a/docs/cugraph/source/graph_support/compatibility.rst +++ b/docs/cugraph/source/graph_support/compatibility.rst @@ -5,4 +5,4 @@ Compatibility .. toctree:: :maxdepth: 3 -Compatibility document coming soon \ No newline at end of file +Compatibility document coming soon diff --git a/docs/cugraph/source/graph_support/cugraph_service.rst b/docs/cugraph/source/graph_support/cugraph_service.rst index 620b22d1e..658f9e731 100644 --- a/docs/cugraph/source/graph_support/cugraph_service.rst +++ b/docs/cugraph/source/graph_support/cugraph_service.rst @@ -5,5 +5,5 @@ CuGraph Service .. toctree:: :maxdepth: 3 - -Cugraph Service for remote access to a server-based cuGraph(https://github.com/rapidsai/cugraph/blob/branch-23.04/python/cugraph-service/README.md) \ No newline at end of file + +Cugraph Service for remote access to a server-based cuGraph(https://github.com/rapidsai/cugraph/blob/branch-23.04/python/cugraph-service/README.md) diff --git a/docs/cugraph/source/graph_support/cugraphops_support.rst b/docs/cugraph/source/graph_support/cugraphops_support.rst index 08ae3b218..fd79564f8 100644 --- a/docs/cugraph/source/graph_support/cugraphops_support.rst +++ b/docs/cugraph/source/graph_support/cugraphops_support.rst @@ -7,4 +7,4 @@ cugraph-ops aims to be a low-level, framework agnostic library providing commonl .. toctree:: :maxdepth: 3 - https://github.com/rapidsai/cugraph-ops/blob/branch-23.04/README.md \ No newline at end of file + https://github.com/rapidsai/cugraph-ops/blob/branch-23.04/README.md diff --git a/docs/cugraph/source/graph_support/datastores.rst b/docs/cugraph/source/graph_support/datastores.rst index f92110077..50d8461e7 100644 --- a/docs/cugraph/source/graph_support/datastores.rst +++ b/docs/cugraph/source/graph_support/datastores.rst @@ -6,4 +6,3 @@ Data Stores property_graph.md knowledge_stores.md feature_stores.md - diff --git a/docs/cugraph/source/graph_support/feature_stores.md b/docs/cugraph/source/graph_support/feature_stores.md index f40cab72e..ef9358c4c 100644 --- a/docs/cugraph/source/graph_support/feature_stores.md +++ b/docs/cugraph/source/graph_support/feature_stores.md @@ -1,3 +1,3 @@ # Feature Store -Coming Soon \ No newline at end of file +Coming Soon diff --git a/docs/cugraph/source/graph_support/gnn_support.rst b/docs/cugraph/source/graph_support/gnn_support.rst index 3c92dc360..639b657c6 100644 --- a/docs/cugraph/source/graph_support/gnn_support.rst +++ b/docs/cugraph/source/graph_support/gnn_support.rst @@ -5,8 +5,8 @@ Graph Neural Network Support .. toctree:: :maxdepth: 2 - + PyG_support.md DGL_support.md cugraphops_support.rst - wholegraph_support.md \ No newline at end of file + wholegraph_support.md diff --git a/docs/cugraph/source/graph_support/graph_algorithms.rst b/docs/cugraph/source/graph_support/graph_algorithms.rst index a8ba01aa9..38dd8ccc2 100644 --- a/docs/cugraph/source/graph_support/graph_algorithms.rst +++ b/docs/cugraph/source/graph_support/graph_algorithms.rst @@ -4,5 +4,5 @@ Algorithms .. toctree:: :maxdepth: 3 - - algorithms.md \ No newline at end of file + + algorithms.md diff --git a/docs/cugraph/source/graph_support/index.rst b/docs/cugraph/source/graph_support/index.rst index 9526fae7e..67aba7428 100644 --- a/docs/cugraph/source/graph_support/index.rst +++ b/docs/cugraph/source/graph_support/index.rst @@ -10,4 +10,4 @@ Graph Support compatibility.rst gnn_support.rst datastores.rst - cugraph_service.rst \ No newline at end of file + cugraph_service.rst diff --git a/docs/cugraph/source/graph_support/knowledge_stores.md b/docs/cugraph/source/graph_support/knowledge_stores.md index 4d6028a59..1749eb2b4 100644 --- a/docs/cugraph/source/graph_support/knowledge_stores.md +++ b/docs/cugraph/source/graph_support/knowledge_stores.md @@ -1,3 +1,3 @@ # Knowledge Store -Coming Soon \ No newline at end of file +Coming Soon diff --git a/docs/cugraph/source/graph_support/property_graph.md b/docs/cugraph/source/graph_support/property_graph.md index 614910b79..ef07be79b 100644 --- a/docs/cugraph/source/graph_support/property_graph.md +++ b/docs/cugraph/source/graph_support/property_graph.md @@ -16,8 +16,8 @@ Property Graph enables: This is an example of using the cuGraph Property Graph in a two stage analysis. ``` -import cudf -import cugraph +import cudf +import cugraph from cugraph.experimental import PropertyGraph # Import a built-in dataset @@ -29,22 +29,22 @@ graph = cugraph.Graph(directed=False) G = karate.get_graph(create_using=graph,fetch=True) df = G.edgelist.edgelist_df -pG = PropertyGraph() +pG = PropertyGraph() pG. add_edge_data(df, vertex_col_names=("src", "dst")) -# Run Louvain to get the partition number for each vertex. -# Set resolution accordingly to identify two primary partitions. +# Run Louvain to get the partition number for each vertex. +# Set resolution accordingly to identify two primary partitions. (partition_info, _) = cugraph.louvain(pG.extract_subgraph(create_using=graph), resolution=0.6) -# Add the partition numbers back to the Property Graph as vertex properties +# Add the partition numbers back to the Property Graph as vertex properties pG.add_vertex_data(partition_info, vertex_col_name="vertex") -# Use the partition properties to extract a Graph for each partition. +# Use the partition properties to extract a Graph for each partition. G0 = pG.extract_subgraph(selection=pG.select_vertices("partition == 0")) G1 = pG.extract_subgraph(selection=pG. select_vertices("partition == 1")) -# Run pagerank on each graph, print results. -pageranks0 = cugraph.pagerank(G0) -pageranks1 = cugraph.pagerank(G1) +# Run pagerank on each graph, print results. +pageranks0 = cugraph.pagerank(G0) +pageranks1 = cugraph.pagerank(G1) print(pageranks0.sort_values (by="pagerank", ascending=False).head(3)) print(pageranks1.sort_values (by="pagerank", ascending=False).head(3)) -``` \ No newline at end of file +``` diff --git a/docs/cugraph/source/graph_support/wholegraph_support.md b/docs/cugraph/source/graph_support/wholegraph_support.md index fa26700a6..d1c5eaf72 100644 --- a/docs/cugraph/source/graph_support/wholegraph_support.md +++ b/docs/cugraph/source/graph_support/wholegraph_support.md @@ -1,4 +1,4 @@ # WholeGraph [RAPIDS](https://rapids.ai) [WholeGraph](https://github.com/rapidsai/wholegraph) is designed to help train large-scale Graph Neural Networks(GNN). -Please see [WholeGraph Introduction](https://github.com/rapidsai/wholegraph/blob/main/README.md) for more details \ No newline at end of file +Please see [WholeGraph Introduction](https://github.com/rapidsai/wholegraph/blob/main/README.md) for more details diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 955eb6d54..b18a79d33 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -18,7 +18,7 @@ RAPIDS Graph documentation - :abbr:`cugraph-service (Graph-as-a-service provides both Client and Server packages)` * - :abbr:`pylibcugraph (light-weight Python wrapper with no guard rails)` - :abbr:`cugraph-dgl (Accelerated extensions for use with the DGL framework)` - - + - * - :abbr:`libcugraph (C++ API)` - :abbr:`cugraph-pyg (Accelerated extensions for use with the PyG framework)` - @@ -33,7 +33,7 @@ Introduction ~~~~~~~~~~~~ cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call -graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or +graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. Note: We are redoing all of our documents, please be patient as we update diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index 0695c690a..4d601bf32 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -1,7 +1,7 @@ # Getting cuGraph Packages -Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) +Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. @@ -65,4 +65,3 @@ Also available: * nx-cugraph-cu12
- diff --git a/docs/cugraph/source/installation/index.rst b/docs/cugraph/source/installation/index.rst index 27858b770..8ad12c589 100644 --- a/docs/cugraph/source/installation/index.rst +++ b/docs/cugraph/source/installation/index.rst @@ -5,6 +5,6 @@ Installation .. toctree:: :maxdepth: 3 - + getting_cugraph source_build diff --git a/docs/cugraph/source/references/cugraph_ref.md b/docs/cugraph/source/references/cugraph_ref.md index a646d6da2..845436a60 100644 --- a/docs/cugraph/source/references/cugraph_ref.md +++ b/docs/cugraph/source/references/cugraph_ref.md @@ -43,4 +43,3 @@

- diff --git a/docs/cugraph/source/references/datasets.md b/docs/cugraph/source/references/datasets.md index 3d45dec18..35234de87 100644 --- a/docs/cugraph/source/references/datasets.md +++ b/docs/cugraph/source/references/datasets.md @@ -4,18 +4,18 @@ karate - W. W. Zachary, *An information flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977). dolphins - D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, - *The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations*, + *The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations*, Behavioral Ecology and Sociobiology 54, 396-405 (2003). netscience - M. E. J. Newman, - *Finding community structure in networks using the eigenvectors of matrices*, + *Finding community structure in networks using the eigenvectors of matrices*, Preprint physics/0605087 (2006). email-Eu-core - Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. - *Local Higher-order Graph Clustering.* + *Local Higher-order Graph Clustering.* In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. - - J. Leskovec, J. Kleinberg and C. Faloutsos. - *Graph Evolution: Densification and Shrinking Diameters*. - ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf + - J. Leskovec, J. Kleinberg and C. Faloutsos. + *Graph Evolution: Densification and Shrinking Diameters*. + ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf polbooks - - V. Krebs, unpublished, http://www.orgnet.com/. \ No newline at end of file + - V. Krebs, unpublished, http://www.orgnet.com/. diff --git a/docs/cugraph/source/references/index.rst b/docs/cugraph/source/references/index.rst index d7a173a83..9ea51a083 100644 --- a/docs/cugraph/source/references/index.rst +++ b/docs/cugraph/source/references/index.rst @@ -5,7 +5,7 @@ References .. toctree:: :maxdepth: 3 - + cugraph_ref.md datasets.md licenses.md diff --git a/docs/cugraph/source/references/licenses.md b/docs/cugraph/source/references/licenses.md index dfc950023..b95905d9f 100644 --- a/docs/cugraph/source/references/licenses.md +++ b/docs/cugraph/source/references/licenses.md @@ -1,7 +1,7 @@ # License Most of the Graph code is open-sourced and developed under the Apache 2.0 licnese. -The cugraph-ops code is closed sourced and developed under a NVIDIA copyright +The cugraph-ops code is closed sourced and developed under a NVIDIA copyright diff --git a/docs/cugraph/source/releases/index.rst b/docs/cugraph/source/releases/index.rst index 7bd4f6dfa..cbd22324b 100644 --- a/docs/cugraph/source/releases/index.rst +++ b/docs/cugraph/source/releases/index.rst @@ -2,4 +2,4 @@ Releases ======== -https://github.com/rapidsai/cugraph/blob/main/CHANGELOG.md \ No newline at end of file +https://github.com/rapidsai/cugraph/blob/main/CHANGELOG.md diff --git a/docs/cugraph/source/tutorials/community_resources.md b/docs/cugraph/source/tutorials/community_resources.md index 572f85a01..1c4362393 100644 --- a/docs/cugraph/source/tutorials/community_resources.md +++ b/docs/cugraph/source/tutorials/community_resources.md @@ -1,2 +1,2 @@ # Commmunity Resources -[Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib) \ No newline at end of file +[Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib) diff --git a/docs/cugraph/source/tutorials/cugraph_blogs.rst b/docs/cugraph/source/tutorials/cugraph_blogs.rst index 368dbcce4..373e846f6 100644 --- a/docs/cugraph/source/tutorials/cugraph_blogs.rst +++ b/docs/cugraph/source/tutorials/cugraph_blogs.rst @@ -3,7 +3,7 @@ cuGraph Blogs and Presentations ************************************************ The RAPIDS team blogs at https://medium.com/rapids-ai, and many of -these blog posts provide deeper dives into features from cuGraph. +these blog posts provide deeper dives into features from cuGraph. Here, we've selected just a few that are of particular interest to cuGraph users: diff --git a/docs/cugraph/source/tutorials/cugraph_notebooks.md b/docs/cugraph/source/tutorials/cugraph_notebooks.md index d4251d4c9..559ba36e9 100644 --- a/docs/cugraph/source/tutorials/cugraph_notebooks.md +++ b/docs/cugraph/source/tutorials/cugraph_notebooks.md @@ -53,7 +53,7 @@ Running the example in these notebooks requires: * The latest version of RAPIDS with cuGraph. * Download via Docker, Conda (See [__Getting Started__](https://rapids.ai/start.html)) - + * cuGraph is dependent on the latest version of cuDF. Please install all components of RAPIDS * Python 3.8+ * A system with an NVIDIA GPU: Pascal architecture or better @@ -66,7 +66,7 @@ Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at -http://www.apache.org/licenses/LICENSE-2.0 +http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. diff --git a/docs/cugraph/source/tutorials/how_to_guides.md b/docs/cugraph/source/tutorials/how_to_guides.md index 42da6ed21..80be5b4ab 100644 --- a/docs/cugraph/source/tutorials/how_to_guides.md +++ b/docs/cugraph/source/tutorials/how_to_guides.md @@ -1,9 +1,9 @@ # How To Guides -- Basic use of cuGraph, on the page -- Property graph with analytic flow -- GNN – model building -- cuGraph Service – client/server setup and use (ucx) -- MNMG Graph – dask, rmm basics and examples -- Pylibcugraph – why and how -- Cugraph for C, C++ users -- Use of nvidia-smi with cugraph \ No newline at end of file +- Basic use of cuGraph, on the page +- Property graph with analytic flow +- GNN – model building +- cuGraph Service – client/server setup and use (ucx) +- MNMG Graph – dask, rmm basics and examples +- Pylibcugraph – why and how +- Cugraph for C, C++ users +- Use of nvidia-smi with cugraph diff --git a/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md b/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md index a5541109c..634539cd2 100644 --- a/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md +++ b/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md @@ -18,7 +18,7 @@ partition data into these GPU devices. The partition method guarantees that each GPU can access one continuous part of the entire memory. Here "can access" means can directly access from CUDA kernels, but the memory doesn't have to be physically on that GPU. For example,it can be on host memory or other GPU's device memory that can be access using P2P. -In that case the stored data has its own granularity that shouldn't be split. Data granularity can be specified while +In that case the stored data has its own granularity that shouldn't be split. Data granularity can be specified while creating WholeMemory. Then each data granularity can be considered as a block of data. The follow figure shows the layout of 15 data block over 4 GPUs. diff --git a/docs/cugraph/source/wholegraph/index.rst b/docs/cugraph/source/wholegraph/index.rst index 2a69544b4..bb2281b13 100644 --- a/docs/cugraph/source/wholegraph/index.rst +++ b/docs/cugraph/source/wholegraph/index.rst @@ -11,4 +11,3 @@ RAPIDS WholeGraph has following package: basics/index installation/index - diff --git a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md index 5b2072b05..57314dcd4 100644 --- a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md +++ b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md @@ -1,7 +1,7 @@ # Getting the WholeGraph Packages -Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) +Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. diff --git a/print_env.sh b/print_env.sh index 6f2d33b0e..43abd1c7e 100644 --- a/print_env.sh +++ b/print_env.sh @@ -1,7 +1,8 @@ #!/usr/bin/env bash +# Copyright (c) 2019-2024, NVIDIA CORPORATION. # Reports relevant environment information useful for diagnosing and # debugging cuGraph issues. -# Usage: +# Usage: # "./print_env.sh" - prints to stdout # "./print_env.sh > env.txt" - prints to file "env.txt" @@ -14,16 +15,16 @@ git submodule status --recursive else echo "Not inside a git repository" fi -echo +echo echo "***OS Information***" cat /etc/*-release uname -a -echo +echo echo "***GPU Information***" nvidia-smi -echo +echo echo "***CPU***" lscpu @@ -31,15 +32,15 @@ echo echo "***CMake***" which cmake && cmake --version -echo +echo echo "***g++***" which g++ && g++ --version -echo +echo echo "***nvcc***" which nvcc && nvcc --version -echo +echo echo "***Python***" which python && python -c "import sys; print('Python {0}.{1}.{2}'.format(sys.version_info[0], sys.version_info[1], sys.version_info[2]))" From b22f56296c15ffa87dea68d1290c875e3e4e6445 Mon Sep 17 00:00:00 2001 From: Kyle Edwards Date: Mon, 11 Mar 2024 17:00:45 -0400 Subject: [PATCH 331/384] Replace local copyright check with pre-commit-hooks verify-copyright (#4130) The local `copyright.py` script is bug-prone. Replace it with a more robust centralized script from `pre-commit-hooks`. Authors: - Kyle Edwards (https://github.com/KyleFromNVIDIA) - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) - Jake Awe (https://github.com/AyodeAwe) URL: https://github.com/rapidsai/cugraph/pull/4130 --- .pre-commit-config.yaml | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3d893e0e5..ddb84d8a0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -46,16 +46,20 @@ repos: ) types_or: [c, c++, cuda] args: ["-fallback-style=none", "-style=file", "-i"] - - repo: local - hooks: - - id: copyright-check - name: copyright-check - entry: python ./ci/checks/copyright.py --git-modified-only --update-current-year - language: python - pass_filenames: false - additional_dependencies: [gitpython] - repo: https://github.com/rapidsai/dependency-file-generator rev: v1.8.0 hooks: - id: rapids-dependency-file-generator args: ["--clean"] + - repo: https://github.com/rapidsai/pre-commit-hooks + rev: v0.0.1 + hooks: + - id: verify-copyright + files: | + (?x) + [.](cmake|cpp|cu|cuh|h|hpp|sh|pxd|py|pyx)$| + CMakeLists[.]txt$| + CMakeLists_standalone[.]txt$| + [.]flake8[.]cython$| + meta[.]yaml$| + setup[.]cfg$ From 0cab94580cb5099674ae103e73e5604eab21c7a3 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 12 Mar 2024 09:18:49 -0400 Subject: [PATCH 332/384] Starting work on blog links and nx_cugraph docs (#4160) This PR starts to address updating cuGraph docs to reflect recent blogs and work on nx_cugraph as a backend for NetworkX Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4160 --- docs/cugraph/source/api_docs/index.rst | 2 +- docs/cugraph/source/basics/nx_transition.rst | 42 ++--- .../source/graph_support/property_graph.md | 2 +- docs/cugraph/source/images/ancestors.png | Bin 0 -> 14156 bytes docs/cugraph/source/images/bfs_tree.png | Bin 0 -> 13763 bytes docs/cugraph/source/images/conn_component.png | Bin 0 -> 13053 bytes docs/cugraph/source/images/descendents.png | Bin 0 -> 13639 bytes docs/cugraph/source/images/k_truss.png | Bin 0 -> 13389 bytes docs/cugraph/source/images/katz.png | Bin 0 -> 13558 bytes docs/cugraph/source/images/pagerank.png | Bin 0 -> 13243 bytes docs/cugraph/source/images/sssp.png | Bin 0 -> 14542 bytes docs/cugraph/source/images/wcc.png | Bin 0 -> 14519 bytes docs/cugraph/source/index.rst | 1 + docs/cugraph/source/nx_cugraph/index.rst | 9 + docs/cugraph/source/nx_cugraph/nx_cugraph.md | 165 ++++++++++++++++++ .../source/tutorials/community_resources.md | 2 + .../source/tutorials/cugraph_blogs.rst | 15 ++ 17 files changed, 208 insertions(+), 30 deletions(-) create mode 100644 docs/cugraph/source/images/ancestors.png create mode 100644 docs/cugraph/source/images/bfs_tree.png create mode 100644 docs/cugraph/source/images/conn_component.png create mode 100644 docs/cugraph/source/images/descendents.png create mode 100644 docs/cugraph/source/images/k_truss.png create mode 100644 docs/cugraph/source/images/katz.png create mode 100644 docs/cugraph/source/images/pagerank.png create mode 100644 docs/cugraph/source/images/sssp.png create mode 100644 docs/cugraph/source/images/wcc.png create mode 100644 docs/cugraph/source/nx_cugraph/index.rst create mode 100644 docs/cugraph/source/nx_cugraph/nx_cugraph.md diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 1b907165a..d76902772 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -15,7 +15,7 @@ Core Graph API Documentation cugraph_c/index.rst cugraph_cpp/index.rst -Graph Nerual Networks API Documentation +Graph Neural Networks API Documentation --------------------------------------- .. toctree:: diff --git a/docs/cugraph/source/basics/nx_transition.rst b/docs/cugraph/source/basics/nx_transition.rst index 984986581..07c2ad26f 100644 --- a/docs/cugraph/source/basics/nx_transition.rst +++ b/docs/cugraph/source/basics/nx_transition.rst @@ -1,30 +1,20 @@ ************************************** -NetworkX Compatibility and Transition +NetworkX by calling cuGraph Algorithms ************************************** + *Note: this is a work in progress and will be updatred and changed as we better flesh out compatibility issues* -One of the goals of RAPIDS cuGraph is to mimic the NetworkX API to simplify -the transition to accelerated GPU data science. However, graph analysis, -also called network science, like most other data science workflow, is more -than just running an algorithm. Graph data requires cleaning and prep (ETL) -and then the construction of a graph object; that is all before the execution -of a graph algorithm. RAPIDS and cuGraph allow a portion or the complete -analytic workflow to be accelerated. To achieve the maximum amount of -acceleration, we encourage fully replacing existing code with cuGraph. -But sometimes it is easier to replace just a portion. - -Last Update -########### +Latest Update +############# -Last Update: Oct 14th, 2020 -Release: 0.16 +Last Update: March 7th, 2024 +Release: 24.04 -Information on `NetworkX `_ - -This transition guide in an expansion of the Medium Blog on `NetworkX Compatibility -`_ +**CuGraph is now a registered backend for networkX. This is described in the following blog: +`Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics +`_ Easy Path – Use NetworkX Graph Objects, Accelerated Algorithms @@ -33,12 +23,11 @@ Easy Path – Use NetworkX Graph Objects, Accelerated Algorithms Rather than updating all of your existing code, simply update the calls to graph algorithms by replacing the module name. This allows all the complicated ETL code to be unchanged while still seeing significate performance +improvements. Again this will be deprecated since networkX dispatching to nx_cugraph +has many advantages. + improvements. -In the following example, the cuGraph module is being imported as “cnx”. -While module can be assigned any name can be used, we picked cnx to reduce -the amount of text to be changed. The text highlighted in yellow indicates -changes. .. image:: ../images/Nx_Cg_1.png :width: 600 @@ -49,9 +38,6 @@ input and match the NetworkX API list of arguments. Currently, cuGraph accepts both NetworkX Graph and DiGraph objects. We will be adding support for Bipartite graph and Multigraph over the next few releases. -| - - Differences in Algorithms ########################## @@ -169,8 +155,8 @@ Graph Building ############## The biggest difference between NetworkX and cuGraph is with how Graph objects -are built. NetworkX, for the most part, stores graph data in a dictionary. -That structure allows easy insertion of new records. Consider the following +are built. NetworkX, for the most part, stores graph data in a dictionary. +That structure allows easy insertion of new records. 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literal 0 HcmV?d00001 diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index b18a79d33..9ea9e4d65 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -46,6 +46,7 @@ the docs and links :caption: Contents: basics/index + nx_cugraph/index installation/index tutorials/index graph_support/index diff --git a/docs/cugraph/source/nx_cugraph/index.rst b/docs/cugraph/source/nx_cugraph/index.rst new file mode 100644 index 000000000..ef6f51601 --- /dev/null +++ b/docs/cugraph/source/nx_cugraph/index.rst @@ -0,0 +1,9 @@ +=============================== +nxCugraph as a NetworkX Backend +=============================== + + +.. toctree:: + :maxdepth: 2 + + nx_cugraph.md diff --git a/docs/cugraph/source/nx_cugraph/nx_cugraph.md b/docs/cugraph/source/nx_cugraph/nx_cugraph.md new file mode 100644 index 000000000..8d497e3a1 --- /dev/null +++ b/docs/cugraph/source/nx_cugraph/nx_cugraph.md @@ -0,0 +1,165 @@ +### nx_cugraph + + +Whereas previous versions of cuGraph have included mechanisms to make it +trivial to plug in cuGraph algorithm calls. Beginning with version 24.02, nx-cuGraph +is now a [networkX backend](). +The user now need only [install nx-cugraph]() +to experience GPU speedups. + +Lets look at some examples of algorithm speedups comparing CPU based NetworkX to dispatched versions run on GPU with nx_cugraph. + +Each chart has three measurements. +* NX - running the algorithm natively with networkX on CPU. +* nx-cugraph - running with GPU accelerated networkX achieved by simply calling the cugraph backend. This pays the overhead of building the GPU resident object for each algorithm called. This achieves significant improvement but stil isn't compleltely optimum. +* nx-cugraph (preconvert) - This is a bit more complicated since it involves building (precomputing) the GPU resident graph ahead and reusing it for each algorithm. + + +![Ancestors](../images/ancestors.png) +![BFS Tree](../images/bfs_tree.png) +![Connected Components](../images/conn_component.png) +![Descendents](../images/descendents.png) +![Katz](../images/katz.png) +![Pagerank](../images/pagerank.png) +![Single Source Shortest Path](../images/sssp.png) +![Weakly Connected Components](../images/wcc.png) + + +The following algorithms are supported and automatically dispatched to nx-cuGraph for acceleration. + +#### Algorithms +``` +bipartite + ├─ basic + │ └─ is_bipartite + └─ generators + └─ complete_bipartite_graph +centrality + ├─ betweenness + │ ├─ betweenness_centrality + │ └─ edge_betweenness_centrality + ├─ degree_alg + │ ├─ degree_centrality + │ ├─ in_degree_centrality + │ └─ out_degree_centrality + ├─ eigenvector + │ └─ eigenvector_centrality + └─ katz + └─ katz_centrality +cluster + ├─ average_clustering + ├─ clustering + ├─ transitivity + └─ triangles +community + └─ louvain + └─ louvain_communities +components + ├─ connected + │ ├─ connected_components + │ ├─ is_connected + │ ├─ node_connected_component + │ └─ number_connected_components + └─ weakly_connected + ├─ is_weakly_connected + ├─ number_weakly_connected_components + └─ weakly_connected_components +core + ├─ core_number + └─ k_truss +dag + ├─ ancestors + └─ descendants +isolate + ├─ is_isolate + ├─ isolates + └─ number_of_isolates +link_analysis + ├─ hits_alg + │ └─ hits + └─ pagerank_alg + └─ pagerank +operators + └─ unary + ├─ complement + └─ reverse +reciprocity + ├─ overall_reciprocity + └─ reciprocity +shortest_paths + └─ unweighted + ├─ single_source_shortest_path_length + └─ single_target_shortest_path_length +traversal + └─ breadth_first_search + ├─ bfs_edges + ├─ bfs_layers + ├─ bfs_predecessors + ├─ bfs_successors + ├─ bfs_tree + ├─ descendants_at_distance + └─ generic_bfs_edges +tree + └─ recognition + ├─ is_arborescence + ├─ is_branching + ├─ is_forest + └─ is_tree +``` + +#### Generators +``` +classic + ├─ barbell_graph + ├─ circular_ladder_graph + ├─ complete_graph + ├─ complete_multipartite_graph + ├─ cycle_graph + ├─ empty_graph + ├─ ladder_graph + ├─ lollipop_graph + ├─ null_graph + ├─ path_graph + ├─ star_graph + ├─ tadpole_graph + ├─ trivial_graph + ├─ turan_graph + └─ wheel_graph +community + └─ caveman_graph +small + ├─ bull_graph + ├─ chvatal_graph + ├─ cubical_graph + ├─ desargues_graph + ├─ diamond_graph + ├─ dodecahedral_graph + ├─ frucht_graph + ├─ heawood_graph + ├─ house_graph + ├─ house_x_graph + ├─ icosahedral_graph + ├─ krackhardt_kite_graph + ├─ moebius_kantor_graph + ├─ octahedral_graph + ├─ pappus_graph + ├─ petersen_graph + ├─ sedgewick_maze_graph + ├─ tetrahedral_graph + ├─ truncated_cube_graph + ├─ truncated_tetrahedron_graph + └─ tutte_graph +social + ├─ davis_southern_women_graph + ├─ florentine_families_graph + ├─ karate_club_graph + └─ les_miserables_graph +``` + +#### Other + +``` +convert_matrix + ├─ from_pandas_edgelist + └─ from_scipy_sparse_array +``` diff --git a/docs/cugraph/source/tutorials/community_resources.md b/docs/cugraph/source/tutorials/community_resources.md index 1c4362393..975f11965 100644 --- a/docs/cugraph/source/tutorials/community_resources.md +++ b/docs/cugraph/source/tutorials/community_resources.md @@ -1,2 +1,4 @@ # Commmunity Resources [Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib) +[RAPIDS Containers on Docker Hub](https://catalog.ngc.nvidia.com/containers) +[RAPIDS PyTorch Container in Docker](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg) diff --git a/docs/cugraph/source/tutorials/cugraph_blogs.rst b/docs/cugraph/source/tutorials/cugraph_blogs.rst index 373e846f6..3665f425e 100644 --- a/docs/cugraph/source/tutorials/cugraph_blogs.rst +++ b/docs/cugraph/source/tutorials/cugraph_blogs.rst @@ -9,6 +9,17 @@ Here, we've selected just a few that are of particular interest to cuGraph users Blogs & Conferences ==================== +2024 +------ +Coming Soon + +2023 +------ + * `Intro to Graph Neural Networks with cuGraph-DGL `_ + * `GTC 2023 Ask the Experts Q&A `_ + * `Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics `_ + * `Introduction to Graph Neural Networks with NVIDIA cuGraph-DGL `_ + * `Supercharge Graph Analytics at Scale with GPU-CPU Fusion for 100x Performance `_ 2022 ------ * `GTC: State of cuGraph (video & slides) `_ @@ -50,6 +61,8 @@ Media Academic Papers =============== + * Seunghwa Kang, Chuck Hastings, Joe Eaton, Brad Rees `cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing `_ + * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press * S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020 @@ -58,6 +71,8 @@ Academic Papers * Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504). + * A Gondhalekar, P Sathre, W Feng `Hybrid CPU-GPU Implementation of Edge-Connected Jaccard Similarity in Graph Datasets `_ + Other Blogs ======================== From cd7a4bd3a4c546f888cf2c1e0f14101a63d1f8cb Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 13 Mar 2024 09:16:46 -0500 Subject: [PATCH 333/384] Adds nx-cugraph benchmarks for APIs added to prior releases that were never benchmarked (#4228) * Adds benchmark for nx-cugraph `pagerank` with a personalization dict (see results below) * Adds several placeholder benchmarks for other nx-cugraph APIs added to prior releases that were never benchmarked. ![image](https://github.com/rapidsai/cugraph/assets/3039903/4692e2a2-e14a-489d-84f7-772eda6fc316) Authors: - Rick Ratzel (https://github.com/rlratzel) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4228 --- .../nx-cugraph/pytest-based/bench_algos.py | 112 ++++++++++++++++++ 1 file changed, 112 insertions(+) diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py index 97eb32e2a..3b085a9bf 100644 --- a/benchmarks/nx-cugraph/pytest-based/bench_algos.py +++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py @@ -242,6 +242,28 @@ def get_highest_degree_node(graph_obj): return max(degrees, key=lambda t: t[1])[0] +def build_personalization_dict(pagerank_dict): + """ + Returns a dictionary that can be used as the personalization value for a + call to nx.pagerank(). The pagerank_dict passed in is used as the initial + source of values for each node, and this function simply treats the list of + dict values as two halves (halves A and B) and swaps them so (most if not + all) nodes/keys are assigned a different value from the dictionary. + """ + num_half = len(pagerank_dict) // 2 + A_half_items = list(pagerank_dict.items())[:num_half] + B_half_items = list(pagerank_dict.items())[num_half:] + + # Support an odd number of items by initializing with B_half_items, which + # will always be one bigger if the number of items is odd. This will leave + # the one remainder (in the case of an odd number) unchanged. + pers_dict = dict(B_half_items) + pers_dict.update({A_half_items[i][0]: B_half_items[i][1] for i in range(num_half)}) + pers_dict.update({B_half_items[i][0]: A_half_items[i][1] for i in range(num_half)}) + + return pers_dict + + ################################################################################ # Benchmarks def bench_from_networkx(benchmark, graph_obj): @@ -431,6 +453,26 @@ def bench_pagerank(benchmark, graph_obj, backend_wrapper): assert type(result) is dict +def bench_pagerank_personalized(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + + # FIXME: This will run for every combination of inputs, even if the + # graph/dataset does not change. Ideally this is run once per + # graph/dataset. + pagerank_dict = nx.pagerank(G) + personalization_dict = build_personalization_dict(pagerank_dict) + + result = benchmark.pedantic( + target=backend_wrapper(nx.pagerank), + args=(G,), + kwargs={"personalization": personalization_dict}, + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + def bench_single_source_shortest_path_length(benchmark, graph_obj, backend_wrapper): G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) node = get_highest_degree_node(graph_obj) @@ -804,3 +846,73 @@ def bench_weakly_connected_components(benchmark, graph_obj, backend_wrapper): warmup_rounds=warmup_rounds, ) assert type(result) is list + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_complete_bipartite_graph(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_connected_components(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_connected(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_node_connected_component(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_number_connected_components(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_isolate(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_isolates(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_number_of_isolates(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_complement(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_reverse(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_arborescence(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_branching(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_forest(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_tree(benchmark, graph_obj, backend_wrapper): + pass From cc8e2ebc89fd392378fd240c466902274f57d101 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 13 Mar 2024 09:18:37 -0500 Subject: [PATCH 334/384] nx-cugraph: add more shortest path algorithms (#4199) This begins by adding more unweighted shortest path algorithms. Next we'll do weighted via `sssp`, then generic. Note that there are some performance improvements that can be made: - add bidirectional search between source and target - for `bidirectional_shortest_path` and `has_path` - alternatively, perform `bfs` from `source` until `target` is reached - run `all_pairs*` in batched groups Authors: - Erik Welch (https://github.com/eriknw) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) - Don Acosta (https://github.com/acostadon) URL: https://github.com/rapidsai/cugraph/pull/4199 --- python/nx-cugraph/README.md | 25 +- python/nx-cugraph/_nx_cugraph/__init__.py | 52 +++- python/nx-cugraph/lint.yaml | 4 +- .../nx_cugraph/algorithms/__init__.py | 2 +- .../algorithms/bipartite/__init__.py | 1 - .../nx_cugraph/algorithms/bipartite/basic.py | 31 -- .../algorithms/centrality/eigenvector.py | 9 +- .../nx_cugraph/algorithms/centrality/katz.py | 9 +- .../algorithms/link_analysis/hits_alg.py | 9 +- .../algorithms/link_analysis/pagerank_alg.py | 7 +- .../algorithms/shortest_paths/__init__.py | 4 +- .../algorithms/shortest_paths/generic.py | 165 ++++++++++ .../algorithms/shortest_paths/unweighted.py | 174 ++++++++++- .../algorithms/shortest_paths/weighted.py | 286 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/interface.py | 14 + python/nx-cugraph/nx_cugraph/utils/misc.py | 14 +- python/nx-cugraph/scripts/update_readme.py | 0 17 files changed, 723 insertions(+), 83 deletions(-) delete mode 100644 python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py mode change 100644 => 100755 python/nx-cugraph/scripts/update_readme.py diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index 8201dc34e..1bf310c8c 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -95,8 +95,6 @@ Below is the list of algorithms that are currently supported in nx-cugraph. 

 bipartite
- ├─ basic
- │   └─ is_bipartite
  └─ generators
      └─ complete_bipartite_graph
 centrality
@@ -152,9 +150,26 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  ├─ overall_reciprocity
  └─ reciprocity
 shortest_paths
- └─ unweighted
-     ├─ single_source_shortest_path_length
-     └─ single_target_shortest_path_length
+ ├─ generic
+ │   ├─ has_path
+ │   ├─ shortest_path
+ │   └─ shortest_path_length
+ ├─ unweighted
+ │   ├─ all_pairs_shortest_path
+ │   ├─ all_pairs_shortest_path_length
+ │   ├─ bidirectional_shortest_path
+ │   ├─ single_source_shortest_path
+ │   ├─ single_source_shortest_path_length
+ │   ├─ single_target_shortest_path
+ │   └─ single_target_shortest_path_length
+ └─ weighted
+     ├─ all_pairs_bellman_ford_path
+     ├─ all_pairs_bellman_ford_path_length
+     ├─ bellman_ford_path
+     ├─ bellman_ford_path_length
+     ├─ single_source_bellman_ford
+     ├─ single_source_bellman_ford_path
+     └─ single_source_bellman_ford_path_length
 traversal
  └─ breadth_first_search
      ├─ bfs_edges
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index b2f13d25f..bc7f63fcd 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -33,15 +33,22 @@
     # "description": "TODO",
     "functions": {
         # BEGIN: functions
+        "all_pairs_bellman_ford_path",
+        "all_pairs_bellman_ford_path_length",
+        "all_pairs_shortest_path",
+        "all_pairs_shortest_path_length",
         "ancestors",
         "average_clustering",
         "barbell_graph",
+        "bellman_ford_path",
+        "bellman_ford_path_length",
         "betweenness_centrality",
         "bfs_edges",
         "bfs_layers",
         "bfs_predecessors",
         "bfs_successors",
         "bfs_tree",
+        "bidirectional_shortest_path",
         "bull_graph",
         "caveman_graph",
         "chvatal_graph",
@@ -70,6 +77,7 @@
         "from_scipy_sparse_array",
         "frucht_graph",
         "generic_bfs_edges",
+        "has_path",
         "heawood_graph",
         "hits",
         "house_graph",
@@ -77,7 +85,6 @@
         "icosahedral_graph",
         "in_degree_centrality",
         "is_arborescence",
-        "is_bipartite",
         "is_branching",
         "is_connected",
         "is_forest",
@@ -110,7 +117,14 @@
         "reciprocity",
         "reverse",
         "sedgewick_maze_graph",
+        "shortest_path",
+        "shortest_path_length",
+        "single_source_bellman_ford",
+        "single_source_bellman_ford_path",
+        "single_source_bellman_ford_path_length",
+        "single_source_shortest_path",
         "single_source_shortest_path_length",
+        "single_target_shortest_path",
         "single_target_shortest_path_length",
         "star_graph",
         "tadpole_graph",
@@ -128,7 +142,11 @@
     },
     "additional_docs": {
         # BEGIN: additional_docs
+        "all_pairs_bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "all_pairs_bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "average_clustering": "Directed graphs and `weight` parameter are not yet supported.",
+        "bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.",
         "bfs_edges": "`sort_neighbors` parameter is not yet supported.",
         "bfs_predecessors": "`sort_neighbors` parameter is not yet supported.",
@@ -147,11 +165,28 @@
         "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.",
         "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.",
         "pagerank": "`dangling` parameter is not supported, but it is checked for validity.",
+        "shortest_path": "Negative weights are not yet supported, and method is ununsed.",
+        "shortest_path_length": "Negative weights are not yet supported, and method is ununsed.",
+        "single_source_bellman_ford": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "single_source_bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "single_source_bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "transitivity": "Directed graphs are not yet supported.",
         # END: additional_docs
     },
     "additional_parameters": {
         # BEGIN: additional_parameters
+        "all_pairs_bellman_ford_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "all_pairs_bellman_ford_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "bellman_ford_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "bellman_ford_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         "eigenvector_centrality": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
@@ -169,6 +204,21 @@
         "pagerank": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
+        "shortest_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "shortest_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_bellman_ford": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_bellman_ford_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_bellman_ford_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         # END: additional_parameters
     },
 }
diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml
index fdd24861d..3239fa151 100644
--- a/python/nx-cugraph/lint.yaml
+++ b/python/nx-cugraph/lint.yaml
@@ -50,7 +50,7 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.2
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.2
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py
index 7aafa85f5..b4a10bcf0 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py
@@ -22,7 +22,7 @@
     traversal,
     tree,
 )
-from .bipartite import complete_bipartite_graph, is_bipartite
+from .bipartite import complete_bipartite_graph
 from .centrality import *
 from .cluster import *
 from .components import *
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py
index e028299c6..bfc7f1d4d 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py
@@ -10,5 +10,4 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from .basic import *
 from .generators import *
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py
deleted file mode 100644
index 46c6b5407..000000000
--- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import cupy as cp
-
-from nx_cugraph.algorithms.cluster import _triangles
-from nx_cugraph.convert import _to_graph
-from nx_cugraph.utils import networkx_algorithm
-
-__all__ = [
-    "is_bipartite",
-]
-
-
-@networkx_algorithm(version_added="24.02", _plc="triangle_count")
-def is_bipartite(G):
-    G = _to_graph(G)
-    # Counting triangles may not be the fastest way to do this, but it is simple.
-    node_ids, triangles, is_single_node = _triangles(
-        G, None, symmetrize="union" if G.is_directed() else None
-    )
-    return int(cp.count_nonzero(triangles)) == 0
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py
index 65a863366..c32b6fbb7 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py
@@ -36,17 +36,12 @@ def eigenvector_centrality(
     G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None
 ):
     """`nstart` parameter is not used, but it is checked for validity."""
-    G = _to_graph(G, weight, np.float32)
+    G = _to_graph(G, weight, 1, np.float32)
     if len(G) == 0:
         raise nx.NetworkXPointlessConcept(
             "cannot compute centrality for the null graph"
         )
-    if dtype is not None:
-        dtype = _get_float_dtype(dtype)
-    elif weight in G.edge_values:
-        dtype = _get_float_dtype(G.edge_values[weight].dtype)
-    else:
-        dtype = np.float32
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         # Check if given nstart is valid even though we don't use it
         nstart = G._dict_to_nodearray(nstart, dtype=dtype)
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py
index 4a0684f72..1c6ed6170 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py
@@ -49,15 +49,10 @@ def katz_centrality(
         # Redundant with the `_can_run` check below when being dispatched by NetworkX,
         # but we raise here in case this funcion is called directly.
         raise NotImplementedError("normalized=False is not supported.")
-    G = _to_graph(G, weight, np.float32)
+    G = _to_graph(G, weight, 1, np.float32)
     if (N := len(G)) == 0:
         return {}
-    if dtype is not None:
-        dtype = _get_float_dtype(dtype)
-    elif weight in G.edge_values:
-        dtype = _get_float_dtype(G.edge_values[weight].dtype)
-    else:
-        dtype = np.float32
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         # Check if given nstart is valid even though we don't use it
         nstart = G._dict_to_nodearray(nstart, 0, dtype)
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py
index e61a931c0..e529b83ab 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py
@@ -46,15 +46,10 @@ def hits(
     weight="weight",
     dtype=None,
 ):
-    G = _to_graph(G, weight, np.float32)
+    G = _to_graph(G, weight, 1, np.float32)
     if (N := len(G)) == 0:
         return {}, {}
-    if dtype is not None:
-        dtype = _get_float_dtype(dtype)
-    elif weight in G.edge_values:
-        dtype = _get_float_dtype(G.edge_values[weight].dtype)
-    else:
-        dtype = np.float32
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         nstart = G._dict_to_nodearray(nstart, 0, dtype)
     if max_iter <= 0:
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py
index 40224e91d..41203a2bc 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py
@@ -48,12 +48,7 @@ def pagerank(
     G = _to_graph(G, weight, 1, np.float32)
     if (N := len(G)) == 0:
         return {}
-    if dtype is not None:
-        dtype = _get_float_dtype(dtype)
-    elif weight in G.edge_values:
-        dtype = _get_float_dtype(G.edge_values[weight].dtype)
-    else:
-        dtype = np.float32
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         nstart = G._dict_to_nodearray(nstart, 0, dtype=dtype)
         if (total := nstart.sum()) == 0:
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py
index b7d6b7421..9d87389a9 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -10,4 +10,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from .generic import *
 from .unweighted import *
+from .weighted import *
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py
new file mode 100644
index 000000000..68dbbace9
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py
@@ -0,0 +1,165 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import networkx as nx
+import numpy as np
+
+import nx_cugraph as nxcg
+from nx_cugraph.convert import _to_graph
+from nx_cugraph.utils import _dtype_param, _get_float_dtype, networkx_algorithm
+
+from .unweighted import _bfs
+from .weighted import _sssp
+
+__all__ = [
+    "shortest_path",
+    "shortest_path_length",
+    "has_path",
+]
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def has_path(G, source, target):
+    # TODO PERF: make faster in core
+    try:
+        nxcg.bidirectional_shortest_path(G, source, target)
+    except nx.NetworkXNoPath:
+        return False
+    return True
+
+
+@networkx_algorithm(
+    extra_params=_dtype_param, version_added="24.04", _plc={"bfs", "sssp"}
+)
+def shortest_path(
+    G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None
+):
+    """Negative weights are not yet supported, and method is ununsed."""
+    if method not in {"dijkstra", "bellman-ford"}:
+        raise ValueError(f"method not supported: {method}")
+    if weight is None:
+        method = "unweighted"
+    if source is None:
+        if target is None:
+            # All pairs
+            if method == "unweighted":
+                paths = nxcg.all_pairs_shortest_path(G)
+            else:
+                # method == "dijkstra":
+                # method == 'bellman-ford':
+                paths = nxcg.all_pairs_bellman_ford_path(G, weight=weight, dtype=dtype)
+            if nx.__version__[:3] <= "3.4":
+                paths = dict(paths)
+        # To target
+        elif method == "unweighted":
+            paths = nxcg.single_target_shortest_path(G, target)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            # XXX: it seems weird that `reverse_path=True` is necessary here
+            G = _to_graph(G, weight, 1, np.float32)
+            dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+            paths = _sssp(
+                G, target, weight, return_type="path", dtype=dtype, reverse_path=True
+            )
+    elif target is None:
+        # From source
+        if method == "unweighted":
+            paths = nxcg.single_source_shortest_path(G, source)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            paths = nxcg.single_source_bellman_ford_path(
+                G, source, weight=weight, dtype=dtype
+            )
+    # From source to target
+    elif method == "unweighted":
+        paths = nxcg.bidirectional_shortest_path(G, source, target)
+    else:
+        # method == "dijkstra":
+        # method == 'bellman-ford':
+        paths = nxcg.bellman_ford_path(G, source, target, weight, dtype=dtype)
+    return paths
+
+
+@shortest_path._can_run
+def _(G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(
+    extra_params=_dtype_param, version_added="24.04", _plc={"bfs", "sssp"}
+)
+def shortest_path_length(
+    G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None
+):
+    """Negative weights are not yet supported, and method is ununsed."""
+    if method not in {"dijkstra", "bellman-ford"}:
+        raise ValueError(f"method not supported: {method}")
+    if weight is None:
+        method = "unweighted"
+    if source is None:
+        if target is None:
+            # All pairs
+            if method == "unweighted":
+                lengths = nxcg.all_pairs_shortest_path_length(G)
+            else:
+                # method == "dijkstra":
+                # method == 'bellman-ford':
+                lengths = nxcg.all_pairs_bellman_ford_path_length(
+                    G, weight=weight, dtype=dtype
+                )
+        # To target
+        elif method == "unweighted":
+            lengths = nxcg.single_target_shortest_path_length(G, target)
+            if nx.__version__[:3] <= "3.4":
+                lengths = dict(lengths)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            lengths = nxcg.single_source_bellman_ford_path_length(
+                G, target, weight=weight, dtype=dtype
+            )
+    elif target is None:
+        # From source
+        if method == "unweighted":
+            lengths = nxcg.single_source_shortest_path_length(G, source)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            lengths = dict(
+                nxcg.single_source_bellman_ford_path_length(
+                    G, source, weight=weight, dtype=dtype
+                )
+            )
+    # From source to target
+    elif method == "unweighted":
+        G = _to_graph(G)
+        lengths = _bfs(G, source, None, "Source", return_type="length", target=target)
+    else:
+        # method == "dijkstra":
+        # method == 'bellman-ford':
+        lengths = nxcg.bellman_ford_path_length(G, source, target, weight, dtype=dtype)
+    return lengths
+
+
+@shortest_path_length._can_run
+def _(G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
index 201249595..714289c5b 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
@@ -10,33 +10,127 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import itertools
+
 import cupy as cp
 import networkx as nx
 import numpy as np
 import pylibcugraph as plc
 
 from nx_cugraph.convert import _to_graph
-from nx_cugraph.utils import index_dtype, networkx_algorithm
+from nx_cugraph.utils import _groupby, index_dtype, networkx_algorithm
+
+__all__ = [
+    "bidirectional_shortest_path",
+    "single_source_shortest_path",
+    "single_source_shortest_path_length",
+    "single_target_shortest_path",
+    "single_target_shortest_path_length",
+    "all_pairs_shortest_path",
+    "all_pairs_shortest_path_length",
+]
 
-__all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"]
+concat = itertools.chain.from_iterable
 
 
 @networkx_algorithm(version_added="23.12", _plc="bfs")
 def single_source_shortest_path_length(G, source, cutoff=None):
-    return _single_shortest_path_length(G, source, cutoff, "Source")
+    G = _to_graph(G)
+    return _bfs(G, source, cutoff, "Source", return_type="length")
 
 
 @networkx_algorithm(version_added="23.12", _plc="bfs")
 def single_target_shortest_path_length(G, target, cutoff=None):
-    return _single_shortest_path_length(G, target, cutoff, "Target")
+    G = _to_graph(G)
+    rv = _bfs(G, target, cutoff, "Target", return_type="length")
+    if nx.__version__[:3] <= "3.4":
+        return iter(rv.items())
+    return rv
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def all_pairs_shortest_path_length(G, cutoff=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G)
+    for n in G:
+        yield (n, _bfs(G, n, cutoff, "Source", return_type="length"))
 
 
-def _single_shortest_path_length(G, source, cutoff, kind):
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def bidirectional_shortest_path(G, source, target):
+    # TODO PERF: do bidirectional traversal in core
     G = _to_graph(G)
+    if source not in G or target not in G:
+        raise nx.NodeNotFound(f"Either source {source} or target {target} is not in G")
+    return _bfs(G, source, None, "Source", return_type="path", target=target)
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def single_source_shortest_path(G, source, cutoff=None):
+    G = _to_graph(G)
+    return _bfs(G, source, cutoff, "Source", return_type="path")
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def single_target_shortest_path(G, target, cutoff=None):
+    G = _to_graph(G)
+    return _bfs(G, target, cutoff, "Target", return_type="path", reverse_path=True)
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def all_pairs_shortest_path(G, cutoff=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G)
+    for n in G:
+        yield (n, _bfs(G, n, cutoff, "Source", return_type="path"))
+
+
+def _bfs(
+    G, source, cutoff, kind, *, return_type, reverse_path=False, target=None, scale=None
+):
+    """BFS for unweighted shortest path algorithms.
+
+    Parameters
+    ----------
+    source: node label
+
+    cutoff: int, optional
+
+    kind: {"Source", "Target"}
+
+    return_type: {"length", "path", "length-path"}
+
+    reverse_path: bool
+
+    target: node label
+
+    scale: int or float, optional
+        The amount to scale the lengths
+    """
+    # DRY: _sssp in weighted.py has similar code
     if source not in G:
-        raise nx.NodeNotFound(f"{kind} {source} is not in G")
-    if G.src_indices.size == 0:
-        return {source: 0}
+        # Different message to pass networkx tests
+        if return_type == "length":
+            raise nx.NodeNotFound(f"{kind} {source} is not in G")
+        raise nx.NodeNotFound(f"{kind} {source} not in G")
+    if target is not None:
+        if source == target or cutoff is not None and cutoff <= 0:
+            if return_type == "path":
+                return [source]
+            if return_type == "length":
+                return 0
+            # return_type == "length-path"
+            return 0, [source]
+        if target not in G or G.src_indices.size == 0:
+            raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+    elif G.src_indices.size == 0 or cutoff is not None and cutoff <= 0:
+        if return_type == "path":
+            return {source: [source]}
+        if return_type == "length":
+            return {source: 0}
+        # return_type == "length-path"
+        return {source: 0}, {source: [source]}
+
     if cutoff is None:
         cutoff = -1
     src_index = source if G.key_to_id is None else G.key_to_id[source]
@@ -46,8 +140,68 @@ def _single_shortest_path_length(G, source, cutoff, kind):
         sources=cp.array([src_index], index_dtype),
         direction_optimizing=False,  # True for undirected only; what's recommended?
         depth_limit=cutoff,
-        compute_predecessors=False,
+        compute_predecessors=return_type != "length",
         do_expensive_check=False,
     )
     mask = distances != np.iinfo(distances.dtype).max
-    return G._nodearrays_to_dict(node_ids[mask], distances[mask])
+    node_ids = node_ids[mask]
+    if return_type != "path":
+        lengths = distances = distances[mask]
+        if scale is not None:
+            lengths = scale * lengths
+        lengths = G._nodearrays_to_dict(node_ids, lengths)
+        if target is not None:
+            if target not in lengths:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            lengths = lengths[target]
+    if return_type != "length":
+        if target is not None:
+            d = dict(zip(node_ids.tolist(), predecessors[mask].tolist()))
+            dst_index = target if G.key_to_id is None else G.key_to_id[target]
+            if dst_index not in d:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            cur = dst_index
+            paths = [dst_index]
+            while cur != src_index:
+                cur = d[cur]
+                paths.append(cur)
+            if (id_to_key := G.id_to_key) is not None:
+                if reverse_path:
+                    paths = [id_to_key[cur] for cur in paths]
+                else:
+                    paths = [id_to_key[cur] for cur in reversed(paths)]
+            elif not reverse_path:
+                paths.reverse()
+        else:
+            if return_type == "path":
+                distances = distances[mask]
+            groups = _groupby(distances, [predecessors[mask], node_ids])
+
+            # `pred_node_iter` does the equivalent as these nested for loops:
+            # for length in range(1, len(groups)):
+            #     preds, nodes = groups[length]
+            #     for pred, node in zip(preds.tolist(), nodes.tolist()):
+            if G.key_to_id is None:
+                pred_node_iter = concat(
+                    zip(*(x.tolist() for x in groups[length]))
+                    for length in range(1, len(groups))
+                )
+            else:
+                pred_node_iter = concat(
+                    zip(*(G._nodeiter_to_iter(x.tolist()) for x in groups[length]))
+                    for length in range(1, len(groups))
+                )
+            # Consider making utility functions for creating paths
+            paths = {source: [source]}
+            if reverse_path:
+                for pred, node in pred_node_iter:
+                    paths[node] = [node, *paths[pred]]
+            else:
+                for pred, node in pred_node_iter:
+                    paths[node] = [*paths[pred], node]
+    if return_type == "path":
+        return paths
+    if return_type == "length":
+        return lengths
+    # return_type == "length-path"
+    return lengths, paths
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py
new file mode 100644
index 000000000..32323dd45
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py
@@ -0,0 +1,286 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import networkx as nx
+import numpy as np
+import pylibcugraph as plc
+
+from nx_cugraph.convert import _to_graph
+from nx_cugraph.utils import (
+    _dtype_param,
+    _get_float_dtype,
+    _groupby,
+    networkx_algorithm,
+)
+
+from .unweighted import _bfs
+
+__all__ = [
+    "bellman_ford_path",
+    "bellman_ford_path_length",
+    "single_source_bellman_ford",
+    "single_source_bellman_ford_path",
+    "single_source_bellman_ford_path_length",
+    "all_pairs_bellman_ford_path",
+    "all_pairs_bellman_ford_path_length",
+]
+
+
+def _add_doc(func):
+    func.__doc__ = (
+        "Negative cycles are not yet supported. ``NotImplementedError`` will be raised "
+        "if there are negative edge weights. We plan to support negative edge weights "
+        "soon. Also, callable ``weight`` argument is not supported."
+    )
+    return func
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def bellman_ford_path(G, source, target, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, target, return_type="path", dtype=dtype)
+
+
+@bellman_ford_path._can_run
+def _(G, source, target, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def bellman_ford_path_length(G, source, target, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, target, return_type="length", dtype=dtype)
+
+
+@bellman_ford_path_length._can_run
+def _(G, source, target, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def single_source_bellman_ford_path(G, source, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, return_type="path", dtype=dtype)
+
+
+@single_source_bellman_ford_path._can_run
+def _(G, source, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def single_source_bellman_ford_path_length(G, source, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, return_type="length", dtype=dtype)
+
+
+@single_source_bellman_ford_path_length._can_run
+def _(G, source, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def single_source_bellman_ford(G, source, target=None, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, target, return_type="length-path", dtype=dtype)
+
+
+@single_source_bellman_ford._can_run
+def _(G, source, target=None, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def all_pairs_bellman_ford_path_length(G, weight="weight", *, dtype=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    for n in G:
+        yield (n, _sssp(G, n, weight, return_type="length", dtype=dtype))
+
+
+@all_pairs_bellman_ford_path_length._can_run
+def _(G, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def all_pairs_bellman_ford_path(G, weight="weight", *, dtype=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    for n in G:
+        yield (n, _sssp(G, n, weight, return_type="path", dtype=dtype))
+
+
+@all_pairs_bellman_ford_path._can_run
+def _(G, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+def _sssp(G, source, weight, target=None, *, return_type, dtype, reverse_path=False):
+    """SSSP for weighted shortest paths.
+
+    Parameters
+    ----------
+    return_type : {"length", "path", "length-path"}
+
+    """
+    # DRY: _bfs in unweighted.py has similar code
+    if source not in G:
+        raise nx.NodeNotFound(f"Node {source} not found in graph")
+    if target is not None:
+        if source == target:
+            if return_type == "path":
+                return [source]
+            if return_type == "length":
+                return 0
+            # return_type == "length-path"
+            return 0, [source]
+        if target not in G or G.src_indices.size == 0:
+            raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+    elif G.src_indices.size == 0:
+        if return_type == "path":
+            return {source: [source]}
+        if return_type == "length":
+            return {source: 0}
+        # return_type == "length-path"
+        return {source: 0}, {source: [source]}
+
+    if callable(weight):
+        raise NotImplementedError("callable `weight` argument is not supported")
+
+    if weight not in G.edge_values:
+        # No edge values, so use BFS instead
+        return _bfs(G, source, None, "Source", return_type=return_type, target=target)
+
+    # Check for negative values since we don't support negative cycles
+    edge_vals = G.edge_values[weight]
+    if weight in G.edge_masks:
+        edge_vals = edge_vals[G.edge_masks[weight]]
+    if (edge_vals < 0).any():
+        raise NotImplementedError("Negative edge weights not yet supported")
+    edge_val = edge_vals[0]
+    if (edge_vals == edge_val).all() and (
+        edge_vals.size == G.src_indices.size or edge_val == 1
+    ):
+        # Edge values are all the same, so use scaled BFS instead
+        return _bfs(
+            G,
+            source,
+            None,
+            "Source",
+            return_type=return_type,
+            target=target,
+            scale=edge_val,
+            reverse_path=reverse_path,
+        )
+
+    src_index = source if G.key_to_id is None else G.key_to_id[source]
+    node_ids, distances, predecessors = plc.sssp(
+        resource_handle=plc.ResourceHandle(),
+        graph=G._get_plc_graph(weight, 1, dtype),
+        source=src_index,
+        cutoff=np.inf,
+        compute_predecessors=True,  # TODO: False is not yet supported
+        # compute_predecessors=return_type != "length",
+        do_expensive_check=False,
+    )
+    mask = distances != np.finfo(distances.dtype).max
+    node_ids = node_ids[mask]
+    if return_type != "path":
+        lengths = G._nodearrays_to_dict(node_ids, distances[mask])
+        if target is not None:
+            if target not in lengths:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            lengths = lengths[target]
+    if return_type != "length":
+        if target is not None:
+            d = dict(zip(node_ids.tolist(), predecessors[mask].tolist()))
+            dst_index = target if G.key_to_id is None else G.key_to_id[target]
+            if dst_index not in d:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            cur = dst_index
+            paths = [dst_index]
+            while cur != src_index:
+                cur = d[cur]
+                paths.append(cur)
+            if (id_to_key := G.id_to_key) is not None:
+                if reverse_path:
+                    paths = [id_to_key[cur] for cur in paths]
+                else:
+                    paths = [id_to_key[cur] for cur in reversed(paths)]
+            elif not reverse_path:
+                paths.reverse()
+        else:
+            groups = _groupby(predecessors[mask], node_ids)
+            if (id_to_key := G.id_to_key) is not None:
+                groups = {id_to_key[k]: v for k, v in groups.items() if k >= 0}
+            paths = {source: [source]}
+            preds = [source]
+            while preds:
+                pred = preds.pop()
+                pred_path = paths[pred]
+                nodes = G._nodearray_to_list(groups[pred])
+                if reverse_path:
+                    for node in nodes:
+                        paths[node] = [node, *pred_path]
+                else:
+                    for node in nodes:
+                        paths[node] = [*pred_path, node]
+                preds.extend(nodes & groups.keys())
+    if return_type == "path":
+        return paths
+    if return_type == "length":
+        return lengths
+    # return_type == "length-path"
+    return lengths, paths
diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py
index d044ba696..0d893ac28 100644
--- a/python/nx-cugraph/nx_cugraph/interface.py
+++ b/python/nx-cugraph/nx_cugraph/interface.py
@@ -67,6 +67,7 @@ def key(testpath):
         no_multigraph = "multigraphs not currently supported"
         louvain_different = "Louvain may be different due to RNG"
         no_string_dtype = "string edge values not currently supported"
+        sssp_path_different = "sssp may choose a different valid path"
 
         xfail = {
             # This is removed while strongly_connected_components() is not
@@ -77,6 +78,19 @@ def key(testpath):
             #     "test_strongly_connected.py:"
             #     "TestStronglyConnected.test_condensation_mapping_and_members"
             # ): "Strongly connected groups in different iteration order",
+            key(
+                "test_cycles.py:TestMinimumCycleBasis.test_unweighted_diamond"
+            ): sssp_path_different,
+            key(
+                "test_cycles.py:TestMinimumCycleBasis.test_weighted_diamond"
+            ): sssp_path_different,
+            key(
+                "test_cycles.py:TestMinimumCycleBasis.test_petersen_graph"
+            ): sssp_path_different,
+            key(
+                "test_cycles.py:TestMinimumCycleBasis."
+                "test_gh6787_and_edge_attribute_names"
+            ): sssp_path_different,
         }
 
         from packaging.version import parse
diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py
index aa06d7fd2..eab4b42c2 100644
--- a/python/nx-cugraph/nx_cugraph/utils/misc.py
+++ b/python/nx-cugraph/nx_cugraph/utils/misc.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -22,7 +22,9 @@
 import numpy as np
 
 if TYPE_CHECKING:
-    from ..typing import Dtype
+    import nx_cugraph as nxcg
+
+    from ..typing import Dtype, EdgeKey
 
 try:
     from itertools import pairwise  # Python >=3.10
@@ -190,10 +192,14 @@ def _get_int_dtype(
         raise ValueError("Value is too large to store as integer: {val}") from exc
 
 
-def _get_float_dtype(dtype: Dtype):
+def _get_float_dtype(
+    dtype: Dtype, *, graph: nxcg.Graph | None = None, weight: EdgeKey | None = None
+):
     """Promote dtype to float32 or float64 as appropriate."""
     if dtype is None:
-        return np.dtype(np.float32)
+        if graph is None or weight not in graph.edge_values:
+            return np.dtype(np.float32)
+        dtype = graph.edge_values[weight].dtype
     rv = np.promote_types(dtype, np.float32)
     if np.float32 != rv != np.float64:
         raise TypeError(
diff --git a/python/nx-cugraph/scripts/update_readme.py b/python/nx-cugraph/scripts/update_readme.py
old mode 100644
new mode 100755

From a6b900bea92503c8a175fc7b9c187f1cc9d52f5e Mon Sep 17 00:00:00 2001
From: Bradley Dice 
Date: Wed, 13 Mar 2024 10:22:13 -0500
Subject: [PATCH 335/384] Add upper bound to prevent usage of NumPy 2 (#4233)

NumPy 2 is expected to be released in the near future. For the RAPIDS 24.04 release, we will pin to `numpy>=1.23,<2.0a0`. This PR adds an upper bound to affected RAPIDS repositories.

xref: https://github.com/rapidsai/build-planning/issues/29

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cugraph/pull/4233
---
 python/nx-cugraph/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index 07ec0eab2..dbdc8dd19 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -33,7 +33,7 @@ classifiers = [
 dependencies = [
     "cupy-cuda11x>=12.0.0",
     "networkx>=3.0",
-    "numpy>=1.23",
+    "numpy>=1.23,<2.0a0",
     "pylibcugraph==24.4.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 

From ae1190e0d0033dd79309694dec8324b37df3bd48 Mon Sep 17 00:00:00 2001
From: Kyle Edwards 
Date: Wed, 13 Mar 2024 15:20:37 -0400
Subject: [PATCH 336/384] Remove hard-coding of RAPIDS version where possible
 (#4217)

* Read `VERSION` file in CMake
* Read `cugraph.__version__` in docs build
* Read `VERSION` file in shell scripts
* Use environment variables in Doxyfile
* Remove updates from `ci/update-version.sh`

Issue: https://github.com/rapidsai/build-planning/issues/15

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Don Acosta (https://github.com/acostadon)
  - Chuck Hastings (https://github.com/ChuckHastings)
  - Brad Rees (https://github.com/BradReesWork)
  - Jake Awe (https://github.com/AyodeAwe)
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cugraph/pull/4217
---
 docs/cugraph/source/conf.py                           |  9 +++++++--
 python/nx-cugraph/{nx_cugraph => _nx_cugraph}/VERSION |  0
 python/nx-cugraph/_nx_cugraph/__init__.py             | 11 +++++++----
 .../{nx_cugraph => _nx_cugraph}/_version.py           |  2 +-
 python/nx-cugraph/nx_cugraph/__init__.py              |  4 ++--
 python/nx-cugraph/pyproject.toml                      |  2 +-
 6 files changed, 18 insertions(+), 10 deletions(-)
 rename python/nx-cugraph/{nx_cugraph => _nx_cugraph}/VERSION (100%)
 rename python/nx-cugraph/{nx_cugraph => _nx_cugraph}/_version.py (91%)

diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py
index 719f94d48..952b962ac 100644
--- a/docs/cugraph/source/conf.py
+++ b/docs/cugraph/source/conf.py
@@ -19,6 +19,10 @@
 import os
 import sys
 
+from packaging.version import Version
+
+import cugraph
+
 # If extensions (or modules to document with autodoc) are in another
 # directory, add these directories to sys.path here. If the directory
 # is relative to the documentation root, use os.path.abspath to make it
@@ -76,10 +80,11 @@
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
+CUGRAPH_VERSION = Version(cugraph.__version__)
 # The short X.Y version.
-version = '24.04'
+version = f"{CUGRAPH_VERSION.major:02}.{CUGRAPH_VERSION.minor:02}"
 # The full version, including alpha/beta/rc tags.
-release = '24.04.00'
+release = f"{CUGRAPH_VERSION.major:02}.{CUGRAPH_VERSION.minor:02}.{CUGRAPH_VERSION.micro:02}"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/python/nx-cugraph/nx_cugraph/VERSION b/python/nx-cugraph/_nx_cugraph/VERSION
similarity index 100%
rename from python/nx-cugraph/nx_cugraph/VERSION
rename to python/nx-cugraph/_nx_cugraph/VERSION
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index bc7f63fcd..8c6a65046 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -23,12 +23,18 @@
 $ python _nx_cugraph/__init__.py
 """
 
+from packaging.version import Version
+
+from _nx_cugraph._version import __version__
+
+_nx_cugraph_version = Version(__version__)
+
 # Entries between BEGIN and END are automatically generated
 _info = {
     "backend_name": "cugraph",
     "project": "nx-cugraph",
     "package": "nx_cugraph",
-    "url": "https://github.com/rapidsai/cugraph/tree/branch-24.04/python/nx-cugraph",
+    "url": f"https://github.com/rapidsai/cugraph/tree/branch-{_nx_cugraph_version.major:02}.{_nx_cugraph_version.minor:02}/python/nx-cugraph",
     "short_summary": "GPU-accelerated backend.",
     # "description": "TODO",
     "functions": {
@@ -252,9 +258,6 @@ def get_info():
     return d
 
 
-# FIXME: can this use the standard VERSION file and update mechanism?
-__version__ = "24.04.00"
-
 if __name__ == "__main__":
     from pathlib import Path
 
diff --git a/python/nx-cugraph/nx_cugraph/_version.py b/python/nx-cugraph/_nx_cugraph/_version.py
similarity index 91%
rename from python/nx-cugraph/nx_cugraph/_version.py
rename to python/nx-cugraph/_nx_cugraph/_version.py
index a528a3bfe..dc2d2a3a3 100644
--- a/python/nx-cugraph/nx_cugraph/_version.py
+++ b/python/nx-cugraph/_nx_cugraph/_version.py
@@ -19,6 +19,6 @@
 # to update version info (including commit hashes) without modifying
 # source files.
 __version__ = (
-    importlib.resources.files("nx_cugraph").joinpath("VERSION").read_text().strip()
+    importlib.resources.files("_nx_cugraph").joinpath("VERSION").read_text().strip()
 )
 __git_commit__ = ""
diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py
index 3a8f0996e..2c54da878 100644
--- a/python/nx-cugraph/nx_cugraph/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -29,4 +29,4 @@
 from . import algorithms
 from .algorithms import *
 
-from nx_cugraph._version import __git_commit__, __version__
+from _nx_cugraph._version import __git_commit__, __version__
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index dbdc8dd19..2ff53c1a3 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -71,7 +71,7 @@ cugraph = "_nx_cugraph:get_info"
 license-files = ["LICENSE"]
 
 [tool.setuptools.dynamic]
-version = {file = "nx_cugraph/VERSION"}
+version = {file = "_nx_cugraph/VERSION"}
 
 [tool.setuptools.packages.find]
 include = [

From 8ccdcaf9e2dfe48235484fc9a5e0120b736d2f2f Mon Sep 17 00:00:00 2001
From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com>
Date: Wed, 13 Mar 2024 17:22:23 -0500
Subject: [PATCH 337/384] Updates nx-cugraph `README` for latest h/w, CUDA,
 python, NX requirements, moves updater to pre-commit (#4225)

* Updates nx-cugraph `README` for latest h/w, CUDA, python, NX requirements
* Moves the call to the nx-cugraph `README` check and auto-updater script from the python CI test script to a pre-commit hook alongside the other codegen calls/checks.  The pre-commit checks are run by the dev at commit-time and by CI as part of the style check job.  This check will only run for changes to files under the `nx-cugraph` subdir.
* Updates the `update_readme.py` script to optionally download the NX `objects.inv` file itself so calling `make` is not required as part of the pre-commit hook.
  * _NOTE: This is less important though, since the pre-commit hook still calls `bash` in order to set the `PYTHONPATH` env var to use the local version of the `update_readme.py` script, which is what I was trying to avoid in the first place. This still has some advantages in making the `update_readme.py` script more usable if the user isn't aware of how to obtain a `objects.inv` file, but feedback is welcome. The hook could also be changed to just call `make` if that ends up being preferred, in which case we'd have to consider if we still want the updates to `update_readme.py` to download the `objects.inv` file itself or not._

Here's the check running as part of the style-check job for this PR:
![image](https://github.com/rapidsai/cugraph/assets/3039903/6a313308-0901-4614-862e-d2596a7754f4)

Authors:
  - Rick Ratzel (https://github.com/rlratzel)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Jake Awe (https://github.com/AyodeAwe)
  - Don Acosta (https://github.com/acostadon)
  - Erik Welch (https://github.com/eriknw)

URL: https://github.com/rapidsai/cugraph/pull/4225
---
 .pre-commit-config.yaml                    | 20 ++++++++
 python/nx-cugraph/README.md                | 20 +++++---
 python/nx-cugraph/_nx_cugraph/__init__.py  | 16 ++++++
 python/nx-cugraph/_nx_cugraph/core.py      | 19 ++++++--
 python/nx-cugraph/scripts/update_readme.py | 57 ++++++++++++++++++++--
 5 files changed, 116 insertions(+), 16 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ddb84d8a0..542e9cacb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -63,3 +63,23 @@ repos:
               [.]flake8[.]cython$|
               meta[.]yaml$|
               setup[.]cfg$
+  - repo: local
+    hooks:
+      - id: nx-cugraph-meta-data-update
+        name: nx-cugraph meta-data updater
+        entry: bash -c "PYTHONPATH=./python/nx-cugraph python ./python/nx-cugraph/_nx_cugraph/__init__.py"
+        files: ^python/nx-cugraph/
+        types: [python]
+        language: python
+        pass_filenames: false
+        additional_dependencies: ["networkx>=3.2"]
+  - repo: local
+    hooks:
+      - id: nx-cugraph-readme-update
+        name: nx-cugraph README updater
+        entry: bash -c "PYTHONPATH=./python/nx-cugraph python ./python/nx-cugraph/scripts/update_readme.py ./python/nx-cugraph/README.md"
+        files: ^python/nx-cugraph/
+        types_or: [python, markdown]
+        language: python
+        pass_filenames: false
+        additional_dependencies: ["networkx>=3.2"]
diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md
index 1bf310c8c..77066356a 100644
--- a/python/nx-cugraph/README.md
+++ b/python/nx-cugraph/README.md
@@ -7,11 +7,10 @@ to run supported algorithms with GPU acceleration.
 ## System Requirements
 
 nx-cugraph requires the following:
-
- * NVIDIA GPU, Pascal architecture or later
+ * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
  * CUDA 11.2, 11.4, 11.5, 11.8, or 12.0
- * Python versions 3.9, 3.10, or 3.11
- * NetworkX >= version 3.2
+ * Python version 3.9, 3.10, or 3.11
+ * NetworkX >= version 3.0 (version 3.2 or higher recommended)
 
 More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).
 
@@ -20,16 +19,25 @@ More details about system requirements can be found in the [RAPIDS System Requir
 nx-cugraph can be installed using either conda or pip.
 
 ### conda
+#### latest nightly version
 ```
 conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph
 ```
+#### latest stable version
+```
+conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph
+```
 ### pip
+#### latest nightly version
+```
+python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
+```
+#### latest stable version
 ```
 python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com
 ```
 Notes:
-
- * Nightly wheel builds will not be available until the 23.12 release, therefore the index URL for the stable release version is being used in the pip install command above.
+ * The pip example above installs for CUDA 11. To install for CUDA 12, replace `-cu11` with `-cu12`
  * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start).
 
 ## Enabling nx-cugraph
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index 8c6a65046..f6e5e3aa5 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -261,6 +261,22 @@ def get_info():
 if __name__ == "__main__":
     from pathlib import Path
 
+    # This script imports nx_cugraph modules, which imports nx_cugraph runtime
+    # dependencies. The modules do not need the runtime deps, so stub them out
+    # to avoid installing them.
+    class Stub:
+        def __getattr__(self, *args, **kwargs):
+            return Stub()
+
+        def __call__(self, *args, **kwargs):
+            return Stub()
+
+    import sys
+
+    sys.modules["cupy"] = Stub()
+    sys.modules["numpy"] = Stub()
+    sys.modules["pylibcugraph"] = Stub()
+
     from _nx_cugraph.core import main
 
     filepath = Path(__file__)
diff --git a/python/nx-cugraph/_nx_cugraph/core.py b/python/nx-cugraph/_nx_cugraph/core.py
index c4de197f3..82ce7bc43 100644
--- a/python/nx-cugraph/_nx_cugraph/core.py
+++ b/python/nx-cugraph/_nx_cugraph/core.py
@@ -45,18 +45,27 @@ def update_text(text, lines_to_add, target, indent=" " * 8):
     return f"{text[:start]}{begin}{to_add}\n{indent}{text[stop:]}"
 
 
+def dq_repr(s):
+    """Return repr(s) quoted with the double quote preference used by black."""
+    rs = repr(s)
+    if rs.startswith("'") and '"' not in rs:
+        rs = rs.strip("'")
+        return f'"{rs}"'
+    return rs
+
+
 def dict_to_lines(d, *, indent=""):
     for key in sorted(d):
         val = d[key]
         if "\n" not in val:
-            yield f"{indent}{key!r}: {val!r},"
+            yield f"{indent}{dq_repr(key)}: {dq_repr(val)},"
         else:
-            yield f"{indent}{key!r}: ("
+            yield f"{indent}{dq_repr(key)}: ("
             *lines, last_line = val.split("\n")
             for line in lines:
                 line += "\n"
-                yield f"    {indent}{line!r}"
-            yield f"    {indent}{last_line!r}"
+                yield f"    {indent}{dq_repr(line)}"
+            yield f"    {indent}{dq_repr(last_line)}"
             yield f"{indent}),"
 
 
@@ -83,7 +92,7 @@ def main(filepath):
     to_add = []
     for name in sorted(additional_parameters):
         params = additional_parameters[name]
-        to_add.append(f"{name!r}: {{")
+        to_add.append(f"{dq_repr(name)}: {{")
         to_add.extend(dict_to_lines(params, indent=" " * 4))
         to_add.append("},")
     text = update_text(text, to_add, "additional_parameters")
diff --git a/python/nx-cugraph/scripts/update_readme.py b/python/nx-cugraph/scripts/update_readme.py
index 1ab5a76c4..fcaa1769d 100755
--- a/python/nx-cugraph/scripts/update_readme.py
+++ b/python/nx-cugraph/scripts/update_readme.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
 # Copyright (c) 2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,12 +12,13 @@
 # limitations under the License.
 import argparse
 import re
+import urllib.request
 import zlib
 from collections import namedtuple
 from pathlib import Path
 from warnings import warn
 
-from nx_cugraph.scripts.print_tree import create_tree, tree_lines
+_objs_file_url = "https://networkx.org/documentation/stable/objects.inv"
 
 # See: https://sphobjinv.readthedocs.io/en/stable/syntax.html
 DocObject = namedtuple(
@@ -75,6 +75,8 @@ def replace_body(text, match, new_body):
 
 def main(readme_file, objects_filename):
     """``readme_file`` must be readable and writable, so use mode ``"a+"``"""
+    from nx_cugraph.scripts.print_tree import create_tree, tree_lines
+
     # Use the `objects.inv` file to determine URLs. For details about this file, see:
     # https://sphobjinv.readthedocs.io/en/stable/syntax.html
     # We might be better off using a library like that, but roll our own for now.
@@ -190,14 +192,59 @@ def get_payload_internal(keys):
     return text
 
 
+def find_or_download_objs_file(objs_file_dir):
+    """
+    Returns the path to /objects.inv, downloading it from
+    _objs_file_url if it does not already exist.
+    """
+    objs_file_path = objs_file_dir / "objects.inv"
+    if not objs_file_path.exists():
+        request = urllib.request.Request(_objs_file_url)
+        with (
+            urllib.request.urlopen(request) as response,
+            Path(objs_file_path).open("wb") as out,
+        ):
+            out.write(response.read())
+    return objs_file_path
+
+
 if __name__ == "__main__":
+    # This script imports a nx_cugraph script module, which imports nx_cugraph
+    # runtime dependencies. The script module does not need the runtime deps,
+    # so stub them out to avoid installing them.
+    class Stub:
+        def __getattr__(self, *args, **kwargs):
+            return Stub()
+
+        def __call__(self, *args, **kwargs):
+            return Stub()
+
+    import sys
+
+    sys.modules["cupy"] = Stub()
+    sys.modules["numpy"] = Stub()
+    sys.modules["pylibcugraph"] = Stub()
+
     parser = argparse.ArgumentParser(
         "Update README.md to show NetworkX functions implemented by nx-cugraph"
     )
     parser.add_argument("readme_filename", help="Path to the README.md file")
     parser.add_argument(
-        "networkx_objects", help="Path to the objects.inv file from networkx docs"
+        "networkx_objects",
+        nargs="?",
+        default=None,
+        help="Optional path to the objects.inv file from the NetworkX docs. Default is "
+        "the objects.inv file in the directory containing the specified README.md. If "
+        "an objects.inv file does not exist in that location, one will be downloaded "
+        "and saved to that location.",
     )
     args = parser.parse_args()
-    with Path(args.readme_filename).open("a+") as readme_file:
-        main(readme_file, args.networkx_objects)
+
+    readme_filename = args.readme_filename
+    readme_path = Path(readme_filename)
+    objects_filename = args.networkx_objects
+    if objects_filename is None:
+        objects_filename = find_or_download_objs_file(readme_path.parent)
+
+    with readme_path.open("a+") as readme_file:
+        main(readme_file, objects_filename)

From 64af488f957f09d92c6bfaac7f9b6cc8a0368b79 Mon Sep 17 00:00:00 2001
From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com>
Date: Fri, 15 Mar 2024 03:26:26 -0500
Subject: [PATCH 338/384] Removes (unintentional?) runtime dependency on
 `packaging` from nx-cugraph (#4240)

A [recent PR](https://github.com/rapidsai/cugraph/pull/4217) was merged that added `packaging` as a runtime dependency to nx-cugraph.  This PR removes that dependency and manually extracts the `major` and `minor` version numbers instead.

Authors:
  - Rick Ratzel (https://github.com/rlratzel)
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Brad Rees (https://github.com/BradReesWork)
  - Erik Welch (https://github.com/eriknw)

URL: https://github.com/rapidsai/cugraph/pull/4240
---
 python/nx-cugraph/_nx_cugraph/__init__.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index f6e5e3aa5..098de46af 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -23,18 +23,19 @@
 $ python _nx_cugraph/__init__.py
 """
 
-from packaging.version import Version
-
 from _nx_cugraph._version import __version__
 
-_nx_cugraph_version = Version(__version__)
+# This is normally handled by packaging.version.Version, but instead of adding
+# an additional runtime dependency on "packaging", assume __version__ will
+# always be in .. format.
+(_version_major, _version_minor) = __version__.split(".")[:2]
 
 # Entries between BEGIN and END are automatically generated
 _info = {
     "backend_name": "cugraph",
     "project": "nx-cugraph",
     "package": "nx_cugraph",
-    "url": f"https://github.com/rapidsai/cugraph/tree/branch-{_nx_cugraph_version.major:02}.{_nx_cugraph_version.minor:02}/python/nx-cugraph",
+    "url": f"https://github.com/rapidsai/cugraph/tree/branch-{_version_major:0>2}.{_version_minor:0>2}/python/nx-cugraph",
     "short_summary": "GPU-accelerated backend.",
     # "description": "TODO",
     "functions": {

From 1ee83fbabbb6d2bc2fe50b4ff85e8bbc88de2629 Mon Sep 17 00:00:00 2001
From: Ray Douglass 
Date: Fri, 15 Mar 2024 11:49:52 -0400
Subject: [PATCH 339/384] DOC v24.06 Updates [skip ci]

---
 VERSION                                      | 2 +-
 docs/cugraph/source/nx_cugraph/nx_cugraph.md | 2 +-
 python/nx-cugraph/pyproject.toml             | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/VERSION b/VERSION
index 4a2fe8aa5..0bff6981a 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.04.00
+24.06.00
diff --git a/docs/cugraph/source/nx_cugraph/nx_cugraph.md b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
index 8d497e3a1..92fbf90a4 100644
--- a/docs/cugraph/source/nx_cugraph/nx_cugraph.md
+++ b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
@@ -4,7 +4,7 @@
 Whereas previous versions of cuGraph have included mechanisms to make it
 trivial to plug in cuGraph algorithm calls. Beginning with version 24.02, nx-cuGraph
 is now a [networkX backend]().
-The user now need only [install nx-cugraph]()
+The user now need only [install nx-cugraph]()
 to experience GPU speedups.
 
 Lets look at some examples of algorithm speedups comparing CPU based NetworkX to dispatched versions run on GPU with nx_cugraph.
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index 2ff53c1a3..1a4a98e5f 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -34,7 +34,7 @@ dependencies = [
     "cupy-cuda11x>=12.0.0",
     "networkx>=3.0",
     "numpy>=1.23,<2.0a0",
-    "pylibcugraph==24.4.*",
+    "pylibcugraph==24.6.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 
 [project.optional-dependencies]

From 8d14daf2f9e5561d1e9f37afff4149e381b8ad7e Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Fri, 15 Mar 2024 13:37:38 -0500
Subject: [PATCH 340/384] nx-cugraph: add `is_negatively_weighted` (#4242)

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4242
---
 python/nx-cugraph/_nx_cugraph/__init__.py     |  1 +
 .../nx-cugraph/nx_cugraph/classes/function.py | 22 +++++++++++-
 .../nx_cugraph/tests/test_classes_function.py | 35 +++++++++++++++++++
 python/nx-cugraph/pyproject.toml              |  2 ++
 python/nx-cugraph/scripts/update_readme.py    |  9 +++--
 5 files changed, 65 insertions(+), 4 deletions(-)
 create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_classes_function.py

diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index 098de46af..013f12367 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -96,6 +96,7 @@
         "is_connected",
         "is_forest",
         "is_isolate",
+        "is_negatively_weighted",
         "is_tree",
         "is_weakly_connected",
         "isolates",
diff --git a/python/nx-cugraph/nx_cugraph/classes/function.py b/python/nx-cugraph/nx_cugraph/classes/function.py
index 7212a4d2d..3dac6c2fd 100644
--- a/python/nx-cugraph/nx_cugraph/classes/function.py
+++ b/python/nx-cugraph/nx_cugraph/classes/function.py
@@ -11,11 +11,31 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import cupy as cp
+import networkx as nx
 
 from nx_cugraph.convert import _to_graph
 from nx_cugraph.utils import networkx_algorithm
 
-__all__ = ["number_of_selfloops"]
+__all__ = [
+    "is_negatively_weighted",
+    "number_of_selfloops",
+]
+
+
+@networkx_algorithm(version_added="24.04")
+def is_negatively_weighted(G, edge=None, weight="weight"):
+    G = _to_graph(G, weight)
+    if edge is not None:
+        data = G.get_edge_data(*edge)
+        if data is None:
+            raise nx.NetworkXError(f"Edge {edge!r} does not exist.")
+        return weight in data and data[weight] < 0
+    if weight not in G.edge_values:
+        return False
+    edge_vals = G.edge_values[weight]
+    if weight in G.edge_masks:
+        edge_vals = edge_vals[G.edge_masks[weight]]
+    return bool((edge_vals < 0).any())
 
 
 @networkx_algorithm(version_added="23.12")
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_classes_function.py b/python/nx-cugraph/nx_cugraph/tests/test_classes_function.py
new file mode 100644
index 000000000..d6152f650
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/tests/test_classes_function.py
@@ -0,0 +1,35 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Test functions from nx_cugraph/classes/function.py"""
+import networkx as nx
+
+import nx_cugraph as nxcg
+
+
+def test_is_negatively_weighted():
+    Gnx = nx.MultiGraph()
+    Gnx.add_edge(0, 1, 2, weight=-3)
+    Gnx.add_edge(2, 3, foo=3)
+    Gcg = nxcg.from_networkx(Gnx, preserve_edge_attrs=True)
+    assert nx.is_negatively_weighted(Gnx)
+    assert nxcg.is_negatively_weighted(Gnx)
+    assert nxcg.is_negatively_weighted(Gcg)
+    assert not nx.is_negatively_weighted(Gnx, weight="foo")
+    assert not nxcg.is_negatively_weighted(Gcg, weight="foo")
+    assert not nx.is_negatively_weighted(Gnx, weight="bar")
+    assert not nxcg.is_negatively_weighted(Gcg, weight="bar")
+    assert nx.is_negatively_weighted(Gnx, (0, 1, 2))
+    assert nxcg.is_negatively_weighted(Gcg, (0, 1, 2))
+    assert nx.is_negatively_weighted(Gnx, (0, 1)) == nxcg.is_negatively_weighted(
+        Gcg, (0, 1)
+    )
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index 2ff53c1a3..c662e710f 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -175,6 +175,7 @@ ignore = [
     # "SIM401",  # Use dict.get ... instead of if-else-block (Note: if-else better for coverage and sometimes clearer)
     # "TRY004",  # Prefer `TypeError` exception for invalid type (Note: good advice, but not worth the nuisance)
     "B904",  # Bare `raise` inside exception clause (like TRY200; sometimes okay)
+    "S310",  # Audit URL open for permitted schemes (Note: we don't download URLs in normal usage)
 
     # Intentionally ignored
     "A003",  # Class attribute ... is shadowing a python builtin
@@ -232,6 +233,7 @@ ignore = [
 "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"]
 "_nx_cugraph/__init__.py" = ["E501"]
 "nx_cugraph/algorithms/**/*py" = ["D205", "D401"]  # Allow flexible docstrings for algorithms
+"scripts/update_readme.py" = ["INP001"]  # Not part of a package
 
 [tool.ruff.lint.flake8-annotations]
 mypy-init-return = true
diff --git a/python/nx-cugraph/scripts/update_readme.py b/python/nx-cugraph/scripts/update_readme.py
index fcaa1769d..5ee49e63a 100755
--- a/python/nx-cugraph/scripts/update_readme.py
+++ b/python/nx-cugraph/scripts/update_readme.py
@@ -70,6 +70,9 @@ def replace_body(text, match, new_body):
         "https://networkx.org/documentation/stable/reference/"
         "algorithms/component.html#weak-connectivity"
     ),
+    "networkx.classes": (
+        "https://networkx.org/documentation/stable/reference/classes/index.html"
+    ),
 }
 
 
@@ -193,9 +196,9 @@ def get_payload_internal(keys):
 
 
 def find_or_download_objs_file(objs_file_dir):
-    """
-    Returns the path to /objects.inv, downloading it from
-    _objs_file_url if it does not already exist.
+    """Return the path to /objects.inv and download it if necessary.
+
+    Download objects.inv from _objs_file_url if it does not already exist.
     """
     objs_file_path = objs_file_dir / "objects.inv"
     if not objs_file_path.exists():

From 890c56193967f803d1d087a718c3e4a5128f7600 Mon Sep 17 00:00:00 2001
From: Kyle Edwards 
Date: Thu, 21 Mar 2024 09:12:22 -0400
Subject: [PATCH 341/384] Update pre-commit-hooks to v0.0.3 (#4264)

This fixes an issue with how the `verify-copyright` hook handles multiple merge bases.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Don Acosta (https://github.com/acostadon)

URL: https://github.com/rapidsai/cugraph/pull/4264
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 542e9cacb..a314b8c71 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -52,7 +52,7 @@ repos:
         - id: rapids-dependency-file-generator
           args: ["--clean"]
   - repo: https://github.com/rapidsai/pre-commit-hooks
-    rev: v0.0.1
+    rev: v0.0.3
     hooks:
       - id: verify-copyright
         files: |

From 7ef61c4c3a70ece0dc61f337ac0609e939af803f Mon Sep 17 00:00:00 2001
From: James Lamb 
Date: Fri, 5 Apr 2024 12:38:36 -0500
Subject: [PATCH 342/384] fix PyPI URL in docs (#4282)

`https://pypi.ngc.nvidia.com` was retired a while ago, and RAPIDS wheels are now available from `https://pypi.nvidia.com` (see https://docs.rapids.ai/install).

This proposes removing one lingering reference to `pypi.ngc.nvidia.com` left over in this project's docs.

### How I tested this

```shell
docker run \
   --rm \
   python:3.10 \
   pip install --dry-run cugraph-cu12 --extra-index-url=https://pypi.nvidia.com
```

saw the expected output

```text
Collecting cugraph-cu12
  Downloading https://pypi.nvidia.com/cugraph-cu12/cugraph_cu12-24.2.0-cp310-cp310-manylinux_2_28_aarch64.whl (1318.6 MB)
```

This is the only use of that `ngc.nvidia.com` URL in this project.

```shell
git grep -E 'ngc\.'
```

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Don Acosta (https://github.com/acostadon)

URL: https://github.com/rapidsai/cugraph/pull/4282
---
 docs/cugraph/source/installation/getting_cugraph.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md
index 4d601bf32..126325c09 100644
--- a/docs/cugraph/source/installation/getting_cugraph.md
+++ b/docs/cugraph/source/installation/getting_cugraph.md
@@ -53,7 +53,7 @@ Note: This conda installation only applies to Linux and Python versions 3.9/3.10
 cuGraph, and all of RAPIDS, is available via pip.
 
 ```
-pip install cugraph-cu12 --extra-index-url=https://pypi.ngc.nvidia.com
+pip install cugraph-cu12 --extra-index-url=https://pypi.nvidia.com
 ```
 
 Replace `-cu12` with `-cu11` for packages supporting CUDA 11.

From 8244b85e98a607e7ca211dace95d1e3d36296932 Mon Sep 17 00:00:00 2001
From: Ralph Liu <137829296+nv-rliu@users.noreply.github.com>
Date: Tue, 9 Apr 2024 14:06:03 -0400
Subject: [PATCH 343/384] Fix Table for Synthetic Data notebook (#4311)

This PR fixes the algorithms table in
`notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb` to render
properly on GH.
---
 .../synth_release_single_gpu.ipynb            | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb b/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb
index 1acef5d55..99ab9a0e0 100644
--- a/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb
+++ b/notebooks/cugraph_benchmarks/synth_release_single_gpu.ipynb
@@ -83,17 +83,17 @@
     "tags": []
    },
    "source": [
-    "|        Algorithm        |  Type         | Undirected Graph | Directed Graph |   Notes\n",
-    "| ------------------------|---------------|------ | ------- |-------------\n",
-    "| Katz                    | Centrality    |   X   |         | \n",
-    "| Betweenness Centrality  | Centrality    |   X   |         | Estimated, k = 100\n",
-    "| Louvain                 | Community     |   X   |         | Uses python-louvain for comparison\n",
-    "| Triangle Counting       | Community     |   X   |         |\n",
-    "| Core Number             | Core          |   X   |         |\n",
-    "| PageRank                | Link Analysis |       |    X    |\n",
-    "| Jaccard                 | Similarity    |   X   |         |\n",
-    "| BFS                     | Traversal     |   X   |         | No depth limit\n",
-    "| SSSP                    | Traversal     |   X   |         | \n",
+    "|        Algorithm        |  Type         | Undirected Graph | Directed Graph |   Notes           |\n",
+    "| ------------------------|---------------|------ | ------- |-------------------------------------|\n",
+    "| Katz                    | Centrality    |   X   |         |                                     |\n",
+    "| Betweenness Centrality  | Centrality    |   X   |         | Estimated, k = 100                  |\n",
+    "| Louvain                 | Community     |   X   |         | Uses python-louvain for comparison  |\n",
+    "| Triangle Counting       | Community     |   X   |         |                                     |\n",
+    "| Core Number             | Core          |   X   |         |                                     |\n",
+    "| PageRank                | Link Analysis |       |    X    |                                     |\n",
+    "| Jaccard                 | Similarity    |   X   |         |                                     |\n",
+    "| BFS                     | Traversal     |   X   |         | No depth limit                      |\n",
+    "| SSSP                    | Traversal     |   X   |         |                                     |\n",
     "\n",
     "\n",
     "### Test Data\n",
@@ -810,7 +810,7 @@
    "metadata": {},
    "source": [
     "___\n",
-    "Copyright (c) 2020-2023, NVIDIA CORPORATION.\n",
+    "Copyright (c) 2020-2024, NVIDIA CORPORATION.\n",
     "\n",
     "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
     "\n",

From 538fcf352d5fe39dd31d91a04eca5e68eecd6901 Mon Sep 17 00:00:00 2001
From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com>
Date: Tue, 9 Apr 2024 13:10:05 -0500
Subject: [PATCH 344/384] Updates the nx-cugraph README.md with the latest
 upstream URLs (#4321)

This PR updates the nx-cugraph README.md with the latest upstream URLs
by running the `update_readme.py` script on a current `objects.inv` file
downloaded from NetworkX. The latest `objects.inv` file has updated
URLs, which results in links in the README being updated too.

This should resolve style check errors recently seen in [other
PRs](https://github.com/rapidsai/cugraph/pull/4320) due to the upstream
change in `objects.inv` in NetworkX. Since CI runs pre-commit on all
files, this caused the nx-cugraph checks to run on PRs that didn't
change nx-cugraph files.
---
 .pre-commit-config.yaml                   |  4 ++--
 python/nx-cugraph/README.md               | 15 +++++++++------
 python/nx-cugraph/_nx_cugraph/__init__.py |  1 -
 3 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a314b8c71..f0aa51f7e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -72,7 +72,7 @@ repos:
         types: [python]
         language: python
         pass_filenames: false
-        additional_dependencies: ["networkx>=3.2"]
+        additional_dependencies: ["networkx>=3.3"]
   - repo: local
     hooks:
       - id: nx-cugraph-readme-update
@@ -82,4 +82,4 @@ repos:
         types_or: [python, markdown]
         language: python
         pass_filenames: false
-        additional_dependencies: ["networkx>=3.2"]
+        additional_dependencies: ["networkx>=3.3"]
diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md
index 77066356a..75b5c1c5a 100644
--- a/python/nx-cugraph/README.md
+++ b/python/nx-cugraph/README.md
@@ -106,16 +106,16 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  └─ generators
      └─ complete_bipartite_graph
 centrality
- ├─ betweenness
+ ├─ betweenness
  │   ├─ betweenness_centrality
  │   └─ edge_betweenness_centrality
- ├─ degree_alg
+ ├─ degree_alg
  │   ├─ degree_centrality
  │   ├─ in_degree_centrality
  │   └─ out_degree_centrality
- ├─ eigenvector
+ ├─ eigenvector
  │   └─ eigenvector_centrality
- └─ katz
+ └─ katz
      └─ katz_centrality
 cluster
  ├─ average_clustering
@@ -126,12 +126,12 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  └─ louvain
      └─ louvain_communities
 components
- ├─ connected
+ ├─ connected
  │   ├─ connected_components
  │   ├─ is_connected
  │   ├─ node_connected_component
  │   └─ number_connected_components
- └─ weakly_connected
+ └─ weakly_connected
      ├─ is_weakly_connected
      ├─ number_weakly_connected_components
      └─ weakly_connected_components
@@ -248,6 +248,9 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
 ### Other
 
 
+classes
+ └─ function
+     └─ is_negatively_weighted
 convert_matrix
  ├─ from_pandas_edgelist
  └─ from_scipy_sparse_array
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index 013f12367..7a073ffb2 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -207,7 +207,6 @@
         },
         "louvain_communities": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
-            "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).",
         },
         "pagerank": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",

From 3e47b1cb163c773aaaedc52f28a873af5044bf30 Mon Sep 17 00:00:00 2001
From: Don Acosta <97529984+acostadon@users.noreply.github.com>
Date: Fri, 12 Apr 2024 17:25:04 -0400
Subject: [PATCH 345/384] Notebook fixes to formulas and outputs (#4272)

Authors:
  - Don Acosta (https://github.com/acostadon)

Approvers:
  - Brad Rees (https://github.com/BradReesWork)

URL: https://github.com/rapidsai/cugraph/pull/4272
---
 .../cugraph/source/installation/source_build.md | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md
index 1a129d452..49af13757 100644
--- a/docs/cugraph/source/installation/source_build.md
+++ b/docs/cugraph/source/installation/source_build.md
@@ -13,8 +13,8 @@ __Compilers:__
 
 __CUDA:__
 * CUDA 11.2+
-* NVIDIA driver 450.80.02+
-* Pascal architecture or better
+* NVIDIA driver 470.42.01 or newer
+* NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
 
 Further details and download links for these prerequisites are available on the
 [RAPIDS System Requirements page](https://docs.rapids.ai/install#system-req).
@@ -42,6 +42,10 @@ files](https://github.com/rapidsai/cugraph/blob/main/conda/environments).
 # for CUDA 11.x
 conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml
 
+# for CUDA 12.x
+conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-12.2_arch-x86_64.yaml
+
+
 # activate the environment
 conda activate cugraph_dev
 
@@ -55,6 +59,13 @@ The environment can be updated as cugraph adds/removes/updates its dependencies.
 # for CUDA 11.x
 conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml
 conda activate cugraph_dev
+
+# for CUDA 12.x
+conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-122_arch-x86_64.yaml
+conda activate cugraph_dev
+
+
+
 ```
 
 ### Build and Install
@@ -167,7 +178,7 @@ Run either the C++ or the Python tests with datasets
    make test
    ```
 
-Note: This conda installation only applies to Linux and Python versions 3.8/3.10.
+Note: This conda installation only applies to Linux and Python versions 3.8/3.11.
 
 ### (OPTIONAL) Set environment variable on activation
 

From 5e6463617e263039688974d22618f416b4b99667 Mon Sep 17 00:00:00 2001
From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com>
Date: Mon, 15 Apr 2024 15:46:07 -0400
Subject: [PATCH 346/384] Update pre-commit hooks to include all C and C++
 files (#4332)

#3062 added pre-commit hooks for formatting C and C++ code.  However, libcugraph_etl and the C API unit tests are currently excluded from that check.  The intention was to get back around to them and include them in the testing.  However, that slipped through the cracks.

This PR adds the pre-commit hooks for those files as well.

Authors:
  - Chuck Hastings (https://github.com/ChuckHastings)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Seunghwa Kang (https://github.com/seunghwak)
  - Mark Harris (https://github.com/harrism)

URL: https://github.com/rapidsai/cugraph/pull/4332
---
 .pre-commit-config.yaml | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f0aa51f7e..07bf1667c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -39,11 +39,6 @@ repos:
     rev: v16.0.6
     hooks:
       - id: clang-format
-        exclude: |
-          (?x)^(
-            cpp/libcugraph_etl|
-            cpp/tests/c_api
-          )
         types_or: [c, c++, cuda]
         args: ["-fallback-style=none", "-style=file", "-i"]
   - repo: https://github.com/rapidsai/dependency-file-generator

From 8dd0b7cf4266c913ba5fd15cc4f1da5ae3689e4a Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Mon, 29 Apr 2024 11:10:15 -0500
Subject: [PATCH 347/384] `nx-cugraph`: support `should_run` that was added in
 NetworkX 3.3 (#4348)

Also, update `complete_bipartite_graph` for nx dev

**edit**: ~This does not yet add per-algorithm `should_run`, but it makes doing so possible.~

Add `should_run` for `triangles`, `clustering`, `is_isolate`

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4348
---
 python/nx-cugraph/lint.yaml                      | 16 ++++++++--------
 .../algorithms/bipartite/generators.py           |  6 +++++-
 .../algorithms/centrality/degree_alg.py          | 15 +++++++++++++++
 .../nx-cugraph/nx_cugraph/algorithms/cluster.py  | 14 ++++++++++++++
 .../nx-cugraph/nx_cugraph/algorithms/isolate.py  |  5 +++++
 python/nx-cugraph/nx_cugraph/classes/function.py |  5 +++++
 python/nx-cugraph/nx_cugraph/interface.py        | 12 ++++++++----
 .../nx_cugraph/tests/test_match_api.py           |  2 ++
 python/nx-cugraph/nx_cugraph/utils/decorators.py | 15 +++++++++++++++
 python/nx-cugraph/pyproject.toml                 |  1 +
 python/nx-cugraph/run_nx_tests.sh                |  2 +-
 python/nx-cugraph/scripts/update_readme.py       |  1 +
 12 files changed, 80 insertions(+), 14 deletions(-)

diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml
index 3239fa151..d220cb18d 100644
--- a/python/nx-cugraph/lint.yaml
+++ b/python/nx-cugraph/lint.yaml
@@ -11,7 +11,7 @@ default_language_version:
     python: python3
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict
@@ -31,7 +31,7 @@ repos:
       - id: validate-pyproject
         name: Validate pyproject.toml
   - repo: https://github.com/PyCQA/autoflake
-    rev: v2.3.0
+    rev: v2.3.1
     hooks:
       - id: autoflake
         args: [--in-place]
@@ -40,17 +40,17 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.1
+    rev: v3.15.2
     hooks:
       - id: pyupgrade
         args: [--py39-plus]
   - repo: https://github.com/psf/black
-    rev: 24.2.0
+    rev: 24.4.2
     hooks:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.2
+    rev: v0.4.2
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
@@ -62,7 +62,7 @@ repos:
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
           - flake8==7.0.0
-          - flake8-bugbear==24.2.6
+          - flake8-bugbear==24.4.21
           - flake8-simplify==0.21.0
   - repo: https://github.com/asottile/yesqa
     rev: v1.5.0
@@ -77,11 +77,11 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.2
+    rev: v0.4.2
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: no-commit-to-branch
         args: [-p, "^branch-2....$"]
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py
index 5a0c970c9..60276b7d4 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py
@@ -48,13 +48,17 @@ def complete_bipartite_graph(n1, n2, create_using=None):
         nodes.extend(range(n2) if nodes2 is None else nodes2)
         if len(set(nodes)) != len(nodes):
             raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes")
+    if nx.__version__[:3] <= "3.3":
+        name = f"complete_bipartite_graph({orig_n1}, {orig_n2})"
+    else:
+        name = f"complete_bipartite_graph({n1}, {n2})"
     G = graph_class.from_coo(
         n1 + n2,
         src_indices,
         dst_indices,
         node_values={"bipartite": bipartite},
         id_to_key=nodes,
-        name=f"complete_bipartite_graph({orig_n1}, {orig_n2})",
+        name=name,
     )
     if inplace:
         return create_using._become(G)
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py
index a319eb3a1..1cc051c69 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py
@@ -26,6 +26,11 @@ def degree_centrality(G):
     return G._nodearray_to_dict(centrality)
 
 
+@degree_centrality._should_run
+def _(G):
+    return "Fast algorithm; not worth converting."
+
+
 @not_implemented_for("undirected")
 @networkx_algorithm(version_added="23.12")
 def in_degree_centrality(G):
@@ -37,6 +42,11 @@ def in_degree_centrality(G):
     return G._nodearray_to_dict(centrality)
 
 
+@in_degree_centrality._should_run
+def _(G):
+    return "Fast algorithm; not worth converting."
+
+
 @not_implemented_for("undirected")
 @networkx_algorithm(version_added="23.12")
 def out_degree_centrality(G):
@@ -46,3 +56,8 @@ def out_degree_centrality(G):
     deg = G._out_degrees_array()
     centrality = deg * (1 / (len(G) - 1))
     return G._nodearray_to_dict(centrality)
+
+
+@out_degree_centrality._should_run
+def _(G):
+    return "Fast algorithm; not worth converting."
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/cluster.py b/python/nx-cugraph/nx_cugraph/algorithms/cluster.py
index a458e6c04..c355a1bb7 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/cluster.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/cluster.py
@@ -56,6 +56,13 @@ def triangles(G, nodes=None):
     return G._nodearrays_to_dict(node_ids, triangles)
 
 
+@triangles._should_run
+def _(G, nodes=None):
+    if nodes is None or nodes not in G:
+        return True
+    return "Fast algorithm when computing for a single node; not worth converting."
+
+
 @not_implemented_for("directed")
 @networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count")
 def clustering(G, nodes=None, weight=None):
@@ -86,6 +93,13 @@ def _(G, nodes=None, weight=None):
     return weight is None and not G.is_directed()
 
 
+@clustering._should_run
+def _(G, nodes=None, weight=None):
+    if nodes is None or nodes not in G:
+        return True
+    return "Fast algorithm when computing for a single node; not worth converting."
+
+
 @not_implemented_for("directed")
 @networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count")
 def average_clustering(G, nodes=None, weight=None, count_zeros=True):
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py
index 9621fbeaa..47a349bcf 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py
@@ -37,6 +37,11 @@ def is_isolate(G, n):
     )
 
 
+@is_isolate._should_run
+def _(G, n):
+    return "Fast algorithm; not worth converting."
+
+
 def _mark_isolates(G, symmetrize=None) -> cp.ndarray[bool]:
     """Return a boolean mask array indicating indices of isolated nodes."""
     mark_isolates = cp.ones(len(G), bool)
diff --git a/python/nx-cugraph/nx_cugraph/classes/function.py b/python/nx-cugraph/nx_cugraph/classes/function.py
index 3dac6c2fd..55cbf19aa 100644
--- a/python/nx-cugraph/nx_cugraph/classes/function.py
+++ b/python/nx-cugraph/nx_cugraph/classes/function.py
@@ -43,3 +43,8 @@ def number_of_selfloops(G):
     G = _to_graph(G)
     is_selfloop = G.src_indices == G.dst_indices
     return int(cp.count_nonzero(is_selfloop))
+
+
+@number_of_selfloops._should_run
+def _(G):
+    return "Fast algorithm; not worth converting."
diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py
index 0d893ac28..8569bbf40 100644
--- a/python/nx-cugraph/nx_cugraph/interface.py
+++ b/python/nx-cugraph/nx_cugraph/interface.py
@@ -330,8 +330,12 @@ def key(testpath):
 
     @classmethod
     def can_run(cls, name, args, kwargs):
-        """Can this backend run the specified algorithms with the given arguments?
-
-        This is a proposed API to add to networkx dispatching machinery and may change.
-        """
+        """Can this backend run the specified algorithms with the given arguments?"""
+        # TODO: drop hasattr when networkx 3.0 support is dropped
         return hasattr(cls, name) and getattr(cls, name).can_run(*args, **kwargs)
+
+    @classmethod
+    def should_run(cls, name, args, kwargs):
+        """Should this backend run the specified algorithms with the given arguments?"""
+        # TODO: drop hasattr when networkx 3.0 support is dropped
+        return hasattr(cls, name) and getattr(cls, name).should_run(*args, **kwargs)
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py
index bd6f20e84..d784d8c13 100644
--- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py
+++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py
@@ -63,6 +63,8 @@ def test_match_signature_and_names():
             )
         if func.can_run is not nxcg.utils.decorators._default_can_run:
             assert func_sig == inspect.signature(func.can_run)
+        if func.should_run is not nxcg.utils.decorators._default_should_run:
+            assert func_sig == inspect.signature(func.should_run)
 
         # Matching function names?
         assert func.__name__ == dispatchable_func.__name__ == orig_func.__name__
diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py
index 011ebfd6e..3c5de4f29 100644
--- a/python/nx-cugraph/nx_cugraph/utils/decorators.py
+++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py
@@ -108,9 +108,11 @@ def __new__(
         if instance.name in _registered_algorithms:
             instance.__doc__ = _registered_algorithms[instance.name].__doc__
         instance.can_run = _default_can_run
+        instance.should_run = _default_should_run
         setattr(BackendInterface, instance.name, instance)
         # Set methods so they are in __dict__
         instance._can_run = instance._can_run
+        instance._should_run = instance._should_run
         if nodes_or_number is not None and nx.__version__[:3] <= "3.2":
             instance = nx.utils.decorators.nodes_or_number(nodes_or_number)(instance)
         return instance
@@ -124,6 +126,15 @@ def _can_run(self, func):
             )
         self.can_run = func
 
+    def _should_run(self, func):
+        """Set the `should_run` attribute to the decorated function."""
+        if not func.__name__.startswith("_"):
+            raise ValueError(
+                "The name of the function used by `_should_run` must begin with '_'; "
+                f"got: {func.__name__!r}"
+            )
+        self.should_run = func
+
     def __call__(self, /, *args, **kwargs):
         return self.__wrapped__(*args, **kwargs)
 
@@ -135,5 +146,9 @@ def _default_can_run(*args, **kwargs):
     return True
 
 
+def _default_should_run(*args, **kwargs):
+    return True
+
+
 def _restore_networkx_dispatched(name):
     return getattr(BackendInterface, name)
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index f3114ca98..a7daf0177 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -233,6 +233,7 @@ ignore = [
 "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"]
 "_nx_cugraph/__init__.py" = ["E501"]
 "nx_cugraph/algorithms/**/*py" = ["D205", "D401"]  # Allow flexible docstrings for algorithms
+"nx_cugraph/interface.py" = ["D401"]  # Flexible docstrings
 "scripts/update_readme.py" = ["INP001"]  # Not part of a package
 
 [tool.ruff.lint.flake8-annotations]
diff --git a/python/nx-cugraph/run_nx_tests.sh b/python/nx-cugraph/run_nx_tests.sh
index da7a2014c..bceec53b7 100755
--- a/python/nx-cugraph/run_nx_tests.sh
+++ b/python/nx-cugraph/run_nx_tests.sh
@@ -24,7 +24,7 @@
 # Warning: cugraph has a .coveragerc file in the /python directory,
 # so be mindful of its contents and the CWD when running.
 # FIXME: should something be added to detect/prevent the above?
-
+set -e
 NETWORKX_GRAPH_CONVERT=cugraph \
 NETWORKX_TEST_BACKEND=cugraph \
 NETWORKX_FALLBACK_TO_NX=True \
diff --git a/python/nx-cugraph/scripts/update_readme.py b/python/nx-cugraph/scripts/update_readme.py
index 5ee49e63a..0dad5d675 100755
--- a/python/nx-cugraph/scripts/update_readme.py
+++ b/python/nx-cugraph/scripts/update_readme.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # Copyright (c) 2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

From b2bcbd5b0d38974b7ceab22cc5ea391d5a9d722d Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Thu, 2 May 2024 10:02:01 -0500
Subject: [PATCH 348/384] Update `k_truss` to work with many connected
 components (#4383)

This case is tested by the networkx tests.

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4383
---
 python/nx-cugraph/_nx_cugraph/__init__.py       | 4 ----
 python/nx-cugraph/nx_cugraph/algorithms/core.py | 9 ---------
 2 files changed, 13 deletions(-)

diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index 7a073ffb2..edc96983b 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -166,10 +166,6 @@
         "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.",
         "from_pandas_edgelist": "cudf.DataFrame inputs also supported; value columns with str is unsuppported.",
         "generic_bfs_edges": "`neighbors` and `sort_neighbors` parameters are not yet supported.",
-        "k_truss": (
-            "Currently raises `NotImplementedError` for graphs with more than one connected\n"
-            "component when k >= 3. We expect to fix this soon."
-        ),
         "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.",
         "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.",
         "pagerank": "`dangling` parameter is not supported, but it is checked for validity.",
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py
index 71f61abf4..8eb9a9946 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/core.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py
@@ -57,10 +57,6 @@ def _(G):
 @not_implemented_for("multigraph")
 @networkx_algorithm(is_incomplete=True, version_added="23.12", _plc="k_truss_subgraph")
 def k_truss(G, k):
-    """
-    Currently raises `NotImplementedError` for graphs with more than one connected
-    component when k >= 3. We expect to fix this soon.
-    """
     if is_nx := isinstance(G, nx.Graph):
         G = nxcg.from_networkx(G, preserve_all_attrs=True)
     if nxcg.number_of_selfloops(G) > 0:
@@ -91,11 +87,6 @@ def k_truss(G, k):
         # Renumber step 1: edge values (no changes needed)
         edge_values = {key: val.copy() for key, val in G.edge_values.items()}
         edge_masks = {key: val.copy() for key, val in G.edge_masks.items()}
-    elif (ncc := nxcg.number_connected_components(G)) > 1:
-        raise NotImplementedError(
-            "nx_cugraph.k_truss does not yet work on graphs with more than one "
-            f"connected component (this graph has {ncc}). We expect to fix this soon."
-        )
     else:
         edge_dtype = _get_int_dtype(G.src_indices.size - 1)
         edge_indices = cp.arange(G.src_indices.size, dtype=edge_dtype)

From ab381e3873571207bd1bccfe349cc71ea7a34822 Mon Sep 17 00:00:00 2001
From: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>
Date: Wed, 8 May 2024 06:10:38 -1000
Subject: [PATCH 349/384] Address dask_cudf.read_csv chunksize deprecation
 (#4379)

xref https://github.com/rapidsai/cugraph/pull/4271

`chunksize` was deprecated in favor of `blocksize`

Also removed an unsupported `chunksize` in a `cudf.read_csv` call

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)

Approvers:
  - Don Acosta (https://github.com/acostadon)
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4379
---
 docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst
index d9ba3f3a7..f5132dd65 100644
--- a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst
+++ b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst
@@ -54,7 +54,7 @@ Example
     # multi-GPU CSV reader
     e_list = dask_cudf.read_csv(
         input_data_path,
-        chunksize=chunksize,
+        blocksize=chunksize,
         names=['src', 'dst'],
         dtype=['int32', 'int32'],
     )

From 1399e5258c0f61f9b716cb87d958b88df6f33999 Mon Sep 17 00:00:00 2001
From: Don Acosta <97529984+acostadon@users.noreply.github.com>
Date: Thu, 9 May 2024 09:05:24 -0400
Subject: [PATCH 350/384] Removed obsolete methods from rst file (#4331)

Removes doc generation for the link prediction algorithm methods that have been taken out
closes #4330

Authors:
  - Don Acosta (https://github.com/acostadon)
  - Alex Barghi (https://github.com/alexbarghi-nv)

Approvers:
  - Brad Rees (https://github.com/BradReesWork)

URL: https://github.com/rapidsai/cugraph/pull/4331
---
 docs/cugraph/source/api_docs/cugraph/link_prediction.rst | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/docs/cugraph/source/api_docs/cugraph/link_prediction.rst b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst
index f05dce6f7..3d2f9562e 100644
--- a/docs/cugraph/source/api_docs/cugraph/link_prediction.rst
+++ b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst
@@ -11,7 +11,6 @@ Jaccard Coefficient
 
    cugraph.jaccard
    cugraph.jaccard_coefficient
-   cugraph.jaccard_w
 
 
 Overlap Coefficient
@@ -21,7 +20,6 @@ Overlap Coefficient
 
    cugraph.overlap
    cugraph.overlap_coefficient
-   cugraph.overlap_w
 
 
 Sorensen Coefficient
@@ -31,4 +29,3 @@ Sorensen Coefficient
 
    cugraph.sorensen
    cugraph.sorensen_coefficient
-   cugraph.sorensen_w

From 7e4ff7d627f16920778c68d604e4ac64bc1479ff Mon Sep 17 00:00:00 2001
From: Ray Bell 
Date: Mon, 20 May 2024 17:09:36 -0400
Subject: [PATCH 351/384] Update operators.rst (#4339)

Closes https://github.com/rapidsai/cugraph/issues/4337

Authors:
  - Ray Bell (https://github.com/raybellwaves)
  - Tingyu Wang (https://github.com/tingyu66)

Approvers:
  - Tingyu Wang (https://github.com/tingyu66)
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4339
---
 .../api_docs/cugraph-ops/python/operators.rst | 24 +++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst
index 3e6664b2d..8b5efd7aa 100644
--- a/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst
+++ b/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst
@@ -47,10 +47,26 @@ Graph Attention (GATConv/GATv2Conv)
 .. autosummary::
    :toctree: ../../api/ops
 
-   operators.mha_gat_n2n_fwd
-   operators.mha_gat_n2n_bwd
-   operators.mha_gat_n2n_efeat_fwd
-   operators.mha_gat_n2n_efeat_bwd
+   operators.mha_gat_n2n_fwd_bf16_fp32
+   operators.mha_gat_n2n_fwd_fp16_fp32
+   operators.mha_gat_n2n_fwd_fp32_fp32
+   operators.mha_gat_n2n_bwd_bf16_bf16_bf16_fp32
+   operators.mha_gat_n2n_bwd_bf16_bf16_fp32_fp32
+   operators.mha_gat_n2n_bwd_bf16_fp32_fp32_fp32
+   operators.mha_gat_n2n_bwd_fp16_fp16_fp16_fp32
+   operators.mha_gat_n2n_bwd_fp16_fp16_fp32_fp32
+   operators.mha_gat_n2n_bwd_fp16_fp32_fp32_fp32
+   operators.mha_gat_n2n_bwd_fp32_fp32_fp32_fp32
+   operators.mha_gat_n2n_efeat_fwd_bf16_fp32
+   operators.mha_gat_n2n_efeat_fwd_fp16_fp32
+   operators.mha_gat_n2n_efeat_fwd_fp32_fp32
+   operators.mha_gat_n2n_efeat_bwd_bf16_bf16_bf16_fp32
+   operators.mha_gat_n2n_efeat_bwd_bf16_bf16_fp32_fp32
+   operators.mha_gat_n2n_efeat_bwd_bf16_fp32_fp32_fp32
+   operators.mha_gat_n2n_efeat_bwd_fp16_fp16_fp16_fp32
+   operators.mha_gat_n2n_efeat_bwd_fp16_fp16_fp32_fp32
+   operators.mha_gat_n2n_efeat_bwd_fp16_fp32_fp32_fp32
+   operators.mha_gat_n2n_efeat_bwd_fp32_fp32_fp32_fp32
 
    operators.mha_gat_v2_n2n_fwd
    operators.mha_gat_v2_n2n_bwd

From 3fdc08ffc74765161f6ea9ee98996df36ebd6a9d Mon Sep 17 00:00:00 2001
From: Ray Douglass 
Date: Mon, 20 May 2024 17:42:47 -0400
Subject: [PATCH 352/384] DOC v24.08 Updates [skip ci]

---
 VERSION                                      | 2 +-
 docs/cugraph/source/nx_cugraph/nx_cugraph.md | 2 +-
 python/nx-cugraph/pyproject.toml             | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/VERSION b/VERSION
index 0bff6981a..ec8489fda 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.06.00
+24.08.00
diff --git a/docs/cugraph/source/nx_cugraph/nx_cugraph.md b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
index 92fbf90a4..82bebecdf 100644
--- a/docs/cugraph/source/nx_cugraph/nx_cugraph.md
+++ b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
@@ -4,7 +4,7 @@
 Whereas previous versions of cuGraph have included mechanisms to make it
 trivial to plug in cuGraph algorithm calls. Beginning with version 24.02, nx-cuGraph
 is now a [networkX backend]().
-The user now need only [install nx-cugraph]()
+The user now need only [install nx-cugraph]()
 to experience GPU speedups.
 
 Lets look at some examples of algorithm speedups comparing CPU based NetworkX to dispatched versions run on GPU with nx_cugraph.
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index a7daf0177..e65a4cb51 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -34,7 +34,7 @@ dependencies = [
     "cupy-cuda11x>=12.0.0",
     "networkx>=3.0",
     "numpy>=1.23,<2.0a0",
-    "pylibcugraph==24.6.*",
+    "pylibcugraph==24.8.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 
 [project.optional-dependencies]

From fc326dd9681ff225371b86e8a83c4a174573e577 Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Mon, 20 May 2024 19:24:47 -0500
Subject: [PATCH 353/384] nx-cugraph: add `ego_graph` (#4395)

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4395
---
 python/nx-cugraph/README.md                   |   2 +
 python/nx-cugraph/_nx_cugraph/__init__.py     |   5 +
 python/nx-cugraph/lint.yaml                   |   8 +-
 python/nx-cugraph/nx_cugraph/convert.py       |   9 +-
 .../nx_cugraph/generators/__init__.py         |   3 +-
 .../nx-cugraph/nx_cugraph/generators/ego.py   | 161 ++++++++++++++++++
 .../nx_cugraph/tests/test_ego_graph.py        |  81 +++++++++
 python/nx-cugraph/pyproject.toml              |   5 +-
 8 files changed, 265 insertions(+), 9 deletions(-)
 create mode 100644 python/nx-cugraph/nx_cugraph/generators/ego.py
 create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_ego_graph.py

diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md
index 75b5c1c5a..27825585c 100644
--- a/python/nx-cugraph/README.md
+++ b/python/nx-cugraph/README.md
@@ -216,6 +216,8 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  └─ wheel_graph
 community
  └─ caveman_graph
+ego
+ └─ ego_graph
 small
  ├─ bull_graph
  ├─ chvatal_graph
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index edc96983b..f57b90eb4 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -77,6 +77,7 @@
         "diamond_graph",
         "dodecahedral_graph",
         "edge_betweenness_centrality",
+        "ego_graph",
         "eigenvector_centrality",
         "empty_graph",
         "florentine_families_graph",
@@ -163,6 +164,7 @@
         "clustering": "Directed graphs and `weight` parameter are not yet supported.",
         "core_number": "Directed graphs are not yet supported.",
         "edge_betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.",
+        "ego_graph": "Weighted ego_graph with negative cycles is not yet supported. `NotImplementedError` will be raised if there are negative `distance` edge weights.",
         "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.",
         "from_pandas_edgelist": "cudf.DataFrame inputs also supported; value columns with str is unsuppported.",
         "generic_bfs_edges": "`neighbors` and `sort_neighbors` parameters are not yet supported.",
@@ -191,6 +193,9 @@
         "bellman_ford_path_length": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
+        "ego_graph": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         "eigenvector_centrality": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml
index d220cb18d..c4422ffb9 100644
--- a/python/nx-cugraph/lint.yaml
+++ b/python/nx-cugraph/lint.yaml
@@ -26,7 +26,7 @@ repos:
       - id: mixed-line-ending
       - id: trailing-whitespace
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.16
+    rev: v0.17
     hooks:
       - id: validate-pyproject
         name: Validate pyproject.toml
@@ -50,7 +50,7 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.2
+    rev: v0.4.4
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
@@ -62,7 +62,7 @@ repos:
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
           - flake8==7.0.0
-          - flake8-bugbear==24.4.21
+          - flake8-bugbear==24.4.26
           - flake8-simplify==0.21.0
   - repo: https://github.com/asottile/yesqa
     rev: v1.5.0
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.2
+    rev: v0.4.4
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks
diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py
index f265540a1..b34245d50 100644
--- a/python/nx-cugraph/nx_cugraph/convert.py
+++ b/python/nx-cugraph/nx_cugraph/convert.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -562,7 +562,12 @@ def to_networkx(G: nxcg.Graph, *, sort_edges: bool = False) -> nx.Graph:
         dst_iter = map(id_to_key.__getitem__, dst_indices)
     if G.is_multigraph() and (G.edge_keys is not None or G.edge_indices is not None):
         if G.edge_keys is not None:
-            edge_keys = G.edge_keys
+            if not G.is_directed():
+                edge_keys = [k for k, m in zip(G.edge_keys, mask.tolist()) if m]
+            else:
+                edge_keys = G.edge_keys
+        elif not G.is_directed():
+            edge_keys = G.edge_indices[mask].tolist()
         else:
             edge_keys = G.edge_indices.tolist()
         if edge_values:
diff --git a/python/nx-cugraph/nx_cugraph/generators/__init__.py b/python/nx-cugraph/nx_cugraph/generators/__init__.py
index c1834a4de..60a9d9237 100644
--- a/python/nx-cugraph/nx_cugraph/generators/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/generators/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -12,5 +12,6 @@
 # limitations under the License.
 from .classic import *
 from .community import *
+from .ego import *
 from .small import *
 from .social import *
diff --git a/python/nx-cugraph/nx_cugraph/generators/ego.py b/python/nx-cugraph/nx_cugraph/generators/ego.py
new file mode 100644
index 000000000..66c9c8b95
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/generators/ego.py
@@ -0,0 +1,161 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+
+import cupy as cp
+import networkx as nx
+import numpy as np
+import pylibcugraph as plc
+
+import nx_cugraph as nxcg
+
+from ..utils import _dtype_param, _get_float_dtype, index_dtype, networkx_algorithm
+
+__all__ = ["ego_graph"]
+
+
+@networkx_algorithm(
+    extra_params=_dtype_param, version_added="24.06", _plc={"bfs", "ego_graph", "sssp"}
+)
+def ego_graph(
+    G, n, radius=1, center=True, undirected=False, distance=None, *, dtype=None
+):
+    """Weighted ego_graph with negative cycles is not yet supported. `NotImplementedError` will be raised if there are negative `distance` edge weights."""  # noqa: E501
+    if isinstance(G, nx.Graph):
+        G = nxcg.from_networkx(G, preserve_all_attrs=True)
+    if n not in G:
+        if distance is None:
+            raise nx.NodeNotFound(f"Source {n} is not in G")
+        raise nx.NodeNotFound(f"Node {n} not found in graph")
+    src_index = n if G.key_to_id is None else G.key_to_id[n]
+    symmetrize = "union" if undirected and G.is_directed() else None
+    if distance is None or distance not in G.edge_values:
+        # Simple BFS to determine nodes
+        if radius is not None and radius <= 0:
+            if center:
+                node_ids = cp.array([src_index], dtype=index_dtype)
+            else:
+                node_ids = cp.empty(0, dtype=index_dtype)
+            node_mask = None
+        else:
+            if radius is None or np.isinf(radius):
+                radius = -1
+            else:
+                radius = math.ceil(radius)
+            distances, unused_predecessors, node_ids = plc.bfs(
+                handle=plc.ResourceHandle(),
+                graph=G._get_plc_graph(symmetrize=symmetrize),
+                sources=cp.array([src_index], index_dtype),
+                direction_optimizing=False,  # True for undirected only; what's best?
+                depth_limit=radius,
+                compute_predecessors=False,
+                do_expensive_check=False,
+            )
+            node_mask = distances != np.iinfo(distances.dtype).max
+    else:
+        # SSSP to determine nodes
+        if callable(distance):
+            raise NotImplementedError("callable `distance` argument is not supported")
+        if symmetrize and G.is_multigraph():
+            # G._get_plc_graph does not implement `symmetrize=True` w/ edge array
+            raise NotImplementedError(
+                "Weighted ego_graph with undirected=True not implemented"
+            )
+        # Check for negative values since we don't support negative cycles
+        edge_vals = G.edge_values[distance]
+        if distance in G.edge_masks:
+            edge_vals = edge_vals[G.edge_masks[distance]]
+        if (edge_vals < 0).any():
+            raise NotImplementedError(
+                "Negative edge weights not yet supported by ego_graph"
+            )
+        # PERF: we could use BFS if all edges are equal
+        if radius is None:
+            radius = np.inf
+        dtype = _get_float_dtype(dtype, graph=G, weight=distance)
+        node_ids, distances, unused_predecessors = plc.sssp(
+            resource_handle=plc.ResourceHandle(),
+            graph=(G.to_undirected() if symmetrize else G)._get_plc_graph(
+                distance, 1, dtype
+            ),
+            source=src_index,
+            cutoff=np.nextafter(radius, np.inf, dtype=np.float64),
+            compute_predecessors=True,  # TODO: False is not yet supported
+            do_expensive_check=False,
+        )
+        node_mask = distances != np.finfo(distances.dtype).max
+
+    if node_mask is not None:
+        if not center:
+            node_mask &= node_ids != src_index
+        node_ids = node_ids[node_mask]
+    if node_ids.size == G._N:
+        return G.copy()
+    # TODO: create renumbering helper function(s)
+    node_ids.sort()  # TODO: is this ever necessary? Keep for safety
+    node_values = {key: val[node_ids] for key, val in G.node_values.items()}
+    node_masks = {key: val[node_ids] for key, val in G.node_masks.items()}
+
+    G._sort_edge_indices()  # TODO: is this ever necessary? Keep for safety
+    edge_mask = cp.isin(G.src_indices, node_ids) & cp.isin(G.dst_indices, node_ids)
+    src_indices = cp.searchsorted(node_ids, G.src_indices[edge_mask]).astype(
+        index_dtype
+    )
+    dst_indices = cp.searchsorted(node_ids, G.dst_indices[edge_mask]).astype(
+        index_dtype
+    )
+    edge_values = {key: val[edge_mask] for key, val in G.edge_values.items()}
+    edge_masks = {key: val[edge_mask] for key, val in G.edge_masks.items()}
+
+    # Renumber nodes
+    if (id_to_key := G.id_to_key) is not None:
+        key_to_id = {
+            id_to_key[old_index]: new_index
+            for new_index, old_index in enumerate(node_ids.tolist())
+        }
+    else:
+        key_to_id = {
+            old_index: new_index
+            for new_index, old_index in enumerate(node_ids.tolist())
+        }
+    kwargs = {
+        "N": node_ids.size,
+        "src_indices": src_indices,
+        "dst_indices": dst_indices,
+        "edge_values": edge_values,
+        "edge_masks": edge_masks,
+        "node_values": node_values,
+        "node_masks": node_masks,
+        "key_to_id": key_to_id,
+    }
+    if G.is_multigraph():
+        if G.edge_keys is not None:
+            kwargs["edge_keys"] = [
+                x for x, m in zip(G.edge_keys, edge_mask.tolist()) if m
+            ]
+        if G.edge_indices is not None:
+            kwargs["edge_indices"] = G.edge_indices[edge_mask]
+    rv = G.__class__.from_coo(**kwargs)
+    rv.graph.update(G.graph)
+    return rv
+
+
+@ego_graph._can_run
+def _(G, n, radius=1, center=True, undirected=False, distance=None, *, dtype=None):
+    if distance is not None and undirected and G.is_directed() and G.is_multigraph():
+        return "Weighted ego_graph with undirected=True not implemented"
+    if distance is not None and nx.is_negatively_weighted(G, weight=distance):
+        return "Weighted ego_graph with negative cycles not yet supported"
+    if callable(distance):
+        return "callable `distance` argument is not supported"
+    return True
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_ego_graph.py b/python/nx-cugraph/nx_cugraph/tests/test_ego_graph.py
new file mode 100644
index 000000000..5474f9d79
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/tests/test_ego_graph.py
@@ -0,0 +1,81 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import networkx as nx
+import pytest
+from packaging.version import parse
+
+import nx_cugraph as nxcg
+
+from .testing_utils import assert_graphs_equal
+
+nxver = parse(nx.__version__)
+
+
+if nxver.major == 3 and nxver.minor < 2:
+    pytest.skip("Need NetworkX >=3.2 to test ego_graph", allow_module_level=True)
+
+
+@pytest.mark.parametrize(
+    "create_using", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+@pytest.mark.parametrize("radius", [-1, 0, 1, 1.5, 2, float("inf"), None])
+@pytest.mark.parametrize("center", [True, False])
+@pytest.mark.parametrize("undirected", [False, True])
+@pytest.mark.parametrize("multiple_edges", [False, True])
+@pytest.mark.parametrize("n", [0, 3])
+def test_ego_graph_cycle_graph(
+    create_using, radius, center, undirected, multiple_edges, n
+):
+    Gnx = nx.cycle_graph(7, create_using=create_using)
+    if multiple_edges:
+        # Test multigraph with multiple edges
+        if not Gnx.is_multigraph():
+            return
+        Gnx.add_edges_from(nx.cycle_graph(7, create_using=nx.DiGraph).edges)
+        Gnx.add_edge(0, 1, 10)
+    Gcg = nxcg.from_networkx(Gnx, preserve_all_attrs=True)
+    assert_graphs_equal(Gnx, Gcg)  # Sanity check
+
+    kwargs = {"radius": radius, "center": center, "undirected": undirected}
+    Hnx = nx.ego_graph(Gnx, n, **kwargs)
+    Hcg = nx.ego_graph(Gnx, n, **kwargs, backend="cugraph")
+    assert_graphs_equal(Hnx, Hcg)
+    with pytest.raises(nx.NodeNotFound, match="not in G"):
+        nx.ego_graph(Gnx, -1, **kwargs)
+    with pytest.raises(nx.NodeNotFound, match="not in G"):
+        nx.ego_graph(Gnx, -1, **kwargs, backend="cugraph")
+    # Using sssp with default weight of 1 should give same answer as bfs
+    nx.set_edge_attributes(Gnx, 1, name="weight")
+    Gcg = nxcg.from_networkx(Gnx, preserve_all_attrs=True)
+    assert_graphs_equal(Gnx, Gcg)  # Sanity check
+
+    kwargs["distance"] = "weight"
+    H2nx = nx.ego_graph(Gnx, n, **kwargs)
+    is_nx32 = nxver.major == 3 and nxver.minor == 2
+    if undirected and Gnx.is_directed() and Gnx.is_multigraph():
+        if is_nx32:
+            # `should_run` was added in nx 3.3
+            match = "Weighted ego_graph with undirected=True not implemented"
+        else:
+            match = "not implemented by cugraph"
+        with pytest.raises(RuntimeError, match=match):
+            nx.ego_graph(Gnx, n, **kwargs, backend="cugraph")
+        with pytest.raises(NotImplementedError, match="ego_graph"):
+            nx.ego_graph(Gcg, n, **kwargs)
+    else:
+        H2cg = nx.ego_graph(Gnx, n, **kwargs, backend="cugraph")
+        assert_graphs_equal(H2nx, H2cg)
+        with pytest.raises(nx.NodeNotFound, match="not found in graph"):
+            nx.ego_graph(Gnx, -1, **kwargs)
+        with pytest.raises(nx.NodeNotFound, match="not found in graph"):
+            nx.ego_graph(Gnx, -1, **kwargs, backend="cugraph")
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index a7daf0177..477fe8bb4 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 
 [build-system]
 
@@ -19,7 +19,7 @@ authors = [
 license = { text = "Apache 2.0" }
 requires-python = ">=3.9"
 classifiers = [
-    "Development Status :: 3 - Alpha",
+    "Development Status :: 4 - Beta",
     "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
@@ -233,6 +233,7 @@ ignore = [
 "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"]
 "_nx_cugraph/__init__.py" = ["E501"]
 "nx_cugraph/algorithms/**/*py" = ["D205", "D401"]  # Allow flexible docstrings for algorithms
+"nx_cugraph/generators/**/*py" = ["D205", "D401"]  # Allow flexible docstrings for generators
 "nx_cugraph/interface.py" = ["D401"]  # Flexible docstrings
 "scripts/update_readme.py" = ["INP001"]  # Not part of a package
 

From 611232892245258b512b9b06f2da582982ce38ba Mon Sep 17 00:00:00 2001
From: Ray Bell 
Date: Mon, 20 May 2024 21:02:17 -0400
Subject: [PATCH 354/384] test sphinx mapping to networkx (#4323)

Closes https://github.com/rapidsai/cugraph/issues/4285

I'll report back if it get to render locally. Ran out of mem building the library on my ec2 machine (g5.2xlarge: 32 Gb RAM) but I did just build cudf. I'll try again soon.

~~Also brainstorming here and I probably should upstream this to the rapids CI process. Would be nice to have /ok to test docs which just builds the docs for this PR~~ created https://github.com/nv-gha-runners/nvidia-runners/issues/25

I tried building the docs locally and got

```
WARNING: [autosummary] failed to import cugraph.jaccard_w.
Possible hints:
* AttributeError: module 'cugraph' has no attribute 'jaccard_w'
* ImportError:
* ModuleNotFoundError: No module named 'cugraph.jaccard_w'
WARNING: [autosummary] failed to import cugraph.overlap_w.
Possible hints:
* ModuleNotFoundError: No module named 'cugraph.overlap_w'
* ImportError:
* AttributeError: module 'cugraph' has no attribute 'overlap_w'
WARNING: [autosummary] failed to import cugraph.sorensen_w.
Possible hints:
* ModuleNotFoundError: No module named 'cugraph.sorensen_w'
* ImportError:
* AttributeError: module 'cugraph' has no attribute 'sorensen_w'
```

Think this comes from https://github.com/rapidsai/cugraph/blob/abe69c0419b67b567d3c8fce91ee1a062d53e385/docs/cugraph/source/api_docs/cugraph/link_prediction.rst#L14

but I may have messed up my build

Authors:
  - Ray Bell (https://github.com/raybellwaves)
  - Rick Ratzel (https://github.com/rlratzel)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4323
---
 docs/cugraph/source/conf.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py
index 952b962ac..66bc3137f 100644
--- a/docs/cugraph/source/conf.py
+++ b/docs/cugraph/source/conf.py
@@ -190,9 +190,17 @@
      'Miscellaneous'),
 ]
 
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'https://docs.python.org/': None}
-
+# Connect docs in other projects
+intersphinx_mapping = {
+    "networkx": (
+        "https://networkx.org/documentation/stable/",
+        "https://networkx.org/documentation/stable/objects.inv",
+    ),
+    "python": (
+        "https://docs.python.org/3",
+        "https://docs.python.org/3/objects.inv",
+    ),
+}
 
 # Config numpydoc
 numpydoc_show_inherited_class_members = False

From fb2c64a642a8dafdbb4dc3b5afa233b5f697b8e4 Mon Sep 17 00:00:00 2001
From: Ray Bell 
Date: Tue, 21 May 2024 12:18:07 -0400
Subject: [PATCH 355/384] Update DGL_support.md (#4327)

Believe the path suggested previously is outdated.

Authors:
  - Ray Bell (https://github.com/raybellwaves)
  - Don Acosta (https://github.com/acostadon)
  - Rick Ratzel (https://github.com/rlratzel)

Approvers:
  - Don Acosta (https://github.com/acostadon)

URL: https://github.com/rapidsai/cugraph/pull/4327
---
 docs/cugraph/source/graph_support/DGL_support.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/cugraph/source/graph_support/DGL_support.md b/docs/cugraph/source/graph_support/DGL_support.md
index dc4f66180..9df462155 100644
--- a/docs/cugraph/source/graph_support/DGL_support.md
+++ b/docs/cugraph/source/graph_support/DGL_support.md
@@ -17,7 +17,7 @@ mamba install cugraph-dgl -c rapidsai-nightly -c rapidsai -c pytorch -c conda-fo
 
 ### Create the conda development environment
 ```
-mamba env create -n cugraph_dgl_dev --file conda/cugraph_dgl_dev_11.6.yml
+conda env create -n cugraph_dgl_dev --file conda/environments/all_cuda-122_arch-x86_64.yaml
 ```
 
 ### Install  in editable mode

From 83b7d159fe60d411342d35aba25e1242c03e1f06 Mon Sep 17 00:00:00 2001
From: Ray Bell 
Date: Tue, 21 May 2024 21:24:18 -0400
Subject: [PATCH 356/384] DOC: doc-update-link-for-cugraphops (#4279)

Fixes a broken link

https://github.com/rapidsai/cugraph-ops/blob/branch-23.04/README.md -> https://github.com/rapidsai/cugraph/blob/branch-24.04/readme_pages/cugraph_ops.md

Authors:
  - Ray Bell (https://github.com/raybellwaves)
  - Alex Barghi (https://github.com/alexbarghi-nv)
  - Rick Ratzel (https://github.com/rlratzel)

Approvers:
  - Don Acosta (https://github.com/acostadon)

URL: https://github.com/rapidsai/cugraph/pull/4279
---
 docs/cugraph/source/graph_support/cugraphops_support.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/cugraph/source/graph_support/cugraphops_support.rst b/docs/cugraph/source/graph_support/cugraphops_support.rst
index fd79564f8..96b13f62a 100644
--- a/docs/cugraph/source/graph_support/cugraphops_support.rst
+++ b/docs/cugraph/source/graph_support/cugraphops_support.rst
@@ -7,4 +7,4 @@ cugraph-ops aims to be a low-level, framework agnostic library providing commonl
 .. toctree::
    :maxdepth: 3
 
-   https://github.com/rapidsai/cugraph-ops/blob/branch-23.04/README.md
+   https://github.com/rapidsai/cugraph/blob/branch-24.06/readme_pages/cugraph_ops.md

From 0b668b6cdcb1094f1cb9b06e8a666e8fed518c1c Mon Sep 17 00:00:00 2001
From: Don Acosta <97529984+acostadon@users.noreply.github.com>
Date: Wed, 29 May 2024 08:43:47 -0400
Subject: [PATCH 357/384] adding notebook to demo nx_cugraph (#4366)

This notebook will be used to demontstrate how to  use nx-cugraph and show the speed-up.

Authors:
  - Don Acosta (https://github.com/acostadon)

Approvers:
  - Brad Rees (https://github.com/BradReesWork)

URL: https://github.com/rapidsai/cugraph/pull/4366
---
 docs/cugraph/source/nx_cugraph/nx_cugraph.md  | 159 +++---------
 .../nx_cugraph_codeless_switching.ipynb       | 244 ++++++++++++++++++
 2 files changed, 275 insertions(+), 128 deletions(-)
 create mode 100644 notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb

diff --git a/docs/cugraph/source/nx_cugraph/nx_cugraph.md b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
index 92fbf90a4..ff2fc3d1d 100644
--- a/docs/cugraph/source/nx_cugraph/nx_cugraph.md
+++ b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
@@ -24,142 +24,45 @@ Each chart has three measurements.
 ![Single Source Shortest Path](../images/sssp.png)
 ![Weakly Connected Components](../images/wcc.png)
 
+### Command line example
+Open bc_demo.ipy and paste the code below.
 
-The following algorithms are supported and automatically dispatched to nx-cuGraph for acceleration.
+```
+import pandas as pd
+import networkx as nx
+
+url = "https://data.rapids.ai/cugraph/datasets/cit-Patents.csv"
+df = pd.read_csv(url, sep=" ", names=["src", "dst"], dtype="int32")
+G = nx.from_pandas_edgelist(df, source="src", target="dst")
 
-#### Algorithms
+%time result = nx.betweenness_centrality(G, k=10)
+```
+Run the command:
 ```
-bipartite
- ├─ basic
- │   └─ is_bipartite
- └─ generators
-     └─ complete_bipartite_graph
-centrality
- ├─ betweenness
- │   ├─ betweenness_centrality
- │   └─ edge_betweenness_centrality
- ├─ degree_alg
- │   ├─ degree_centrality
- │   ├─ in_degree_centrality
- │   └─ out_degree_centrality
- ├─ eigenvector
- │   └─ eigenvector_centrality
- └─ katz
-     └─ katz_centrality
-cluster
- ├─ average_clustering
- ├─ clustering
- ├─ transitivity
- └─ triangles
-community
- └─ louvain
-     └─ louvain_communities
-components
- ├─ connected
- │   ├─ connected_components
- │   ├─ is_connected
- │   ├─ node_connected_component
- │   └─ number_connected_components
- └─ weakly_connected
-     ├─ is_weakly_connected
-     ├─ number_weakly_connected_components
-     └─ weakly_connected_components
-core
- ├─ core_number
- └─ k_truss
-dag
- ├─ ancestors
- └─ descendants
-isolate
- ├─ is_isolate
- ├─ isolates
- └─ number_of_isolates
-link_analysis
- ├─ hits_alg
- │   └─ hits
- └─ pagerank_alg
-     └─ pagerank
-operators
- └─ unary
-     ├─ complement
-     └─ reverse
-reciprocity
- ├─ overall_reciprocity
- └─ reciprocity
-shortest_paths
- └─ unweighted
-     ├─ single_source_shortest_path_length
-     └─ single_target_shortest_path_length
-traversal
- └─ breadth_first_search
-     ├─ bfs_edges
-     ├─ bfs_layers
-     ├─ bfs_predecessors
-     ├─ bfs_successors
-     ├─ bfs_tree
-     ├─ descendants_at_distance
-     └─ generic_bfs_edges
-tree
- └─ recognition
-     ├─ is_arborescence
-     ├─ is_branching
-     ├─ is_forest
-     └─ is_tree
+user@machine:/# ipython bc_demo.ipy
 ```
 
-#### Generators
+You will observe a run time of approximately 7 minutes...more or less depending on your cpu.
+
+Run the command again, this time specifiying cugraph as the NetworkX backend of choice.
+```
+user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy
 ```
-classic
- ├─ barbell_graph
- ├─ circular_ladder_graph
- ├─ complete_graph
- ├─ complete_multipartite_graph
- ├─ cycle_graph
- ├─ empty_graph
- ├─ ladder_graph
- ├─ lollipop_graph
- ├─ null_graph
- ├─ path_graph
- ├─ star_graph
- ├─ tadpole_graph
- ├─ trivial_graph
- ├─ turan_graph
- └─ wheel_graph
-community
- └─ caveman_graph
-small
- ├─ bull_graph
- ├─ chvatal_graph
- ├─ cubical_graph
- ├─ desargues_graph
- ├─ diamond_graph
- ├─ dodecahedral_graph
- ├─ frucht_graph
- ├─ heawood_graph
- ├─ house_graph
- ├─ house_x_graph
- ├─ icosahedral_graph
- ├─ krackhardt_kite_graph
- ├─ moebius_kantor_graph
- ├─ octahedral_graph
- ├─ pappus_graph
- ├─ petersen_graph
- ├─ sedgewick_maze_graph
- ├─ tetrahedral_graph
- ├─ truncated_cube_graph
- ├─ truncated_tetrahedron_graph
- └─ tutte_graph
-social
- ├─ davis_southern_women_graph
- ├─ florentine_families_graph
- ├─ karate_club_graph
- └─ les_miserables_graph
+This run will be much faster, typically around 20 seconds depending on your GPU.
+```
+user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy
+```
+There is also an option to add caching. This will dramatically help performance when running multiple algorithms on the same graph.
+```
+NETWORKX_BACKEND_PRIORITY=cugraph CACHE_CONVERTED_GRAPH=True ipython bc_demo.ipy
 ```
 
-#### Other
+When running Python interactively, cugraph backend can be specified as an argument in the algorithm call.
 
+For example:
 ```
-convert_matrix
- ├─ from_pandas_edgelist
- └─ from_scipy_sparse_array
+nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph")
 ```
+
+
+The latest list of algorithms that can be dispatched to nx-cuGraph for acceleration is found [here](https://github.com/rapidsai/cugraph/blob/main/python/nx-cugraph/README.md#algorithms).
diff --git a/notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb b/notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb
new file mode 100644
index 000000000..e05544448
--- /dev/null
+++ b/notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb
@@ -0,0 +1,244 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmarking Performance of NetworkX with Rapids GPU-based nx_cugraph backend vs on cpu\n",
+    "# Skip notebook test\n",
+    "This notebook demonstrates compares the performance of nx_cugraph as a dispatcher for NetworkX algorithms. \n",
+    "\n",
+    "We do this by executing Betweenness Centrality, Breadth First Search and Louvain Community Detection, collecting run times with and without nx_cugraph backend and graph caching enabled. nx_cugraph is a registered NetworkX backend. Using it is a zero code change solution.\n",
+    "\n",
+    "In the notebook switching to the nx-cugraph backend is done via variables set using the [NetworkX config package](https://networkx.org/documentation/stable/reference/backends.html#networkx.utils.configs.NetworkXConfig) **which requires networkX 3.3 or later !!**\n",
+    "\n",
+    "\n",
+    "They can be set at the command line as well.\n",
+    "\n",
+    "### See this example from GTC Spring 2024\n",
+    "\n",
+    "\n",
+    "\n",
+    "Here is a sample minimal script to demonstrate No-code-change GPU acceleration using nx-cugraph.\n",
+    "\n",
+    "----\n",
+    "bc_demo.ipy:\n",
+    "\n",
+    "```\n",
+    "import pandas as pd\n",
+    "import networkx as nx\n",
+    "\n",
+    "url = \"https://data.rapids.ai/cugraph/datasets/cit-Patents.csv\"\n",
+    "df = pd.read_csv(url, sep=\" \", names=[\"src\", \"dst\"], dtype=\"int32\")\n",
+    "G = nx.from_pandas_edgelist(df, source=\"src\", target=\"dst\")\n",
+    "\n",
+    "%time result = nx.betweenness_centrality(G, k=10)\n",
+    "```\n",
+    "----\n",
+    "Running it with the nx-cugraph backend looks like this:\n",
+    "```\n",
+    "user@machine:/# ipython bc_demo.ipy\n",
+    "CPU times: user 7min 38s, sys: 5.6 s, total: 7min 44s\n",
+    "Wall time: 7min 44s\n",
+    "\n",
+    "user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy\n",
+    "CPU times: user 18.4 s, sys: 1.44 s, total: 19.9 s\n",
+    "Wall time: 20 s\n",
+    "```\n",
+    "----\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First import the needed packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import networkx as nx\n",
+    "import time\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This installs the NetworkX cuGraph dispatcher if not already present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try: \n",
+    "    import nx_cugraph\n",
+    "except ModuleNotFoundError:\n",
+    "    os.system('conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is boiler plate NetworkX code to run:\n",
+    "* betweenness Centrality\n",
+    "* Bredth first Search\n",
+    "* Louvain community detection\n",
+    "\n",
+    "and report times. it is completely unaware of cugraph or GPU-based tools.\n",
+    "[NetworkX configurations](https://networkx.org/documentation/stable/reference/utils.html#backends) can determine how they are run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_algos(G):\n",
+    "    runtime = time.time()\n",
+    "    result = nx.betweenness_centrality(G, k=10)\n",
+    "    print (\"Betweenness Centrality time: \" + str(round(time.time() - runtime))+ \" seconds\")\n",
+    "    runtime = time.time()\n",
+    "    result = nx.bfs_tree(G,source=1)\n",
+    "    print (\"Breadth First Search time:  \" + str(round(time.time() - runtime))+ \" seconds\")\n",
+    "    runtime = time.time()\n",
+    "    result = nx.community.louvain_communities(G,threshold=1e-04)\n",
+    "    print (\"Louvain time: \" + str(round(time.time() - runtime))+ \" seconds\")\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloads a patent citation dataset containing 3774768 nodes and 16518948 edges and loads it into a NetworkX graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filepath = \"./data/cit-Patents.csv\"\n",
+    "\n",
+    "if os.path.exists(filepath):\n",
+    "    print(\"File found\")\n",
+    "    url = filepath\n",
+    "else:\n",
+    "    url = \"https://data.rapids.ai/cugraph/datasets/cit-Patents.csv\"\n",
+    "df = pd.read_csv(url, sep=\" \", names=[\"src\", \"dst\"], dtype=\"int32\")\n",
+    "G = nx.from_pandas_edgelist(df, source=\"src\", target=\"dst\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setting the NetworkX dispatcher with an environment variable or in code using NetworkX config package which is new to [NetworkX 3.3 config](https://networkx.org/documentation/stable/reference/backends.html#networkx.utils.configs.NetworkXConfig).\n",
+    "\n",
+    "These convenience settinge allow turning off caching and cugraph dispatching if you want to see how long cpu-only takes.\n",
+    "This example using an AMD Ryzen Threadripper PRO 3975WX 32-Cores cpu completed in slightly over 40 minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "use_cugraph = True\n",
+    "cache_graph = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if use_cugraph:\n",
+    "    nx.config[\"backend_priority\"]=['cugraph']\n",
+    "else:\n",
+    "    # Use this setting to turn off the cugraph dispatcher running in legacy cpu mode.\n",
+    "    nx.config[\"backend_priority\"]=[]\n",
+    "if cache_graph:\n",
+    "    nx.config[\"cache_converted_graphs\"]= True\n",
+    "else:\n",
+    "    # Use this setting to turn off graph caching which will convertthe NetworkX to a gpu-resident graph each time an algorithm is run.\n",
+    "    nx.config[\"cache_converted_graphs\"]= False\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the algorithms on GPU. \n",
+    "\n",
+    "**Note the messages NetworkX generates to remind us cached graph shouldn't be modified.**\n",
+    "\n",
+    "```\n",
+    "For the cache to be consistent (i.e., correct), the input graph must not have been manually mutated since the cached graph was created.\n",
+    "\n",
+    "Using cached graph for 'cugraph' backend in call to bfs_edges.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "run_algos(G)\n",
+    "print (\"Total Algorithm run time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "___\n",
+    "Copyright (c) 2024, NVIDIA CORPORATION.\n",
+    "\n",
+    "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
+    "\n",
+    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
+    "___"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From bce633af383aeff9a71d79ad835f60fdbb368ad0 Mon Sep 17 00:00:00 2001
From: Don Acosta <97529984+acostadon@users.noreply.github.com>
Date: Wed, 29 May 2024 15:16:07 -0400
Subject: [PATCH 358/384] Fixed links and added c++ docs per issue 4431 (#4435)

added content to document c++ algorithms and fixed links that were pointing to the previously removed content
resolves #4431
Resolves #4116

Authors:
  - Don Acosta (https://github.com/acostadon)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)
  - Brad Rees (https://github.com/BradReesWork)
  - Chuck Hastings (https://github.com/ChuckHastings)

URL: https://github.com/rapidsai/cugraph/pull/4435
---
 .../source/graph_support/algorithms.md        | 10 +--
 .../cpp_algorithms/centrality_cpp.md          | 81 +++++++++++++++++++
 .../algorithms/cpp_algorithms/linear_cpp.md   | 37 +++++++++
 .../cpp_algorithms/traversal_cpp.md           | 56 +++++++++++++
 4 files changed, 179 insertions(+), 5 deletions(-)
 create mode 100644 docs/cugraph/source/graph_support/algorithms/cpp_algorithms/centrality_cpp.md
 create mode 100644 docs/cugraph/source/graph_support/algorithms/cpp_algorithms/linear_cpp.md
 create mode 100644 docs/cugraph/source/graph_support/algorithms/cpp_algorithms/traversal_cpp.md

diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md
index 8a5158f2f..2aac61325 100644
--- a/docs/cugraph/source/graph_support/algorithms.md
+++ b/docs/cugraph/source/graph_support/algorithms.md
@@ -50,10 +50,10 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M
 | Layout            |                                    |                     |                                                                 |
 |                   | [Force Atlas 2](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/layout/Force-Atlas2.ipynb)              | Single-GPU          |        |
 | Linear Assignment |                                    |                     |                                                                 |
-|                   | [Hungarian]()                      | Single-GPU          | [README](cpp/src/linear_assignment/README-hungarian.md)         |
+|                   | [Hungarian](https://docs.rapids.ai/api/cugraph/nightly/api_docs/cugraph/linear_assignment/#hungarian)                      | Single-GPU          | [README](./algorithms/cpp_algorithms/linear_cpp.html)         |
 | Link Analysis     |                                    |                     |                                                                 |
-|                   | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb)                | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank)                |
-|                   | [Personal Pagerank]()                  | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank)   |
+|                   | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb)                | __Multi-GPU__ | [C++ README](./algorithms/cpp_algorithms/centrality_cpp.html#Pagerank)                |
+|                   | [Personal Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb)                  | __Multi-GPU__ | [C++ README](./algorithms/cpp_algorithms/centrality_cpp.html#Personalized-Pagerank)   |
 |                   | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb)        | __Multi-GPU__ |                |
 | [Link Prediction](algorithms/Similarity.html)   |                                    |                     |                                                                 |
 |                   | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb)                 | __Multi-GPU__      | Directed graph only                         |
@@ -68,8 +68,8 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M
 |                   | Node2Vec                           | __Multi-GPU__       |                                             |
 |                   | Neighborhood sampling      | __Multi-GPU__ |                                                                 |
 | Traversal         |                                    |                     |                                                                 |
-|                   | Breadth First Search (BFS)         | __Multi-GPU__ | with cutoff support [C++ README](cpp/src/traversal/README.md#BFS) |
-|                   | Single Source Shortest Path (SSSP) | __Multi-GPU__ | [C++ README](cpp/src/traversal/README.md#SSSP)                     |
+|                   | Breadth First Search (BFS)         | __Multi-GPU__ | [C++ README](algorithms/cpp_algorithms/traversal_cpp.html#BFS) |
+|                   | Single Source Shortest Path (SSSP) | __Multi-GPU__ | [C++ README](algorithms/cpp_algorithms/traversal_cpp.html#SSSP)                     |
 |                   | _ASSP / APSP_                    | ---                 |                                                                 |
 | Tree              |                                    |                     |                                                                 |
 |                   | Minimum Spanning Tree              | Single-GPU          |                                                                 |
diff --git a/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/centrality_cpp.md b/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/centrality_cpp.md
new file mode 100644
index 000000000..b3f7ac17d
--- /dev/null
+++ b/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/centrality_cpp.md
@@ -0,0 +1,81 @@
+# Centrality algorithms
+cuGraph Pagerank is implemented using our graph primitive library
+
+## Pagerank
+
+The unit test code is the best place to search for examples on calling pagerank.
+
+ * [SG Implementation](https://github.com/rapidsai/cugraph/blob/main/cpp/tests/link_analysis/pagerank_test.cpp)
+ * [MG Implementation](https://github.com/rapidsai/cugraph/blob/main/cpp/tests/link_analysis/mg_pagerank_test.cpp)
+
+## Simple pagerank
+
+The example assumes that you create an SG or MG graph somehow.  The caller must create the pageranks vector in device memory and pass in the raw pointer to that vector into the pagerank function.
+
+```cpp
+#include 
+...
+using vertex_t = int32_t;       // or int64_t, whichever is appropriate
+using weight_t = float;         // or double, whichever is appropriate
+using result_t = weight_t;      // could specify float or double also
+raft::handle_t handle;          // Must be configured if MG
+auto graph_view = graph.view(); // assumes you have created a graph somehow
+
+result_t constexpr alpha{0.85};
+result_t constexpr epsilon{1e-6};
+
+rmm::device_uvector pageranks_v(graph_view.number_of_vertices(), handle.get_stream());
+
+// pagerank optionally supports three additional parameters:
+//     max_iterations     - maximum number of iterations, if pagerank doesn't coverge by
+//                          then we abort
+//     has_initial_guess  - if true, values in the pagerank array when the call is initiated
+//                          will be used as the initial pagerank values.  These values will
+//                          be normalized before use.  If false (the default), the values
+//                          in the pagerank array will be set to 1/num_vertices before
+//                          starting the computation.
+//     do_expensive_check - perform extensive validation of the input data before
+//                          executing algorithm.  Off by default.  Note: turning this on
+//                          is expensive
+cugraph::pagerank(handle, graph_view, nullptr, nullptr, nullptr, vertex_t{0},
+                                pageranks_v.data(), alpha, epsilon);
+```
+
+## Personalized Pagerank
+
+The example assumes that you create an SG or MG graph somehow.  The caller must create the pageranks vector in device memory and pass in the raw pointer to that vector into the pagerank function.  Additionally, the caller must create personalization_vertices and personalized_values vectors in device memory, populate them and pass in the raw pointers to those vectors.
+
+```cpp
+#include 
+...
+using vertex_t = int32_t;                    // or int64_t, whichever is appropriate
+using weight_t = float;                      // or double, whichever is appropriate
+using result_t = weight_t;                   // could specify float or double also
+raft::handle_t handle;                       // Must be configured if MG
+auto graph_view = graph.view();              // assumes you have created a graph somehow
+vertex_t number_of_personalization_vertices; // Provided by caller
+
+result_t constexpr alpha{0.85};
+result_t constexpr epsilon{1e-6};
+
+rmm::device_uvector pageranks_v(graph_view.number_of_vertices(), handle.get_stream());
+rmm::device_uvector personalization_vertices(number_of_personalization_vertices, handle.get_stream());
+rmm::device_uvector personalization_values(number_of_personalization_vertices, handle.get_stream());
+
+//  Populate personalization_vertices, personalization_values with user provided data
+
+// pagerank optionally supports three additional parameters:
+//     max_iterations     - maximum number of iterations, if pagerank doesn't coverge by
+//                          then we abort
+//     has_initial_guess  - if true, values in the pagerank array when the call is initiated
+//                          will be used as the initial pagerank values.  These values will
+//                          be normalized before use.  If false (the default), the values
+//                          in the pagerank array will be set to 1/num_vertices before
+//                          starting the computation.
+//     do_expensive_check - perform extensive validation of the input data before
+//                          executing algorithm.  Off by default.  Note: turning this on
+//                          is expensive
+cugraph::pagerank(handle, graph_view, nullptr, personalization_vertices.data(),
+                                personalization_values.data(), number_of_personalization_vertices,
+                                pageranks_v.data(), alpha, epsilon);
+```
diff --git a/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/linear_cpp.md b/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/linear_cpp.md
new file mode 100644
index 000000000..8af4a5042
--- /dev/null
+++ b/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/linear_cpp.md
@@ -0,0 +1,37 @@
+# LAP
+
+Implementation of ***O(n^3) Alternating Tree Variant*** of Hungarian Algorithm on NVIDIA CUDA-enabled GPU.
+
+This implementation solves a batch of ***k*** **Linear Assignment Problems (LAP)**, each with ***nxn*** matrix of single floating point cost values. At optimality, the algorithm produces an assignment with ***minimum*** cost.
+
+The API can be used to query optimal primal and dual costs, optimal assignment vector, and optimal row/column dual vectors for each subproblem in the batch.
+
+cuGraph exposes the Hungarian algorithm, the actual implementation is contained in the RAFT library which contains some common tools and kernels shared between cuGraph and cuML.
+
+Following parameters can be used to tune the performance of algorithm:
+
+1. epsilon: (in raft/lap/lap_kernels.cuh) This parameter controls the tolerance on the floating point precision. Setting this too small will result in increased solution time because the algorithm will search for precise solutions. Setting it too high may cause some inaccuracies.
+
+2. BLOCKDIMX, BLOCKDIMY: (in raft/lap/lap_functions.cuh) These parameters control threads_per_block to be used along the given dimension. Set these according to the device specifications and occupancy calculation.
+
+***This library is licensed under Apache License 2.0. Please cite our paper, if this library helps you in your research.***
+
+- Harvard citation style
+
+  Date, K. and Nagi, R., 2016. GPU-accelerated Hungarian algorithms for the Linear Assignment Problem. Parallel Computing, 57, pp.52-72.
+
+- BibTeX Citation block to be used in LaTeX bibliography file:
+
+```
+@article{date2016gpu,
+  title={GPU-accelerated Hungarian algorithms for the Linear Assignment Problem},
+  author={Date, Ketan and Nagi, Rakesh},
+  journal={Parallel Computing},
+  volume={57},
+  pages={52--72},
+  year={2016},
+  publisher={Elsevier}
+}
+```
+
+The paper is available online on [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S016781911630045X).
diff --git a/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/traversal_cpp.md b/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/traversal_cpp.md
new file mode 100644
index 000000000..6480d885a
--- /dev/null
+++ b/docs/cugraph/source/graph_support/algorithms/cpp_algorithms/traversal_cpp.md
@@ -0,0 +1,56 @@
+# Traversal
+cuGraph traversal algorithms are contained in this directory
+
+## SSSP
+
+The unit test code is the best place to search for examples on calling SSSP.
+
+ * [SG Implementation](https://github.com/rapidsai/cugraph/blob/main/cpp/tests/traversal/sssp_test.cpp)
+ * [MG Implementation](https://github.com/rapidsai/cugraph/blob/main/cpp/tests/traversal/mg_sssp_test.cpp)
+
+## Simple SSSP
+
+The example assumes that you create an SG or MG graph somehow.  The caller must create the distances and predecessors vectors in device memory and pass in the raw pointers to those vectors into the SSSP function.
+
+```cpp
+#include 
+...
+using vertex_t = int32_t;       // or int64_t, whichever is appropriate
+using weight_t = float;         // or double, whichever is appropriate
+using result_t = weight_t;      // could specify float or double also
+raft::handle_t handle;          // Must be configured if MG
+auto graph_view = graph.view(); // assumes you have created a graph somehow
+vertex_t source;                // Initialized by user
+
+rmm::device_uvector distances_v(graph_view.number_of_vertices(), handle.get_stream());
+rmm::device_uvector predecessors_v(graph_view.number_of_vertices(), handle.get_stream());
+
+cugraph::sssp(handle, graph_view, distances_v.begin(), predecessors_v.begin(), source, std::numeric_limits::max(), false);
+```
+
+## BFS
+
+The unit test code is the best place to search for examples on calling BFS.
+
+ * [SG Implementation](https://github.com/rapidsai/cugraph/blob/main/cpp/tests/traversal/bfs_test.cpp)
+ * [MG Implementation](https://github.com/rapidsai/cugraph/blob/main/cpp/tests/traversal/mg_bfs_test.cpp)
+
+## Simple BFS
+
+The example assumes that you create an SG or MG graph somehow.  The caller must create the distances and predecessors vectors in device memory and pass in the raw pointers to those vectors into the BFS function.
+
+```cpp
+#include 
+...
+using vertex_t = int32_t;       // or int64_t, whichever is appropriate
+using weight_t = float;         // or double, whichever is appropriate
+using result_t = weight_t;      // could specify float or double also
+raft::handle_t handle;          // Must be configured if MG
+auto graph_view = graph.view(); // assumes you have created a graph somehow
+vertex_t source;                // Initialized by user
+
+rmm::device_uvector distances_v(graph_view.number_of_vertices(), handle.get_stream());
+rmm::device_uvector predecessors_v(graph_view.number_of_vertices(), handle.get_stream());
+
+cugraph::bfs(handle, graph_view, d_distances.begin(), d_predecessors.begin(), source, false, std::numeric_limits::max(), false);
+```

From 310914b4b0e907ae2aedd424e553bcd488a68de3 Mon Sep 17 00:00:00 2001
From: Don Acosta <97529984+acostadon@users.noreply.github.com>
Date: Wed, 29 May 2024 19:16:55 -0400
Subject: [PATCH 359/384] first copy of general cugraph tutorial. (#4396)

Adding a tutorial to get started with cugraph.

There are more to follow but this is identified as an important one.

closes #4385

Authors:
  - Don Acosta (https://github.com/acostadon)

Approvers:
  - Brad Rees (https://github.com/BradReesWork)

URL: https://github.com/rapidsai/cugraph/pull/4396
---
 .../cugraph/source/tutorials/basic_cugraph.md | 38 +++++++++++++++++++
 .../cugraph/source/tutorials/how_to_guides.md |  2 +-
 2 files changed, 39 insertions(+), 1 deletion(-)
 create mode 100644 docs/cugraph/source/tutorials/basic_cugraph.md

diff --git a/docs/cugraph/source/tutorials/basic_cugraph.md b/docs/cugraph/source/tutorials/basic_cugraph.md
new file mode 100644
index 000000000..783254724
--- /dev/null
+++ b/docs/cugraph/source/tutorials/basic_cugraph.md
@@ -0,0 +1,38 @@
+# Getting started with cuGraph
+
+## Required hardware/software
+
+CuGraph is part of [Rapids](https://docs.rapids.ai/user-guide) and has the following system requirements:
+ * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
+ * CUDA 11.2, 11.4, 11.5, 11.8, 12.0 or 12.2
+ * Python version 3.9, 3.10, or 3.11
+ * NetworkX >= version 3.3 or newer in order to use use [NetworkX Configs](https://networkx.org/documentation/stable/reference/backends.html#module-networkx.utils.configs) **This is required for use of nx-cuGraph, [see below](#cugraph-using-networkx-code).**
+
+## Installation
+The latest RAPIDS System Requirements documentation is located [here](https://docs.rapids.ai/install#system-req).
+
+This includes several ways to set up cuGraph
+* From Unix
+    * [Conda](https://docs.rapids.ai/install#wsl-conda)
+    * [Docker](https://docs.rapids.ai/install#wsl-docker)
+    * [pip](https://docs.rapids.ai/install#wsl-pip)
+
+* In windows you must install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) and then choose one of the following:
+    * [Conda](https://docs.rapids.ai/install#wsl-conda)
+    * [Docker](https://docs.rapids.ai/install#wsl-docker)
+    * [pip](https://docs.rapids.ai/install#wsl-pip)
+
+* Build From Source
+
+To build from source, check each RAPIDS GitHub README for set up and build instructions. Further links are provided in the [selector tool](https://docs.rapids.ai/install#selector). If additional help is needed reach out on our [Slack Channel](https://rapids-goai.slack.com/archives/C5E06F4DC).
+
+## CuGraph Using NetworkX Code
+While the steps above are required to use the full suite of cuGraph graph analytics, cuGraph is now supported as a NetworkX backend using [nx-cugraph](https://docs.rapids.ai/api/cugraph/nightly/nx_cugraph/nx_cugraph/).
+Nx-cugraph offers those with existing NetworkX code, a **zero code change** option with a growing list of supported algorithms.
+
+
+## Cugraph API Example
+Coming soon !
+
+
+Until then, [the cuGraph notebook repository](https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md) has many examples of loading graph data and running algorithms in Jupyter notebooks. The [cuGraph test code](https://github.com/rapidsai/cugraph/tree/main/python/cugraph/cugraph/tests) gives examples of python scripts settng up and calling cuGraph algorithms. A simple example of [testing the degree centrality algorithm](https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py) is a good place to start. Some of these examples show [multi-GPU tests/examples with larger data sets](https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py) as well.
diff --git a/docs/cugraph/source/tutorials/how_to_guides.md b/docs/cugraph/source/tutorials/how_to_guides.md
index 80be5b4ab..998957afe 100644
--- a/docs/cugraph/source/tutorials/how_to_guides.md
+++ b/docs/cugraph/source/tutorials/how_to_guides.md
@@ -1,5 +1,5 @@
 # How To Guides
-- Basic use of cuGraph, on the page
+- [Basic use of cuGraph](./basic_cugraph.md)
 - Property graph with analytic flow
 - GNN – model building
 - cuGraph Service – client/server setup and use (ucx)

From 62013d72c622f10237108db1cad244f9333995f2 Mon Sep 17 00:00:00 2001
From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com>
Date: Wed, 29 May 2024 21:30:00 -0500
Subject: [PATCH 360/384] Adds benchmark for `nx_cugraph.ego_graph` (#4451)

Adds benchmark for `nx_cugraph.ego_graph`

_Note: this code is not part of any installed package and does not affect the release.  It is being added so it can be included in the `branch-24.06` sources for reference._

Authors:
  - Rick Ratzel (https://github.com/rlratzel)

Approvers:
  - Alex Barghi (https://github.com/alexbarghi-nv)
  - Erik Welch (https://github.com/eriknw)

URL: https://github.com/rapidsai/cugraph/pull/4451
---
 .../nx-cugraph/pytest-based/bench_algos.py      | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py
index 3b085a9bf..d40b51308 100644
--- a/benchmarks/nx-cugraph/pytest-based/bench_algos.py
+++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py
@@ -848,6 +848,23 @@ def bench_weakly_connected_components(benchmark, graph_obj, backend_wrapper):
     assert type(result) is list
 
 
+def bench_ego_graph(benchmark, graph_obj, backend_wrapper):
+    G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper)
+    node = get_highest_degree_node(graph_obj)
+    result = benchmark.pedantic(
+        target=backend_wrapper(nx.ego_graph),
+        args=(G,),
+        kwargs=dict(
+            n=node,
+            radius=100,
+        ),
+        rounds=rounds,
+        iterations=iterations,
+        warmup_rounds=warmup_rounds,
+    )
+    assert isinstance(result, (nx.Graph, nxcg.Graph))
+
+
 @pytest.mark.skip(reason="benchmark not implemented")
 def bench_complete_bipartite_graph(benchmark, graph_obj, backend_wrapper):
     pass

From f63e1c3414104b7c6049fbf23341432b4b621956 Mon Sep 17 00:00:00 2001
From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com>
Date: Thu, 30 May 2024 00:44:27 -0400
Subject: [PATCH 361/384] Distributed Sampling in cuGraph-PyG (#4384)

Distributed sampling in cuGraph-PyG.  Also renames the existing API to clarify that it is dask based.
Adds a dependency on `tensordict` for `cuGraph-PyG` which supports the new `TensorDictFeatureStore`.
Also no longer installs `torch-cluster` and `torch-spline-conv` in CI for testing since that results in an `ImportError` and neither of those packages are needed.

Requires PyG 2.5.  Should be merged after #4335

Merge after #4355

Closes #4248
Closes #4249
Closes #3383
Closes #3942
Closes #3836
Closes #4202
Closes #4051
Closes #4326
Closes #4252
Partially addresses #3805

Authors:
  - Alex Barghi (https://github.com/alexbarghi-nv)
  - Seunghwa Kang (https://github.com/seunghwak)
  - Tingyu Wang (https://github.com/tingyu66)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Tingyu Wang (https://github.com/tingyu66)
  - Brad Rees (https://github.com/BradReesWork)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cugraph/pull/4384
---
 .../api_docs/cugraph-pyg/cugraph_pyg.rst      | 33 +++++++++++++++++--
 1 file changed, 31 insertions(+), 2 deletions(-)

diff --git a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
index a150d4db9..5475fd6c5 100644
--- a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
+++ b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
@@ -6,8 +6,37 @@ cugraph-pyg
 
 .. currentmodule:: cugraph_pyg
 
+Graph Storage
+-------------
 .. autosummary::
    :toctree: ../api/cugraph-pyg/
 
-..   cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore
-..   cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler
+   cugraph_pyg.data.dask_graph_store.DaskGraphStore
+   cugraph_pyg.data.graph_store.GraphStore
+
+Feature Storage
+---------------
+.. autosummary::
+   :toctree: ../api/cugraph-pyg/
+
+   cugraph_pyg.data.feature_store.TensorDictFeatureStore
+
+Data Loaders
+------------
+.. autosummary::
+   :toctree: ../api/cugraph-pyg/
+
+   cugraph_pyg.loader.dask_node_loader.DaskNeighborLoader
+   cugraph_pyg.loader.dask_node_loader.BulkSampleLoader
+   cugraph_pyg.loader.node_loader.NodeLoader
+   cugraph_pyg.loader.neighbor_loader.NeighborLoader
+
+Samplers
+--------
+.. autosummary::
+   :toctree: ../api/cugraph-pyg/
+
+   cugraph_pyg.sampler.sampler.BaseSampler
+   cugraph_pyg.sampler.sampler.SampleReader
+   cugraph_pyg.sampler.sampler.HomogeneousSampleReader
+   cugraph_pyg.sampler.sampler.SampleIterator

From c515bcbdb3d5c7cf30c2005b09853a644cf2b7b3 Mon Sep 17 00:00:00 2001
From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com>
Date: Thu, 30 May 2024 15:40:17 -0700
Subject: [PATCH 362/384] [FEA] New WholeGraph Feature Store for PyG (#4432)

Reimplements the WG feature store for PyG using the `FeatureStore` interface.
Merge after #4384

Closes rapidsai/wholegraph#47
Closes #4399

Authors:
  - Alex Barghi (https://github.com/alexbarghi-nv)
  - Seunghwa Kang (https://github.com/seunghwak)
  - Tingyu Wang (https://github.com/tingyu66)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Tingyu Wang (https://github.com/tingyu66)
  - Vibhu Jawa (https://github.com/VibhuJawa)
  - Brad Rees (https://github.com/BradReesWork)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cugraph/pull/4432
---
 docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
index 5475fd6c5..d2b1d124c 100644
--- a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
+++ b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
@@ -20,6 +20,7 @@ Feature Storage
    :toctree: ../api/cugraph-pyg/
 
    cugraph_pyg.data.feature_store.TensorDictFeatureStore
+   cugraph_pyg.data.feature_store.WholeFeatureStore
 
 Data Loaders
 ------------

From 5ef723f62478df794efc6ebe26f67741017cb4b0 Mon Sep 17 00:00:00 2001
From: Ray Douglass 
Date: Wed, 5 Jun 2024 13:44:21 -0400
Subject: [PATCH 363/384] REL v24.06.00 release


From 66e2279be4cd3589a70ecbce07e97d16632b2552 Mon Sep 17 00:00:00 2001
From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com>
Date: Thu, 6 Jun 2024 13:33:04 -0400
Subject: [PATCH 364/384] [DOC] Minor Improvements to cuGraph-PyG Documentation
 (#4460)

Makes some minor updates and clarifications to the cuGraph-PyG API docs.

Authors:
  - Alex Barghi (https://github.com/alexbarghi-nv)

Approvers:
  - Don Acosta (https://github.com/acostadon)
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4460
---
 docs/cugraph/source/api_docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst
index d76902772..c4b90b579 100644
--- a/docs/cugraph/source/api_docs/index.rst
+++ b/docs/cugraph/source/api_docs/index.rst
@@ -20,7 +20,7 @@ Graph Neural Networks API Documentation
 
 .. toctree::
     :maxdepth: 3
-    :caption: Graph Nerual Networks API Documentation
+    :caption: Graph Neural Networks API Documentation
 
     cugraph-dgl/cugraph_dgl.rst
     cugraph-pyg/cugraph_pyg.rst

From 8c190460edf9f38f9b59c9fa47a74bdb4e943365 Mon Sep 17 00:00:00 2001
From: Ray Douglass 
Date: Thu, 13 Jun 2024 13:24:26 -0400
Subject: [PATCH 365/384] REL v24.06.01 release

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index 0bff6981a..e2cfb4c64 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.06.00
+24.06.01

From 74f282789e1cf77e947f5380bb0fdbf906eec86a Mon Sep 17 00:00:00 2001
From: Ray Bell 
Date: Mon, 1 Jul 2024 13:38:34 -0400
Subject: [PATCH 366/384] DOC: typo in nx_transition.rst (#4491)

Authors:
  - Ray Bell (https://github.com/raybellwaves)
  - Ralph Liu (https://github.com/nv-rliu)
  - Don Acosta (https://github.com/acostadon)

Approvers:
  - Don Acosta (https://github.com/acostadon)

URL: https://github.com/rapidsai/cugraph/pull/4491
---
 docs/cugraph/source/basics/nx_transition.rst | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/cugraph/source/basics/nx_transition.rst b/docs/cugraph/source/basics/nx_transition.rst
index 07c2ad26f..9da2fe9b4 100644
--- a/docs/cugraph/source/basics/nx_transition.rst
+++ b/docs/cugraph/source/basics/nx_transition.rst
@@ -26,8 +26,6 @@ ETL code to be unchanged while still seeing significate performance
 improvements. Again this will be deprecated since networkX dispatching to nx_cugraph
 has many advantages.
 
-improvements.
-
 
 .. image:: ../images/Nx_Cg_1.png
   :width: 600

From 84695a66bbba3db8b94f6cb31d663e9c1e3098ba Mon Sep 17 00:00:00 2001
From: Kyle Edwards 
Date: Mon, 1 Jul 2024 18:47:57 -0400
Subject: [PATCH 367/384] Use rapids-build-backend (#4393)

Contributes to https://github.com/rapidsai/build-planning/issues/31
Contributes to https://github.com/rapidsai/dependency-file-generator/issues/89

Proposes introducing `rapids-build-backend` as this project's build backend, to reduce the complexity of various CI/build scripts.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Mike Sarahan (https://github.com/msarahan)
  - Vyas Ramasubramani (https://github.com/vyasr)
  - Brad Rees (https://github.com/BradReesWork)

URL: https://github.com/rapidsai/cugraph/pull/4393
---
 .pre-commit-config.yaml                            | 13 +++++++------
 python/nx-cugraph/_nx_cugraph/_version.py          | 14 ++++++++++++--
 python/nx-cugraph/nx_cugraph/tests/test_version.py | 12 ++++++++++++
 python/nx-cugraph/pyproject.toml                   | 10 ++++++++--
 4 files changed, 39 insertions(+), 10 deletions(-)
 create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_version.py

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 07bf1667c..36c5fa841 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -41,13 +41,8 @@ repos:
       - id: clang-format
         types_or: [c, c++, cuda]
         args: ["-fallback-style=none", "-style=file", "-i"]
-  - repo: https://github.com/rapidsai/dependency-file-generator
-    rev: v1.8.0
-    hooks:
-        - id: rapids-dependency-file-generator
-          args: ["--clean"]
   - repo: https://github.com/rapidsai/pre-commit-hooks
-    rev: v0.0.3
+    rev: v0.2.0
     hooks:
       - id: verify-copyright
         files: |
@@ -58,6 +53,12 @@ repos:
               [.]flake8[.]cython$|
               meta[.]yaml$|
               setup[.]cfg$
+      - id: verify-alpha-spec
+  - repo: https://github.com/rapidsai/dependency-file-generator
+    rev: v1.13.11
+    hooks:
+        - id: rapids-dependency-file-generator
+          args: ["--clean"]
   - repo: local
     hooks:
       - id: nx-cugraph-meta-data-update
diff --git a/python/nx-cugraph/_nx_cugraph/_version.py b/python/nx-cugraph/_nx_cugraph/_version.py
index dc2d2a3a3..3cf8b23da 100644
--- a/python/nx-cugraph/_nx_cugraph/_version.py
+++ b/python/nx-cugraph/_nx_cugraph/_version.py
@@ -19,6 +19,16 @@
 # to update version info (including commit hashes) without modifying
 # source files.
 __version__ = (
-    importlib.resources.files("_nx_cugraph").joinpath("VERSION").read_text().strip()
+    importlib.resources.files(__package__).joinpath("VERSION").read_text().strip()
 )
-__git_commit__ = ""
+try:
+    __git_commit__ = (
+        importlib.resources.files(__package__)
+        .joinpath("GIT_COMMIT")
+        .read_text()
+        .strip()
+    )
+except FileNotFoundError:
+    __git_commit__ = ""
+
+__all__ = ["__git_commit__", "__version__"]
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_version.py b/python/nx-cugraph/nx_cugraph/tests/test_version.py
new file mode 100644
index 000000000..c45702b60
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/tests/test_version.py
@@ -0,0 +1,12 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+import nx_cugraph
+
+
+def test_version_constants_are_populated():
+    # __git_commit__ will only be non-empty in a built distribution
+    assert isinstance(nx_cugraph.__git_commit__, str)
+
+    # __version__ should always be non-empty
+    assert isinstance(nx_cugraph.__version__, str)
+    assert len(nx_cugraph.__version__) > 0
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index 9df6d745b..50881d5db 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -3,10 +3,11 @@
 [build-system]
 
 requires = [
+    "rapids-build-backend>=0.3.1,<0.4.0.dev0",
     "setuptools>=61.0.0",
     "wheel",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-build-backend = "setuptools.build_meta"
+build-backend = "rapids_build_backend.build"
 
 [project]
 name = "nx-cugraph"
@@ -34,7 +35,7 @@ dependencies = [
     "cupy-cuda11x>=12.0.0",
     "networkx>=3.0",
     "numpy>=1.23,<2.0a0",
-    "pylibcugraph==24.8.*",
+    "pylibcugraph==24.8.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 
 [project.optional-dependencies]
@@ -81,6 +82,11 @@ include = [
     "_nx_cugraph.*",
 ]
 
+[tool.rapids-build-backend]
+build-backend = "setuptools.build_meta"
+commit-files = ["_nx_cugraph/GIT_COMMIT"]
+dependencies-file = "../../dependencies.yaml"
+
 [tool.black]
 line-length = 88
 target-version = ["py39", "py310", "py311"]

From 8df1d53b4376c44c0c07d33d8b33d5c43d48e982 Mon Sep 17 00:00:00 2001
From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com>
Date: Tue, 2 Jul 2024 15:38:24 -0500
Subject: [PATCH 368/384] Doc cleanup for nx-cugraph: fixed typos, cleaned up
 various descriptions, renamed notebook to match naming convetion. (#4478)

closes #4466

* Fixed typos
* Changed various descriptions to match existing terminology and hopefully clarify
* Renamed notebook to match file name conventions

Authors:
  - Rick Ratzel (https://github.com/rlratzel)
  - Ralph Liu (https://github.com/nv-rliu)
  - Don Acosta (https://github.com/acostadon)

Approvers:
  - Don Acosta (https://github.com/acostadon)
  - James Lamb (https://github.com/jameslamb)
  - Erik Welch (https://github.com/eriknw)

URL: https://github.com/rapidsai/cugraph/pull/4478
---
 docs/cugraph/source/nx_cugraph/nx_cugraph.md  |  27 +-
 .../nx_cugraph_benchmark.ipynb                | 365 ++++++++++++++++++
 .../nx_cugraph_codeless_switching.ipynb       | 244 ------------
 3 files changed, 378 insertions(+), 258 deletions(-)
 create mode 100644 notebooks/cugraph_benchmarks/nx_cugraph_benchmark.ipynb
 delete mode 100644 notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb

diff --git a/docs/cugraph/source/nx_cugraph/nx_cugraph.md b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
index 854f755cb..75a30b0be 100644
--- a/docs/cugraph/source/nx_cugraph/nx_cugraph.md
+++ b/docs/cugraph/source/nx_cugraph/nx_cugraph.md
@@ -1,18 +1,17 @@
 ### nx_cugraph
 
 
-Whereas previous versions of cuGraph have included mechanisms to make it
-trivial to plug in cuGraph algorithm calls. Beginning with version 24.02, nx-cuGraph
-is now a [networkX backend]().
-The user now need only [install nx-cugraph]()
-to experience GPU speedups.
+nx-cugraph is a [NetworkX
+backend]() that provides GPU acceleration to many popular NetworkX algorithms.
 
-Lets look at some examples of algorithm speedups comparing CPU based NetworkX to dispatched versions run on GPU with nx_cugraph.
+By simply [installing and enabling nx-cugraph](), users can see significant speedup on workflows where performance is hindered by the default NetworkX implementation.  With nx-cugraph, users can have GPU-based, large-scale performance without changing their familiar and easy-to-use NetworkX code.
+
+Let's look at some examples of algorithm speedups comparing NetworkX with and without GPU acceleration using nx-cugraph.
 
 Each chart has three measurements.
-* NX - running the algorithm natively with networkX on CPU.
-* nx-cugraph - running with GPU accelerated networkX achieved by simply calling the cugraph backend. This pays the overhead of building the GPU resident object for each algorithm called. This achieves significant improvement but stil isn't compleltely optimum.
-* nx-cugraph (preconvert) - This is a bit more complicated since it involves building (precomputing) the GPU resident graph ahead and reusing it for each algorithm.
+* NX - default NetworkX, no GPU acceleration
+* nx-cugraph - GPU-accelerated NetworkX using nx-cugraph. This involves an internal conversion/transfer of graph data from CPU to GPU memory
+* nx-cugraph (preconvert) - GPU-accelerated NetworkX using nx-cugraph with the graph data pre-converted/transferred to GPU
 
 
 ![Ancestors](../images/ancestors.png)
@@ -44,7 +43,7 @@ user@machine:/# ipython bc_demo.ipy
 
 You will observe a run time of approximately 7 minutes...more or less depending on your cpu.
 
-Run the command again, this time specifiying cugraph as the NetworkX backend of choice.
+Run the command again, this time specifying cugraph as the NetworkX backend.
 ```
 user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy
 ```
@@ -52,12 +51,12 @@ This run will be much faster, typically around 20 seconds depending on your GPU.
 ```
 user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy
 ```
-There is also an option to add caching. This will dramatically help performance when running multiple algorithms on the same graph.
+There is also an option to cache the graph conversion to GPU. This can dramatically improve performance when running multiple algorithms on the same graph.
 ```
-NETWORKX_BACKEND_PRIORITY=cugraph CACHE_CONVERTED_GRAPH=True ipython bc_demo.ipy
+NETWORKX_BACKEND_PRIORITY=cugraph NETWORKX_CACHE_CONVERTED_GRAPHS=True ipython bc_demo.ipy
 ```
 
-When running Python interactively, cugraph backend can be specified as an argument in the algorithm call.
+When running Python interactively, the cugraph backend can be specified as an argument in the algorithm call.
 
 For example:
 ```
@@ -65,4 +64,4 @@ nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph")
 ```
 
 
-The latest list of algorithms that can be dispatched to nx-cuGraph for acceleration is found [here](https://github.com/rapidsai/cugraph/blob/main/python/nx-cugraph/README.md#algorithms).
+The latest list of algorithms supported by nx-cugraph can be found [here](https://github.com/rapidsai/cugraph/blob/main/python/nx-cugraph/README.md#algorithms).
diff --git a/notebooks/cugraph_benchmarks/nx_cugraph_benchmark.ipynb b/notebooks/cugraph_benchmarks/nx_cugraph_benchmark.ipynb
new file mode 100644
index 000000000..bc57947f2
--- /dev/null
+++ b/notebooks/cugraph_benchmarks/nx_cugraph_benchmark.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmarking Performance of NetworkX without and with the RAPIDS GPU-based nx-cugraph backend\n",
+    "\n",
+    "This notebook collects the run-times without and with the nx-cugraph backend enabled for three popular NetworkX algorithms: Betweenness Centrality, Breadth First Search, and Louvain Community Detection.\n",
+    "\n",
+    "Here is a sample minimal script to demonstrate no-code-change GPU acceleration using nx-cugraph.\n",
+    "\n",
+    "----\n",
+    "bc_demo.ipy:\n",
+    "\n",
+    "```\n",
+    "import pandas as pd\n",
+    "import networkx as nx\n",
+    "\n",
+    "url = \"https://data.rapids.ai/cugraph/datasets/cit-Patents.csv\"\n",
+    "df = pd.read_csv(url, sep=\" \", names=[\"src\", \"dst\"], dtype=\"int32\")\n",
+    "G = nx.from_pandas_edgelist(df, source=\"src\", target=\"dst\")\n",
+    "\n",
+    "%time result = nx.betweenness_centrality(G, k=10)\n",
+    "```\n",
+    "----\n",
+    "Running it with the nx-cugraph backend looks like this:\n",
+    "```\n",
+    "user@machine:/# ipython bc_demo.ipy\n",
+    "CPU times: user 7min 38s, sys: 5.6 s, total: 7min 44s\n",
+    "Wall time: 7min 44s\n",
+    "\n",
+    "user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy\n",
+    "CPU times: user 18.4 s, sys: 1.44 s, total: 19.9 s\n",
+    "Wall time: 20 s\n",
+    "```\n",
+    "----\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First import the needed packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This installs nx-cugraph if not already present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try: \n",
+    "    import nx_cugraph\n",
+    "except ModuleNotFoundError:\n",
+    "    os.system('conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download a patent citation dataset containing 3774768 nodes and 16518948 edges and loads it into a NetworkX graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File ./data/cit-Patents.csv not found, downloading https://data.rapids.ai/cugraph/datasets/cit-Patents.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "filepath = \"./data/cit-Patents.csv\"\n",
+    "\n",
+    "if os.path.exists(filepath):\n",
+    "    url = filepath\n",
+    "else:\n",
+    "    url = \"https://data.rapids.ai/cugraph/datasets/cit-Patents.csv\"\n",
+    "    print(f\"File {filepath} not found, downloading {url}\")\n",
+    "\n",
+    "df = pd.read_csv(url, sep=\" \", names=[\"src\", \"dst\"], dtype=\"int32\")\n",
+    "G = nx.from_pandas_edgelist(df, source=\"src\", target=\"dst\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function that can be used to run various NetworkX algorithms on the Graph created above. This can be used to compare run-times for NetworkX both without `nx-cugraph` and with `nx-cugraph` enabled.\n",
+    "\n",
+    "The following NetworkX calls will be run:\n",
+    "* [Betweenness Centrality](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.betweenness_centrality.html)\n",
+    "* [Breadth First Search](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.bfs_tree.html)\n",
+    "* [Louvain Community Detection](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.louvain.louvain_communities.html)\n",
+    "\n",
+    "This code does not require modification to use with nx-cugraph and can be used with NetworkX as-is even when no backends are installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_algos():\n",
+    "   print(\"\\nRunning Betweenness Centrality...\")\n",
+    "   %time nx.betweenness_centrality(G, k=10)\n",
+    "\n",
+    "   print(\"\\nRunning Breadth First Search (bfs_edges)...\")\n",
+    "   %time list(nx.bfs_edges(G, source=1))  # yields individual edges, use list() to force the full computation\n",
+    "\n",
+    "   print(\"\\nRunning Louvain...\")\n",
+    "   %time nx.community.louvain_communities(G, threshold=1e-04)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NetworkX (no backend) Benchmark Runs\n",
+    "**_NOTE: NetworkX benchmarks without a backend for the graph used in this notebook can take very long time.  Using a Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz with 45GB of memory, the three algo runs took approximately 50 minutes._**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Running Betweenness Centrality...\n",
+      "CPU times: user 7min 47s, sys: 5.61 s, total: 7min 53s\n",
+      "Wall time: 7min 52s\n",
+      "\n",
+      "Running Breadth First Search (bfs_edges)...\n",
+      "CPU times: user 28.9 s, sys: 336 ms, total: 29.2 s\n",
+      "Wall time: 29.1 s\n",
+      "\n",
+      "Running Louvain...\n",
+      "CPU times: user 42min 46s, sys: 4.8 s, total: 42min 51s\n",
+      "Wall time: 42min 50s\n"
+     ]
+    }
+   ],
+   "source": [
+    "run_algos()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NetworkX with `nx-cugraph` Benchmark Runs\n",
+    "Use the `nx.config` API introduced in ([NetworkX 3.3](https://networkx.org/documentation/stable/reference/backends.html#networkx.utils.configs.NetworkXConfig)) to configure NetworkX to use nx-cugraph.  Both options used below can also be set using environment variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the prioritized list of backends to automatically try. If none of the backends in the list\n",
+    "# support the algorithm, NetworkX will use the default implementation).\n",
+    "#\n",
+    "# This can also be set using the environment variable NETWORKX_BACKEND_PRIORITY which accepts a\n",
+    "# comma-separated list.\n",
+    "nx.config.backend_priority = [\"cugraph\"]  # Try the \"cugraph\" (nx-cugraph) backend first, then\n",
+    "                                          # fall back to NetworkX\n",
+    "#nx.config.backend_priority = []          # Do not use any backends\n",
+    "\n",
+    "# Enable caching of graph conversions. When set to False (the default) nx-cugraph will convert\n",
+    "# the CPU-based NetworkX graph object to a nx-cugraph GPU-based graph object each time an algorithm\n",
+    "# is run. When True, the conversion will happen once and be saved for future use *if* the graph has\n",
+    "# not been modified via a supported method such as G.add_edge(u, v, weight=val)\n",
+    "#\n",
+    "# This can also be set using the environment variable NETWORKX_CACHE_CONVERTED_GRAPHS\n",
+    "nx.config.cache_converted_graphs = True\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note the warning message NetworkX generates to remind us a cached graph should not be manually mutated. This is shown because caching was enabled, and the initial call resulted in a cached graph conversion for use with subsequent nx-cugraph calls.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Running Betweenness Centrality...\n",
+      "CPU times: user 17.9 s, sys: 1.5 s, total: 19.4 s\n",
+      "Wall time: 19.1 s\n",
+      "\n",
+      "Running Breadth First Search (bfs_edges)...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/networkx/utils/backends.py:1101: UserWarning: Using cached graph for 'cugraph' backend in call to bfs_edges.\n",
+      "\n",
+      "For the cache to be consistent (i.e., correct), the input graph must not have been manually mutated since the cached graph was created. Examples of manually mutating the graph data structures resulting in an inconsistent cache include:\n",
+      "\n",
+      "    >>> G[u][v][key] = val\n",
+      "\n",
+      "and\n",
+      "\n",
+      "    >>> for u, v, d in G.edges(data=True):\n",
+      "    ...     d[key] = val\n",
+      "\n",
+      "Using methods such as `G.add_edge(u, v, weight=val)` will correctly clear the cache to keep it consistent. You may also use `G.__networkx_cache__.clear()` to manually clear the cache, or set `G.__networkx_cache__` to None to disable caching for G. Enable or disable caching via `nx.config.cache_converted_graphs` config.\n",
+      "  warnings.warn(warning_message)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 50.5 s, sys: 589 ms, total: 51 s\n",
+      "Wall time: 50.7 s\n",
+      "\n",
+      "Running Louvain...\n",
+      "CPU times: user 27.4 s, sys: 3.36 s, total: 30.7 s\n",
+      "Wall time: 30.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "run_algos()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Betweenness Centrality call above resulted in a conversion from a NetworkX Graph to a nx-cugraph Graph due to it being the first to use nx-cugraph. However, since caching was enabled, a second call will show the run-time for Betweenness Centrality without the need to convert the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Running Betweenness Centrality (again)...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/networkx/utils/backends.py:1128: UserWarning: Using cached graph for 'cugraph' backend in call to betweenness_centrality.\n",
+      "\n",
+      "For the cache to be consistent (i.e., correct), the input graph must not have been manually mutated since the cached graph was created. Examples of manually mutating the graph data structures resulting in an inconsistent cache include:\n",
+      "\n",
+      "    >>> G[u][v][key] = val\n",
+      "\n",
+      "and\n",
+      "\n",
+      "    >>> for u, v, d in G.edges(data=True):\n",
+      "    ...     d[key] = val\n",
+      "\n",
+      "Using methods such as `G.add_edge(u, v, weight=val)` will correctly clear the cache to keep it consistent. You may also use `G.__networkx_cache__.clear()` to manually clear the cache, or set `G.__networkx_cache__` to None to disable caching for G. Enable or disable caching via `nx.config.cache_converted_graphs` config.\n",
+      "  warnings.warn(warning_message)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.84 s, sys: 312 ms, total: 2.15 s\n",
+      "Wall time: 2.12 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nRunning Betweenness Centrality (again)...\")\n",
+    "%time result = nx.betweenness_centrality(G, k=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "___\n",
+    "Each user is responsible for checking the content of datasets and the applicable licenses and determining if suitable for the intended use.\n",
+    "\n",
+    "Information on the U.S. Patent Citation Network dataset used in this notebook is as follows:\n",
+    "Authors: Jure Leskovec and Andrej Krevl\n",
+    "Title: SNAP Datasets, Stanford Large Network Dataset Collection\n",
+    "URL: http://snap.stanford.edu/data\n",
+    "Date: June 2014 \n",
+    "___\n",
+    "Copyright (c) 2024, NVIDIA CORPORATION.\n",
+    "\n",
+    "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
+    "\n",
+    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
+    "___"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb b/notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb
deleted file mode 100644
index e05544448..000000000
--- a/notebooks/cugraph_benchmarks/nx_cugraph_codeless_switching.ipynb
+++ /dev/null
@@ -1,244 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Benchmarking Performance of NetworkX with Rapids GPU-based nx_cugraph backend vs on cpu\n",
-    "# Skip notebook test\n",
-    "This notebook demonstrates compares the performance of nx_cugraph as a dispatcher for NetworkX algorithms. \n",
-    "\n",
-    "We do this by executing Betweenness Centrality, Breadth First Search and Louvain Community Detection, collecting run times with and without nx_cugraph backend and graph caching enabled. nx_cugraph is a registered NetworkX backend. Using it is a zero code change solution.\n",
-    "\n",
-    "In the notebook switching to the nx-cugraph backend is done via variables set using the [NetworkX config package](https://networkx.org/documentation/stable/reference/backends.html#networkx.utils.configs.NetworkXConfig) **which requires networkX 3.3 or later !!**\n",
-    "\n",
-    "\n",
-    "They can be set at the command line as well.\n",
-    "\n",
-    "### See this example from GTC Spring 2024\n",
-    "\n",
-    "\n",
-    "\n",
-    "Here is a sample minimal script to demonstrate No-code-change GPU acceleration using nx-cugraph.\n",
-    "\n",
-    "----\n",
-    "bc_demo.ipy:\n",
-    "\n",
-    "```\n",
-    "import pandas as pd\n",
-    "import networkx as nx\n",
-    "\n",
-    "url = \"https://data.rapids.ai/cugraph/datasets/cit-Patents.csv\"\n",
-    "df = pd.read_csv(url, sep=\" \", names=[\"src\", \"dst\"], dtype=\"int32\")\n",
-    "G = nx.from_pandas_edgelist(df, source=\"src\", target=\"dst\")\n",
-    "\n",
-    "%time result = nx.betweenness_centrality(G, k=10)\n",
-    "```\n",
-    "----\n",
-    "Running it with the nx-cugraph backend looks like this:\n",
-    "```\n",
-    "user@machine:/# ipython bc_demo.ipy\n",
-    "CPU times: user 7min 38s, sys: 5.6 s, total: 7min 44s\n",
-    "Wall time: 7min 44s\n",
-    "\n",
-    "user@machine:/# NETWORKX_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy\n",
-    "CPU times: user 18.4 s, sys: 1.44 s, total: 19.9 s\n",
-    "Wall time: 20 s\n",
-    "```\n",
-    "----\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First import the needed packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import networkx as nx\n",
-    "import time\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This installs the NetworkX cuGraph dispatcher if not already present."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try: \n",
-    "    import nx_cugraph\n",
-    "except ModuleNotFoundError:\n",
-    "    os.system('conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is boiler plate NetworkX code to run:\n",
-    "* betweenness Centrality\n",
-    "* Bredth first Search\n",
-    "* Louvain community detection\n",
-    "\n",
-    "and report times. it is completely unaware of cugraph or GPU-based tools.\n",
-    "[NetworkX configurations](https://networkx.org/documentation/stable/reference/utils.html#backends) can determine how they are run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_algos(G):\n",
-    "    runtime = time.time()\n",
-    "    result = nx.betweenness_centrality(G, k=10)\n",
-    "    print (\"Betweenness Centrality time: \" + str(round(time.time() - runtime))+ \" seconds\")\n",
-    "    runtime = time.time()\n",
-    "    result = nx.bfs_tree(G,source=1)\n",
-    "    print (\"Breadth First Search time:  \" + str(round(time.time() - runtime))+ \" seconds\")\n",
-    "    runtime = time.time()\n",
-    "    result = nx.community.louvain_communities(G,threshold=1e-04)\n",
-    "    print (\"Louvain time: \" + str(round(time.time() - runtime))+ \" seconds\")\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Downloads a patent citation dataset containing 3774768 nodes and 16518948 edges and loads it into a NetworkX graph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filepath = \"./data/cit-Patents.csv\"\n",
-    "\n",
-    "if os.path.exists(filepath):\n",
-    "    print(\"File found\")\n",
-    "    url = filepath\n",
-    "else:\n",
-    "    url = \"https://data.rapids.ai/cugraph/datasets/cit-Patents.csv\"\n",
-    "df = pd.read_csv(url, sep=\" \", names=[\"src\", \"dst\"], dtype=\"int32\")\n",
-    "G = nx.from_pandas_edgelist(df, source=\"src\", target=\"dst\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Setting the NetworkX dispatcher with an environment variable or in code using NetworkX config package which is new to [NetworkX 3.3 config](https://networkx.org/documentation/stable/reference/backends.html#networkx.utils.configs.NetworkXConfig).\n",
-    "\n",
-    "These convenience settinge allow turning off caching and cugraph dispatching if you want to see how long cpu-only takes.\n",
-    "This example using an AMD Ryzen Threadripper PRO 3975WX 32-Cores cpu completed in slightly over 40 minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "use_cugraph = True\n",
-    "cache_graph = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if use_cugraph:\n",
-    "    nx.config[\"backend_priority\"]=['cugraph']\n",
-    "else:\n",
-    "    # Use this setting to turn off the cugraph dispatcher running in legacy cpu mode.\n",
-    "    nx.config[\"backend_priority\"]=[]\n",
-    "if cache_graph:\n",
-    "    nx.config[\"cache_converted_graphs\"]= True\n",
-    "else:\n",
-    "    # Use this setting to turn off graph caching which will convertthe NetworkX to a gpu-resident graph each time an algorithm is run.\n",
-    "    nx.config[\"cache_converted_graphs\"]= False\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the algorithms on GPU. \n",
-    "\n",
-    "**Note the messages NetworkX generates to remind us cached graph shouldn't be modified.**\n",
-    "\n",
-    "```\n",
-    "For the cache to be consistent (i.e., correct), the input graph must not have been manually mutated since the cached graph was created.\n",
-    "\n",
-    "Using cached graph for 'cugraph' backend in call to bfs_edges.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "run_algos(G)\n",
-    "print (\"Total Algorithm run time\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "___\n",
-    "Copyright (c) 2024, NVIDIA CORPORATION.\n",
-    "\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
-    "\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
-    "___"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 5303de0cead5df950bd4ca3bd9c2bb2b15d71158 Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Tue, 9 Jul 2024 01:25:11 +0200
Subject: [PATCH 369/384] Better handle cudf.pandas in `from_pandas_edgelist`
 (#4525)

Optimistically use cupy, but fall back to numpy if necessary.

Also, bump lint versions.

CC @rlratzel

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4525
---
 python/nx-cugraph/lint.yaml                   | 14 ++++++-------
 python/nx-cugraph/nx_cugraph/classes/graph.py |  8 ++++----
 .../nx-cugraph/nx_cugraph/convert_matrix.py   | 20 ++++++++++++++-----
 3 files changed, 26 insertions(+), 16 deletions(-)

diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml
index c4422ffb9..317d5b8d4 100644
--- a/python/nx-cugraph/lint.yaml
+++ b/python/nx-cugraph/lint.yaml
@@ -26,7 +26,7 @@ repos:
       - id: mixed-line-ending
       - id: trailing-whitespace
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.17
+    rev: v0.18
     hooks:
       - id: validate-pyproject
         name: Validate pyproject.toml
@@ -40,7 +40,7 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.2
+    rev: v3.16.0
     hooks:
       - id: pyupgrade
         args: [--py39-plus]
@@ -50,18 +50,18 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.5.1
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
   - repo: https://github.com/PyCQA/flake8
-    rev: 7.0.0
+    rev: 7.1.0
     hooks:
       - id: flake8
         args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=SIM105']  # Why is this necessary?
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
-          - flake8==7.0.0
+          - flake8==7.1.0
           - flake8-bugbear==24.4.26
           - flake8-simplify==0.21.0
   - repo: https://github.com/asottile/yesqa
@@ -70,14 +70,14 @@ repos:
       - id: yesqa
         additional_dependencies: *flake8_dependencies
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.6
+    rev: v2.3.0
     hooks:
       - id: codespell
         types_or: [python, rst, markdown]
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.5.1
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks
diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py
index 5132e6a54..ad7cf3191 100644
--- a/python/nx-cugraph/nx_cugraph/classes/graph.py
+++ b/python/nx-cugraph/nx_cugraph/classes/graph.py
@@ -124,13 +124,13 @@ def from_coo(
         # Easy and fast sanity checks
         if size != new_graph.dst_indices.size:
             raise ValueError
-        for attr in ["edge_values", "edge_masks"]:
-            if datadict := getattr(new_graph, attr):
+        for edge_attr in ["edge_values", "edge_masks"]:
+            if datadict := getattr(new_graph, edge_attr):
                 for key, val in datadict.items():
                     if val.shape[0] != size:
                         raise ValueError(key)
-        for attr in ["node_values", "node_masks"]:
-            if datadict := getattr(new_graph, attr):
+        for node_attr in ["node_values", "node_masks"]:
+            if datadict := getattr(new_graph, node_attr):
                 for key, val in datadict.items():
                     if val.shape[0] != N:
                         raise ValueError(key)
diff --git a/python/nx-cugraph/nx_cugraph/convert_matrix.py b/python/nx-cugraph/nx_cugraph/convert_matrix.py
index 1a2ecde9b..67f638698 100644
--- a/python/nx-cugraph/nx_cugraph/convert_matrix.py
+++ b/python/nx-cugraph/nx_cugraph/convert_matrix.py
@@ -35,11 +35,21 @@ def from_pandas_edgelist(
 ):
     """cudf.DataFrame inputs also supported; value columns with str is unsuppported."""
     graph_class, inplace = _create_using_class(create_using)
+    # Try to be optimal whether using pandas, cudf, or cudf.pandas
     src_array = df[source].to_numpy()
     dst_array = df[target].to_numpy()
+    try:
+        # Optimistically try to use cupy, but fall back to numpy if necessary
+        src_array = cp.asarray(src_array)
+        dst_array = cp.asarray(dst_array)
+        np_or_cp = cp
+    except ValueError:
+        src_array = np.asarray(src_array)
+        dst_array = np.asarray(dst_array)
+        np_or_cp = np
     # TODO: create renumbering helper function(s)
     # Renumber step 0: node keys
-    nodes = np.unique(np.concatenate([src_array, dst_array]))
+    nodes = np_or_cp.unique(np_or_cp.concatenate([src_array, dst_array]))
     N = nodes.size
     kwargs = {}
     if N > 0 and (
@@ -47,13 +57,13 @@ def from_pandas_edgelist(
         or nodes[N - 1] != N - 1
         or (
             nodes.dtype.kind not in {"i", "u"}
-            and not (nodes == np.arange(N, dtype=np.int64)).all()
+            and not (nodes == np_or_cp.arange(N, dtype=np.int64)).all()
         )
     ):
-        # We need to renumber indices--np.searchsorted to the rescue!
+        # We need to renumber indices--np_or_cp.searchsorted to the rescue!
         kwargs["id_to_key"] = nodes.tolist()
-        src_indices = cp.array(np.searchsorted(nodes, src_array), index_dtype)
-        dst_indices = cp.array(np.searchsorted(nodes, dst_array), index_dtype)
+        src_indices = cp.asarray(np_or_cp.searchsorted(nodes, src_array), index_dtype)
+        dst_indices = cp.asarray(np_or_cp.searchsorted(nodes, dst_array), index_dtype)
     else:
         src_indices = cp.array(src_array)
         dst_indices = cp.array(dst_array)

From 943f6f32975062f98bead202a5aca88460570c3d Mon Sep 17 00:00:00 2001
From: Kyle Edwards 
Date: Wed, 17 Jul 2024 12:43:36 -0400
Subject: [PATCH 370/384] Build and test with CUDA 12.5.1 (#4534)

This PR updates the latest CUDA build/test version 12.2.2 to 12.5.1.

Contributes to https://github.com/rapidsai/build-planning/issues/73

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - https://github.com/jakirkham

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - https://github.com/jakirkham
  - Chuck Hastings (https://github.com/ChuckHastings)

URL: https://github.com/rapidsai/cugraph/pull/4534
---
 docs/cugraph/source/graph_support/DGL_support.md | 2 +-
 docs/cugraph/source/installation/source_build.md | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/cugraph/source/graph_support/DGL_support.md b/docs/cugraph/source/graph_support/DGL_support.md
index 9df462155..ba9a28e31 100644
--- a/docs/cugraph/source/graph_support/DGL_support.md
+++ b/docs/cugraph/source/graph_support/DGL_support.md
@@ -17,7 +17,7 @@ mamba install cugraph-dgl -c rapidsai-nightly -c rapidsai -c pytorch -c conda-fo
 
 ### Create the conda development environment
 ```
-conda env create -n cugraph_dgl_dev --file conda/environments/all_cuda-122_arch-x86_64.yaml
+conda env create -n cugraph_dgl_dev --file conda/environments/all_cuda-125_arch-x86_64.yaml
 ```
 
 ### Install  in editable mode
diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md
index 49af13757..89e63bade 100644
--- a/docs/cugraph/source/installation/source_build.md
+++ b/docs/cugraph/source/installation/source_build.md
@@ -43,7 +43,7 @@ files](https://github.com/rapidsai/cugraph/blob/main/conda/environments).
 conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml
 
 # for CUDA 12.x
-conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-12.2_arch-x86_64.yaml
+conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-125_arch-x86_64.yaml
 
 
 # activate the environment
@@ -61,7 +61,7 @@ conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_
 conda activate cugraph_dev
 
 # for CUDA 12.x
-conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-122_arch-x86_64.yaml
+conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-125_arch-x86_64.yaml
 conda activate cugraph_dev
 
 

From b8b0a7094765e07c38da583003dbf5a5714d5a25 Mon Sep 17 00:00:00 2001
From: Ralph Liu <137829296+nv-rliu@users.noreply.github.com>
Date: Thu, 18 Jul 2024 20:14:23 -0400
Subject: [PATCH 371/384] Add Additional Check For SSSP Source Vertex & Fix
 SSSP Benchmark (#4541)

Closes https://github.com/rapidsai/cugraph/issues/4511. Part of https://github.com/rapidsai/graph_dl/issues/513

### Fixes
 - `bench_algos.py` will properly convert `source` vertex values returned from MG and SG graphs before calling the actual algorithm.

---
*Other minor changes*
- Fix white-space formatting of `pytest.ini`

Authors:
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4541
---
 benchmarks/pytest.ini | 106 +++++++++++++++++++++---------------------
 1 file changed, 53 insertions(+), 53 deletions(-)

diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini
index b3d8a8bb3..fe7fc31b6 100644
--- a/benchmarks/pytest.ini
+++ b/benchmarks/pytest.ini
@@ -1,67 +1,67 @@
 [pytest]
 pythonpath =
-             shared/python
+    shared/python
 
 testpaths =
-            cugraph/pytest_based
-	    cugraph-service/pytest_based
+    cugraph/pytest_based
+    cugraph-service/pytest_based
 
 addopts =
-          --benchmark-columns="min, max, mean, stddev, outliers"
+    --benchmark-columns="min, max, mean, stddev, outliers"
 
 markers =
-          managedmem_on: RMM managed memory enabled
-          managedmem_off: RMM managed memory disabled
-          poolallocator_on: RMM pool allocator enabled
-          poolallocator_off: RMM pool allocator disabled
-          tiny: tiny datasets
-          small: small datasets
-	  medium: medium datasets
-          large: large datasets
-          directed: directed datasets
-          undirected: undirected datasets
-	  matrix_types: inputs are matrices
-	  nx_types: inputs are NetowrkX Graph objects
-	  cugraph_types: inputs are cuGraph Graph objects
-	  sg: single-GPU
-	  mg: multi-GPU
-	  snmg: single-node multi-GPU
-	  mnmg: multi-node multi-GPU
-	  local: local cugraph
-	  remote: cugraph-service
-	  batch_size_100: batch size of 100 for sampling algos
-	  batch_size_500: batch size of 500 for sampling algos
-	  batch_size_1000: batch size of 1000 for sampling algos
-	  batch_size_2500: batch size of 2500 for sampling algos
-	  batch_size_5000: batch size of 5000 for sampling algos
-	  batch_size_10000: batch size of 10000 for sampling algos
-	  batch_size_20000: batch size of 20000 for sampling algos
-	  batch_size_30000: batch size of 30000 for sampling algos
-	  batch_size_40000: batch size of 40000 for sampling algos
-	  batch_size_50000: batch size of 50000 for sampling algos
-	  batch_size_60000: batch size of 60000 for sampling algos
-	  batch_size_70000: batch size of 70000 for sampling algos
-	  batch_size_80000: batch size of 80000 for sampling algos
-	  batch_size_90000: batch size of 90000 for sampling algos
-	  batch_size_100000: batch size of 100000 for sampling algos
-	  num_clients_2: start 2 cugraph-service clients
-	  num_clients_4: start 4 cugraph-service clients
-	  num_clients_8: start 8 cugraph-service clients
-	  num_clients_16: start 16 cugraph-service clients
-	  num_clients_32: start 32 cugraph-service clients
-	  fanout_10_25: fanout [10, 25] for sampling algos
-	  fanout_5_10_15: fanout [5, 10, 15] for sampling algos
-	  rmat_data: RMAT-generated synthetic datasets
-	  file_data: datasets from $RAPIDS_DATASET_ROOT_DIR
+    managedmem_on: RMM managed memory enabled
+    managedmem_off: RMM managed memory disabled
+    poolallocator_on: RMM pool allocator enabled
+    poolallocator_off: RMM pool allocator disabled
+    tiny: tiny datasets
+    small: small datasets
+    medium: medium datasets
+    large: large datasets
+    directed: directed datasets
+    undirected: undirected datasets
+    matrix_types: inputs are matrices
+    nx_types: inputs are NetowrkX Graph objects
+    cugraph_types: inputs are cuGraph Graph objects
+    sg: single-GPU
+    mg: multi-GPU
+    snmg: single-node multi-GPU
+    mnmg: multi-node multi-GPU
+    local: local cugraph
+    remote: cugraph-service
+    batch_size_100: batch size of 100 for sampling algos
+    batch_size_500: batch size of 500 for sampling algos
+    batch_size_1000: batch size of 1000 for sampling algos
+    batch_size_2500: batch size of 2500 for sampling algos
+    batch_size_5000: batch size of 5000 for sampling algos
+    batch_size_10000: batch size of 10000 for sampling algos
+    batch_size_20000: batch size of 20000 for sampling algos
+    batch_size_30000: batch size of 30000 for sampling algos
+    batch_size_40000: batch size of 40000 for sampling algos
+    batch_size_50000: batch size of 50000 for sampling algos
+    batch_size_60000: batch size of 60000 for sampling algos
+    batch_size_70000: batch size of 70000 for sampling algos
+    batch_size_80000: batch size of 80000 for sampling algos
+    batch_size_90000: batch size of 90000 for sampling algos
+    batch_size_100000: batch size of 100000 for sampling algos
+    num_clients_2: start 2 cugraph-service clients
+    num_clients_4: start 4 cugraph-service clients
+    num_clients_8: start 8 cugraph-service clients
+    num_clients_16: start 16 cugraph-service clients
+    num_clients_32: start 32 cugraph-service clients
+    fanout_10_25: fanout [10, 25] for sampling algos
+    fanout_5_10_15: fanout [5, 10, 15] for sampling algos
+    rmat_data: RMAT-generated synthetic datasets
+    file_data: datasets from $RAPIDS_DATASET_ROOT_DIR
 
 python_classes =
-                 Bench*
-                 Test*
+    Bench*
+    Test*
 
 python_files =
-                 bench_*
-                 test_*
+    bench_*
+    test_*
 
 python_functions =
-                   bench_*
-                   test_*
+    bench_*
+    test_*

From d03bfac093e03dac86caf45fb9a9c87acd64f5fa Mon Sep 17 00:00:00 2001
From: James Lamb 
Date: Tue, 30 Jul 2024 00:14:13 -0500
Subject: [PATCH 372/384] split up CUDA-suffixed dependencies in
 dependencies.yaml (#4552)

Contributes to https://github.com/rapidsai/build-planning/issues/31

In short, RAPIDS DLFW builds want to produce wheels with unsuffixed dependencies, e.g. `cudf` depending on `rmm`, not `rmm-cu12`.

This PR is part of a series across all of RAPIDS to try to support that type of build by setting up CUDA-suffixed and CUDA-unsuffixed dependency lists in `dependencies.yaml`.

For more details, see:

* https://github.com/rapidsai/build-planning/issues/31#issuecomment-2245815818
* https://github.com/rapidsai/cudf/pull/16183

## Notes for Reviewers

### Why target 24.08?

This is targeting 24.08 because:

1. it should be very low-risk
2. getting these changes into 24.08 prevents the need to carry around patches for every library in DLFW builds using RAPIDS 24.08

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cugraph/pull/4552
---
 python/nx-cugraph/pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index 50881d5db..07e09201c 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -86,6 +86,7 @@ include = [
 build-backend = "setuptools.build_meta"
 commit-files = ["_nx_cugraph/GIT_COMMIT"]
 dependencies-file = "../../dependencies.yaml"
+matrix-entry = "cuda_suffixed=true"
 
 [tool.black]
 line-length = 88

From cbcffaf1efdb5999dc875626bd02cd1672e7823f Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Tue, 30 Jul 2024 08:47:39 +0200
Subject: [PATCH 373/384] nx-cugraph: add `from_dict_of_lists` and
 `to_dict_of_lists` (#4537)

Tests were added to improve coverage. Perhaps we could/should upstream tests to networkx.

Authors:
  - Erik Welch (https://github.com/eriknw)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4537
---
 python/nx-cugraph/README.md                   |   3 +
 python/nx-cugraph/_nx_cugraph/__init__.py     |   2 +
 python/nx-cugraph/nx_cugraph/convert.py       | 102 +++++++++++++++++-
 .../nx_cugraph/tests/test_convert.py          |  50 ++++++++-
 4 files changed, 154 insertions(+), 3 deletions(-)

diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md
index 27825585c..088f2fd20 100644
--- a/python/nx-cugraph/README.md
+++ b/python/nx-cugraph/README.md
@@ -253,6 +253,9 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
 classes
  └─ function
      └─ is_negatively_weighted
+convert
+ ├─ from_dict_of_lists
+ └─ to_dict_of_lists
 convert_matrix
  ├─ from_pandas_edgelist
  └─ from_scipy_sparse_array
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index f57b90eb4..3d27e4b9e 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -81,6 +81,7 @@
         "eigenvector_centrality",
         "empty_graph",
         "florentine_families_graph",
+        "from_dict_of_lists",
         "from_pandas_edgelist",
         "from_scipy_sparse_array",
         "frucht_graph",
@@ -138,6 +139,7 @@
         "star_graph",
         "tadpole_graph",
         "tetrahedral_graph",
+        "to_dict_of_lists",
         "transitivity",
         "triangles",
         "trivial_graph",
diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py
index b34245d50..9e6c080d6 100644
--- a/python/nx-cugraph/nx_cugraph/convert.py
+++ b/python/nx-cugraph/nx_cugraph/convert.py
@@ -14,7 +14,7 @@
 
 import itertools
 import operator as op
-from collections import Counter
+from collections import Counter, defaultdict
 from collections.abc import Mapping
 from typing import TYPE_CHECKING
 
@@ -24,7 +24,8 @@
 
 import nx_cugraph as nxcg
 
-from .utils import index_dtype
+from .utils import index_dtype, networkx_algorithm
+from .utils.misc import pairwise
 
 if TYPE_CHECKING:  # pragma: no cover
     from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue, any_ndarray
@@ -32,6 +33,8 @@
 __all__ = [
     "from_networkx",
     "to_networkx",
+    "from_dict_of_lists",
+    "to_dict_of_lists",
 ]
 
 concat = itertools.chain.from_iterable
@@ -653,3 +656,98 @@ def _to_undirected_graph(
         )
     # TODO: handle cugraph.Graph
     raise TypeError
+
+
+@networkx_algorithm(version_added="24.08")
+def from_dict_of_lists(d, create_using=None):
+    from .generators._utils import _create_using_class
+
+    graph_class, inplace = _create_using_class(create_using)
+    key_to_id = defaultdict(itertools.count().__next__)
+    src_indices = cp.array(
+        # cp.repeat is slow to use here, so use numpy instead
+        np.repeat(
+            np.fromiter(map(key_to_id.__getitem__, d), index_dtype),
+            np.fromiter(map(len, d.values()), index_dtype),
+        )
+    )
+    dst_indices = cp.fromiter(
+        map(key_to_id.__getitem__, concat(d.values())), index_dtype
+    )
+    # Initialize as directed first them symmetrize if undirected.
+    G = graph_class.to_directed_class().from_coo(
+        len(key_to_id),
+        src_indices,
+        dst_indices,
+        key_to_id=key_to_id,
+    )
+    if not graph_class.is_directed():
+        G = G.to_undirected()
+    if inplace:
+        return create_using._become(G)
+    return G
+
+
+@networkx_algorithm(version_added="24.08")
+def to_dict_of_lists(G, nodelist=None):
+    G = _to_graph(G)
+    src_indices = G.src_indices
+    dst_indices = G.dst_indices
+    if nodelist is not None:
+        try:
+            node_ids = G._nodekeys_to_nodearray(nodelist)
+        except KeyError as exc:
+            gname = "digraph" if G.is_directed() else "graph"
+            raise nx.NetworkXError(
+                f"The node {exc.args[0]} is not in the {gname}."
+            ) from exc
+        mask = cp.isin(src_indices, node_ids) & cp.isin(dst_indices, node_ids)
+        src_indices = src_indices[mask]
+        dst_indices = dst_indices[mask]
+    # Sort indices so we can use `cp.unique` to determine boundaries.
+    # This is like exporting to DCSR.
+    if G.is_multigraph():
+        stacked = cp.unique(cp.vstack((src_indices, dst_indices)), axis=1)
+        src_indices = stacked[0]
+        dst_indices = stacked[1]
+    else:
+        stacked = cp.vstack((dst_indices, src_indices))
+        indices = cp.lexsort(stacked)
+        src_indices = src_indices[indices]
+        dst_indices = dst_indices[indices]
+    compressed_srcs, left_bounds = cp.unique(src_indices, return_index=True)
+    # Ensure we include isolate nodes in the result (and in proper order)
+    rv = None
+    if nodelist is not None:
+        if compressed_srcs.size != len(nodelist):
+            if G.key_to_id is None:
+                # `G._nodekeys_to_nodearray` does not check for valid node keys.
+                container = range(G._N)
+                for key in nodelist:
+                    if key not in container:
+                        gname = "digraph" if G.is_directed() else "graph"
+                        raise nx.NetworkXError(f"The node {key} is not in the {gname}.")
+            rv = {key: [] for key in nodelist}
+    elif compressed_srcs.size != G._N:
+        rv = {key: [] for key in G}
+    # We use `boundaries` like this in `_groupby` too
+    boundaries = pairwise(itertools.chain(left_bounds.tolist(), [src_indices.size]))
+    dst_indices = dst_indices.tolist()
+    if G.key_to_id is None:
+        it = zip(compressed_srcs.tolist(), boundaries)
+        if rv is None:
+            return {src: dst_indices[start:end] for src, (start, end) in it}
+        rv.update((src, dst_indices[start:end]) for src, (start, end) in it)
+        return rv
+    to_key = G.id_to_key.__getitem__
+    it = zip(compressed_srcs.tolist(), boundaries)
+    if rv is None:
+        return {
+            to_key(src): list(map(to_key, dst_indices[start:end]))
+            for src, (start, end) in it
+        }
+    rv.update(
+        (to_key(src), list(map(to_key, dst_indices[start:end])))
+        for src, (start, end) in it
+    )
+    return rv
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_convert.py b/python/nx-cugraph/nx_cugraph/tests/test_convert.py
index 1a71b7968..634b28e96 100644
--- a/python/nx-cugraph/nx_cugraph/tests/test_convert.py
+++ b/python/nx-cugraph/nx_cugraph/tests/test_convert.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -13,10 +13,13 @@
 import cupy as cp
 import networkx as nx
 import pytest
+from packaging.version import parse
 
 import nx_cugraph as nxcg
 from nx_cugraph import interface
 
+nxver = parse(nx.__version__)
+
 
 @pytest.mark.parametrize(
     "graph_class", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
@@ -224,3 +227,48 @@ def test_multigraph(graph_class):
     H = nxcg.to_networkx(Gcg)
     assert type(G) is type(H)
     assert nx.utils.graphs_equal(G, H)
+
+
+def test_to_dict_of_lists():
+    G = nx.MultiGraph()
+    G.add_edge("a", "b")
+    G.add_edge("a", "c")
+    G.add_edge("a", "b")
+    expected = nx.to_dict_of_lists(G)
+    result = nxcg.to_dict_of_lists(G)
+    assert expected == result
+    expected = nx.to_dict_of_lists(G, nodelist=["a", "b"])
+    result = nxcg.to_dict_of_lists(G, nodelist=["a", "b"])
+    assert expected == result
+    with pytest.raises(nx.NetworkXError, match="The node d is not in the graph"):
+        nx.to_dict_of_lists(G, nodelist=["a", "d"])
+    with pytest.raises(nx.NetworkXError, match="The node d is not in the graph"):
+        nxcg.to_dict_of_lists(G, nodelist=["a", "d"])
+    G.add_node("d")  # No edges
+    expected = nx.to_dict_of_lists(G)
+    result = nxcg.to_dict_of_lists(G)
+    assert expected == result
+    expected = nx.to_dict_of_lists(G, nodelist=["a", "d"])
+    result = nxcg.to_dict_of_lists(G, nodelist=["a", "d"])
+    assert expected == result
+    # Now try with default node ids
+    G = nx.DiGraph()
+    G.add_edge(0, 1)
+    G.add_edge(0, 2)
+    expected = nx.to_dict_of_lists(G)
+    result = nxcg.to_dict_of_lists(G)
+    assert expected == result
+    expected = nx.to_dict_of_lists(G, nodelist=[0, 1])
+    result = nxcg.to_dict_of_lists(G, nodelist=[0, 1])
+    assert expected == result
+    with pytest.raises(nx.NetworkXError, match="The node 3 is not in the digraph"):
+        nx.to_dict_of_lists(G, nodelist=[0, 3])
+    with pytest.raises(nx.NetworkXError, match="The node 3 is not in the digraph"):
+        nxcg.to_dict_of_lists(G, nodelist=[0, 3])
+    G.add_node(3)  # No edges
+    expected = nx.to_dict_of_lists(G)
+    result = nxcg.to_dict_of_lists(G)
+    assert expected == result
+    expected = nx.to_dict_of_lists(G, nodelist=[0, 3])
+    result = nxcg.to_dict_of_lists(G, nodelist=[0, 3])
+    assert expected == result

From 9742e59b176659ace49252f45d04a621f3bc42bd Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Tue, 30 Jul 2024 08:49:57 +0200
Subject: [PATCH 374/384] nx-cugraph: fix `from_pandas_edgekey` given edgekey
 but not edgeattr (#4550)

This fixes this: https://github.com/networkx/networkx/pull/7466

We would catch this (and other changes) if we tested networkx nightlies. Someday!

I kept the buggy behavior for older versions of networkx in order to match networkx, which feels kinda silly, but also okay-ish.

Authors:
  - Erik Welch (https://github.com/eriknw)
  - Rick Ratzel (https://github.com/rlratzel)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4550
---
 .../nx-cugraph/nx_cugraph/convert_matrix.py   | 35 ++++++++++++-------
 1 file changed, 22 insertions(+), 13 deletions(-)

diff --git a/python/nx-cugraph/nx_cugraph/convert_matrix.py b/python/nx-cugraph/nx_cugraph/convert_matrix.py
index 67f638698..10793c641 100644
--- a/python/nx-cugraph/nx_cugraph/convert_matrix.py
+++ b/python/nx-cugraph/nx_cugraph/convert_matrix.py
@@ -111,19 +111,28 @@ def from_pandas_edgelist(
             }
         kwargs["edge_values"] = edge_values
 
-        if graph_class.is_multigraph() and edge_key is not None:
-            try:
-                edge_keys = df[edge_key].to_list()
-            except (KeyError, TypeError) as exc:
-                raise nx.NetworkXError(
-                    f"Invalid edge_key argument: {edge_key}"
-                ) from exc
-            if not graph_class.is_directed():
-                # Symmetrize the edges
-                edge_keys = cp.hstack(
-                    (edge_keys, edge_keys[mask] if mask is not None else edge_keys)
-                )
-            kwargs["edge_keys"] = edge_keys
+    if (
+        graph_class.is_multigraph()
+        and edge_key is not None
+        and (
+            # In nx <= 3.3, `edge_key` was ignored if `edge_attr` is None
+            edge_attr is not None
+            or nx.__version__[:3] > "3.3"
+        )
+    ):
+        try:
+            edge_keys = df[edge_key].to_list()
+        except (KeyError, TypeError) as exc:
+            raise nx.NetworkXError(f"Invalid edge_key argument: {edge_key}") from exc
+        if not graph_class.is_directed():
+            # Symmetrize the edges; remember, `edge_keys` is a list!
+            if mask is None:
+                edge_keys *= 2
+            else:
+                edge_keys += [
+                    key for keep, key in zip(mask.tolist(), edge_keys) if keep
+                ]
+        kwargs["edge_keys"] = edge_keys
 
     G = graph_class.from_coo(N, src_indices, dst_indices, **kwargs)
     if inplace:

From 5e5d59869bd7baedaae3d8dcad11c04e6e7e7840 Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Tue, 30 Jul 2024 08:53:35 +0200
Subject: [PATCH 375/384] nx-cugraph: add dijkstra sssp functions (#4538)

This does add new functionality (so it's not just sugar!), since dijstra methods have `cutoff=` arguments.

Note that we are missing multi-source dijstra methods, since `plc.sssp` only accepts a single source.

We also do not have `bidirectional_dijkstra`, since PLC does not implement this algorithm.

Authors:
  - Erik Welch (https://github.com/eriknw)
  - Rick Ratzel (https://github.com/rlratzel)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4538
---
 python/nx-cugraph/README.md                   |  10 +-
 python/nx-cugraph/_nx_cugraph/__init__.py     |  36 +++-
 .../algorithms/shortest_paths/generic.py      |  54 +++---
 .../algorithms/shortest_paths/unweighted.py   |   9 +-
 .../algorithms/shortest_paths/weighted.py     | 170 +++++++++++++++---
 .../nx_cugraph/tests/test_match_api.py        |  14 +-
 6 files changed, 230 insertions(+), 63 deletions(-)

diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md
index 088f2fd20..8a1824a7a 100644
--- a/python/nx-cugraph/README.md
+++ b/python/nx-cugraph/README.md
@@ -173,11 +173,19 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  └─ weighted
      ├─ all_pairs_bellman_ford_path
      ├─ all_pairs_bellman_ford_path_length
+     ├─ all_pairs_dijkstra
+     ├─ all_pairs_dijkstra_path
+     ├─ all_pairs_dijkstra_path_length
      ├─ bellman_ford_path
      ├─ bellman_ford_path_length
+     ├─ dijkstra_path
+     ├─ dijkstra_path_length
      ├─ single_source_bellman_ford
      ├─ single_source_bellman_ford_path
-     └─ single_source_bellman_ford_path_length
+     ├─ single_source_bellman_ford_path_length
+     ├─ single_source_dijkstra
+     ├─ single_source_dijkstra_path
+     └─ single_source_dijkstra_path_length
 traversal
  └─ breadth_first_search
      ├─ bfs_edges
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index 3d27e4b9e..2d6017fa2 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -42,6 +42,9 @@
         # BEGIN: functions
         "all_pairs_bellman_ford_path",
         "all_pairs_bellman_ford_path_length",
+        "all_pairs_dijkstra",
+        "all_pairs_dijkstra_path",
+        "all_pairs_dijkstra_path_length",
         "all_pairs_shortest_path",
         "all_pairs_shortest_path_length",
         "ancestors",
@@ -75,6 +78,8 @@
         "descendants",
         "descendants_at_distance",
         "diamond_graph",
+        "dijkstra_path",
+        "dijkstra_path_length",
         "dodecahedral_graph",
         "edge_betweenness_centrality",
         "ego_graph",
@@ -132,6 +137,9 @@
         "single_source_bellman_ford",
         "single_source_bellman_ford_path",
         "single_source_bellman_ford_path_length",
+        "single_source_dijkstra",
+        "single_source_dijkstra_path",
+        "single_source_dijkstra_path_length",
         "single_source_shortest_path",
         "single_source_shortest_path_length",
         "single_target_shortest_path",
@@ -173,8 +181,8 @@
         "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.",
         "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.",
         "pagerank": "`dangling` parameter is not supported, but it is checked for validity.",
-        "shortest_path": "Negative weights are not yet supported, and method is ununsed.",
-        "shortest_path_length": "Negative weights are not yet supported, and method is ununsed.",
+        "shortest_path": "Negative weights are not yet supported.",
+        "shortest_path_length": "Negative weights are not yet supported.",
         "single_source_bellman_ford": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "single_source_bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "single_source_bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
@@ -189,12 +197,27 @@
         "all_pairs_bellman_ford_path_length": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
+        "all_pairs_dijkstra": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "all_pairs_dijkstra_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "all_pairs_dijkstra_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         "bellman_ford_path": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
         "bellman_ford_path_length": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
+        "dijkstra_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "dijkstra_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         "ego_graph": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
@@ -229,6 +252,15 @@
         "single_source_bellman_ford_path_length": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
+        "single_source_dijkstra": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_dijkstra_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_dijkstra_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         # END: additional_parameters
     },
 }
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py
index 68dbbace9..7d6d77f34 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py
@@ -43,7 +43,7 @@ def has_path(G, source, target):
 def shortest_path(
     G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None
 ):
-    """Negative weights are not yet supported, and method is ununsed."""
+    """Negative weights are not yet supported."""
     if method not in {"dijkstra", "bellman-ford"}:
         raise ValueError(f"method not supported: {method}")
     if weight is None:
@@ -53,9 +53,9 @@ def shortest_path(
             # All pairs
             if method == "unweighted":
                 paths = nxcg.all_pairs_shortest_path(G)
-            else:
-                # method == "dijkstra":
-                # method == 'bellman-ford':
+            elif method == "dijkstra":
+                paths = nxcg.all_pairs_dijkstra_path(G, weight=weight, dtype=dtype)
+            else:  # method == 'bellman-ford':
                 paths = nxcg.all_pairs_bellman_ford_path(G, weight=weight, dtype=dtype)
             if nx.__version__[:3] <= "3.4":
                 paths = dict(paths)
@@ -75,9 +75,11 @@ def shortest_path(
         # From source
         if method == "unweighted":
             paths = nxcg.single_source_shortest_path(G, source)
-        else:
-            # method == "dijkstra":
-            # method == 'bellman-ford':
+        elif method == "dijkstra":
+            paths = nxcg.single_source_dijkstra_path(
+                G, source, weight=weight, dtype=dtype
+            )
+        else:  # method == 'bellman-ford':
             paths = nxcg.single_source_bellman_ford_path(
                 G, source, weight=weight, dtype=dtype
             )
@@ -106,7 +108,7 @@ def _(G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None
 def shortest_path_length(
     G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None
 ):
-    """Negative weights are not yet supported, and method is ununsed."""
+    """Negative weights are not yet supported."""
     if method not in {"dijkstra", "bellman-ford"}:
         raise ValueError(f"method not supported: {method}")
     if weight is None:
@@ -116,9 +118,11 @@ def shortest_path_length(
             # All pairs
             if method == "unweighted":
                 lengths = nxcg.all_pairs_shortest_path_length(G)
-            else:
-                # method == "dijkstra":
-                # method == 'bellman-ford':
+            elif method == "dijkstra":
+                lengths = nxcg.all_pairs_dijkstra_path_length(
+                    G, weight=weight, dtype=dtype
+                )
+            else:  # method == 'bellman-ford':
                 lengths = nxcg.all_pairs_bellman_ford_path_length(
                     G, weight=weight, dtype=dtype
                 )
@@ -127,9 +131,11 @@ def shortest_path_length(
             lengths = nxcg.single_target_shortest_path_length(G, target)
             if nx.__version__[:3] <= "3.4":
                 lengths = dict(lengths)
-        else:
-            # method == "dijkstra":
-            # method == 'bellman-ford':
+        elif method == "dijkstra":
+            lengths = nxcg.single_source_dijkstra_path_length(
+                G, target, weight=weight, dtype=dtype
+            )
+        else:  # method == 'bellman-ford':
             lengths = nxcg.single_source_bellman_ford_path_length(
                 G, target, weight=weight, dtype=dtype
             )
@@ -137,21 +143,21 @@ def shortest_path_length(
         # From source
         if method == "unweighted":
             lengths = nxcg.single_source_shortest_path_length(G, source)
-        else:
-            # method == "dijkstra":
-            # method == 'bellman-ford':
-            lengths = dict(
-                nxcg.single_source_bellman_ford_path_length(
-                    G, source, weight=weight, dtype=dtype
-                )
+        elif method == "dijkstra":
+            lengths = nxcg.single_source_dijkstra_path_length(
+                G, source, weight=weight, dtype=dtype
+            )
+        else:  # method == 'bellman-ford':
+            lengths = nxcg.single_source_bellman_ford_path_length(
+                G, source, weight=weight, dtype=dtype
             )
     # From source to target
     elif method == "unweighted":
         G = _to_graph(G)
         lengths = _bfs(G, source, None, "Source", return_type="length", target=target)
-    else:
-        # method == "dijkstra":
-        # method == 'bellman-ford':
+    elif method == "dijkstra":
+        lengths = nxcg.dijkstra_path_length(G, source, target, weight, dtype=dtype)
+    else:  # method == 'bellman-ford':
         lengths = nxcg.bellman_ford_path_length(G, source, target, weight, dtype=dtype)
     return lengths
 
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
index 714289c5b..0e98c366e 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
@@ -61,7 +61,12 @@ def bidirectional_shortest_path(G, source, target):
     # TODO PERF: do bidirectional traversal in core
     G = _to_graph(G)
     if source not in G or target not in G:
-        raise nx.NodeNotFound(f"Either source {source} or target {target} is not in G")
+        if nx.__version__[:3] <= "3.3":
+            raise nx.NodeNotFound(
+                f"Either source {source} or target {target} is not in G"
+            )
+        missing = f"Source {source}" if source not in G else f"Target {target}"
+        raise nx.NodeNotFound(f"{missing} is not in G")
     return _bfs(G, source, None, "Source", return_type="path", target=target)
 
 
@@ -131,7 +136,7 @@ def _bfs(
         # return_type == "length-path"
         return {source: 0}, {source: [source]}
 
-    if cutoff is None:
+    if cutoff is None or np.isinf(cutoff):
         cutoff = -1
     src_index = source if G.key_to_id is None else G.key_to_id[source]
     distances, predecessors, node_ids = plc.bfs(
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py
index 32323dd45..032ef2c7f 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py
@@ -25,6 +25,14 @@
 from .unweighted import _bfs
 
 __all__ = [
+    "dijkstra_path",
+    "dijkstra_path_length",
+    "single_source_dijkstra",
+    "single_source_dijkstra_path",
+    "single_source_dijkstra_path_length",
+    "all_pairs_dijkstra",
+    "all_pairs_dijkstra_path",
+    "all_pairs_dijkstra_path_length",
     "bellman_ford_path",
     "bellman_ford_path_length",
     "single_source_bellman_ford",
@@ -44,14 +52,24 @@ def _add_doc(func):
     return func
 
 
-@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
-@_add_doc
-def bellman_ford_path(G, source, target, weight="weight", *, dtype=None):
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def dijkstra_path(G, source, target, weight="weight", *, dtype=None):
     G = _to_graph(G, weight, 1, np.float32)
     dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     return _sssp(G, source, weight, target, return_type="path", dtype=dtype)
 
 
+@dijkstra_path._can_run
+def _(G, source, target, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def bellman_ford_path(G, source, target, weight="weight", *, dtype=None):
+    return dijkstra_path(G, source, target, weight=weight, dtype=dtype)
+
+
 @bellman_ford_path._can_run
 def _(G, source, target, weight="weight", *, dtype=None):
     return (
@@ -61,14 +79,24 @@ def _(G, source, target, weight="weight", *, dtype=None):
     )
 
 
-@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
-@_add_doc
-def bellman_ford_path_length(G, source, target, weight="weight", *, dtype=None):
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def dijkstra_path_length(G, source, target, weight="weight", *, dtype=None):
     G = _to_graph(G, weight, 1, np.float32)
     dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     return _sssp(G, source, weight, target, return_type="length", dtype=dtype)
 
 
+@dijkstra_path._can_run
+def _(G, source, target, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def bellman_ford_path_length(G, source, target, weight="weight", *, dtype=None):
+    return dijkstra_path_length(G, source, target, weight=weight, dtype=dtype)
+
+
 @bellman_ford_path_length._can_run
 def _(G, source, target, weight="weight", *, dtype=None):
     return (
@@ -78,12 +106,22 @@ def _(G, source, target, weight="weight", *, dtype=None):
     )
 
 
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def single_source_dijkstra_path(G, source, cutoff=None, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, return_type="path", dtype=dtype, cutoff=cutoff)
+
+
+@single_source_dijkstra_path._can_run
+def _(G, source, cutoff=None, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
 @networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
 @_add_doc
 def single_source_bellman_ford_path(G, source, weight="weight", *, dtype=None):
-    G = _to_graph(G, weight, 1, np.float32)
-    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
-    return _sssp(G, source, weight, return_type="path", dtype=dtype)
+    return single_source_dijkstra_path(G, source, weight=weight, dtype=dtype)
 
 
 @single_source_bellman_ford_path._can_run
@@ -95,12 +133,24 @@ def _(G, source, weight="weight", *, dtype=None):
     )
 
 
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def single_source_dijkstra_path_length(
+    G, source, cutoff=None, weight="weight", *, dtype=None
+):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, return_type="length", dtype=dtype, cutoff=cutoff)
+
+
+@single_source_dijkstra_path_length._can_run
+def _(G, source, cutoff=None, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
 @networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
 @_add_doc
 def single_source_bellman_ford_path_length(G, source, weight="weight", *, dtype=None):
-    G = _to_graph(G, weight, 1, np.float32)
-    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
-    return _sssp(G, source, weight, return_type="length", dtype=dtype)
+    return single_source_dijkstra_path_length(G, source, weight=weight, dtype=dtype)
 
 
 @single_source_bellman_ford_path_length._can_run
@@ -112,12 +162,26 @@ def _(G, source, weight="weight", *, dtype=None):
     )
 
 
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def single_source_dijkstra(
+    G, source, target=None, cutoff=None, weight="weight", *, dtype=None
+):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(
+        G, source, weight, target, return_type="length-path", dtype=dtype, cutoff=cutoff
+    )
+
+
+@single_source_dijkstra._can_run
+def _(G, source, target=None, cutoff=None, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
 @networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
 @_add_doc
 def single_source_bellman_ford(G, source, target=None, weight="weight", *, dtype=None):
-    G = _to_graph(G, weight, 1, np.float32)
-    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
-    return _sssp(G, source, weight, target, return_type="length-path", dtype=dtype)
+    return single_source_dijkstra(G, source, target=target, weight=weight, dtype=dtype)
 
 
 @single_source_bellman_ford._can_run
@@ -129,14 +193,41 @@ def _(G, source, target=None, weight="weight", *, dtype=None):
     )
 
 
-@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
-@_add_doc
-def all_pairs_bellman_ford_path_length(G, weight="weight", *, dtype=None):
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def all_pairs_dijkstra(G, cutoff=None, weight="weight", *, dtype=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    for n in G:
+        yield (
+            n,
+            _sssp(G, n, weight, return_type="length-path", dtype=dtype, cutoff=cutoff),
+        )
+
+
+@all_pairs_dijkstra._can_run
+def _(G, cutoff=None, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def all_pairs_dijkstra_path_length(G, cutoff=None, weight="weight", *, dtype=None):
     # TODO PERF: batched bfs to compute many at once
     G = _to_graph(G, weight, 1, np.float32)
     dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     for n in G:
-        yield (n, _sssp(G, n, weight, return_type="length", dtype=dtype))
+        yield (n, _sssp(G, n, weight, return_type="length", dtype=dtype, cutoff=cutoff))
+
+
+@all_pairs_dijkstra_path_length._can_run
+def _(G, cutoff=None, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def all_pairs_bellman_ford_path_length(G, weight="weight", *, dtype=None):
+    return all_pairs_dijkstra_path_length(G, weight=weight, dtype=None)
 
 
 @all_pairs_bellman_ford_path_length._can_run
@@ -148,14 +239,24 @@ def _(G, weight="weight", *, dtype=None):
     )
 
 
-@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
-@_add_doc
-def all_pairs_bellman_ford_path(G, weight="weight", *, dtype=None):
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.08", _plc="sssp")
+def all_pairs_dijkstra_path(G, cutoff=None, weight="weight", *, dtype=None):
     # TODO PERF: batched bfs to compute many at once
     G = _to_graph(G, weight, 1, np.float32)
     dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     for n in G:
-        yield (n, _sssp(G, n, weight, return_type="path", dtype=dtype))
+        yield (n, _sssp(G, n, weight, return_type="path", dtype=dtype, cutoff=cutoff))
+
+
+@all_pairs_dijkstra_path._can_run
+def _(G, cutoff=None, weight="weight", *, dtype=None):
+    return not callable(weight)
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def all_pairs_bellman_ford_path(G, weight="weight", *, dtype=None):
+    return all_pairs_dijkstra_path(G, weight=weight, dtype=None)
 
 
 @all_pairs_bellman_ford_path._can_run
@@ -167,7 +268,17 @@ def _(G, weight="weight", *, dtype=None):
     )
 
 
-def _sssp(G, source, weight, target=None, *, return_type, dtype, reverse_path=False):
+def _sssp(
+    G,
+    source,
+    weight,
+    target=None,
+    *,
+    return_type,
+    dtype,
+    reverse_path=False,
+    cutoff=None,
+):
     """SSSP for weighted shortest paths.
 
     Parameters
@@ -201,7 +312,7 @@ def _sssp(G, source, weight, target=None, *, return_type, dtype, reverse_path=Fa
 
     if weight not in G.edge_values:
         # No edge values, so use BFS instead
-        return _bfs(G, source, None, "Source", return_type=return_type, target=target)
+        return _bfs(G, source, cutoff, "Source", return_type=return_type, target=target)
 
     # Check for negative values since we don't support negative cycles
     edge_vals = G.edge_values[weight]
@@ -217,7 +328,7 @@ def _sssp(G, source, weight, target=None, *, return_type, dtype, reverse_path=Fa
         return _bfs(
             G,
             source,
-            None,
+            None if cutoff is None else cutoff / edge_val,
             "Source",
             return_type=return_type,
             target=target,
@@ -226,11 +337,16 @@ def _sssp(G, source, weight, target=None, *, return_type, dtype, reverse_path=Fa
         )
 
     src_index = source if G.key_to_id is None else G.key_to_id[source]
+    if cutoff is None:
+        cutoff = np.inf
+    else:
+        cutoff = np.nextafter(cutoff, np.inf, dtype=np.float64)
+
     node_ids, distances, predecessors = plc.sssp(
         resource_handle=plc.ResourceHandle(),
         graph=G._get_plc_graph(weight, 1, dtype),
         source=src_index,
-        cutoff=np.inf,
+        cutoff=cutoff,
         compute_predecessors=True,  # TODO: False is not yet supported
         # compute_predecessors=return_type != "length",
         do_expensive_check=False,
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py
index d784d8c13..176b531a6 100644
--- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py
+++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py
@@ -48,7 +48,7 @@ def test_match_signature_and_names():
         orig_sig = inspect.signature(orig_func)
         func_sig = inspect.signature(func)
         if not func.extra_params:
-            assert orig_sig == func_sig
+            assert orig_sig == func_sig, name
         else:
             # Ignore extra parameters added to nx-cugraph algorithm
             # The key of func.extra_params may be like "max_level : int, optional",
@@ -60,14 +60,14 @@ def test_match_signature_and_names():
                     for name, p in func_sig.parameters.items()
                     if name not in extra_params
                 ]
-            )
+            ), name
         if func.can_run is not nxcg.utils.decorators._default_can_run:
-            assert func_sig == inspect.signature(func.can_run)
+            assert func_sig == inspect.signature(func.can_run), name
         if func.should_run is not nxcg.utils.decorators._default_should_run:
-            assert func_sig == inspect.signature(func.should_run)
+            assert func_sig == inspect.signature(func.should_run), name
 
         # Matching function names?
-        assert func.__name__ == dispatchable_func.__name__ == orig_func.__name__
+        assert func.__name__ == dispatchable_func.__name__ == orig_func.__name__, name
 
         # Matching dispatch names?
         # nx version >=3.2 uses name, version >=3.0,<3.2 uses dispatchname
@@ -75,14 +75,14 @@ def test_match_signature_and_names():
             dispatchname = dispatchable_func.dispatchname
         else:
             dispatchname = dispatchable_func.name
-        assert func.name == dispatchname
+        assert func.name == dispatchname, name
 
         # Matching modules (i.e., where function defined)?
         assert (
             "networkx." + func.__module__.split(".", 1)[1]
             == dispatchable_func.__module__
             == orig_func.__module__
-        )
+        ), name
 
         # Matching package layout (i.e., which modules have the function)?
         nxcg_path = func.__module__

From 4dffdd9c9dc54b008dacd79869f56d20e30118cf Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Tue, 30 Jul 2024 15:52:02 +0200
Subject: [PATCH 376/384] Further optimize `from_pandas_edgelist` with cudf
 (#4528)

This continues #4525 (and [this comment](https://github.com/rapidsai/cugraph/pull/4525#discussion_r1669847549)) to avoid copies and to be more optimal whether using pandas, cudf, or cudf.pandas. Notably, using `s.to_numpy` with cudf will return a _numpy_ array, but `cudf.pandas` may return a _cupy_ array (proxy).

Also, `s.to_numpy(copy=False)` ([from comment](https://github.com/rapidsai/cugraph/pull/4525#discussion_r1669020347)) is not used, b/c cudf's `to_numpy` raises if `copy=False`. We get the behavior we want by not specifying `copy=`.

I don't know if this is the best way to determine whether a copy occurred or not, but this seems like a useful pattern to establish, because we want to make ingest more efficient.

CC @rlratzel

Authors:
  - Erik Welch (https://github.com/eriknw)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4528
---
 .../nx-cugraph/nx_cugraph/convert_matrix.py   | 35 ++++++--
 .../nx_cugraph/tests/test_convert_matrix.py   | 86 +++++++++++++++++++
 .../nx-cugraph/nx_cugraph/tests/test_utils.py | 22 ++++-
 python/nx-cugraph/nx_cugraph/utils/misc.py    | 32 +++++++
 4 files changed, 166 insertions(+), 9 deletions(-)
 create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_convert_matrix.py

diff --git a/python/nx-cugraph/nx_cugraph/convert_matrix.py b/python/nx-cugraph/nx_cugraph/convert_matrix.py
index 10793c641..38139b913 100644
--- a/python/nx-cugraph/nx_cugraph/convert_matrix.py
+++ b/python/nx-cugraph/nx_cugraph/convert_matrix.py
@@ -15,7 +15,7 @@
 import numpy as np
 
 from .generators._utils import _create_using_class
-from .utils import index_dtype, networkx_algorithm
+from .utils import _cp_iscopied_asarray, index_dtype, networkx_algorithm
 
 __all__ = [
     "from_pandas_edgelist",
@@ -34,16 +34,30 @@ def from_pandas_edgelist(
     edge_key=None,
 ):
     """cudf.DataFrame inputs also supported; value columns with str is unsuppported."""
+    # This function never shares ownership of the underlying arrays of the DataFrame
+    # columns. We will perform a copy if necessary even if given e.g. a cudf.DataFrame.
     graph_class, inplace = _create_using_class(create_using)
     # Try to be optimal whether using pandas, cudf, or cudf.pandas
-    src_array = df[source].to_numpy()
-    dst_array = df[target].to_numpy()
+    src_series = df[source]
+    dst_series = df[target]
     try:
         # Optimistically try to use cupy, but fall back to numpy if necessary
-        src_array = cp.asarray(src_array)
-        dst_array = cp.asarray(dst_array)
+        src_array = src_series.to_cupy()
+        dst_array = dst_series.to_cupy()
+    except (AttributeError, TypeError, ValueError, NotImplementedError):
+        src_array = src_series.to_numpy()
+        dst_array = dst_series.to_numpy()
+    try:
+        # Minimize unnecessary data copies by tracking whether we copy or not
+        is_src_copied, src_array = _cp_iscopied_asarray(
+            src_array, orig_object=src_series
+        )
+        is_dst_copied, dst_array = _cp_iscopied_asarray(
+            dst_array, orig_object=dst_series
+        )
         np_or_cp = cp
     except ValueError:
+        is_src_copied = is_dst_copied = False
         src_array = np.asarray(src_array)
         dst_array = np.asarray(dst_array)
         np_or_cp = np
@@ -65,8 +79,15 @@ def from_pandas_edgelist(
         src_indices = cp.asarray(np_or_cp.searchsorted(nodes, src_array), index_dtype)
         dst_indices = cp.asarray(np_or_cp.searchsorted(nodes, dst_array), index_dtype)
     else:
-        src_indices = cp.array(src_array)
-        dst_indices = cp.array(dst_array)
+        # Copy if necessary so we don't share ownership of input arrays.
+        if is_src_copied:
+            src_indices = src_array
+        else:
+            src_indices = cp.array(src_array)
+        if is_dst_copied:
+            dst_indices = dst_array
+        else:
+            dst_indices = cp.array(dst_array)
 
     if not graph_class.is_directed():
         # Symmetrize the edges
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_convert_matrix.py b/python/nx-cugraph/nx_cugraph/tests/test_convert_matrix.py
new file mode 100644
index 000000000..0a9cc087c
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/tests/test_convert_matrix.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import networkx as nx
+import pandas as pd
+import pytest
+
+import nx_cugraph as nxcg
+from nx_cugraph.utils import _cp_iscopied_asarray
+
+try:
+    import cudf
+except ModuleNotFoundError:
+    cudf = None
+
+
+DATA = [
+    {"source": [0, 1], "target": [1, 2]},  # nodes are 0, 1, 2
+    {"source": [0, 1], "target": [1, 3]},  # nodes are 0, 1, 3 (need renumbered!)
+    {"source": ["a", "b"], "target": ["b", "c"]},  # nodes are 'a', 'b', 'c'
+]
+CREATE_USING = [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+
+
+@pytest.mark.skipif("not cudf")
+@pytest.mark.parametrize("data", DATA)
+@pytest.mark.parametrize("create_using", CREATE_USING)
+def test_from_cudf_edgelist(data, create_using):
+    df = cudf.DataFrame(data)
+    nxcg.from_pandas_edgelist(df, create_using=create_using)  # Basic smoke test
+    source = df["source"]
+    if source.dtype == int:
+        is_copied, src_array = _cp_iscopied_asarray(source)
+        assert is_copied is False
+        is_copied, src_array = _cp_iscopied_asarray(source.to_cupy())
+        assert is_copied is False
+        is_copied, src_array = _cp_iscopied_asarray(source, orig_object=source)
+        assert is_copied is False
+        is_copied, src_array = _cp_iscopied_asarray(
+            source.to_cupy(), orig_object=source
+        )
+        assert is_copied is False
+        # to numpy
+        is_copied, src_array = _cp_iscopied_asarray(source.to_numpy())
+        assert is_copied is True
+        is_copied, src_array = _cp_iscopied_asarray(
+            source.to_numpy(), orig_object=source
+        )
+        assert is_copied is True
+    else:
+        with pytest.raises(TypeError):
+            _cp_iscopied_asarray(source)
+        with pytest.raises(TypeError):
+            _cp_iscopied_asarray(source.to_cupy())
+        with pytest.raises(ValueError, match="Unsupported dtype"):
+            _cp_iscopied_asarray(source.to_numpy())
+        with pytest.raises(ValueError, match="Unsupported dtype"):
+            _cp_iscopied_asarray(source.to_numpy(), orig_object=source)
+
+
+@pytest.mark.parametrize("data", DATA)
+@pytest.mark.parametrize("create_using", CREATE_USING)
+def test_from_pandas_edgelist(data, create_using):
+    df = pd.DataFrame(data)
+    nxcg.from_pandas_edgelist(df, create_using=create_using)  # Basic smoke test
+    source = df["source"]
+    if source.dtype == int:
+        is_copied, src_array = _cp_iscopied_asarray(source)
+        assert is_copied is True
+        is_copied, src_array = _cp_iscopied_asarray(source, orig_object=source)
+        assert is_copied is True
+        is_copied, src_array = _cp_iscopied_asarray(source.to_numpy())
+        assert is_copied is True
+        is_copied, src_array = _cp_iscopied_asarray(
+            source.to_numpy(), orig_object=source
+        )
+        assert is_copied is True
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_utils.py b/python/nx-cugraph/nx_cugraph/tests/test_utils.py
index fdd0c9199..d38a286fa 100644
--- a/python/nx-cugraph/nx_cugraph/tests/test_utils.py
+++ b/python/nx-cugraph/nx_cugraph/tests/test_utils.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -10,10 +10,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import cupy as cp
 import numpy as np
 import pytest
 
-from nx_cugraph.utils import _get_int_dtype
+from nx_cugraph.utils import _cp_iscopied_asarray, _get_int_dtype
 
 
 def test_get_int_dtype():
@@ -85,3 +86,20 @@ def test_get_int_dtype():
         _get_int_dtype(7, signed=True, unsigned=True)
     assert _get_int_dtype(7, signed=True, unsigned=False) == np.int8
     assert _get_int_dtype(7, signed=False, unsigned=True) == np.uint8
+
+
+def test_cp_iscopied_asarray():
+    # We don't yet run doctest, so do simple copy/paste test here.
+    #
+    # >>> is_copied, a = _cp_iscopied_asarray([1, 2, 3])
+    # >>> is_copied
+    # True
+    # >>> a
+    # array([1, 2, 3])
+    # >>> _cp_iscopied_asarray(a)
+    # (False, array([1, 2, 3]))
+    is_copied, a = _cp_iscopied_asarray([1, 2, 3])
+    assert is_copied is True
+    assert isinstance(a, cp.ndarray)
+    assert repr(a) == "array([1, 2, 3])"
+    assert _cp_iscopied_asarray(a)[0] is False
diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py
index eab4b42c2..8526524f1 100644
--- a/python/nx-cugraph/nx_cugraph/utils/misc.py
+++ b/python/nx-cugraph/nx_cugraph/utils/misc.py
@@ -45,6 +45,7 @@ def pairwise(it):
     "_get_int_dtype",
     "_get_float_dtype",
     "_dtype_param",
+    "_cp_iscopied_asarray",
 ]
 
 # This may switch to np.uint32 at some point
@@ -206,3 +207,34 @@ def _get_float_dtype(
             f"Dtype {dtype} cannot be safely promoted to float32 or float64"
         )
     return rv
+
+
+def _cp_iscopied_asarray(a, *args, orig_object=None, **kwargs):
+    """Like ``cp.asarray``, but also returns whether the input was copied.
+
+    Use this to avoid unnecessary copies. If given, ``orig_object`` will
+    also be inspected to determine if it was copied.
+
+    >>> is_copied, a = _cp_iscopied_asarray([1, 2, 3])
+    >>> is_copied
+    True
+    >>> a
+    array([1, 2, 3])
+    >>> _cp_iscopied_asarray(a)
+    (False, array([1, 2, 3]))
+    """
+    arr = cp.asarray(a, *args, **kwargs)
+    ptr = arr.__cuda_array_interface__["data"][0]
+    if (
+        hasattr(a, "__cuda_array_interface__")
+        and a.__cuda_array_interface__["data"][0] == ptr
+        and (
+            orig_object is None
+            or hasattr(orig_object, "__cuda_array_interface__")
+            and orig_object.__cuda_array_interface__["data"][0] == ptr
+        )
+        # Should we also check device_id?
+        # and getattr(getattr(a, "data", None), "device_id", None) == arr.data.device_id
+    ):
+        return False, arr
+    return True, arr

From c28474041d9561ca1bc20dcb0c29e6a617d54174 Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Tue, 30 Jul 2024 20:42:58 +0200
Subject: [PATCH 377/384] nx-cugraph: add `relabel_nodes` and
 `convert_node_labels_to_integers` (#4531)

This was pretty tricky in places! This begins with `Graph` and `DiGraph`, which are probably tricker, because of the way edge data get merged when nodes are combined (and I wouldn't be surprised if there are other ways to do this operation).

This is one of the most heavily used functions in networkx and also networkx dependents.

Up next: multigraphs

Authors:
  - Erik Welch (https://github.com/eriknw)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cugraph/pull/4531
---
 python/nx-cugraph/.flake8                     |   3 +-
 python/nx-cugraph/README.md                   |   3 +
 python/nx-cugraph/_nx_cugraph/__init__.py     |   2 +
 python/nx-cugraph/lint.yaml                   |   6 +-
 python/nx-cugraph/nx_cugraph/__init__.py      |   3 +
 .../nx_cugraph/algorithms/operators/unary.py  |   5 +
 .../traversal/breadth_first_search.py         |  52 ++--
 python/nx-cugraph/nx_cugraph/convert.py       |  42 ++-
 python/nx-cugraph/nx_cugraph/interface.py     |  95 ++++++
 python/nx-cugraph/nx_cugraph/relabel.py       | 282 ++++++++++++++++++
 .../nx_cugraph/tests/test_relabel.py          |  63 ++++
 .../nx_cugraph/tests/testing_utils.py         |   8 +-
 python/nx-cugraph/pyproject.toml              |   3 +
 13 files changed, 536 insertions(+), 31 deletions(-)
 create mode 100644 python/nx-cugraph/nx_cugraph/relabel.py
 create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_relabel.py

diff --git a/python/nx-cugraph/.flake8 b/python/nx-cugraph/.flake8
index c5874e54f..cdda8d108 100644
--- a/python/nx-cugraph/.flake8
+++ b/python/nx-cugraph/.flake8
@@ -1,9 +1,10 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 
 [flake8]
 max-line-length = 88
 inline-quotes = "
 extend-ignore =
+    B020,
     E203,
     SIM105,
     SIM401,
diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md
index 8a1824a7a..458421e2b 100644
--- a/python/nx-cugraph/README.md
+++ b/python/nx-cugraph/README.md
@@ -267,6 +267,9 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
 convert_matrix
  ├─ from_pandas_edgelist
  └─ from_scipy_sparse_array
+relabel
+ ├─ convert_node_labels_to_integers
+ └─ relabel_nodes
 

 
 To request nx-cugraph backend support for a NetworkX API that is not listed
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index 2d6017fa2..d18fc53b8 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -69,6 +69,7 @@
         "complete_graph",
         "complete_multipartite_graph",
         "connected_components",
+        "convert_node_labels_to_integers",
         "core_number",
         "cubical_graph",
         "cycle_graph",
@@ -130,6 +131,7 @@
         "path_graph",
         "petersen_graph",
         "reciprocity",
+        "relabel_nodes",
         "reverse",
         "sedgewick_maze_graph",
         "shortest_path",
diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml
index 317d5b8d4..ce46360e2 100644
--- a/python/nx-cugraph/lint.yaml
+++ b/python/nx-cugraph/lint.yaml
@@ -50,7 +50,7 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.1
+    rev: v0.5.4
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
@@ -58,7 +58,7 @@ repos:
     rev: 7.1.0
     hooks:
       - id: flake8
-        args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=SIM105']  # Why is this necessary?
+        args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=B020,SIM105']  # Why is this necessary?
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
           - flake8==7.1.0
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.1
+    rev: v0.5.4
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks
diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py
index 2c54da878..7d6a2fbe4 100644
--- a/python/nx-cugraph/nx_cugraph/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/__init__.py
@@ -23,6 +23,9 @@
 from . import convert_matrix
 from .convert_matrix import *
 
+from . import relabel
+from .relabel import *
+
 from . import generators
 from .generators import *
 
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py b/python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py
index 08abc9f28..f53b34589 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py
@@ -51,5 +51,10 @@ def reverse(G, copy=True):
     if not G.is_directed():
         raise nx.NetworkXError("Cannot reverse an undirected graph.")
     if isinstance(G, nx.Graph):
+        if not copy:
+            raise RuntimeError(
+                "Using `copy=False` is invalid when using a NetworkX graph "
+                "as input to `nx_cugraph.reverse`"
+            )
         G = nxcg.from_networkx(G, preserve_all_attrs=True)
     return G.reverse(copy=copy)
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py
index f5d5e2a99..5e4466d7d 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py
@@ -57,23 +57,41 @@ def _bfs(G, source, *, depth_limit=None, reverse=False):
     return distances[mask], predecessors[mask], node_ids[mask]
 
 
-@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
-def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
-    """`neighbors` and `sort_neighbors` parameters are not yet supported."""
-    if neighbors is not None:
-        raise NotImplementedError(
-            "neighbors argument in generic_bfs_edges is not currently supported"
-        )
-    if sort_neighbors is not None:
-        raise NotImplementedError(
-            "sort_neighbors argument in generic_bfs_edges is not currently supported"
-        )
-    return bfs_edges(G, source, depth_limit=depth_limit)
-
-
-@generic_bfs_edges._can_run
-def _(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
-    return neighbors is None and sort_neighbors is None
+if nx.__version__[:3] <= "3.3":
+
+    @networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
+    def generic_bfs_edges(
+        G, source, neighbors=None, depth_limit=None, sort_neighbors=None
+    ):
+        """`neighbors` and `sort_neighbors` parameters are not yet supported."""
+        if neighbors is not None:
+            raise NotImplementedError(
+                "neighbors argument in generic_bfs_edges is not currently supported"
+            )
+        if sort_neighbors is not None:
+            raise NotImplementedError(
+                "sort_neighbors argument in generic_bfs_edges is not supported"
+            )
+        return bfs_edges(G, source, depth_limit=depth_limit)
+
+    @generic_bfs_edges._can_run
+    def _(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
+        return neighbors is None and sort_neighbors is None
+
+else:
+
+    @networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
+    def generic_bfs_edges(G, source, neighbors=None, depth_limit=None):
+        """`neighbors` parameter is not yet supported."""
+        if neighbors is not None:
+            raise NotImplementedError(
+                "neighbors argument in generic_bfs_edges is not currently supported"
+            )
+        return bfs_edges(G, source, depth_limit=depth_limit)
+
+    @generic_bfs_edges._can_run
+    def _(G, source, neighbors=None, depth_limit=None):
+        return neighbors is None
 
 
 @networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py
index 9e6c080d6..56d16d837 100644
--- a/python/nx-cugraph/nx_cugraph/convert.py
+++ b/python/nx-cugraph/nx_cugraph/convert.py
@@ -411,13 +411,21 @@ def func(it, edge_attr=edge_attr, dtype=dtype):
                 # Node values may be numpy or cupy arrays (useful for str, object, etc).
                 # Someday we'll let the user choose np or cp, and support edge values.
                 node_mask = np.fromiter(iter_mask, bool)
-                node_value = np.array(vals, dtype)
                 try:
-                    node_value = cp.array(node_value)
+                    node_value = np.array(vals, dtype)
                 except ValueError:
-                    pass
+                    # Handle e.g. list elements
+                    if dtype is None or dtype == object:
+                        node_value = np.fromiter(vals, object)
+                    else:
+                        raise
                 else:
-                    node_mask = cp.array(node_mask)
+                    try:
+                        node_value = cp.array(node_value)
+                    except ValueError:
+                        pass
+                    else:
+                        node_mask = cp.array(node_mask)
                 node_values[node_attr] = node_value
                 node_masks[node_attr] = node_mask
                 # if vals.ndim > 1: ...
@@ -431,7 +439,12 @@ def func(it, edge_attr=edge_attr, dtype=dtype):
                 # Node values may be numpy or cupy arrays (useful for str, object, etc).
                 # Someday we'll let the user choose np or cp, and support edge values.
                 if dtype is None:
-                    node_value = np.array(list(iter_values))
+                    vals = list(iter_values)
+                    try:
+                        node_value = np.array(vals)
+                    except ValueError:
+                        # Handle e.g. list elements
+                        node_value = np.fromiter(vals, object)
                 else:
                     node_value = np.fromiter(iter_values, dtype)
                 try:
@@ -477,6 +490,23 @@ def func(it, edge_attr=edge_attr, dtype=dtype):
     return rv
 
 
+def _to_tuples(ndim, L):
+    if ndim > 2:
+        L = list(map(_to_tuples.__get__(ndim - 1), L))
+    return list(map(tuple, L))
+
+
+def _array_to_tuples(a):
+    """Like ``a.tolist()``, but nested structures are tuples instead of lists.
+
+    This is only different from ``a.tolist()`` if ``a.ndim > 1``. It is used to
+    try to return tuples instead of lists for e.g. node values.
+    """
+    if a.ndim > 1:
+        return _to_tuples(a.ndim, a.tolist())
+    return a.tolist()
+
+
 def _iter_attr_dicts(
     values: dict[AttrKey, any_ndarray[EdgeValue | NodeValue]],
     masks: dict[AttrKey, any_ndarray[bool]],
@@ -485,7 +515,7 @@ def _iter_attr_dicts(
     if full_attrs:
         full_dicts = (
             dict(zip(full_attrs, vals))
-            for vals in zip(*(values[attr].tolist() for attr in full_attrs))
+            for vals in zip(*(_array_to_tuples(values[attr]) for attr in full_attrs))
         )
     partial_attrs = list(values.keys() & masks.keys())
     if partial_attrs:
diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py
index 8569bbf40..4007230ef 100644
--- a/python/nx-cugraph/nx_cugraph/interface.py
+++ b/python/nx-cugraph/nx_cugraph/interface.py
@@ -68,6 +68,13 @@ def key(testpath):
         louvain_different = "Louvain may be different due to RNG"
         no_string_dtype = "string edge values not currently supported"
         sssp_path_different = "sssp may choose a different valid path"
+        no_object_dtype_for_edges = (
+            "Edges don't support object dtype (lists, strings, etc.)"
+        )
+        tuple_elements_preferred = "elements are tuples instead of lists"
+        nx_cugraph_in_test_setup = (
+            "nx-cugraph Graph is incompatible in test setup in nx versions < 3.3"
+        )
 
         xfail = {
             # This is removed while strongly_connected_components() is not
@@ -91,6 +98,81 @@ def key(testpath):
                 "test_cycles.py:TestMinimumCycleBasis."
                 "test_gh6787_and_edge_attribute_names"
             ): sssp_path_different,
+            key(
+                "test_graph_hashing.py:test_isomorphic_edge_attr"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_graph_hashing.py:test_isomorphic_edge_attr_and_node_attr"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_graph_hashing.py:test_isomorphic_edge_attr_subgraph_hash"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_graph_hashing.py:"
+                "test_isomorphic_edge_attr_and_node_attr_subgraph_hash"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_summarization.py:TestSNAPNoEdgeTypes.test_summary_graph"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_summarization.py:TestSNAPUndirected.test_summary_graph"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_summarization.py:TestSNAPDirected.test_summary_graph"
+            ): no_object_dtype_for_edges,
+            key("test_gexf.py:TestGEXF.test_relabel"): no_object_dtype_for_edges,
+            key(
+                "test_gml.py:TestGraph.test_parse_gml_cytoscape_bug"
+            ): no_object_dtype_for_edges,
+            key("test_gml.py:TestGraph.test_parse_gml"): no_object_dtype_for_edges,
+            key("test_gml.py:TestGraph.test_read_gml"): no_object_dtype_for_edges,
+            key("test_gml.py:TestGraph.test_data_types"): no_object_dtype_for_edges,
+            key(
+                "test_gml.py:TestPropertyLists.test_reading_graph_with_list_property"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_relabel.py:"
+                "test_relabel_preserve_node_order_partial_mapping_with_copy_false"
+            ): "Node order is preserved when relabeling with partial mapping",
+            key(
+                "test_gml.py:"
+                "TestPropertyLists.test_reading_graph_with_single_element_list_property"
+            ): tuple_elements_preferred,
+            key(
+                "test_relabel.py:"
+                "TestRelabel.test_relabel_multidigraph_inout_merge_nodes"
+            ): no_string_dtype,
+            key(
+                "test_relabel.py:TestRelabel.test_relabel_multigraph_merge_inplace"
+            ): no_string_dtype,
+            key(
+                "test_relabel.py:TestRelabel.test_relabel_multidigraph_merge_inplace"
+            ): no_string_dtype,
+            key(
+                "test_relabel.py:TestRelabel.test_relabel_multidigraph_inout_copy"
+            ): no_string_dtype,
+            key(
+                "test_relabel.py:TestRelabel.test_relabel_multigraph_merge_copy"
+            ): no_string_dtype,
+            key(
+                "test_relabel.py:TestRelabel.test_relabel_multidigraph_merge_copy"
+            ): no_string_dtype,
+            key(
+                "test_relabel.py:TestRelabel.test_relabel_multigraph_nonnumeric_key"
+            ): no_string_dtype,
+            key("test_contraction.py:test_multigraph_path"): no_object_dtype_for_edges,
+            key(
+                "test_contraction.py:test_directed_multigraph_path"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_contraction.py:test_multigraph_blockmodel"
+            ): no_object_dtype_for_edges,
+            key(
+                "test_summarization.py:TestSNAPUndirectedMulti.test_summary_graph"
+            ): no_string_dtype,
+            key(
+                "test_summarization.py:TestSNAPDirectedMulti.test_summary_graph"
+            ): no_string_dtype,
         }
 
         from packaging.version import parse
@@ -118,6 +200,19 @@ def key(testpath):
                         "test_strongly_connected.py:"
                         "TestStronglyConnected.test_connected_raise"
                     ): "test is incompatible with pytest>=8",
+                    # NetworkX 3.3 introduced logic around functions that return graphs
+                    key(
+                        "test_vf2pp_helpers.py:TestGraphTinoutUpdating.test_updating"
+                    ): nx_cugraph_in_test_setup,
+                    key(
+                        "test_vf2pp_helpers.py:TestGraphTinoutUpdating.test_restoring"
+                    ): nx_cugraph_in_test_setup,
+                    key(
+                        "test_vf2pp_helpers.py:TestDiGraphTinoutUpdating.test_updating"
+                    ): nx_cugraph_in_test_setup,
+                    key(
+                        "test_vf2pp_helpers.py:TestDiGraphTinoutUpdating.test_restoring"
+                    ): nx_cugraph_in_test_setup,
                 }
             )
 
diff --git a/python/nx-cugraph/nx_cugraph/relabel.py b/python/nx-cugraph/nx_cugraph/relabel.py
new file mode 100644
index 000000000..20d1337a9
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/relabel.py
@@ -0,0 +1,282 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import itertools
+from collections import defaultdict
+
+import cupy as cp
+import networkx as nx
+import numpy as np
+
+import nx_cugraph as nxcg
+
+from .utils import _get_int_dtype, _groupby, index_dtype, networkx_algorithm
+
+__all__ = [
+    "convert_node_labels_to_integers",
+    "relabel_nodes",
+]
+
+
+@networkx_algorithm(version_added="24.08")
+def relabel_nodes(G, mapping, copy=True):
+    if isinstance(G, nx.Graph):
+        if not copy:
+            raise RuntimeError(
+                "Using `copy=False` is invalid when using a NetworkX graph "
+                "as input to `nx_cugraph.relabel_nodes`"
+            )
+        G = nxcg.from_networkx(G, preserve_all_attrs=True)
+    it = range(G._N) if G.key_to_id is None else G.id_to_key
+    if callable(mapping):
+        previd_to_key = [mapping(node) for node in it]
+    else:
+        previd_to_key = [mapping.get(node, node) for node in it]
+    if not copy:
+        # Our implementation does not need to raise here, but do so to match networkx.
+        it = range(G._N) if G.key_to_id is None else G.id_to_key
+        D = nx.DiGraph([(x, y) for x, y in zip(it, previd_to_key) if x != y])
+        if nx.algorithms.dag.has_cycle(D):
+            raise nx.NetworkXUnfeasible(
+                "The node label sets are overlapping and no ordering can "
+                "resolve the mapping. Use copy=True."
+            )
+    key_to_previd = {val: i for i, val in enumerate(previd_to_key)}
+    newid_to_key = list(key_to_previd)
+    key_to_newid = dict(zip(newid_to_key, range(len(newid_to_key))))
+
+    src_indices = G.src_indices
+    dst_indices = G.dst_indices
+    edge_values = G.edge_values
+    edge_masks = G.edge_masks
+    node_values = G.node_values
+    node_masks = G.node_masks
+    if G.is_multigraph():
+        edge_indices = G.edge_indices
+        edge_keys = G.edge_keys
+    if len(key_to_previd) != G._N:
+        # Some nodes were combined.
+        # Node data doesn't get merged, so use the data from the last shared index
+        int_dtype = _get_int_dtype(G._N - 1)
+        node_indices = cp.fromiter(key_to_previd.values(), int_dtype)
+        node_indices_np = node_indices.get()  # Node data may be cupy or numpy arrays
+        node_values = {key: val[node_indices_np] for key, val in node_values.items()}
+        node_masks = {key: val[node_indices_np] for key, val in node_masks.items()}
+
+        # Renumber, but will have duplicates
+        translations = cp.fromiter(
+            (key_to_newid[key] for key in previd_to_key), index_dtype
+        )
+        src_indices_dup = translations[src_indices]
+        dst_indices_dup = translations[dst_indices]
+
+        if G.is_multigraph():
+            # No merging necessary for multigraphs.
+            if G.is_directed():
+                src_indices = src_indices_dup
+                dst_indices = dst_indices_dup
+            else:
+                # New self-edges should have one edge entry, not two
+                mask = (
+                    # Not self-edges, no need to deduplicate
+                    (src_indices_dup != dst_indices_dup)
+                    # == : already self-edges; no need to deduplicate
+                    # < : if new self-edges, keep where src < dst
+                    | (src_indices <= dst_indices)
+                )
+                if mask.all():
+                    src_indices = src_indices_dup
+                    dst_indices = dst_indices_dup
+                else:
+                    src_indices = src_indices_dup[mask]
+                    dst_indices = dst_indices_dup[mask]
+                    if edge_values:
+                        edge_values = {
+                            key: val[mask] for key, val in edge_values.items()
+                        }
+                        edge_masks = {key: val[mask] for key, val in edge_masks.items()}
+                    if edge_keys is not None:
+                        edge_keys = [
+                            key for keep, key in zip(mask.tolist(), edge_keys) if keep
+                        ]
+                    if edge_indices is not None:
+                        edge_indices = edge_indices[mask]
+            # Handling of `edge_keys` and `edge_indices` is pure Python to match nx.
+            # This may be slower than we'd like; if it's way too slow, should we
+            # direct users to use the defaults of None?
+            if edge_keys is not None:
+                seen = set()
+                new_edge_keys = []
+                for key in zip(src_indices.tolist(), dst_indices.tolist(), edge_keys):
+                    if key in seen:
+                        src, dst, edge_key = key
+                        if not isinstance(edge_key, (int, float)):
+                            edge_key = 0
+                        for edge_key in itertools.count(edge_key):
+                            if (src, dst, edge_key) not in seen:
+                                seen.add((src, dst, edge_key))
+                                break
+                    else:
+                        seen.add(key)
+                        edge_key = key[2]
+                    new_edge_keys.append(edge_key)
+                edge_keys = new_edge_keys
+            if edge_indices is not None:
+                # PERF: can we do this using cupy?
+                seen = set()
+                new_edge_indices = []
+                for key in zip(
+                    src_indices.tolist(), dst_indices.tolist(), edge_indices.tolist()
+                ):
+                    if key in seen:
+                        src, dst, edge_index = key
+                        for edge_index in itertools.count(edge_index):
+                            if (src, dst, edge_index) not in seen:
+                                seen.add((src, dst, edge_index))
+                                break
+                    else:
+                        seen.add(key)
+                        edge_index = key[2]
+                    new_edge_indices.append(edge_index)
+                edge_indices = cp.array(new_edge_indices, index_dtype)
+        else:
+            stacked_dup = cp.vstack((src_indices_dup, dst_indices_dup))
+            if not edge_values:
+                # Drop duplicates
+                stacked = cp.unique(stacked_dup, axis=1)
+            else:
+                # Drop duplicates. This relies heavily on `_groupby`.
+                # It has not been compared to alternative implementations.
+                # I wonder if there are ways to use assignment using duplicate indices.
+                (stacked, ind, inv) = cp.unique(
+                    stacked_dup, axis=1, return_index=True, return_inverse=True
+                )
+                if ind.dtype != int_dtype:
+                    ind = ind.astype(int_dtype)
+                if inv.dtype != int_dtype:
+                    inv = inv.astype(int_dtype)
+
+                # We need to merge edge data
+                mask = cp.ones(src_indices.size, dtype=bool)
+                mask[ind] = False
+                edge_data = [val[mask] for val in edge_values.values()]
+                edge_data.extend(val[mask] for val in edge_masks.values())
+                groups = _groupby(inv[mask], edge_data)
+
+                edge_values = {key: val[ind] for key, val in edge_values.items()}
+                edge_masks = {key: val[ind] for key, val in edge_masks.items()}
+
+                value_keys = list(edge_values.keys())
+                mask_keys = list(edge_masks.keys())
+
+                values_to_update = defaultdict(list)
+                masks_to_update = defaultdict(list)
+                for k, v in groups.items():
+                    it = iter(v)
+                    vals = dict(zip(value_keys, it))  # zip(strict=False)
+                    masks = dict(zip(mask_keys, it))  # zip(strict=True)
+                    for key, val in vals.items():
+                        if key in masks:
+                            val = val[masks[key]]
+                            if val.size > 0:
+                                values_to_update[key].append((k, val[-1]))
+                                masks_to_update[key].append((k, True))
+                        else:
+                            values_to_update[key].append((k, val[-1]))
+                            if key in edge_masks:
+                                masks_to_update[key].append((k, True))
+
+                int_dtype = _get_int_dtype(src_indices.size - 1)
+                for k, v in values_to_update.items():
+                    ii, jj = zip(*v)
+                    edge_val = edge_values[k]
+                    edge_val[cp.array(ii, dtype=int_dtype)] = cp.array(
+                        jj, dtype=edge_val.dtype
+                    )
+                for k, v in masks_to_update.items():
+                    ii, jj = zip(*v)
+                    edge_masks[k][cp.array(ii, dtype=int_dtype)] = cp.array(
+                        jj, dtype=bool
+                    )
+            src_indices = stacked[0]
+            dst_indices = stacked[1]
+
+    if G.is_multigraph():
+        # `edge_keys` and `edge_indices` are preserved for free if no nodes were merged
+        extra_kwargs = {"edge_keys": edge_keys, "edge_indices": edge_indices}
+    else:
+        extra_kwargs = {}
+    rv = G.__class__.from_coo(
+        len(key_to_previd),
+        src_indices,
+        dst_indices,
+        edge_values=edge_values,
+        edge_masks=edge_masks,
+        node_values=node_values,
+        node_masks=node_masks,
+        id_to_key=newid_to_key,
+        key_to_id=key_to_newid,
+        **extra_kwargs,
+    )
+    rv.graph.update(G.graph)
+    if not copy:
+        G._become(rv)
+        return G
+    return rv
+
+
+@networkx_algorithm(version_added="24.08")
+def convert_node_labels_to_integers(
+    G, first_label=0, ordering="default", label_attribute=None
+):
+    if ordering not in {"default", "sorted", "increasing degree", "decreasing degree"}:
+        raise nx.NetworkXError(f"Unknown node ordering: {ordering}")
+    if isinstance(G, nx.Graph):
+        G = nxcg.from_networkx(G, preserve_all_attrs=True)
+    G = G.copy()
+    if label_attribute is not None:
+        prev_vals = G.id_to_key
+        if prev_vals is None:
+            prev_vals = cp.arange(G._N, dtype=_get_int_dtype(G._N - 1))
+        else:
+            try:
+                prev_vals = np.array(prev_vals)
+            except ValueError:
+                prev_vals = np.fromiter(prev_vals, object)
+            else:
+                try:
+                    prev_vals = cp.array(prev_vals)
+                except ValueError:
+                    pass
+        G.node_values[label_attribute] = prev_vals
+        G.node_masks.pop(label_attribute, None)
+    id_to_key = None
+    if ordering == "default" or ordering == "sorted" and G.key_to_id is None:
+        if first_label == 0:
+            G.key_to_id = None
+        else:
+            id_to_key = list(range(first_label, first_label + G._N))
+            G.key_to_id = dict(zip(id_to_key, range(G._N)))
+    elif ordering == "sorted":
+        key_to_id = G.key_to_id
+        G.key_to_id = {
+            i: key_to_id[n] for i, n in enumerate(sorted(key_to_id), first_label)
+        }
+    else:
+        pairs = sorted(
+            ((d, n) for (n, d) in G._nodearray_to_dict(G._degrees_array()).items()),
+            reverse=ordering == "decreasing degree",
+        )
+        key_to_id = G.key_to_id
+        G.key_to_id = {i: key_to_id[n] for i, (d, n) in enumerate(pairs, first_label)}
+    G._id_to_key = id_to_key
+    return G
diff --git a/python/nx-cugraph/nx_cugraph/tests/test_relabel.py b/python/nx-cugraph/nx_cugraph/tests/test_relabel.py
new file mode 100644
index 000000000..40bf851d3
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/tests/test_relabel.py
@@ -0,0 +1,63 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import networkx as nx
+import pytest
+
+import nx_cugraph as nxcg
+
+from .testing_utils import assert_graphs_equal
+
+
+@pytest.mark.parametrize(
+    "create_using", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_relabel(create_using):
+    G = nx.complete_graph(3, create_using=create_using)
+    Hnx = nx.relabel_nodes(G, {2: 1})
+    Hcg = nxcg.relabel_nodes(G, {2: 1})
+    assert_graphs_equal(Hnx, Hcg)
+
+    G.add_edge(0, 2, a=11)
+    G.add_edge(1, 2, b=22)
+    Hnx = nx.relabel_nodes(G, {2: 10, 1: 10})
+    Hcg = nxcg.relabel_nodes(G, {2: 10, 1: 10})
+    assert_graphs_equal(Hnx, Hcg)
+
+    G = nx.path_graph(3, create_using=create_using)
+    Hnx = nx.relabel_nodes(G, {2: 0})
+    Hcg = nxcg.relabel_nodes(G, {2: 0})
+    assert_graphs_equal(Hnx, Hcg)
+
+
+@pytest.mark.parametrize("create_using", [nx.MultiGraph, nx.MultiDiGraph])
+def test_relabel_multigraph(create_using):
+    G = nx.empty_graph(create_using=create_using)
+    G.add_edge(0, 1, "x", a=11)
+    G.add_edge(0, 2, "y", a=10, b=6)
+    G.add_edge(0, 0, c=7)
+    G.add_edge(0, 0, "x", a=-1, b=-1, c=-1)
+    Hnx = nx.relabel_nodes(G, {0: 1, 2: 1})
+    Hcg = nxcg.relabel_nodes(G, {0: 1, 2: 1})
+    assert_graphs_equal(Hnx, Hcg)
+    Hnx = nx.relabel_nodes(G, {2: 3, 1: 3, 0: 3})
+    Hcg = nxcg.relabel_nodes(G, {2: 3, 1: 3, 0: 3})
+    assert_graphs_equal(Hnx, Hcg)
+
+
+def test_relabel_nx_input():
+    G = nx.complete_graph(3)
+    with pytest.raises(RuntimeError, match="Using `copy=False` is invalid"):
+        nxcg.relabel_nodes(G, {0: 1}, copy=False)
+    Hnx = nx.relabel_nodes(G, {0: 1}, copy=True)
+    Hcg = nxcg.relabel_nodes(G, {0: 1}, copy=True)
+    assert_graphs_equal(Hnx, Hcg)
diff --git a/python/nx-cugraph/nx_cugraph/tests/testing_utils.py b/python/nx-cugraph/nx_cugraph/tests/testing_utils.py
index 6d4741c9c..529a96efd 100644
--- a/python/nx-cugraph/nx_cugraph/tests/testing_utils.py
+++ b/python/nx-cugraph/nx_cugraph/tests/testing_utils.py
@@ -18,10 +18,10 @@
 def assert_graphs_equal(Gnx, Gcg):
     assert isinstance(Gnx, nx.Graph)
     assert isinstance(Gcg, nxcg.Graph)
-    assert Gnx.number_of_nodes() == Gcg.number_of_nodes()
-    assert Gnx.number_of_edges() == Gcg.number_of_edges()
-    assert Gnx.is_directed() == Gcg.is_directed()
-    assert Gnx.is_multigraph() == Gcg.is_multigraph()
+    assert (a := Gnx.number_of_nodes()) == (b := Gcg.number_of_nodes()), (a, b)
+    assert (a := Gnx.number_of_edges()) == (b := Gcg.number_of_edges()), (a, b)
+    assert (a := Gnx.is_directed()) == (b := Gcg.is_directed()), (a, b)
+    assert (a := Gnx.is_multigraph()) == (b := Gcg.is_multigraph()), (a, b)
     G = nxcg.to_networkx(Gcg)
     rv = nx.utils.graphs_equal(G, Gnx)
     if not rv:
diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml
index 07e09201c..881e5aa4d 100644
--- a/python/nx-cugraph/pyproject.toml
+++ b/python/nx-cugraph/pyproject.toml
@@ -181,6 +181,7 @@ ignore = [
     # "SIM300",  # Yoda conditions are discouraged, use ... instead (Note: we're not this picky)
     # "SIM401",  # Use dict.get ... instead of if-else-block (Note: if-else better for coverage and sometimes clearer)
     # "TRY004",  # Prefer `TypeError` exception for invalid type (Note: good advice, but not worth the nuisance)
+    "B020",  # Found for loop that reassigns the iterable it is iterating with each iterable value (too strict)
     "B904",  # Bare `raise` inside exception clause (like TRY200; sometimes okay)
     "S310",  # Audit URL open for permitted schemes (Note: we don't download URLs in normal usage)
 
@@ -207,6 +208,7 @@ ignore = [
     "RET502",  # Do not implicitly `return None` in function able to return non-`None` value
     "RET503",  # Missing explicit `return` at the end of function able to return non-`None` value
     "RET504",  # Unnecessary variable assignment before `return` statement
+    "RUF018",  # Avoid assignment expressions in `assert` statements
     "S110",  # `try`-`except`-`pass` detected, consider logging the exception (Note: good advice, but we don't log)
     "S112",  # `try`-`except`-`continue` detected, consider logging the exception (Note: good advice, but we don't log)
     "SIM102",  # Use a single `if` statement instead of nested `if` statements (Note: often necessary)
@@ -242,6 +244,7 @@ ignore = [
 "nx_cugraph/algorithms/**/*py" = ["D205", "D401"]  # Allow flexible docstrings for algorithms
 "nx_cugraph/generators/**/*py" = ["D205", "D401"]  # Allow flexible docstrings for generators
 "nx_cugraph/interface.py" = ["D401"]  # Flexible docstrings
+"nx_cugraph/convert.py" = ["E721"]  # Allow `dtype == object`
 "scripts/update_readme.py" = ["INP001"]  # Not part of a package
 
 [tool.ruff.lint.flake8-annotations]

From bfaf50cf6122a4aa39233d76f23e7576cdb06a5e Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Wed, 31 Jul 2024 07:21:43 +0200
Subject: [PATCH 378/384] nx-cugraph: add `G.__networkx_cache__` to enable
 graph conversion caching (#4567)

Adding this now, because this was an oversight.

By having a `G.__networkx_cache__` MutableMapping, we give NetworkX the ability to cache graph conversions. This will be most useful in dev and future versions of NetworkX. As such, it would be nice (but not strictly essential) to get this in 24.08.

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4567
---
 python/nx-cugraph/nx_cugraph/classes/graph.py | 26 +++++++++++++++++++
 .../nx_cugraph/classes/multigraph.py          |  9 +++++++
 2 files changed, 35 insertions(+)

diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py
index ad7cf3191..7425eacb2 100644
--- a/python/nx-cugraph/nx_cugraph/classes/graph.py
+++ b/python/nx-cugraph/nx_cugraph/classes/graph.py
@@ -50,6 +50,10 @@ class Graph:
     __networkx_backend__: ClassVar[str] = "cugraph"  # nx >=3.2
     __networkx_plugin__: ClassVar[str] = "cugraph"  # nx <3.2
 
+    # Allow networkx dispatch machinery to cache conversions.
+    # This means we should clear the cache if we ever mutate the object!
+    __networkx_cache__: dict | None
+
     # networkx properties
     graph: dict
     graph_attr_dict_factory: ClassVar[type] = dict
@@ -108,6 +112,7 @@ def from_coo(
         **attr,
     ) -> Graph:
         new_graph = object.__new__(cls)
+        new_graph.__networkx_cache__ = {}
         new_graph.src_indices = src_indices
         new_graph.dst_indices = dst_indices
         new_graph.edge_values = {} if edge_values is None else dict(edge_values)
@@ -420,6 +425,8 @@ def clear(self) -> None:
         self._node_ids = None
         self.key_to_id = None
         self._id_to_key = None
+        if cache := self.__networkx_cache__:
+            cache.clear()
 
     @networkx_api
     def clear_edges(self) -> None:
@@ -427,6 +434,8 @@ def clear_edges(self) -> None:
         self.edge_masks.clear()
         self.src_indices = cp.empty(0, self.src_indices.dtype)
         self.dst_indices = cp.empty(0, self.dst_indices.dtype)
+        if cache := self.__networkx_cache__:
+            cache.clear()
 
     @networkx_api
     def copy(self, as_view: bool = False) -> Graph:
@@ -553,6 +562,12 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False):
         node_masks = self.node_masks
         key_to_id = self.key_to_id
         id_to_key = None if key_to_id is None else self._id_to_key
+        if self.__networkx_cache__ is None:
+            __networkx_cache__ = None
+        elif not reverse and cls is self.__class__:
+            __networkx_cache__ = self.__networkx_cache__
+        else:
+            __networkx_cache__ = {}
         if not as_view:
             src_indices = src_indices.copy()
             dst_indices = dst_indices.copy()
@@ -564,6 +579,8 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False):
                 key_to_id = key_to_id.copy()
                 if id_to_key is not None:
                     id_to_key = id_to_key.copy()
+            if __networkx_cache__ is not None:
+                __networkx_cache__ = __networkx_cache__.copy()
         if reverse:
             src_indices, dst_indices = dst_indices, src_indices
         rv = cls.from_coo(
@@ -581,6 +598,7 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False):
             rv.graph = self.graph
         else:
             rv.graph.update(deepcopy(self.graph))
+        rv.__networkx_cache__ = __networkx_cache__
         return rv
 
     def _get_plc_graph(
@@ -719,18 +737,26 @@ def _become(self, other: Graph):
         edge_masks = self.edge_masks
         node_values = self.node_values
         node_masks = self.node_masks
+        __networkx_cache__ = self.__networkx_cache__
         graph = self.graph
         edge_values.update(other.edge_values)
         edge_masks.update(other.edge_masks)
         node_values.update(other.node_values)
         node_masks.update(other.node_masks)
         graph.update(other.graph)
+        if other.__networkx_cache__ is None:
+            __networkx_cache__ = None
+        else:
+            if __networkx_cache__ is None:
+                __networkx_cache__ = {}
+            __networkx_cache__.update(other.__networkx_cache__)
         self.__dict__.update(other.__dict__)
         self.edge_values = edge_values
         self.edge_masks = edge_masks
         self.node_values = node_values
         self.node_masks = node_masks
         self.graph = graph
+        self.__networkx_cache__ = __networkx_cache__
         return self
 
     def _degrees_array(self, *, ignore_selfloops=False):
diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py
index de58474de..23d9faa87 100644
--- a/python/nx-cugraph/nx_cugraph/classes/multigraph.py
+++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py
@@ -415,6 +415,12 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False):
         key_to_id = self.key_to_id
         id_to_key = None if key_to_id is None else self._id_to_key
         edge_keys = self.edge_keys
+        if self.__networkx_cache__ is None:
+            __networkx_cache__ = None
+        elif not reverse and cls is self.__class__:
+            __networkx_cache__ = self.__networkx_cache__
+        else:
+            __networkx_cache__ = {}
         if not as_view:
             src_indices = src_indices.copy()
             dst_indices = dst_indices.copy()
@@ -429,6 +435,8 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False):
                     id_to_key = id_to_key.copy()
             if edge_keys is not None:
                 edge_keys = edge_keys.copy()
+            if __networkx_cache__ is not None:
+                __networkx_cache__ = __networkx_cache__.copy()
         if reverse:
             src_indices, dst_indices = dst_indices, src_indices
         rv = cls.from_coo(
@@ -448,6 +456,7 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False):
             rv.graph = self.graph
         else:
             rv.graph.update(deepcopy(self.graph))
+        rv.__networkx_cache__ = __networkx_cache__
         return rv
 
     def _sort_edge_indices(self, primary="src"):

From 6c1d4ae2ece22d10c34b20a54a42e5d5959ab295 Mon Sep 17 00:00:00 2001
From: Erik Welch 
Date: Wed, 31 Jul 2024 17:33:18 +0200
Subject: [PATCH 379/384] nx-cugraph: check networkx version (#4571)

This is to address the feedback from @bdice in https://github.com/rapidsai/cugraph/pull/4531#discussion_r1697414065, which I was unable to do in that PR.

I know checking version strings is playing it fast-and-loose, but I believe we are abundantly safe to do so here, as networkx releases have been, and are expected to be, slow and predictable. Also, we do not have `packaging` as a runtime dependency (it _is_ a test dependency for finer control).

So, even better than using `packaging.version.parse`, this PR performs a sanity check on the networkx version. I put it in both `nx_cugraph.__init__` and `_nx_cugraph.__init__` to give us obvious breadcrumbs to discover and follow.

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: https://github.com/rapidsai/cugraph/pull/4571
---
 python/nx-cugraph/_nx_cugraph/__init__.py | 21 +++++++++++++++++++++
 python/nx-cugraph/nx_cugraph/__init__.py  |  3 +++
 2 files changed, 24 insertions(+)

diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index d18fc53b8..f58a6e229 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -296,6 +296,27 @@ def get_info():
     return d
 
 
+def _check_networkx_version():
+    import warnings
+
+    import networkx as nx
+
+    version_major, version_minor = nx.__version__.split(".")[:2]
+    if version_major != "3":
+        warnings.warn(
+            f"nx-cugraph version {__version__} is only known to work with networkx "
+            f"versions 3.x, but networkx {nx.__version__} is installed. "
+            "Perhaps try upgrading your Python environment.",
+            UserWarning,
+            stacklevel=2,
+        )
+    if len(version_minor) > 1:
+        raise RuntimeWarning(
+            f"nx-cugraph version {__version__} does not work with networkx version "
+            f"{nx.__version__}. Please upgrade (or fix) your Python environment."
+        )
+
+
 if __name__ == "__main__":
     from pathlib import Path
 
diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py
index 7d6a2fbe4..542256fa7 100644
--- a/python/nx-cugraph/nx_cugraph/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/__init__.py
@@ -33,3 +33,6 @@
 from .algorithms import *
 
 from _nx_cugraph._version import __git_commit__, __version__
+from _nx_cugraph import _check_networkx_version
+
+_check_networkx_version()

From 4d923738b55e2f6de7ce28e2f98bbb8ed079eef7 Mon Sep 17 00:00:00 2001
From: Ralph Liu 
Date: Wed, 21 Aug 2024 09:26:51 -0700
Subject: [PATCH 380/384] Copied files while preserving history

---
 .github/CODEOWNERS       | 23 +++++++++++
 .github/copy-py-bot.yaml |  4 ++
 .github/labeler.yml      | 19 +++++++++
 .github/ops-bot.yaml     |  9 +++++
 CHANGELOG.md             |  0
 lint.yaml                | 87 ++++++++++++++++++++++++++++++++++++++++
 6 files changed, 142 insertions(+)
 create mode 100644 .github/CODEOWNERS
 create mode 100644 .github/copy-py-bot.yaml
 create mode 100644 .github/labeler.yml
 create mode 100644 .github/ops-bot.yaml
 create mode 100644 CHANGELOG.md
 create mode 100644 lint.yaml

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
new file mode 100644
index 000000000..86c489c25
--- /dev/null
+++ b/.github/CODEOWNERS
@@ -0,0 +1,23 @@
+# https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/about-code-owners
+# Order matters - match of highest importance goes last (last match wins)
+
+
+# doc code owners
+# 
+
+
+# python code owners
+# example
+# python/nx_cugraph			@rapidsai/nx-cugraph-python-codeowners
+
+
+# CI code owners
+# /ci/			@rapidsai/ci-codeowners
+
+
+# packaging code owners
+# /conda/            @rapidsai/packaging-codeowners
+# /dependencies.yaml @rapidsai/packaging-codeowners
+# /build.sh          @rapidsai/packaging-codeowners
+# pyproject.toml     @rapidsai/packaging-codeowners
+# VERSION            @rapidsai/packaging-codeowners
diff --git a/.github/copy-py-bot.yaml b/.github/copy-py-bot.yaml
new file mode 100644
index 000000000..895ba83ee
--- /dev/null
+++ b/.github/copy-py-bot.yaml
@@ -0,0 +1,4 @@
+# Configuration file for `copy-pr-bot` GitHub App
+# https://docs.gha-runners.nvidia.com/apps/copy-pr-bot/
+
+enabled: true
diff --git a/.github/labeler.yml b/.github/labeler.yml
new file mode 100644
index 000000000..4a5b2174d
--- /dev/null
+++ b/.github/labeler.yml
@@ -0,0 +1,19 @@
+# https://github.com/actions/labeler#common-examples
+# Adapted from https://github.com/rapidsai/cugraph/blob/main/.github/CODEOWNERS
+# Labels culled from https://github.com/rapidsai/cugraph/labels
+
+python:
+  - 'python/**'
+  - 'notebooks/**'
+
+benchmarks:
+  - 'benchmarks/**'
+
+datasets:
+  - 'datasets/**'
+
+ci:
+   - 'ci/**'
+
+conda:
+  - 'conda/**'
diff --git a/.github/ops-bot.yaml b/.github/ops-bot.yaml
new file mode 100644
index 000000000..2ed5231ae
--- /dev/null
+++ b/.github/ops-bot.yaml
@@ -0,0 +1,9 @@
+# This file controls which features from the `ops-bot` repository below are enabled.
+# - https://github.com/rapidsai/ops-bot
+
+auto_merger: true
+branch_checker: true
+label_checker: true
+release_drafter: true
+recently_updated: true
+forward_merger: true
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 000000000..e69de29bb
diff --git a/lint.yaml b/lint.yaml
new file mode 100644
index 000000000..ce46360e2
--- /dev/null
+++ b/lint.yaml
@@ -0,0 +1,87 @@
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# https://pre-commit.com/
+#
+# Before first use: `pre-commit install`
+# To run: `make lint`
+# To update: `make lint-update`
+#  - &flake8_dependencies below needs updated manually
+fail_fast: false
+default_language_version:
+    python: python3
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: check-added-large-files
+      - id: check-case-conflict
+      - id: check-merge-conflict
+      - id: check-symlinks
+      - id: check-ast
+      - id: check-toml
+      - id: check-yaml
+      - id: debug-statements
+      - id: end-of-file-fixer
+        exclude_types: [svg]
+      - id: mixed-line-ending
+      - id: trailing-whitespace
+  - repo: https://github.com/abravalheri/validate-pyproject
+    rev: v0.18
+    hooks:
+      - id: validate-pyproject
+        name: Validate pyproject.toml
+  - repo: https://github.com/PyCQA/autoflake
+    rev: v2.3.1
+    hooks:
+      - id: autoflake
+        args: [--in-place]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.16.0
+    hooks:
+      - id: pyupgrade
+        args: [--py39-plus]
+  - repo: https://github.com/psf/black
+    rev: 24.4.2
+    hooks:
+      - id: black
+      # - id: black-jupyter
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.5.4
+    hooks:
+      - id: ruff
+        args: [--fix-only, --show-fixes]  # --unsafe-fixes]
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.1.0
+    hooks:
+      - id: flake8
+        args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=B020,SIM105']  # Why is this necessary?
+        additional_dependencies: &flake8_dependencies
+          # These versions need updated manually
+          - flake8==7.1.0
+          - flake8-bugbear==24.4.26
+          - flake8-simplify==0.21.0
+  - repo: https://github.com/asottile/yesqa
+    rev: v1.5.0
+    hooks:
+      - id: yesqa
+        additional_dependencies: *flake8_dependencies
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.3.0
+    hooks:
+      - id: codespell
+        types_or: [python, rst, markdown]
+        additional_dependencies: [tomli]
+        files: ^(nx_cugraph|docs)/
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.5.4
+    hooks:
+      - id: ruff
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: no-commit-to-branch
+        args: [-p, "^branch-2....$"]

From 3f0112102eb4ae68ed75f6d903a4c8b25cbedf52 Mon Sep 17 00:00:00 2001
From: Ralph Liu 
Date: Wed, 21 Aug 2024 09:27:34 -0700
Subject: [PATCH 381/384] Update README

---
 README.md | 275 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 274 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 6710fd646..458421e2b 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,276 @@
 # nx-cugraph
 
-This will eventually be the repository for nx-cugraph, but for now see the nx-cugraph subdirectory within the [cugraph repo](https://github.com/rapidsai/cugraph/tree/HEAD/python/nx-cugraph).
+## Description
+[RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/utils.html#backends)
+to run supported algorithms with GPU acceleration.
+
+## System Requirements
+
+nx-cugraph requires the following:
+ * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
+ * CUDA 11.2, 11.4, 11.5, 11.8, or 12.0
+ * Python version 3.9, 3.10, or 3.11
+ * NetworkX >= version 3.0 (version 3.2 or higher recommended)
+
+More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).
+
+## Installation
+
+nx-cugraph can be installed using either conda or pip.
+
+### conda
+#### latest nightly version
+```
+conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph
+```
+#### latest stable version
+```
+conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph
+```
+### pip
+#### latest nightly version
+```
+python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
+```
+#### latest stable version
+```
+python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com
+```
+Notes:
+ * The pip example above installs for CUDA 11. To install for CUDA 12, replace `-cu11` with `-cu12`
+ * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start).
+
+## Enabling nx-cugraph
+
+NetworkX will use nx-cugraph as the graph analytics backend if any of the
+following are used:
+
+### `NETWORKX_AUTOMATIC_BACKENDS` environment variable.
+The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports.
+Set `NETWORKX_AUTOMATIC_BACKENDS=cugraph` to use nx-cugraph to GPU accelerate supported APIs with no code changes.
+Example:
+```
+bash> NETWORKX_AUTOMATIC_BACKENDS=cugraph python my_networkx_script.py
+```
+
+### `backend=` keyword argument
+To explicitly specify a particular backend for an API, use the `backend=`
+keyword argument. This argument takes precedence over the
+`NETWORKX_AUTOMATIC_BACKENDS` environment variable. This requires anyone
+running code that uses the `backend=` keyword argument to have the specified
+backend installed.
+
+Example:
+```
+nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph")
+```
+
+### Type-based dispatching
+
+NetworkX also supports automatically dispatching to backends associated with
+specific graph types. Like the `backend=` keyword argument example above, this
+requires the user to write code for a specific backend, and therefore requires
+the backend to be installed, but has the advantage of ensuring a particular
+behavior without the potential for runtime conversions.
+
+To use type-based dispatching with nx-cugraph, the user must import the backend
+directly in their code to access the utilities provided to create a Graph
+instance specifically for the nx-cugraph backend.
+
+Example:
+```
+import networkx as nx
+import nx_cugraph as nxcg
+
+G = nx.Graph()
+...
+nxcg_G = nxcg.from_networkx(G)             # conversion happens once here
+nx.betweenness_centrality(nxcg_G, k=1000)  # nxcg Graph type causes cugraph backend
+                                           # to be used, no conversion necessary
+```
+
+## Supported Algorithms
+
+The nx-cugraph backend to NetworkX connects
+[pylibcugraph](../../readme_pages/pylibcugraph.md) (cuGraph's low-level python
+interface to its CUDA-based graph analytics library) and
+[CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's
+familiar and easy-to-use API.
+
+Below is the list of algorithms that are currently supported in nx-cugraph.
+
+### [Algorithms](https://networkx.org/documentation/latest/reference/algorithms/index.html)
+
+
+bipartite
+ └─ generators
+     └─ complete_bipartite_graph
+centrality
+ ├─ betweenness
+ │   ├─ betweenness_centrality
+ │   └─ edge_betweenness_centrality
+ ├─ degree_alg
+ │   ├─ degree_centrality
+ │   ├─ in_degree_centrality
+ │   └─ out_degree_centrality
+ ├─ eigenvector
+ │   └─ eigenvector_centrality
+ └─ katz
+     └─ katz_centrality
+cluster
+ ├─ average_clustering
+ ├─ clustering
+ ├─ transitivity
+ └─ triangles
+community
+ └─ louvain
+     └─ louvain_communities
+components
+ ├─ connected
+ │   ├─ connected_components
+ │   ├─ is_connected
+ │   ├─ node_connected_component
+ │   └─ number_connected_components
+ └─ weakly_connected
+     ├─ is_weakly_connected
+     ├─ number_weakly_connected_components
+     └─ weakly_connected_components
+core
+ ├─ core_number
+ └─ k_truss
+dag
+ ├─ ancestors
+ └─ descendants
+isolate
+ ├─ is_isolate
+ ├─ isolates
+ └─ number_of_isolates
+link_analysis
+ ├─ hits_alg
+ │   └─ hits
+ └─ pagerank_alg
+     └─ pagerank
+operators
+ └─ unary
+     ├─ complement
+     └─ reverse
+reciprocity
+ ├─ overall_reciprocity
+ └─ reciprocity
+shortest_paths
+ ├─ generic
+ │   ├─ has_path
+ │   ├─ shortest_path
+ │   └─ shortest_path_length
+ ├─ unweighted
+ │   ├─ all_pairs_shortest_path
+ │   ├─ all_pairs_shortest_path_length
+ │   ├─ bidirectional_shortest_path
+ │   ├─ single_source_shortest_path
+ │   ├─ single_source_shortest_path_length
+ │   ├─ single_target_shortest_path
+ │   └─ single_target_shortest_path_length
+ └─ weighted
+     ├─ all_pairs_bellman_ford_path
+     ├─ all_pairs_bellman_ford_path_length
+     ├─ all_pairs_dijkstra
+     ├─ all_pairs_dijkstra_path
+     ├─ all_pairs_dijkstra_path_length
+     ├─ bellman_ford_path
+     ├─ bellman_ford_path_length
+     ├─ dijkstra_path
+     ├─ dijkstra_path_length
+     ├─ single_source_bellman_ford
+     ├─ single_source_bellman_ford_path
+     ├─ single_source_bellman_ford_path_length
+     ├─ single_source_dijkstra
+     ├─ single_source_dijkstra_path
+     └─ single_source_dijkstra_path_length
+traversal
+ └─ breadth_first_search
+     ├─ bfs_edges
+     ├─ bfs_layers
+     ├─ bfs_predecessors
+     ├─ bfs_successors
+     ├─ bfs_tree
+     ├─ descendants_at_distance
+     └─ generic_bfs_edges
+tree
+ └─ recognition
+     ├─ is_arborescence
+     ├─ is_branching
+     ├─ is_forest
+     └─ is_tree
+

+
+### [Generators](https://networkx.org/documentation/latest/reference/generators.html)
+
+
+classic
+ ├─ barbell_graph
+ ├─ circular_ladder_graph
+ ├─ complete_graph
+ ├─ complete_multipartite_graph
+ ├─ cycle_graph
+ ├─ empty_graph
+ ├─ ladder_graph
+ ├─ lollipop_graph
+ ├─ null_graph
+ ├─ path_graph
+ ├─ star_graph
+ ├─ tadpole_graph
+ ├─ trivial_graph
+ ├─ turan_graph
+ └─ wheel_graph
+community
+ └─ caveman_graph
+ego
+ └─ ego_graph
+small
+ ├─ bull_graph
+ ├─ chvatal_graph
+ ├─ cubical_graph
+ ├─ desargues_graph
+ ├─ diamond_graph
+ ├─ dodecahedral_graph
+ ├─ frucht_graph
+ ├─ heawood_graph
+ ├─ house_graph
+ ├─ house_x_graph
+ ├─ icosahedral_graph
+ ├─ krackhardt_kite_graph
+ ├─ moebius_kantor_graph
+ ├─ octahedral_graph
+ ├─ pappus_graph
+ ├─ petersen_graph
+ ├─ sedgewick_maze_graph
+ ├─ tetrahedral_graph
+ ├─ truncated_cube_graph
+ ├─ truncated_tetrahedron_graph
+ └─ tutte_graph
+social
+ ├─ davis_southern_women_graph
+ ├─ florentine_families_graph
+ ├─ karate_club_graph
+ └─ les_miserables_graph
+

+
+### Other
+
+
+classes
+ └─ function
+     └─ is_negatively_weighted
+convert
+ ├─ from_dict_of_lists
+ └─ to_dict_of_lists
+convert_matrix
+ ├─ from_pandas_edgelist
+ └─ from_scipy_sparse_array
+relabel
+ ├─ convert_node_labels_to_integers
+ └─ relabel_nodes
+

+
+To request nx-cugraph backend support for a NetworkX API that is not listed
+above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph).

From 055ceb30ddf3000b9a42dc4d21df176529b3e98a Mon Sep 17 00:00:00 2001
From: Ralph Liu 
Date: Tue, 10 Sep 2024 17:39:23 -0700
Subject: [PATCH 382/384] Remove unnecessary files

---
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 rename docs/{cugraph => nx-cugraph}/source/images/wcc.png (100%)
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 rename docs/{cugraph => nx-cugraph}/source/installation/getting_cugraph.md (100%)
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 rename docs/{cugraph => nx-cugraph}/source/nx_cugraph/index.rst (100%)
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 rename docs/{cugraph => nx-cugraph}/source/releases/index.rst (100%)
 rename docs/{cugraph => nx-cugraph}/source/sphinxext/github_link.py (100%)
 rename docs/{cugraph => nx-cugraph}/source/tutorials/basic_cugraph.md (100%)
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 rename docs/{cugraph => nx-cugraph}/source/tutorials/cugraph_blogs.rst (100%)
 rename docs/{cugraph => nx-cugraph}/source/tutorials/cugraph_notebooks.md (100%)
 rename docs/{cugraph => nx-cugraph}/source/tutorials/how_to_guides.md (100%)
 rename docs/{cugraph => nx-cugraph}/source/tutorials/index.rst (100%)

diff --git a/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst b/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst
deleted file mode 100644
index 4ffecd8d0..000000000
--- a/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst
+++ /dev/null
@@ -1,15 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-cugraph-dgl API Reference
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-cugraph-dgl
-
-.. currentmodule:: cugraph_dgl
-
-Methods
--------
-.. autosummary::
-   :toctree: ../api/cugraph-dgl/
-
-   convert.cugraph_storage_from_heterograph
-   cugraph_storage.CuGraphStorage
diff --git a/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst
deleted file mode 100644
index 39dae955e..000000000
--- a/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-cugraph-ops C++ API Reference
-=============================
diff --git a/docs/cugraph/source/api_docs/cugraph-ops/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/index.rst
deleted file mode 100644
index 0f6a6c937..000000000
--- a/docs/cugraph/source/api_docs/cugraph-ops/index.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-cugraph-ops API reference
-=========================
-
-This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace.
-
-.. toctree::
-    :maxdepth: 2
-    :caption: API Documentation
-
-    python/index
-    c_cpp/index
diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst
deleted file mode 100644
index 6fadcc57b..000000000
--- a/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-=================
-Dimenet operators
-=================
-
-.. currentmodule:: pylibcugraphops
-
-Radial Basis Functions
-----------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   dimenet.radial_basis_fwd
-   dimenet.radial_basis_bwd
-   dimenet.radial_basis_bwd_bwd
-
-Edge-to-Edge Aggregation
--------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   dimenet.agg_edge_to_edge_fwd
-   dimenet.agg_edge_to_edge_bwd
-   dimenet.agg_edge_to_edge_bwd2_grad
-   dimenet.agg_edge_to_edge_bwd2_main
diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst
deleted file mode 100644
index 141d40393..000000000
--- a/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-===========
-Graph types
-===========
-
-.. currentmodule:: pylibcugraphops
-
-
-CSC Graph
------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   make_csc
-
-Heterogenous CSC Graph
-----------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   make_csc_hg
-
-Bipartite Graph
------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   make_bipartite_csc
-
-Heterogenous Bipartite Graph
-----------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   make_bipartite_csc_hg
diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst
deleted file mode 100644
index fb25f2fa0..000000000
--- a/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-cugraph-ops Python API reference
-================================
-
-This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace.
-
-.. toctree::
-    :maxdepth: 2
-    :caption: API Documentation
-
-    graph_types
-    operators
-    dimenet
-    pytorch
diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst
deleted file mode 100644
index 8b5efd7aa..000000000
--- a/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst
+++ /dev/null
@@ -1,109 +0,0 @@
-=============================
-Operators for Message-Passing
-=============================
-
-.. currentmodule:: pylibcugraphops
-
-Simple Neighborhood Aggregator (SAGEConv)
------------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.agg_simple_n2n_fwd
-   operators.agg_simple_n2n_bwd
-   operators.agg_simple_e2n_fwd
-   operators.agg_simple_e2n_bwd
-   operators.agg_simple_n2n_e2n_fwd
-   operators.agg_simple_n2n_e2n_bwd
-
-   operators.agg_concat_n2n_fwd
-   operators.agg_concat_n2n_bwd
-   operators.agg_concat_e2n_fwd
-   operators.agg_concat_e2n_bwd
-   operators.agg_concat_n2n_e2n_fwd
-   operators.agg_concat_n2n_e2n_bwd
-
-
-Weighted Neighborhood Aggregation
----------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.agg_weighted_n2n_fwd
-   operators.agg_weighted_n2n_bwd
-   operators.agg_concat_weighted_n2n_fwd
-   operators.agg_concat_weighted_n2n_bwd
-
-Heterogenous Aggregator using Basis Decomposition (RGCNConv)
-------------------------------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.agg_hg_basis_n2n_post_fwd
-   operators.agg_hg_basis_n2n_post_bwd
-
-Graph Attention (GATConv/GATv2Conv)
------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.mha_gat_n2n_fwd_bf16_fp32
-   operators.mha_gat_n2n_fwd_fp16_fp32
-   operators.mha_gat_n2n_fwd_fp32_fp32
-   operators.mha_gat_n2n_bwd_bf16_bf16_bf16_fp32
-   operators.mha_gat_n2n_bwd_bf16_bf16_fp32_fp32
-   operators.mha_gat_n2n_bwd_bf16_fp32_fp32_fp32
-   operators.mha_gat_n2n_bwd_fp16_fp16_fp16_fp32
-   operators.mha_gat_n2n_bwd_fp16_fp16_fp32_fp32
-   operators.mha_gat_n2n_bwd_fp16_fp32_fp32_fp32
-   operators.mha_gat_n2n_bwd_fp32_fp32_fp32_fp32
-   operators.mha_gat_n2n_efeat_fwd_bf16_fp32
-   operators.mha_gat_n2n_efeat_fwd_fp16_fp32
-   operators.mha_gat_n2n_efeat_fwd_fp32_fp32
-   operators.mha_gat_n2n_efeat_bwd_bf16_bf16_bf16_fp32
-   operators.mha_gat_n2n_efeat_bwd_bf16_bf16_fp32_fp32
-   operators.mha_gat_n2n_efeat_bwd_bf16_fp32_fp32_fp32
-   operators.mha_gat_n2n_efeat_bwd_fp16_fp16_fp16_fp32
-   operators.mha_gat_n2n_efeat_bwd_fp16_fp16_fp32_fp32
-   operators.mha_gat_n2n_efeat_bwd_fp16_fp32_fp32_fp32
-   operators.mha_gat_n2n_efeat_bwd_fp32_fp32_fp32_fp32
-
-   operators.mha_gat_v2_n2n_fwd
-   operators.mha_gat_v2_n2n_bwd
-   operators.mha_gat_v2_n2n_efeat_fwd
-   operators.mha_gat_v2_n2n_efeat_bwd
-
-Transformer-like Graph Attention (TransformerConv)
---------------------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.mha_gat_v2_n2n_fwd
-   operators.mha_gat_v2_n2n_bwd
-   operators.mha_gat_v2_n2n_efeat_fwd
-   operators.mha_gat_v2_n2n_efeat_bwd
-
-Directional Message-Passing (DMPNN)
------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.agg_dmpnn_e2e_fwd
-   operators.agg_dmpnn_e2e_bwd
-
-Update Edges: Concatenation or Sum of Edge and Node Features
-------------------------------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.update_efeat_e2e_concat_fwd
-   operators.update_efeat_e2e_concat_bwd
-
-   operators.update_efeat_e2e_sum_fwd
-   operators.update_efeat_e2e_sum_bwd
-
-   operators.update_efeat_e2e_concat_fwd
-   operators.update_efeat_e2e_concat_bwd
-
-   operators.update_efeat_e2e_sum_fwd
-   operators.update_efeat_e2e_sum_bwd
diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst
deleted file mode 100644
index d2074df15..000000000
--- a/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst
+++ /dev/null
@@ -1,36 +0,0 @@
-==========================
-PyTorch Autograd Wrappers
-==========================
-
-.. currentmodule:: pylibcugraphops.pytorch
-
-Simple Neighborhood Aggregator (SAGEConv)
------------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.agg_concat_n2n
-
-Graph Attention (GATConv/GATv2Conv)
------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.mha_gat_n2n
-   operators.mha_gat_v2_n2n
-
-Heterogenous Aggregator using Basis Decomposition (RGCNConv)
-------------------------------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.agg_hg_basis_n2n_post
-
-
-Update Edges: Concatenation or Sum of Edge and Node Features
-------------------------------------------------------------
-.. autosummary::
-   :toctree: ../../api/ops
-
-   operators.update_efeat_e2e
-   operators.update_efeat_e2e
diff --git a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
deleted file mode 100644
index d2b1d124c..000000000
--- a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst
+++ /dev/null
@@ -1,43 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~~~~~
-cugraph-pyg API Reference
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-cugraph-pyg
-
-.. currentmodule:: cugraph_pyg
-
-Graph Storage
--------------
-.. autosummary::
-   :toctree: ../api/cugraph-pyg/
-
-   cugraph_pyg.data.dask_graph_store.DaskGraphStore
-   cugraph_pyg.data.graph_store.GraphStore
-
-Feature Storage
----------------
-.. autosummary::
-   :toctree: ../api/cugraph-pyg/
-
-   cugraph_pyg.data.feature_store.TensorDictFeatureStore
-   cugraph_pyg.data.feature_store.WholeFeatureStore
-
-Data Loaders
-------------
-.. autosummary::
-   :toctree: ../api/cugraph-pyg/
-
-   cugraph_pyg.loader.dask_node_loader.DaskNeighborLoader
-   cugraph_pyg.loader.dask_node_loader.BulkSampleLoader
-   cugraph_pyg.loader.node_loader.NodeLoader
-   cugraph_pyg.loader.neighbor_loader.NeighborLoader
-
-Samplers
---------
-.. autosummary::
-   :toctree: ../api/cugraph-pyg/
-
-   cugraph_pyg.sampler.sampler.BaseSampler
-   cugraph_pyg.sampler.sampler.SampleReader
-   cugraph_pyg.sampler.sampler.HomogeneousSampleReader
-   cugraph_pyg.sampler.sampler.SampleIterator
diff --git a/docs/cugraph/source/api_docs/cugraph/centrality.rst b/docs/cugraph/source/api_docs/cugraph/centrality.rst
deleted file mode 100644
index 344c95195..000000000
--- a/docs/cugraph/source/api_docs/cugraph/centrality.rst
+++ /dev/null
@@ -1,71 +0,0 @@
-==========
-Centrality
-==========
-.. currentmodule:: cugraph
-
-
-
-Betweenness Centrality
-----------------------
-single-GPU
-^^^^^^^^^^
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.centrality.betweenness_centrality
-   cugraph.centrality.edge_betweenness_centrality
-
-multi-GPU
-^^^^^^^^^^
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.centrality.betweenness_centrality
-
-
-
-Katz Centrality
----------------
-single-GPU
-^^^^^^^^^^
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.centrality.katz_centrality
-
-multi-GPU
-^^^^^^^^^^
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.centrality.katz_centrality.katz_centrality
-
-
-Degree Centrality
------------------
-single-GPU
-^^^^^^^^^^
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.centrality.degree_centrality
-
-multi-GPU
-^^^^^^^^^^
-
-
-Eigenvector Centrality
-----------------------
-single-GPU
-^^^^^^^^^^
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.centrality.eigenvector_centrality
-
-multi-GPU
-^^^^^^^^^^
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.centrality.eigenvector_centrality.eigenvector_centrality
diff --git a/docs/cugraph/source/api_docs/cugraph/community.rst b/docs/cugraph/source/api_docs/cugraph/community.rst
deleted file mode 100644
index acbaa086f..000000000
--- a/docs/cugraph/source/api_docs/cugraph/community.rst
+++ /dev/null
@@ -1,80 +0,0 @@
-=========
-Community
-=========
-.. currentmodule:: cugraph
-
-
-
-EgoNet
-------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.batched_ego_graphs
-   cugraph.ego_graph
-
-Ensemble clustering for graphs (ECG)
-------------------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.ecg
-
-
-K-Truss
--------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.k_truss
-   cugraph.ktruss_subgraph
-
-Leiden
-------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.leiden
-
-
-Louvain
--------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.louvain
-
-
-Louvain (MG)
-------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.community.louvain.louvain
-
-Spectral Clustering
--------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.analyzeClustering_edge_cut
-   cugraph.analyzeClustering_modularity
-   cugraph.analyzeClustering_ratio_cut
-   cugraph.spectralBalancedCutClustering
-   cugraph.spectralModularityMaximizationClustering
-
-
-Subgraph Extraction
--------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.subgraph
-
-
-Triangle Counting
------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.triangle_count
diff --git a/docs/cugraph/source/api_docs/cugraph/components.rst b/docs/cugraph/source/api_docs/cugraph/components.rst
deleted file mode 100644
index e61291fcc..000000000
--- a/docs/cugraph/source/api_docs/cugraph/components.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-==========
-Components
-==========
-.. currentmodule:: cugraph
-
-
-Connected Components
---------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.connected_components
-   cugraph.strongly_connected_components
-   cugraph.weakly_connected_components
-
-
-Connected Components (MG)
--------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.components.connectivity.weakly_connected_components
diff --git a/docs/cugraph/source/api_docs/cugraph/cores.rst b/docs/cugraph/source/api_docs/cugraph/cores.rst
deleted file mode 100644
index 9d274d1c4..000000000
--- a/docs/cugraph/source/api_docs/cugraph/cores.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-=====
-Cores
-=====
-.. currentmodule:: cugraph
-
-
-
-Core Number
------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.core_number
-
-
-K-Core
-------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.k_core
diff --git a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst b/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst
deleted file mode 100644
index f5132dd65..000000000
--- a/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst
+++ /dev/null
@@ -1,78 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~~
-Multi-GPU with cuGraph
-~~~~~~~~~~~~~~~~~~~~~~
-
-cuGraph supports multi-GPU leveraging `Dask `_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cuda `_.
-
-Distributed graph analytics
-===========================
-
-The current solution is able to scale across multiple GPUs on multiple machines. Distributing the graph and computation lets you analyze datasets far larger than a single GPU’s memory.
-
-With cuGraph and Dask, whether you’re using a single NVIDIA GPU or multiple nodes, your RAPIDS workflow will run smoothly, intelligently distributing the workload across the available resources.
-
-If your graph comfortably fits in memory on a single GPU, you would want to use the single-GPU version of cuGraph. If you want to distribute your workflow across multiple GPUs and have more data than you can fit in memory on a single GPU, you would want to use cuGraph's multi-GPU features.
-
-Example
-========
-
-.. code-block:: python
-
-    import dask_cudf
-    from dask.distributed import Client
-    from dask_cuda import LocalCUDACluster
-
-    import cugraph
-    import cugraph.dask as dask_cugraph
-    import cugraph.dask.comms.comms as Comms
-    from cugraph.generators.rmat import rmat
-
-    input_data_path = "input_data.csv"
-
-    # cluster initialization
-    cluster = LocalCUDACluster()
-    client = Client(cluster)
-    Comms.initialize(p2p=True)
-
-    # helper function to generate random input data
-    input_data = rmat(
-        scale=5,
-        num_edges=400,
-        a=0.30,
-        b=0.65,
-        c=0.05,
-        seed=456,
-        clip_and_flip=False,
-        scramble_vertex_ids=False,
-        create_using=None,
-    )
-    input_data.to_csv(input_data_path, index=False)
-
-    # helper function to set the reader chunk size to automatically get one partition per GPU
-    chunksize = dask_cugraph.get_chunksize(input_data_path)
-
-    # multi-GPU CSV reader
-    e_list = dask_cudf.read_csv(
-        input_data_path,
-        blocksize=chunksize,
-        names=['src', 'dst'],
-        dtype=['int32', 'int32'],
-    )
-
-    # create graph from input data
-    G = cugraph.Graph(directed=True)
-    G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')
-
-    # run PageRank
-    pr_df = dask_cugraph.pagerank(G, tol=1e-4)
-
-    # need to call compute to generate results
-    pr_df.compute()
-
-    # cluster clean up
-    Comms.destroy()
-    client.close()
-    cluster.close()
-
-
-|
diff --git a/docs/cugraph/source/api_docs/cugraph/generators.rst b/docs/cugraph/source/api_docs/cugraph/generators.rst
deleted file mode 100644
index f5180a172..000000000
--- a/docs/cugraph/source/api_docs/cugraph/generators.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-==========
-Generators
-==========
-.. currentmodule:: cugraph
-
-
-
-RMAT
-----
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.generators.rmat
diff --git a/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst b/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst
deleted file mode 100644
index ae14306ce..000000000
--- a/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-====================
-Graph Implementation
-====================
-.. currentmodule:: cugraph.structure.graph_implementation.simpleGraphImpl
-
-
-Graph Implementation
---------------------
-.. autosummary::
-    :toctree: ../api/cugraph/
-
-    view_edge_list
-    delete_edge_list
-    view_adj_list
-    view_transposed_adj_list
-    delete_adj_list
-
-    enable_batch
-
-    get_two_hop_neighbors
-    number_of_vertices
-    number_of_nodes
-    number_of_edges
-    in_degree
-    out_degree
-    degree
-    degrees
-    has_edge
-    has_node
-    has_self_loop
-    edges
-    nodes
-    neighbors
-    vertex_column_size
diff --git a/docs/cugraph/source/api_docs/cugraph/helper_functions.rst b/docs/cugraph/source/api_docs/cugraph/helper_functions.rst
deleted file mode 100644
index e7091c50c..000000000
--- a/docs/cugraph/source/api_docs/cugraph/helper_functions.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-========================
-DASK MG Helper functions
-========================
-.. currentmodule:: cugraph
-
-
-Methods
--------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.comms.comms.initialize
-   cugraph.dask.comms.comms.destroy
-   cugraph.dask.comms.comms.is_initialized
-   cugraph.dask.comms.comms.get_comms
-   cugraph.dask.comms.comms.get_workers
-   cugraph.dask.comms.comms.get_session_id
-   cugraph.dask.comms.comms.get_2D_partition
-   cugraph.dask.comms.comms.get_default_handle
-   cugraph.dask.comms.comms.get_handle
-   cugraph.dask.comms.comms.get_worker_id
-   cugraph.dask.common.read_utils.get_chunksize
diff --git a/docs/cugraph/source/api_docs/cugraph/index.rst b/docs/cugraph/source/api_docs/cugraph/index.rst
deleted file mode 100644
index 20b63d50a..000000000
--- a/docs/cugraph/source/api_docs/cugraph/index.rst
+++ /dev/null
@@ -1,25 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~
-cugraph API Reference
-~~~~~~~~~~~~~~~~~~~~~
-
-.. toctree::
-    :maxdepth: 2
-    :caption: API Documentation
-
-    structure
-    graph_implementation
-    property_graph
-    centrality
-    community
-    components
-    cores
-    layout
-    linear_assignment
-    link_analysis
-    link_prediction
-    sampling
-    traversal
-    tree
-    generators
-    helper_functions
-    dask-cugraph.rst
diff --git a/docs/cugraph/source/api_docs/cugraph/layout.rst b/docs/cugraph/source/api_docs/cugraph/layout.rst
deleted file mode 100644
index d416676a6..000000000
--- a/docs/cugraph/source/api_docs/cugraph/layout.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-======
-Layout
-======
-.. currentmodule:: cugraph
-
-
-Force Atlas 2
--------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.force_atlas2
diff --git a/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst b/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst
deleted file mode 100644
index e0b0b4d11..000000000
--- a/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-=================
-Linear Assignment
-=================
-.. currentmodule:: cugraph
-
-
-Hungarian
----------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.hungarian
-   cugraph.dense_hungarian
diff --git a/docs/cugraph/source/api_docs/cugraph/link_analysis.rst b/docs/cugraph/source/api_docs/cugraph/link_analysis.rst
deleted file mode 100644
index 698880c1b..000000000
--- a/docs/cugraph/source/api_docs/cugraph/link_analysis.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-=============
-Link Analysis
-=============
-.. currentmodule:: cugraph
-
-
-HITS
-----
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.hits
-
-HITS (MG)
----------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.link_analysis.hits.hits
-
-
-Pagerank
---------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.pagerank
-
-Pagerank (MG)
--------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.link_analysis.pagerank.pagerank
diff --git a/docs/cugraph/source/api_docs/cugraph/link_prediction.rst b/docs/cugraph/source/api_docs/cugraph/link_prediction.rst
deleted file mode 100644
index 3d2f9562e..000000000
--- a/docs/cugraph/source/api_docs/cugraph/link_prediction.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-===============
-Link Prediction
-===============
-.. currentmodule:: cugraph
-
-
-Jaccard Coefficient
--------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.jaccard
-   cugraph.jaccard_coefficient
-
-
-Overlap Coefficient
--------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.overlap
-   cugraph.overlap_coefficient
-
-
-Sorensen Coefficient
---------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.sorensen
-   cugraph.sorensen_coefficient
diff --git a/docs/cugraph/source/api_docs/cugraph/property_graph.rst b/docs/cugraph/source/api_docs/cugraph/property_graph.rst
deleted file mode 100644
index 672aa7dae..000000000
--- a/docs/cugraph/source/api_docs/cugraph/property_graph.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-====================
-Property Graph
-====================
-.. currentmodule:: cugraph.experimental
-
-
-Property Graph
--------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   PropertySelection
-   PropertyGraph
-   PropertyGraph.add_edge_data
-   PropertyGraph.add_vertex_data
-   PropertyGraph.annotate_dataframe
-   PropertyGraph.edge_props_to_graph
-   PropertyGraph.extract_subgraph
-   PropertyGraph.get_edge_data
-   PropertyGraph.get_num_edges
-   PropertyGraph.get_num_vertices
-   PropertyGraph.get_vertex_data
-   PropertyGraph.get_vertices
-   PropertyGraph.has_duplicate_edges
-   PropertyGraph.is_multigraph
-   PropertyGraph.renumber_edges_by_type
-   PropertyGraph.renumber_vertices_by_type
-   PropertyGraph.select_edges
-   PropertyGraph.select_vertices
diff --git a/docs/cugraph/source/api_docs/cugraph/sampling.rst b/docs/cugraph/source/api_docs/cugraph/sampling.rst
deleted file mode 100644
index 52004a5b1..000000000
--- a/docs/cugraph/source/api_docs/cugraph/sampling.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-========
-Sampling
-========
-.. currentmodule:: cugraph
-
-
-
-Random Walks
-------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.random_walks
-   cugraph.ego_graph
-   cugraph.uniform_neighbor_sample
-
-Node2Vec
----------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.node2vec
diff --git a/docs/cugraph/source/api_docs/cugraph/structure.rst b/docs/cugraph/source/api_docs/cugraph/structure.rst
deleted file mode 100644
index 6369e1bb3..000000000
--- a/docs/cugraph/source/api_docs/cugraph/structure.rst
+++ /dev/null
@@ -1,99 +0,0 @@
-=============
-Graph Classes
-=============
-.. currentmodule:: cugraph
-
-Constructors
-------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   Graph
-   MultiGraph
-
-
-Adding Data
------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   Graph.from_cudf_adjlist
-   Graph.from_cudf_edgelist
-   Graph.from_dask_cudf_edgelist
-   Graph.from_pandas_adjacency
-   Graph.from_pandas_edgelist
-   Graph.from_numpy_array
-   Graph.from_numpy_matrix
-   Graph.add_internal_vertex_id
-   Graph.add_nodes_from
-   Graph.clear
-   Graph.unrenumber
-
-Checks
-------
-.. autosummary::
-    :toctree: ../api/cugraph/
-
-   Graph.has_isolated_vertices
-   Graph.is_bipartite
-   Graph.is_directed
-   Graph.is_multigraph
-   Graph.is_multipartite
-   Graph.is_renumbered
-   Graph.is_weighted
-   Graph.lookup_internal_vertex_id
-   Graph.to_directed
-   Graph.to_undirected
-
-
-Symmetrize
-----------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.symmetrize
-   cugraph.symmetrize_ddf
-   cugraph.symmetrize_df
-
-
-Conversion from Other Formats
------------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.from_adjlist
-   cugraph.from_cudf_edgelist
-   cugraph.from_edgelist
-   cugraph.from_numpy_array
-   cugraph.from_numpy_matrix
-   cugraph.from_pandas_adjacency
-   cugraph.from_pandas_edgelist
-   cugraph.to_numpy_array
-   cugraph.to_numpy_matrix
-   cugraph.to_pandas_adjacency
-   cugraph.to_pandas_edgelist
-
-NumberMap
------------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.structure.NumberMap
-   cugraph.structure.NumberMap.from_internal_vertex_id
-   cugraph.structure.NumberMap.to_internal_vertex_id
-   cugraph.structure.NumberMap.add_internal_vertex_id
-   cugraph.structure.NumberMap.compute_vals
-   cugraph.structure.NumberMap.compute_vals_types
-   cugraph.structure.NumberMap.generate_unused_column_name
-   cugraph.structure.NumberMap.renumber
-   cugraph.structure.NumberMap.renumber_and_segment
-   cugraph.structure.NumberMap.set_renumbered_col_names
-   cugraph.structure.NumberMap.unrenumber
-   cugraph.structure.NumberMap.vertex_column_size
-
-Other
------------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.hypergraph
diff --git a/docs/cugraph/source/api_docs/cugraph/traversal.rst b/docs/cugraph/source/api_docs/cugraph/traversal.rst
deleted file mode 100644
index 31296f3b8..000000000
--- a/docs/cugraph/source/api_docs/cugraph/traversal.rst
+++ /dev/null
@@ -1,37 +0,0 @@
-=========
-Traversal
-=========
-.. currentmodule:: cugraph
-
-
-Breadth-first-search
---------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.bfs
-   cugraph.bfs_edges
-
-Breadth-first-search (MG)
--------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.traversal.bfs.bfs
-
-Single-source-shortest-path
----------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.filter_unreachable
-   cugraph.shortest_path
-   cugraph.shortest_path_length
-   cugraph.sssp
-
-Single-source-shortest-path (MG)
---------------------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.dask.traversal.sssp.sssp
diff --git a/docs/cugraph/source/api_docs/cugraph/tree.rst b/docs/cugraph/source/api_docs/cugraph/tree.rst
deleted file mode 100644
index d8a89046a..000000000
--- a/docs/cugraph/source/api_docs/cugraph/tree.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-====
-Tree
-====
-.. currentmodule:: cugraph
-
-
-Minimum Spanning Tree
----------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.tree.minimum_spanning_tree.minimum_spanning_tree
-
-
-Maximum Spanning Tree
----------------------
-.. autosummary::
-   :toctree: ../api/cugraph/
-
-   cugraph.tree.minimum_spanning_tree.maximum_spanning_tree
diff --git a/docs/cugraph/source/api_docs/cugraph_c/centrality.rst b/docs/cugraph/source/api_docs/cugraph_c/centrality.rst
deleted file mode 100644
index 3bea608fd..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/centrality.rst
+++ /dev/null
@@ -1,50 +0,0 @@
-Centrality
-==========
-
-PageRank
---------
-.. doxygenfunction:: cugraph_pagerank
-    :project: libcugraph
-
-.. doxygenfunction:: cugraph_pagerank_allow_nonconvergence
-    :project: libcugraph
-
-Personalized PageRank
----------------------
-.. doxygenfunction:: cugraph_personalized_pagerank
-    :project: libcugraph
-
-.. doxygenfunction:: cugraph_personalized_pagerank_allow_nonconvergence
-    :project: libcugraph
-
-Eigenvector Centrality
-----------------------
-.. doxygenfunction:: cugraph_eigenvector_centrality
-    :project: libcugraph
-
-Katz Centrality
----------------
-.. doxygenfunction:: cugraph_katz_centrality
-    :project: libcugraph
-
-Betweenness Centrality
-----------------------
-.. doxygenfunction:: cugraph_betweenness_centrality
-    :project: libcugraph
-
-Edge Betweenness Centrality
----------------------------
-.. doxygenfunction:: cugraph_edge_betweenness_centrality
-    :project: libcugraph
-
-HITS Centrality
----------------
-.. doxygenfunction:: cugraph_hits
-    :project: libcugraph
-
-Centrality Support Functions
-----------------------------
- .. doxygengroup:: centrality
-     :project: libcugraph
-     :members:
-     :content-only:
diff --git a/docs/cugraph/source/api_docs/cugraph_c/community.rst b/docs/cugraph/source/api_docs/cugraph_c/community.rst
deleted file mode 100644
index 6b500a972..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/community.rst
+++ /dev/null
@@ -1,56 +0,0 @@
-Community
-=========
-
-Triangle Counting
------------------
-.. doxygenfunction:: cugraph_triangle_count
-    :project: libcugraph
-
-Louvain
--------
-.. doxygenfunction:: cugraph_louvain
-    :project: libcugraph
-
-Leiden
-------
-.. doxygenfunction:: cugraph_leiden
-    :project: libcugraph
-
-ECG
----
-.. doxygenfunction:: cugraph_ecg
-    :project: libcugraph
-
-Extract Egonet
---------------
-.. doxygenfunction:: cugraph_extract_ego
-    :project: libcugraph
-
-Balanced Cut
-------------
-.. doxygenfunction:: cugraph_balanced_cut_clustering
-    :project: libcugraph
-
-Spectral Clustering - Modularity Maximization
----------------------------------------------
-.. doxygenfunction:: cugraph_spectral_modularity_maximization
-    :project: libcugraph
-
-.. doxygenfunction:: cugraph_analyze_clustering_modularity
-    :project: libcugraph
-
-Spectral Clustering - Edge Cut
-------------------------------
-.. doxygenfunction:: cugraph_analyze_clustering_edge_cut
-    :project: libcugraph
-
-.. doxygenfunction:: cugraph_analyze_clustering_ratio_cut
-    :project: libcugraph
-
-
-Community Support Functions
----------------------------
- .. doxygengroup:: community
-     :project: libcugraph
-     :members:
-     :content-only:
diff --git a/docs/cugraph/source/api_docs/cugraph_c/core.rst b/docs/cugraph/source/api_docs/cugraph_c/core.rst
deleted file mode 100644
index 34456c65e..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/core.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-Core
-====
-
-
-Core Number
------------
-.. doxygenfunction:: cugraph_core_number
-    :project: libcugraph
-
-K-Core
-------
-.. doxygenfunction:: cugraph_k_core
-    :project: libcugraph
-
-
-Core Support Functions
-----------------------
- .. doxygengroup:: core
-     :project: libcugraph
-     :members:
-     :content-only:
diff --git a/docs/cugraph/source/api_docs/cugraph_c/index.rst b/docs/cugraph/source/api_docs/cugraph_c/index.rst
deleted file mode 100644
index 3dd37dbc3..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/index.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-===========================
-cuGraph C API documentation
-===========================
-
-
-.. toctree::
-    :maxdepth: 3
-    :caption: API Documentation
-
-    centrality.rst
-    community.rst
-    core.rst
-    labeling.rst
-    sampling.rst
-    similarity.rst
-    traversal.rst
diff --git a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst
deleted file mode 100644
index 2b709ebd3..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-Components
-==========
-
-
-Weakly Connected Components
----------------------------
-.. doxygenfunction:: cugraph_weakly_connected_components
-    :project: libcugraph
-
-Strongly Connected Components
------------------------------
-.. doxygenfunction:: cugraph_strongly_connected_components
-    :project: libcugraph
-
-Labeling Support Functions
---------------------------
- .. doxygengroup:: labeling
-     :project: libcugraph
-     :members:
-     :content-only:
diff --git a/docs/cugraph/source/api_docs/cugraph_c/sampling.rst b/docs/cugraph/source/api_docs/cugraph_c/sampling.rst
deleted file mode 100644
index 3d5af713c..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/sampling.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-Sampling
-========
-
-Uniform Random Walks
---------------------
-.. doxygenfunction:: cugraph_uniform_random_walks
-    :project: libcugraph
-
-Biased Random Walks
--------------------
-.. doxygenfunction:: cugraph_biased_random_walks
-    :project: libcugraph
-
-Random Walks via Node2Vec
--------------------------
-.. doxygenfunction:: cugraph_node2vec_random_walks
-    :project: libcugraph
-
-Node2Vec
---------
-.. doxygenfunction:: cugraph_node2vec
-    :project: libcugraph
-
-Uniform Neighbor Sampling
--------------------------
-.. doxygenfunction:: cugraph_uniform_neighbor_sample
-    :project: libcugraph
-
-Sampling Support Functions
---------------------------
-.. doxygengroup:: samplingC
-     :project: libcugraph
-     :members:
-     :content-only:
diff --git a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst
deleted file mode 100644
index 75735925e..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst
+++ /dev/null
@@ -1,25 +0,0 @@
-Similarity
-==========
-
-
-Jaccard
--------
-.. doxygenfunction:: cugraph_jaccard_coefficients
-    :project: libcugraph
-
-Sorensen
---------
-.. doxygenfunction:: cugraph_sorensen_coefficients
-    :project: libcugraph
-
-Overlap
--------
-.. doxygenfunction:: cugraph_overlap_coefficients
-    :project: libcugraph
-
-Similarty Support Functions
----------------------------
-.. doxygengroup:: similarity
-     :project: libcugraph
-     :members:
-     :content-only:
diff --git a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst
deleted file mode 100644
index bde30f4fa..000000000
--- a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst
+++ /dev/null
@@ -1,30 +0,0 @@
-Traversal
-==========
-
-
-Breadth First Search (BFS)
---------------------------
-.. doxygenfunction:: cugraph_bfs
-    :project: libcugraph
-
-Single-Source Shortest-Path (SSSP)
-----------------------------------
-.. doxygenfunction:: cugraph_sssp
-    :project: libcugraph
-
-Path Extraction
----------------
-.. doxygenfunction:: cugraph_extract_paths
-    :project: libcugraph
-
-Extract Max Path Length
------------------------
-.. doxygenfunction:: cugraph_extract_paths_result_get_max_path_length
-    :project: libcugraph
-
-Traversal Support Functions
----------------------------
-.. doxygengroup:: traversal
-     :project: libcugraph
-     :members:
-     :content-only:
diff --git a/docs/cugraph/source/api_docs/plc/pylibcugraph.rst b/docs/cugraph/source/api_docs/plc/pylibcugraph.rst
deleted file mode 100644
index 7ebdd67e9..000000000
--- a/docs/cugraph/source/api_docs/plc/pylibcugraph.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-pylibcugraph API reference
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-pylibcugraph
-
-.. currentmodule:: pylibcugraph
-
-Methods
--------
-.. autosummary::
-   :toctree: ../api/plc/
-
-   pylibcugraph.eigenvector_centrality
-   pylibcugraph.katz_centrality
-   pylibcugraph.strongly_connected_components
-   pylibcugraph.weakly_connected_components
-   pylibcugraph.pagerank
-   pylibcugraph.hits
-   pylibcugraph.node2vec
-   pylibcugraph.bfs
-   pylibcugraph.sssp
diff --git a/docs/cugraph/source/api_docs/service/cugraph_service_client.rst b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst
deleted file mode 100644
index 7e344d326..000000000
--- a/docs/cugraph/source/api_docs/service/cugraph_service_client.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-cugraph-service-client API Reference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-cugraph-service
-
-.. currentmodule:: cugraph-service
-
-.. autosummary::
-   :toctree: ../api/service/
-
-..   cugraph_service_client.client.RunAsyncioThread
-   cugraph_service_client.client.run_async
-   cugraph_service_client.client.DeviceArrayAllocator
-   cugraph_service_client.client.CugraphServiceClient
-   cugraph_service_client.remote_graph_utils
-   cugraph_service_client.remote_graph.RemoteGraph
-   cugraph_service_client.types.UnionWrapper
-   cugraph_service_client.types.ValueWrapper
-   cugraph_service_client.types.GraphVertexEdgeIDWrapper
diff --git a/docs/cugraph/source/api_docs/service/cugraph_service_server.rst b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst
deleted file mode 100644
index 09ca8360b..000000000
--- a/docs/cugraph/source/api_docs/service/cugraph_service_server.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-cugraph-service-server API Reference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-cugraph-service
-
-.. currentmodule:: cugraph-service
-
-.. autosummary::
-   :toctree: ../api/service/
-
-..   cugraph_service_server.cugraph_handler.call_algo
-   cugraph_service_server.cugraph_handler.ExtensionServerFacade
-   cugraph_service_server.cugraph_handler.CugraphHandler
diff --git a/docs/cugraph/source/api_docs/service/index.rst b/docs/cugraph/source/api_docs/service/index.rst
deleted file mode 100644
index ca251e475..000000000
--- a/docs/cugraph/source/api_docs/service/index.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-cugraph-service API Reference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. toctree::
-    :maxdepth: 2
-    :caption: cugraph-service API Documentation
-
-    cugraph_service_client
-    cugraph_service_server
diff --git a/docs/cugraph/source/api_docs/wholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/index.rst
deleted file mode 100644
index 80e231d46..000000000
--- a/docs/cugraph/source/api_docs/wholegraph/index.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-WholeGraph API reference
-========================
-
-This page provides WholeGraph API reference
-
-.. toctree::
-    :maxdepth: 2
-    :caption: WholeGraph API Documentation
-
-    libwholegraph/index.rst
-    pylibwholegraph/index.rst
diff --git a/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst
deleted file mode 100644
index 4ef68abef..000000000
--- a/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst
+++ /dev/null
@@ -1,228 +0,0 @@
-=====================
-libwholegraph API doc
-=====================
-
-Doxygen WholeGraph C API documentation
---------------------------------------
-For doxygen documentation, please refer to `Doxygen Documentation <../../doxygen_docs/libwholegraph/html/index.html>`_
-
-WholeGraph C API documentation
-------------------------------
-
-Library Level APIs
-++++++++++++++++++
-
-.. doxygenenum:: wholememory_error_code_t
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_init
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_finalize
-    :project: libwholegraph
-.. doxygenfunction:: fork_get_device_count
-    :project: libwholegraph
-
-WholeMemory Communicator APIs
-+++++++++++++++++++++++++++++
-
-.. doxygentypedef:: wholememory_comm_t
-    :project: libwholegraph
-.. doxygenstruct:: wholememory_unique_id_t
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_unique_id
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_communicator
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_destroy_communicator
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_communicator_get_rank
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_communicator_get_size
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_communicator_barrier
-    :project: libwholegraph
-
-WholeMemoryHandle APIs
-++++++++++++++++++++++
-
-.. doxygenenum:: wholememory_memory_type_t
-    :project: libwholegraph
-.. doxygenenum:: wholememory_memory_location_t
-    :project: libwholegraph
-.. doxygentypedef:: wholememory_handle_t
-    :project: libwholegraph
-.. doxygenstruct:: wholememory_gref_t
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_malloc
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_free
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_communicator
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_type
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_location
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_total_size
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_data_granularity
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_local_memory
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_rank_memory
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_global_pointer
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_global_reference
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_determine_partition_plan
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_determine_entry_partition_plan
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_partition_plan
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_load_from_file
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_store_to_file
-    :project: libwholegraph
-
-WholeMemoryTensor APIs
-++++++++++++++++++++++
-
-.. doxygenenum:: wholememory_dtype_t
-    :project: libwholegraph
-.. doxygenstruct:: wholememory_array_description_t
-    :project: libwholegraph
-.. doxygenstruct:: wholememory_matrix_description_t
-    :project: libwholegraph
-.. doxygenstruct:: wholememory_tensor_description_t
-    :project: libwholegraph
-.. doxygentypedef:: wholememory_tensor_t
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_dtype_get_element_size
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_dtype_is_floating_number
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_dtype_is_integer_number
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_array_desc
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_matrix_desc
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_initialize_tensor_desc
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_copy_array_desc_to_matrix
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_copy_array_desc_to_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_copy_matrix_desc_to_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_convert_tensor_desc_to_array
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_convert_tensor_desc_to_matrix
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_element_count_from_array
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_size_from_array
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_element_count_from_matrix
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_size_from_matrix
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_element_count_from_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_get_memory_size_from_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_unsqueeze_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_destroy_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_make_tensor_from_pointer
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_make_tensor_from_handle
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_has_handle
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_get_memory_handle
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_get_tensor_description
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_get_global_reference
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_map_local_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_get_data_pointer
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_get_entry_per_partition
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_get_subtensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_tensor_get_root
-    :project: libwholegraph
-
-Ops on WholeMemory Tensors
-++++++++++++++++++++++++++
-
-.. doxygenfunction:: wholememory_gather
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_scatter
-    :project: libwholegraph
-
-WholeTensorEmbedding APIs
-+++++++++++++++++++++++++
-
-.. doxygentypedef:: wholememory_embedding_cache_policy_t
-    :project: libwholegraph
-.. doxygentypedef:: wholememory_embedding_optimizer_t
-    :project: libwholegraph
-.. doxygentypedef:: wholememory_embedding_t
-    :project: libwholegraph
-.. doxygenenum:: wholememory_access_type_t
-    :project: libwholegraph
-.. doxygenenum:: wholememory_optimizer_type_t
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_embedding_optimizer
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_optimizer_set_parameter
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_destroy_embedding_optimizer
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_embedding_cache_policy
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_destroy_embedding_cache_policy
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_create_embedding
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_destroy_embedding
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_embedding_get_embedding_tensor
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_embedding_gather
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_embedding_gather_gradient_apply
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_embedding_get_optimizer_state_names
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_embedding_get_optimizer_state
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_embedding_writeback_cache
-    :project: libwholegraph
-.. doxygenfunction:: wholememory_embedding_drop_all_cache
-    :project: libwholegraph
-
-Ops on graphs stored in WholeMemory
-+++++++++++++++++++++++++++++++++++
-
-.. doxygenfunction:: wholegraph_csr_unweighted_sample_without_replacement
-    :project: libwholegraph
-.. doxygenfunction:: wholegraph_csr_weighted_sample_without_replacement
-    :project: libwholegraph
-
-Miscellaneous Ops for graph
-+++++++++++++++++++++++++++
-
-.. doxygenfunction:: graph_append_unique
-    :project: libwholegraph
-.. doxygenfunction:: csr_add_self_loop
-    :project: libwholegraph
diff --git a/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst
deleted file mode 100644
index 67aab00ac..000000000
--- a/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst
+++ /dev/null
@@ -1,38 +0,0 @@
-=======================
-pylibwholegraph API doc
-=======================
-
-.. currentmodule:: pylibwholegraph
-
-APIs
-----
-.. autosummary::
-    :toctree: ../../api/wg
-
-    torch.initialize.init_torch_env
-    torch.initialize.init_torch_env_and_create_wm_comm
-    torch.initialize.finalize
-    torch.comm.WholeMemoryCommunicator
-    torch.comm.set_world_info
-    torch.comm.create_group_communicator
-    torch.comm.destroy_communicator
-    torch.comm.get_global_communicator
-    torch.comm.get_local_node_communicator
-    torch.comm.get_local_device_communicator
-    torch.tensor.WholeMemoryTensor
-    torch.tensor.create_wholememory_tensor
-    torch.tensor.create_wholememory_tensor_from_filelist
-    torch.tensor.destroy_wholememory_tensor
-    torch.embedding.WholeMemoryOptimizer
-    torch.embedding.create_wholememory_optimizer
-    torch.embedding.destroy_wholememory_optimizer
-    torch.embedding.WholeMemoryCachePolicy
-    torch.embedding.create_wholememory_cache_policy
-    torch.embedding.create_builtin_cache_policy
-    torch.embedding.destroy_wholememory_cache_policy
-    torch.embedding.WholeMemoryEmbedding
-    torch.embedding.create_embedding
-    torch.embedding.create_embedding_from_filelist
-    torch.embedding.destroy_embedding
-    torch.embedding.WholeMemoryEmbeddingModule
-    torch.graph_structure.GraphStructure
diff --git a/docs/cugraph/source/wholegraph/basics/index.rst b/docs/cugraph/source/wholegraph/basics/index.rst
deleted file mode 100644
index 429fe35d6..000000000
--- a/docs/cugraph/source/wholegraph/basics/index.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-======
-Basics
-======
-
-
-.. toctree::
-   :maxdepth: 2
-
-   wholegraph_intro
-   wholememory_intro
-   wholememory_implementation_details
diff --git a/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md b/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md
deleted file mode 100644
index 360f8e0e3..000000000
--- a/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# WholeGraph Introduction
-WholeGraph helps train large-scale Graph Neural Networks(GNN).
-WholeGraph provides underlying storage structure called WholeMemory.
-WholeMemory is a Tensor like storage and provides multi-GPU support.
-It is optimized for NVLink systems like DGX A100 servers.
-By working together with cuGraph, cuGraph-Ops, cuGraph-DGL, cuGraph-PyG, and upstream DGL and PyG,
-it will be easy to build GNN applications.
-
-## WholeMemory
-WholeMemory can be regarded as a whole view of GPU memory.
-WholeMemory exposes a handle of the memory instance no matter how the underlying data is stored across multiple GPUs.
-WholeMemory assumes that separate process is used to control each GPU.
-
-### WholeMemory Basics
-To define WholeMemory, we need to specify the following:
-
-#### 1. Specify the set of GPU to handle the Memory
-
-Since WholeMemory is owned by a set of GPUs, you must specify the set of GPUs.
-This is done by creating [WholeMemory Communicator](#wholememory-communicator) and specifying the WholeMemory Communicator when creating WholeMemory.
-
-#### 2. Specify the location of the memory
-
-Although WholeMemory is owned by a set of GPUs, the memory itself can be located in host memory or in device memory.
-The location of the memory need to be specified, two types of locations can be specified.
-
-- **Host memory**: will use pinned host memory as underlying storage.
-- **Device memory**: will use GPU device memory as underlying storage.
-
-#### 3. Specify the address mapping mode of the memory
-
-As WholeMemory is owned by multiple GPUs, each GPU will access the whole memory space, so we need address mapping.
-There are three types of address mapping modes (also known as WholeMemory types), they are:
-
-- **Continuous**: All memory from each GPU will be mapped into a single continuous memory address space for each GPU.
-  In this mode, each GPU can directly access the whole memory using a single pointer and offset, just like using normal
-  device memory. Software will see no difference. Hardware peer to peer access will handle the underlying communication.
-
-- **Chunked**: Memory from each GPU will be mapped into different memory chunks, one chunk for each GPU.
-  In this mode, direct access is also supported, but not using a single pointer. Software will see the chunked memory.
-  However, an abstract layer may help to hide this.
-
-- **Distributed**: Memory from other GPUs are not mapped into current GPU, so no direct access is supported.
-  To access memory of other GPU, explicit communication is needed.
-
-To learn more details about WholeMemory locations and WholeMemory types, please refer to
-[WholeMemory Implementation Details](wholememory_implementation_details.md)
-
-### WholeMemory Communicator
-WholeMemory Communicator has two main purpose:
-
-- **Defines a set of GPUs which works together on WholeMemory.** WholeMemory Communicator is created by all GPUs that
-  wants to work together. A WholeMemory Communicator can be reused as long as the GPU set needed is the same.
-- **Provides underlying communication channel needed by WholeMemory.** WholeMemory may need commuincator between GPUs
-  during the WholeMemory creation and some OPs on some types of WholeMemory.
-
-To Create WholeMemory Communicator, a WholeMemory Unique ID needs to be created first, it is usually created by the first GPU in the set of GPUs, and then broadcasted to all GPUs that want to work together. Then all GPUs in this communicator
-will call WholeMemory Communicator creation function using this WholeMemory Unique ID, and the rank of current GPU as
-well as all GPU count.
-
-### WholeMemory Granularity
-As underlying storage may be partitioned into multiple GPUs physically, this is usually not wanted inside one single
-user data block. To help on this, when creating WholeMemory, the granularity of data can be specified. Then the
-WholeMemory is considered as multiple block of the same granularity and will not get split inside the granularity.
-
-### WholeMemory Mapping
-As WholeMemory provides a whole view of memory to GPU, to access WholeMemory, mapping is usually needed.
-Different types of WholeMemory have different mapping methods supported as their names.
-Some mappings supported include
-- All the WholeMemory types support mapping the memory range that local GPU is responsible for.
-  That is, each rank can directly access "Local" memory in all types of WholeMemory.
-  Here "Local" memory doesn't have to be on current GPU's memory, it can be on host memory or even maybe on other GPU,
-  but it is guaranteed to be directly accessed by current GPU.
-- Chunked and Continuous WholeMemory also support Chunked mapping. That is, memory of all GPUs can be mapped into
-  current GPU, one continuous chunk for one GPU. Each chunk can be directly accessed by current GPU. But the memory of
-  different chunks are not guaranteed to be continuous.
-- Continuous WholeMemory can be mapped into continuous memory space. That is, memory of all GPUs are mapped into a
-  single range of virtual memory, accessing to different position of this memory will physically access to different
-  GPUs. This mapping will be handled by hardware (CPU pagetable or GPU pagetable).
-
-### Operations on WholeMemory
-There are some operations that can be performed on WholeMemory. They are based on the mapping of WholeMemory.
-#### Local Operation
-As all WholeMemory supports mapping of local memory, so operation on local memory is supported. The operation can be
-either read or write. Just use it as GPU memory of current device is OK.
-#### Load and Store
-To facilitate file operation, Load / Store WholeMemory from file or to file is supported. WholeMemory uses raw binary
-file format for disk operation. For Load, the input file can be a single file or a list of files, if it is a list, they
-will be logically concatenated together and then loaded. For store, each GPU stores its local memory to file, producing
-a list of files.
-#### Gather and Scatter
-WholeMemory also supports Gather / Scatter operation, usually they operate on a
-[WholeMemory Tensor](#wholememory-tensor).
-
-### WholeMemory Tensor
-Compared to PyTorch, WholeMemory is like PyTorch Storage while a WholeMemory Tensor is like a PyTorch Tensor.
-For now, WholeMemory supports only 1D and 2D tensors, or arrays and matrices. Only first dimension is partitioned.
-
-### WholeMemory Embedding
-WholeMemory Embedding is just like a 2D WholeMemory Tensor, with two features added. They support cache and sparse
-optimizers.
-#### Cache Support
-To create WholeMemory Embedding with a cache, WholeMemory CachePolicy needs to be be created first. WholeMemoryCachePolicy can be created with following fields:
-- **WholeMemory Communicator**: WholeMemory CachePolicy also needs WholeMemory Communicator.
-  WholeMemory Communicator defines the set of GPUs that cache all the Embedding.
-  It can be the same as the WholeMemory Communicator used to create WholeMemory Embedding.
-- **WholeMemory type**: WholeMemory CachePolicy uses WholeMemory type to specify the WholeMemory type of cache.
-- **WholeMemory location**: WholeMemory CachePolicy use WholeMemory location to specify the location of the cache.
-- **Access type**: Access type can be readonly or readwrite.
-- **Cache ratio**: Specify how much memory the cache will use. This ratio is computed for each GPU set that caches the
-  whole embedding.
-
-The two most commonly used caches are:
-- **Device cached host memory**: When the WholeMemory Communicator for Cache Policy is the same as the WholeMemory
-  Communicator used to create WholeMemory Embedding, it means that the cache has same GPU set as WholeMemory Embedding.
-  So each GPU just caches its own part of raw Embedding.
-  Most commonly, when raw WholeMemory Embedding is located on host memory, and the cache is on device
-  memory, each GPU just caches its own part of host memory.
-- **Local cached global memory**: The WholeMemory Communicator of WholeMemory CachePolicy can also be a subset of the
-  WholeMemory Communicator of WholeMemory Embedding. In this case, the subset of GPUs together cache all the embeddings.
-  Normally, when raw WholeMemory Embedding is partitioned on different machine nodes, and we
-  want to cache some embeddings in local machine or local GPU, then the subset of GPU can be all the GPUs in the local
-  machine. For local cached global memory, only readonly is supported.
-
-#### WholeMemory Embedding Sparse Optimizer
-Another feature of WholeMemory Embedding is that WholeMemory Embedding supports embedding training.
-To efficiently train large embedding tables, a sparse optimizer is needed.
-WholeMemory Embedding Sparse Optimizer can run on a cached or noncached WholeMemory Embedding.
-Currently supported optimizers include SGD, Adam, RMSProp and AdaGrad.
-
-## Graph Structure
-Graph structure in WholeGraph is also based on WholeMemory.
-In WholeGraph, graph is stored in [CSR format](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format)).
-Both ROW_INDEX (noted as `csr_row_ptr`) and COL_INDEX (notated as `csr_col_ind`) are stored in a
-WholeMemory Tensor. So loading Graph Structure can use [WholeMemory Tensor Loading mechanism](#load-and-store).
diff --git a/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md b/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md
deleted file mode 100644
index 634539cd2..000000000
--- a/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# WholeMemory Implementation Details
-As described in [WholeMemory Introduction](wholegraph_intro.md), there are two WholeMemory location and three
-WholeMemory types. So there will be total six WholeMemory.
-
-|     Type      | CONTINUOUS  | CONTINUOUS |  CHUNKED  |  CHUNKED  | DISTRIBUTED | DISTRIBUTED |
-|:-------------:|:-----------:|:----------:|:---------:|:---------:|:-----------:|:-----------:|
-|   Location    |   DEVICE    |    HOST    |  DEVICE   |   HOST    |   DEVICE    |    HOST     |
-| Allocated by  |    EACH     |   FIRST    |   EACH    |   FIRST   |    EACH     |    EACH     |
-| Allocate API  |   Driver    |    Host    |  Runtime  |   Host    |   Runtime   |   Runtime   |
-|  IPC Mapping  |   Unix fd   |    mmap    |  cudaIpc  |   mmap    | No IPC map  | No IPC map  |
-
-For "Continuous" and "Chunked" types of WholeMemory, all memory is mapped to each GPU,
-so these two types are all "Mapped" WholeMemory, in contrast to "Distributed" WholeMemory where all are not mapped.
-
-## WholeMemory Layout
-Since the underlying memory of a single WholeMemory object may be on multiple GPU devices, the WholeGraph library will
-partition data into these GPU devices.
-The partition method guarantees that each GPU can access one continuous part of the entire memory.
-Here "can access" means can directly access from CUDA kernels, but the memory doesn't have to be physically on that GPU.
-For example,it can be on host memory or other GPU's device memory that can be access using P2P.
-In that case the stored data has its own granularity that shouldn't be split. Data granularity can be specified while
-creating WholeMemory. Then each data granularity can be considered as a block of data.
-
-The follow figure shows the layout of 15 data block over 4 GPUs.
-![WholeMemory Layout](../imgs/general_wholememory.png)
-
-For WholeMemory Tensors, they can be 1D or 2D tensors.
-For 1D tensor, data granularity is one element. For 2D tensor, data granularity is its 1D tensor.
-The layout will be like this:
-![WholeMemory Tensor Layout](../imgs/wholememory_tensor.png)
-
-## WholeMemory Allocation
-As there are six types of WholeMemory, the allocation process of each type are as follows:
-
-### Device Continuous WholeMemory
-For Device Continuous WholeMemory, first a range of virtual address space is reserved in each GPU, which covers the
-entire memory range. Then a part of pyhsical memory is allocated in each GPU, as shown in the following figure.
-![Device Continuous WholeMemory Allocation Step 1](../imgs/device_continuous_wholememory_step1.png)
-After that, each GPU gathers all the memory handles from all GPUs, and maps them to the reserved address space.
-![Device Continuous WholeMemory Allocation Step 2](../imgs/device_continuous_wholememory_step2.png)
-
-### Device Chunked WholeMemory
-For Device Chunked WholeMemory, first each GPU allocates its own part of memory using CUDA runtime API, this will create
-both a virtual address space and physical memory for its own memory.
-![Device Chunked WholeMemory Allocation Step 1](../imgs/device_chunked_wholememory_step1.png)
-Each GPU gathers the Ipc handle of memory from all other GPUs, and maps that into its own virtual address space.
-![Device Chunked WholeMemory Allocation Step 2](../imgs/device_chunked_wholememory_step2.png)
-
-### Host Mapped WholeMemory
-For Host, Continuous and Chunked are using the same method. First, rank and allocate the host physical and share that to all
-ranks.
-![Host Mapped WholeMemory Allocation Step 1](../imgs/host_mapped_wholememory_step1.png)
-Then each rank registers that host memory to GPU address space.
-![Host Mapped WholeMemory Allocation Step 2](../imgs/host_mapped_wholememory_step2.png)
-
-### Distributed WholeMemory
-For Distributed WholeMemory, each GPU just malloc its own part of memory, no need to share to other GPUs.
-![Distributed WholeMemory Allocation](../imgs/distributed_wholememory.png)
diff --git a/docs/cugraph/source/wholegraph/basics/wholememory_intro.md b/docs/cugraph/source/wholegraph/basics/wholememory_intro.md
deleted file mode 100644
index 7209da947..000000000
--- a/docs/cugraph/source/wholegraph/basics/wholememory_intro.md
+++ /dev/null
@@ -1,123 +0,0 @@
-## WholeMemory
-WholeMemory can be regarded as a whole view of GPU memory.
-WholeMemory exposes a handle to the memory instance no matter how the underlying data is stored across multiple GPUs.
-WholeMemory assumes that a separate process is used to control each GPU.
-
-### WholeMemory Basics
-To define WholeMemory, we need to specify the following:
-
-#### 1. Specify the set of GPU to handle the Memory
-
-As WholeMemory is owned by a set of GPUs, so the set of GPUs need to be specified.
-This is done by creating [WholeMemory Communicator](#wholememory-communicator) and specify the WholeMemory Communicator
-when creating WholeMemory.
-
-#### 2. Specify the location of the memory
-
-Although WholeMemory is owned by a set of GPUs,  the memory itself can be located on host memory or on device memory.
-So the location of the memory needs to be specified. Two types of location can be specified.
-
-- **Host memory**: will use pinned host memory as underlying storage.
-- **Device memory**: will use GPU device memory as underlying storage.
-
-#### 3. Specify the address mapping mode of the memory
-
-As WholeMemory is owned by multiple GPUs, each GPU will access the whole memory space, so we need address mapping.
-There are three types of address mapping modes (also known as WholeMemory types), they are:
-
-- **Continuous**: All memory from each GPU will be mapped into a single continuous memory address space for each GPU.
-  In this mode, each GPU can directly access the whole memory using a single pointer and offset, just like using normal
-  device memory. Software will see no difference. Hardware peer-to-peer access will handle the underlying communication.
-
-- **Chunked**: Memory from each GPU will be mapped into different memory chunks, one chunk for each GPU.
-  In this mode, direct access is also supported, but not using a single pointer. Software will see the chunked memory.
-  However, an abstract layer can hide this.
-
-- **Distributed**: Memory from other GPUs is not mapped into current GPU, so no direct access is supported.
-  To access memory of another GPU, explicit communication is needed.
-
-If you would like to know more details about WholeMemory locations and WholeMemory types, please refer to
-[WholeMemory Implementation Details](wholememory_implementation_details.md)
-
-### WholeMemory Communicator
-WholeMemory Communicator has two main purpose:
-
-- **Defines a set of GPUs which works together on WholeMemory.** WholeMemory Communicator is created by all GPUs that
-  wants to work together. A WholeMemory Communicator can be reused as long as the GPU set needed is the same.
-- **Provides underlying communication channel needed by WholeMemory.** WholeMemory may need commuincator between GPUs
-  during the WholeMemory creation and some OPs on some types of WholeMemory.
-
-To Create WholeMemory Communicator, a WholeMemory Unique ID need to be created first, it is usually created by the first
-GPU in the set of GPUs, and then broadcasted to all GPUs that want to work together. Then all GPUs in this communicator
-will call WholeMemory Communicator creation function using this WholeMemory Unique ID, and the rank of current GPU as
-well as all GPU count.
-
-### WholeMemory Granularity
-As underlying storage may be physically partitioned into multiple GPUs, it is usually not wanted inside one single
-user data block. To help with this, when creating WholeMemory, the granularity of data can be specified. Therefore
-WholeMemory is considered as multiple blocks of the same granularity and will not get split inside the granularity.
-
-### WholeMemory Mapping
-Since WholeMemory provides a whole view of memory to GPU,  mapping is usually needed to access WholeMemory.
-Different types of WholeMemory have different mapping methods supported as their names.
-Some mappings supported include:
-- All the WholeMemory types support mapping the memory range that local GPU is responsible for.
-  That is, each rank can directly access "Local" memory in all types of WholeMemory.
-  Here "Local" memory doesn't have to be on current GPU's memory, it can be on host memory or even maybe on other GPU,
-  but it is guaranteed to be directly accessed by current GPU.
-- Chunked and Continuous WholeMemory also support Chunked mapping. That is, memory of all GPUs can be mapped into
-  current GPU, one continuous chunk for one GPU. Each chunk can be directly accessed by current GPU. But the memory of
-  different chunks are not guaranteed to be continuous.
-- Continuous WholeMemory can be mapped into continuous memory space. That is, memory of all GPUs are mapped into a
-  single range of virtual memory, accessing different positions of this memory will physically access different
-  GPUs. This mapping will be handled by hardware (CPU pagetable or GPU pagetable).
-
-### Operations on WholeMemory
-There are some operations that can be performed on WholeMemory. They are based on the mapping of WholeMemory.
-#### Local Operation
-As all WholeMemory supports mapping of local memory, so operation on local memory is supported. The operation can be
-either read or write. Just use it as GPU memory of current device is OK.
-#### Load / Store
-To facilitate file operation, Load / Store WholeMemory from file or to file is supported. WholeMemory use raw binary
-file format for disk operation. For Load, the input file can be single file or a list of files, if it is a list, they
-will be logically concatenated together and then loaded. For store, each GPU stores its local memory to file, producing
-a list of files.
-#### Gather / Scatter
-WholeMemory also supports Gather / Scatter operations, usually they operate on a
-[WholeMemory Tensor](#wholememory-tensor).
-
-### WholeMemory Tensor
-Compared to PyTorch, WholeMemory is like PyTorch Storage while WholeMemory Tensor is like PyTorch Tensor.
-For now, WholeMemory supports only 1D and 2D tensor, or array and matrix. Only first dimension is partitioned.
-
-### WholeMemory Embedding
-WholeMemory Embedding is just like 2D WholeMemory Tensor, with  cache support and sparse optimizer support added.
-#### Cache Support
-WholeMemory Embedding supports cache. To create WholeMemory Embedding with cache, WholeMemory CachePolicy need first be
-created. WholeMemoryCachePolicy can be created with following fields:
-- **WholeMemory Communicator**: WholeMemory CachePolicy also need WholeMemory Communicator.
-  This WholeMemory Communicator defines the set of GPUs that cache the all the Embedding.
-  It can be the same as the WholeMemory Communicator used to create WholeMemory Embedding.
-- **WholeMemory type**: WholeMemory CachePolicy uses WholeMemory type to specify the WholeMemory type of the cache.
-- **WholeMemory location**: WholeMemory CachePolicy uses WholeMemory location to specify the location of the cache.
-- **Access type**: Access type can be readonly or readwrite.
-- **Cache ratio**: Specify how much memory the cache will use. This ratio is computed for each GPU set that caches the
-  whole embedding.
-
-There are two most commonly used caches. They are:
-- **Device cached host memory**: When the WholeMemory Communicator for Cache Policy is the same as the WholeMemory
-  Communicator used to create WholeMemory Embedding, it means that cache has the same GPU set as WholeMemory Embedding.
-  So each GPU just cache its own part of raw Embedding.
-  Normally, when raw WholeMemory Embedding is located on host memory, and the cache is on device
-  memory, each GPU just caches its own part of host memory.
-- **Local cached global memory**: The WholeMemory Communicator of WholeMemory CachePolicy can also be a subset of the
-  WholeMemory Communicator of WholeMemory Embedding. In this case, the subset of GPUs together cache all the embeddings.
-  Typically, raw WholeMemory Embedding is partitioned on different machine nodes, and we
-  want to cache some embeddings in local machine or local GPU, then the subset of GPUs can be all the GPUs on the local
-  machine. For local cached global memory supports just readonly.
-
-#### WholeMemory Embedding Sparse Optimizer
-Another feature of WholeMemory Embedding is that WholeMemory Embedding supports embedding training.
-To efficiently train large embedding tables, a sparse optimizer is needed.
-The WholeMemory Embedding Sparse Optimizer can run on cached or non-cached WholeMemory Embedding.
-Currently supported optimizers include SGD, Adam, RMSProp and AdaGrad.
diff --git a/docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png b/docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png
deleted file mode 100644
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diff --git a/docs/cugraph/source/wholegraph/index.rst b/docs/cugraph/source/wholegraph/index.rst
deleted file mode 100644
index bb2281b13..000000000
--- a/docs/cugraph/source/wholegraph/index.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-WholeGraph
-==========
-RAPIDS WholeGraph has following package:
-
-* pylibwholegraph: shared memory-based GPU-accelerated GNN training
-
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-   basics/index
-   installation/index
diff --git a/docs/cugraph/source/wholegraph/installation/container.md b/docs/cugraph/source/wholegraph/installation/container.md
deleted file mode 100644
index 3a2c627c5..000000000
--- a/docs/cugraph/source/wholegraph/installation/container.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Build Container for WholeGraph
-To run WholeGraph or build WholeGraph from source, set up the environment first.
-We recommend using Docker images.
-For example, to build the WholeGraph base image from the NGC pytorch 22.10 image, you can follow `Dockerfile`:
-```dockerfile
-FROM nvcr.io/nvidia/pytorch:22.10-py3
-
-RUN apt update && DEBIAN_FRONTEND=noninteractive apt install -y lsb-core software-properties-common wget libspdlog-dev
-
-#RUN remove old cmake to update
-RUN conda remove --force -y cmake
-RUN rm -rf /usr/local/bin/cmake && rm -rf /usr/local/lib/cmake && rm -rf /usr/lib/cmake
-
-RUN apt-key adv --fetch-keys https://apt.kitware.com/keys/kitware-archive-latest.asc && \
-    export LSB_CODENAME=$(lsb_release -cs) && \
-    apt-add-repository -y "deb https://apt.kitware.com/ubuntu/ ${LSB_CODENAME} main" && \
-    apt update && apt install -y cmake
-
-# update py for pytest
-RUN pip3 install -U py
-RUN pip3 install Cython setuputils3 scikit-build nanobind pytest-forked pytest
-```
-
-To run GNN applications, you may also need cuGraphOps, DGL and/or PyG libraries to run the GNN layers.
-You may refer to [DGL](https://www.dgl.ai/pages/start.html) or [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
-For example, to install DGL, you may need to add:
-```dockerfile
-RUN pip3 install  dgl -f https://data.dgl.ai/wheels/cu118/repo.html
-```
diff --git a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md
deleted file mode 100644
index 57314dcd4..000000000
--- a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md
+++ /dev/null
@@ -1,48 +0,0 @@
-
-# Getting the WholeGraph Packages
-
-Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install)
-and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options.
-
-
-There are 4 ways to get WholeGraph packages:
-1. [Quick start with Docker Repo](#docker)
-2. [Conda Installation](#conda)
-3. [Pip Installation](#pip)
-4. [Build from Source](./source_build.md)
-
-
-

-
-## Docker
-The RAPIDS Docker containers (as of Release 23.10) contain all RAPIDS packages, including WholeGraph, as well as all required supporting packages.   To download a container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running.  This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries.
-
-

-
-
-## Conda
-It is easy to install WholeGraph using conda. You can get a minimal conda installation with [Miniconda](https://conda.io/miniconda.html) or get the full installation with [Anaconda](https://www.anaconda.com/download).
-
-WholeGraph conda packages
- * libwholegraph
- * pylibwholegraph
-
-Replace the package name in the example below to the one you want to install.
-
-
-Install and update WholeGraph using the conda command:
-
-```bash
-conda install -c rapidsai -c conda-forge -c nvidia wholegraph cudatoolkit=11.8
-```
-
-

-
-## PIP
-wholegraph, and all of RAPIDS, is available via pip.
-
-```
-pip install wholegraph-cu11 --extra-index-url=https://pypi.nvidia.com
-```
-
-

diff --git a/docs/cugraph/source/wholegraph/installation/index.rst b/docs/cugraph/source/wholegraph/installation/index.rst
deleted file mode 100644
index 09f1cb44a..000000000
--- a/docs/cugraph/source/wholegraph/installation/index.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-Installation
-============
-
-.. toctree::
-    :maxdepth: 2
-
-    getting_wholegraph
-    container
-    source_build
diff --git a/docs/cugraph/source/wholegraph/installation/source_build.md b/docs/cugraph/source/wholegraph/installation/source_build.md
deleted file mode 100644
index a7727ac40..000000000
--- a/docs/cugraph/source/wholegraph/installation/source_build.md
+++ /dev/null
@@ -1,187 +0,0 @@
-# Building from Source
-
-The following instructions are for users wishing to build wholegraph from source code. These instructions are tested on supported distributions of Linux,CUDA,
-and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for a list of supported environments.
-Other operating systems _might be_ compatible, but are not currently tested.
-
-The wholegraph package includes both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly.
-The C/C++ CUDA library is `libwholegraph` and the python library is `pylibwholegraph`.
-
-## Prerequisites
-
-__Compiler__:
-* `gcc`         version 11.0+
-* `nvcc`        version 11.0+
-* `cmake`       version 3.26.4+
-
-__CUDA__:
-* CUDA 11.8+
-* NVIDIA driver 450.80.02+
-* Pascal architecture or better
-
-You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads).
-
-__Other Packages__:
-* ninja
-* nccl
-* cython
-* setuputils3
-* scikit-learn
-* scikit-build-core
-* nanobind>=0.2.0
-
-## Building wholegraph
-To install wholegraph from source, ensure the dependencies are met.
-
-### Clone Repo and Configure Conda Environment
-__GIT clone a version of the repository__
-
-  ```bash
-  # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME
-  export WHOLEGRAPH_HOME=$(pwd)/wholegraph
-
-  # Download the wholegraph repo - if you have a forked version, use that path here instead
-  git clone https://github.com/rapidsai/wholegraph.git $WHOLEGRAPH_HOME
-
-  cd $WHOLEGRAPH_HOME
-  ```
-
-__Create the conda development environment__
-
-```bash
-# create the conda environment (assuming in base `wholegraph` directory)
-
-# for CUDA 11.x
-conda env create --name wholegraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml
-
-# activate the environment
-conda activate wholegraph_dev
-
-# to deactivate an environment
-conda deactivate
-```
-
-  - The environment can be updated as development includes/changes the dependencies. To do so, run:
-
-
-```bash
-
-# Where XXX is the CUDA version
-conda env update --name wholegraph_dev --file conda/environments/all_cuda-XXX_arch-x86_64.yaml
-
-conda activate wholegraph_dev
-```
-
-
-### Build and Install Using the `build.sh` Script
-Using the `build.sh` script make compiling and installing wholegraph a
-breeze. To build and install, simply do:
-
-```bash
-$ cd $WHOLEGRAPH_HOME
-$ ./build.sh clean
-$ ./build.sh libwholegraph
-$ ./build.sh pylibwholegraph
-```
-
-There are several other options available on the build script for advanced users.
-`build.sh` options:
-```bash
-build.sh [ ...] [ ...]
- where  is:
-   clean                    - remove all existing build artifacts and configuration (start over).
-   uninstall                - uninstall libwholegraph and pylibwholegraph from a prior build/install (see also -n)
-   libwholegraph            - build the libwholegraph C++ library.
-   pylibwholegraph          - build the pylibwholegraph Python package.
-   tests                    - build the C++ (OPG) tests.
-   benchmarks               - build benchmarks.
-   docs                     - build the docs
- and  is:
-   -v                          - verbose build mode
-   -g                          - build for debug
-   -n                          - no install step
-   --allgpuarch               - build for all supported GPU architectures
-   --cmake-args=\\\"\\\" - add arbitrary CMake arguments to any cmake call
-   --compile-cmd               - only output compile commands (invoke CMake without build)
-   --clean                    - clean an individual target (note: to do a complete rebuild, use the clean target described above)
-   -h | --h[elp]               - print this text
-
- default action (no args) is to build and install 'libwholegraph' then 'pylibwholegraph' targets
-
-examples:
-$ ./build.sh clean                        # remove prior build artifacts (start over)
-$ ./build.sh
-
-# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4)
-$ PARALLEL_LEVEL=4 ./build.sh libwholegraph
-
-Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`.
-```
-
-
-## Building each section independently
-### Build and Install the C++/CUDA `libwholegraph` Library
-CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`.
-
-This project uses cmake for building the C/C++ library. To configure cmake, run:
-
-  ```bash
-  # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME
-  export WHOLEGRAPH_HOME=$(pwd)/wholegraph
-
-  cd $WHOLEGRAPH_HOME
-  cd cpp                                        # enter cpp directory
-  mkdir build                                   # create build directory
-  cd build                                      # enter the build directory
-  cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX
-
-  # now build the code
-  make -j                                       # "-j" starts multiple threads
-  make install                                  # install the libraries
-  ```
-The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/wholegraph` respectively.
-
-### Building and installing the Python package
-
-Build and Install the Python packages to your Python path:
-
-```bash
-cd $WHOLEGRAPH_HOME
-cd python
-cd pylibwholegraph
-python setup.py build_ext --inplace
-python setup.py install    # install pylibwholegraph
-```
-
-## Run tests
-
-Run either the C++ or the Python tests with datasets
-
-  - **Python tests with datasets**
-
-    ```bash
-    cd $WHOLEGRAPH_HOME
-    cd python
-    pytest
-    ```
-
-  - **C++ stand alone tests**
-
-    From the build directory :
-
-    ```bash
-    # Run the tests
-    cd $WHOLEGRAPH_HOME
-    cd cpp/build
-    gtests/PARALLEL_UTILS_TESTS		# this is an executable file
-    ```
-
-
-Note: This conda installation only applies to Linux and Python versions 3.8/3.10.
-
-## Creating documentation
-
-Python API documentation can be generated from _./docs/wholegraph directory_. Or through using "./build.sh docs"
-
-## Attribution
-Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md
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From eb74be7499666e06d4e5efbc3dd988ff3ecf1f40 Mon Sep 17 00:00:00 2001
From: Ralph Liu 
Date: Mon, 16 Sep 2024 10:44:29 -0700
Subject: [PATCH 383/384] Add initial build file

---
 build.sh | 165 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 165 insertions(+)
 create mode 100755 build.sh

diff --git a/build.sh b/build.sh
new file mode 100755
index 000000000..a2eceaa30
--- /dev/null
+++ b/build.sh
@@ -0,0 +1,165 @@
+#!/bin/bash
+
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
+
+# nx-cugraph build script
+
+# This script is used to build the component(s) in this repo from
+# source, and can be called with various options to customize the
+# build as needed (see the help output for details)
+
+# Abort script on first error
+set -e
+
+NUMARGS=$#
+ARGS=$*
+
+# NOTE: ensure all dir changes are relative to the location of this
+# script, and that this script resides in the repo dir!
+REPODIR=$(cd $(dirname $0); pwd)
+
+RAPIDS_VERSION="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2/' VERSION)"
+
+# Valid args to this script (all possible targets and options) - only one per line
+VALIDARGS="
+   clean
+   uninstall
+   nx-cugraph
+   docs
+   all
+   -v
+   -g
+   -n
+   --pydevelop
+   --allgpuarch
+   --clean
+   -h
+   --help
+"
+
+HELP="$0 [ ...] [ ...]
+ where  is:
+   clean                      - remove all existing build artifacts and configuration (start over)
+   uninstall                  - uninstall libcugraph and cugraph from a prior build/install (see also -n)
+   nx-cugraph                 - build the nx-cugraph Python package
+   docs                       - build the docs
+   all                        - build everything
+ and  is:
+   -v                         - verbose build mode
+   -g                         - build for debug
+   -n                         - do not install after a successful build (does not affect Python packages)
+   --pydevelop                - install the Python packages in editable mode
+   --allgpuarch               - build for all supported GPU architectures
+   --clean                    - clean an individual target (note: to do a complete rebuild, use the clean target described above)
+   -h                         - print this text
+
+ default action (no args) is to build and install 'nx-cugraph'
+"
+
+# Set defaults for vars modified by flags to this script
+VERBOSE_FLAG=""
+BUILD_ALL_GPU_ARCH=0
+PYTHON_ARGS_FOR_INSTALL="-m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true"
+
+function hasArg {
+    (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+}
+
+function buildDefault {
+    (( ${NUMARGS} == 0 )) || !(echo " ${ARGS} " | grep -q " [^-][a-zA-Z0-9\_\-]\+ ")
+}
+
+function cleanPythonDir {
+    pushd $1 > /dev/null
+    rm -rf dist dask-worker-space cugraph/raft *.egg-info
+    find . -type d -name __pycache__ -print | xargs rm -rf
+    find . -type d -name build -print | xargs rm -rf
+    find . -type d -name dist -print | xargs rm -rf
+    find . -type f -name "*.cpp" -delete
+    find . -type f -name "*.cpython*.so" -delete
+    find . -type d -name _external_repositories -print | xargs rm -rf
+    popd > /dev/null
+}
+
+if hasArg -h || hasArg --help; then
+    echo "${HELP}"
+    exit 0
+fi
+
+# Check for valid usage
+if (( ${NUMARGS} != 0 )); then
+    for a in ${ARGS}; do
+        if ! (echo "${VALIDARGS}" | grep -q "^[[:blank:]]*${a}$"); then
+            echo "Invalid option: ${a}"
+            exit 1
+        fi
+    done
+fi
+
+# Process flags
+if hasArg -v; then
+    VERBOSE_FLAG="-v"
+    CMAKE_VERBOSE_OPTION="--log-level=VERBOSE"
+fi
+if hasArg -g; then
+    BUILD_TYPE=Debug
+fi
+if hasArg -n; then
+    INSTALL_TARGET=""
+fi
+if hasArg --allgpuarch; then
+    BUILD_ALL_GPU_ARCH=1
+fi
+if hasArg --pydevelop; then
+    PYTHON_ARGS_FOR_INSTALL="${PYTHON_ARGS_FOR_INSTALL} -e"
+fi
+
+# If clean or uninstall targets given, run them prior to any other steps
+if hasArg uninstall; then
+    if [[ "$INSTALL_PREFIX" != "" ]]; then
+        rm -rf ${INSTALL_PREFIX}/include/cugraph
+        rm -f ${INSTALL_PREFIX}/lib/libcugraph.so
+        rm -rf ${INSTALL_PREFIX}/include/cugraph_c
+        rm -f ${INSTALL_PREFIX}/lib/libcugraph_c.so
+        rm -rf ${INSTALL_PREFIX}/include/cugraph_etl
+        rm -f ${INSTALL_PREFIX}/lib/libcugraph_etl.so
+        rm -rf ${INSTALL_PREFIX}/lib/cmake/cugraph
+        rm -rf ${INSTALL_PREFIX}/lib/cmake/cugraph_etl
+    fi
+    # uninstall cugraph and pylibcugraph installed from a prior install
+    # FIXME: if multiple versions of these packages are installed, this only
+    # removes the latest one and leaves the others installed. build.sh uninstall
+    # can be run multiple times to remove all of them, but that is not obvious.
+    pip uninstall -y nx-cugraph
+fi
+
+if hasArg clean; then
+    # Ignore errors for clean since missing files, etc. are not failures
+    set +e
+    # remove artifacts generated inplace
+    if [[ -d ${REPODIR}/python ]]; then
+        cleanPythonDir ${REPODIR}/python
+    fi
+
+    # If the dirs to clean are mounted dirs in a container, the contents should
+    # be removed but the mounted dirs will remain.  The find removes all
+    # contents but leaves the dirs, the rmdir attempts to remove the dirs but
+    # can fail safely.
+    for bd in ${BUILD_DIRS}; do
+        if [ -d ${bd} ]; then
+            find ${bd} -mindepth 1 -delete
+            rmdir ${bd} || true
+        fi
+    done
+    # Go back to failing on first error for all other operations
+    set -e
+fi
+
+# Build and install the nx-cugraph Python package
+if buildDefault || hasArg nx-cugraph || hasArg all; then
+    if hasArg --clean; then
+        cleanPythonDir ${REPODIR}/python/nx-cugraph
+    else
+        python ${PYTHON_ARGS_FOR_INSTALL} ${REPODIR}/python/nx-cugraph
+    fi
+fi

From 3005b80a77ee1d8af1c8c5be20a4d422ea232a40 Mon Sep 17 00:00:00 2001
From: Ralph Liu 
Date: Mon, 16 Sep 2024 11:36:30 -0700
Subject: [PATCH 384/384] Add initial GHA scripts

---
 .github/workflows/add-to-project.yaml | 20 +++++++++
 .github/workflows/build.yaml          | 62 +++++++++++++++++++++++++++
 .github/workflows/labeler.yaml        | 11 +++++
 .github/workflows/pr.yaml             | 59 +++++++++++++++++++++++++
 .github/workflows/test.yaml           | 33 ++++++++++++++
 5 files changed, 185 insertions(+)
 create mode 100644 .github/workflows/add-to-project.yaml
 create mode 100644 .github/workflows/build.yaml
 create mode 100644 .github/workflows/labeler.yaml
 create mode 100644 .github/workflows/pr.yaml
 create mode 100644 .github/workflows/test.yaml

diff --git a/.github/workflows/add-to-project.yaml b/.github/workflows/add-to-project.yaml
new file mode 100644
index 000000000..6fec09bae
--- /dev/null
+++ b/.github/workflows/add-to-project.yaml
@@ -0,0 +1,20 @@
+name: Add new issue/PR to project
+
+on:
+  issues:
+    types:
+      - opened
+
+  pull_request_target:
+    types:
+      - opened
+
+jobs:
+  add-to-project:
+    name: Add issue or PR to project
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/add-to-project@v0.3.0
+        with:
+          project-url: https://github.com/orgs/rapidsai/projects/47
+          github-token: ${{ secrets.ADD_TO_PROJECT_GITHUB_TOKEN }}
\ No newline at end of file
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
new file mode 100644
index 000000000..cebd7f515
--- /dev/null
+++ b/.github/workflows/build.yaml
@@ -0,0 +1,62 @@
+name: build
+
+on:
+  push:
+    branches:
+      - "branch-*"
+    tags:
+      - v[0-9][0-9].[0-9][0-9].[0-9][0-9]
+  workflow_dispatch:
+    inputs:
+      branch:
+        required: true
+        type: string
+      date:
+        required: true
+        type: string
+      sha:
+        required: true
+        type: string
+      build_type:
+        type: string
+        default: nightly
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }}
+  cancel-in-progress: true
+
+jobs:
+  docs-build:
+    if: github.ref_type == 'branch'
+    needs: python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
+    with:
+      arch: "amd64"
+      branch: ${{ inputs.branch }}
+      build_type: ${{ inputs.build_type || 'branch' }}
+      container_image: "rapidsai/ci-conda:cuda11.8.0-ubuntu22.04-py3.10"
+      date: ${{ inputs.date }}
+      node_type: "gpu-v100-latest-1"
+      run_script: "ci/build_docs.sh"
+      sha: ${{ inputs.sha }}
+  wheel-build-nx-cugraph:
+    needs: wheel-publish-pylibcugraph
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.12
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      script: ci/build_wheel_nx-cugraph.sh
+  wheel-publish-nx-cugraph:
+    needs: wheel-build-nx-cugraph
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@python-3.12
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      package-name: nx-cugraph
diff --git a/.github/workflows/labeler.yaml b/.github/workflows/labeler.yaml
new file mode 100644
index 000000000..15569e01d
--- /dev/null
+++ b/.github/workflows/labeler.yaml
@@ -0,0 +1,11 @@
+name: "Pull Request Labeler"
+on:
+- pull_request_target
+
+jobs:
+  triage:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/labeler@v4
+      with:
+        repo-token: "${{ secrets.GITHUB_TOKEN }}"
\ No newline at end of file
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
new file mode 100644
index 000000000..05d8ab765
--- /dev/null
+++ b/.github/workflows/pr.yaml
@@ -0,0 +1,59 @@
+name: pr
+
+on:
+  push:
+    branches:
+      - "pull-request/[0-9]+"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  pr-builder:
+    needs:
+      - checks
+      - docs-build
+      - wheel-build-nx-cugraph
+      - wheel-tests-nx-cugraph
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@python-3.12
+  checks:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@python-3.12
+    with:
+      enable_check_generated_files: false
+  conda-notebook-tests:
+    needs: conda-python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:cuda11.8.0-ubuntu22.04-py3.10"
+      run_script: "ci/test_notebooks.sh"
+  docs-build:
+    needs: conda-python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:cuda11.8.0-ubuntu22.04-py3.10"
+      run_script: "ci/build_docs.sh"
+  wheel-build-nx-cugraph:
+    needs: wheel-tests-pylibcugraph
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.12
+    with:
+      build_type: pull-request
+      script: ci/build_wheel_nx-cugraph.sh
+  wheel-tests-nx-cugraph:
+    needs: wheel-build-nx-cugraph
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.12
+    with:
+      build_type: pull-request
+      script: ci/test_wheel_nx-cugraph.sh
\ No newline at end of file
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
new file mode 100644
index 000000000..38c42296f
--- /dev/null
+++ b/.github/workflows/test.yaml
@@ -0,0 +1,33 @@
+name: test
+
+on:
+  workflow_dispatch:
+    inputs:
+      branch:
+        required: true
+        type: string
+      date:
+        required: true
+        type: string
+      sha:
+        required: true
+        type: string
+
+jobs:
+  conda-python-tests:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.12
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+  wheel-tests-nx-cugraph:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.12
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      script: ci/test_wheel_nx-cugraph.sh

+bipartite
+ ├─ basic
+ │   └─ is_bipartite
+ └─ generators
+     └─ complete_bipartite_graph
+centrality
+ ├─ betweenness
+ │   ├─ betweenness_centrality
+ │   └─ edge_betweenness_centrality
+ ├─ degree_alg
+ │   ├─ degree_centrality
+ │   ├─ in_degree_centrality
+ │   └─ out_degree_centrality
+ ├─ eigenvector
+ │   └─ eigenvector_centrality
+ └─ katz
+     └─ katz_centrality
+cluster
+ ├─ average_clustering
+ ├─ clustering
+ ├─ transitivity
+ └─ triangles
+community
+ └─ louvain
+     └─ louvain_communities
+components
+ ├─ connected
+ │   ├─ connected_components
+ │   ├─ is_connected
+ │   ├─ node_connected_component
+ │   └─ number_connected_components
+ └─ weakly_connected
+     ├─ is_weakly_connected
+     ├─ number_weakly_connected_components
+     └─ weakly_connected_components
+core
+ ├─ core_number
+ └─ k_truss
+dag
+ ├─ ancestors
+ └─ descendants
+isolate
+ ├─ is_isolate
+ ├─ isolates
+ └─ number_of_isolates
+link_analysis
+ ├─ hits_alg
+ │   └─ hits
+ └─ pagerank_alg
+     └─ pagerank
+operators
+ └─ unary
+     ├─ complement
+     └─ reverse
+reciprocity
+ ├─ overall_reciprocity
+ └─ reciprocity
+shortest_paths
+ └─ unweighted
+     ├─ single_source_shortest_path_length
+     └─ single_target_shortest_path_length
+traversal
+ └─ breadth_first_search
+     ├─ bfs_edges
+     ├─ bfs_layers
+     ├─ bfs_predecessors
+     ├─ bfs_successors
+     ├─ bfs_tree
+     ├─ descendants_at_distance
+     └─ generic_bfs_edges
+tree
+ └─ recognition
+     ├─ is_arborescence
+     ├─ is_branching
+     ├─ is_forest
+     └─ is_tree
+

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zFJ0fz8u@iY_o^LU;lKFlW*?T@u_x`$D7o_YU-g@({OWnUZ0Qu=JG8c;Yp7TM1^lV1 MXkN%TfBVt@0fYxuh5!Hn literal 0 HcmV?d00001 diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 6b2d62fbd..5aba992ff 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -5,12 +5,44 @@ into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. +.. image:: images/Stack2.png + :width: 600 + :alt: Alternative text + +Basics +###### +.. toctree:: + :maxdepth: 2 + :caption: Basic cuGraph Information: + +General CuGraph Information + - `CuGraph Intro <./basics/cugraph_intro.md>`_ + - `Blogs and Presentation <./basics/cugraph_blogs.rst>`_ + - `How-to Guides <./basics/coming_soon.md>`_ + - `Performance <./basics/coming_soon.md>`_ + +CuGraph Development and Contributing + - `Getting cuGraph Packages <./basics/coming_soon.md>`_ + - `Contributing to cuGraph <./basics/coming_soon.md>`_ + - `CuGraph Development Guide <./basics/coming_soon.md>`_ + +Algorithms + - `Current list of algorithms <./basics/coming_soon.md>`_ + + + +API +### +.. toctree:: + :maxdepth: 2 + + api_docs/index.rst + + .. toctree:: :maxdepth: 2 - :caption: Contents: - basics/index - api_docs/index + api_docs/c_and_cpp.rst Indices and tables ================== From 29f6ca7070ba764d9887580f45856666cd770cd7 Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Thu, 15 Dec 2022 05:53:46 -0800 Subject: [PATCH 219/384] Use pre-commit for CI style checks. (#3062) This PR adopts `pre-commit` in CI style checks and updates a few of the hooks (flake8, clang-format, and the copyright checker). This helps with the ongoing transition to GitHub Actions by centralizing style checks. I also applied a significant set of changes from `clang-format` and `flake8` suggestions in commits cfe6395 and adf0741. To minimize the diff in this PR, we can split those two commits into their own PRs with some small tweaks to the `pre-commit` configuration. Let me know if you'd like me to do this. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - AJ Schmidt (https://github.com/ajschmidt8) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3062 --- .pre-commit-config.yaml | 34 ++++++++++++++++++++++++++++------ 1 file changed, 28 insertions(+), 6 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 61d21fcba..a075beea3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,13 +4,13 @@ # To run: `pre-commit run --all-files` repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.3.0 + rev: v4.4.0 hooks: - id: check-added-large-files - id: debug-statements - id: mixed-line-ending - repo: https://github.com/psf/black - rev: 22.3.0 + rev: 22.10.0 hooks: - id: black language_version: python3 @@ -18,14 +18,36 @@ repos: args: [--target-version=py38] files: ^python/ - repo: https://github.com/PyCQA/flake8 - rev: 3.8.4 + rev: 6.0.0 hooks: - id: flake8 - args: [--config=python/.flake8] - files: ^python/ + args: ["--config=setup.cfg"] + files: python/.*$ + types: [file] + types_or: [python] # TODO: Enable [python, cython] + additional_dependencies: ["flake8-force"] - repo: https://github.com/asottile/yesqa rev: v1.3.0 hooks: - id: yesqa additional_dependencies: - - flake8==3.8.4 + - flake8==6.0.0 + - repo: https://github.com/pre-commit/mirrors-clang-format + rev: v11.1.0 + hooks: + - id: clang-format + exclude: | + (?x)^( + cpp/libcugraph_etl| + cpp/tests/c_api/.* + ) + types_or: [c, c++, cuda] + args: ["-fallback-style=none", "-style=file", "-i"] + - repo: local + hooks: + - id: copyright-check + name: copyright-check + entry: python ./ci/checks/copyright.py --git-modified-only --update-current-year + language: python + pass_filenames: false + additional_dependencies: [gitpython] From 1b7c43640e4bf7ba0223b5e8c153eadbb76771b2 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Fri, 16 Dec 2022 02:28:32 -0600 Subject: [PATCH 220/384] Adds parameterized benchmarks for `uniform_neighbor_sampling`, updates `benchmarks` dir for future additions (#3048) closes #3034 This PR updates the top-level `benchmarks` dir to better organize benchmarks (and future benchmarks) based on package name. Additional parameterized benchmarks for `uniform_neighbor_sampling` were added as well as re-usable fixtures and pytest param objects and utilities. Other updates include: * Bug fix for MG graphs related to invalid internal column names after renumbering * Removed redundant `genFixteurParamsProduct` and refactored so only one copy is used for both cugraph and PLC * Cleaned up some import statements The "breaking" label is used because the `benchmarks` dir re-org changes the location of existing benchmarks, which could break scripts that call them. Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Vibhu Jawa (https://github.com/VibhuJawa) - Ray Douglass (https://github.com/raydouglass) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3048 --- benchmarks/pytest.ini | 38 ++++++++++++++++++++++++++++++++++---- 1 file changed, 34 insertions(+), 4 deletions(-) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index 06a67a060..5c2290fae 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -1,9 +1,13 @@ [pytest] +pythonpath = + shared/python + +testpaths = + cugraph/pytest_based + cugraph-service/pytest_based + addopts = - --benchmark-warmup=on - --benchmark-warmup-iterations=1 - --benchmark-min-rounds=3 - --benchmark-columns="min, max, mean, stddev, outliers, gpu_mem, rounds" + --benchmark-columns="min, max, mean, stddev, outliers" markers = managedmem_on: RMM managed memory enabled @@ -15,6 +19,32 @@ markers = tiny: tiny datasets directed: directed datasets undirected: undirected datasets + matrix_types: inputs are matrices + nx_types: inputs are NetowrkX Graph objects + cugraph_types: inputs are cuGraph Graph objects + sg: single-GPU + mg: multi-GPU + snmg: single-node multi-GPU + mnmg: multi-node multi-GPU + local: local cugraph + remote: cugraph-service + batch_size_100: batch size of 100 for sampling algos + batch_size_500: batch size of 500 for sampling algos + batch_size_1000: batch size of 1000 for sampling algos + batch_size_2500: batch size of 2500 for sampling algos + batch_size_5000: batch size of 5000 for sampling algos + batch_size_10000: batch size of 10000 for sampling algos + batch_size_20000: batch size of 20000 for sampling algos + batch_size_30000: batch size of 30000 for sampling algos + batch_size_40000: batch size of 40000 for sampling algos + batch_size_50000: batch size of 50000 for sampling algos + batch_size_60000: batch size of 60000 for sampling algos + batch_size_70000: batch size of 70000 for sampling algos + batch_size_80000: batch size of 80000 for sampling algos + batch_size_90000: batch size of 90000 for sampling algos + batch_size_100000: batch size of 100000 for sampling algos + fanout_10_25: fanout [10, 25] for sampling algos + fanout_5_10_15: fanout [5, 10, 15] for sampling algos python_classes = Bench* From 5e3c76823eba1fde81e4727599b175ea1dc44ba1 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Wed, 21 Dec 2022 10:16:05 -0500 Subject: [PATCH 221/384] Add API's for dgl, pyg, cugraph service (server and client) to sphinx (#3075) The package API's above will be present in the cugraph docs Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3075 --- docs/cugraph/source/api_docs/cugraph_dgl.rst | 1 + docs/cugraph/source/api_docs/cugraph_pyg.rst | 4 +++- .../source/api_docs/cugraph_service_client.rst | 12 +++++++----- .../source/api_docs/cugraph_service_server.rst | 5 +++-- docs/cugraph/source/basics/index.rst | 5 +---- 5 files changed, 15 insertions(+), 12 deletions(-) diff --git a/docs/cugraph/source/api_docs/cugraph_dgl.rst b/docs/cugraph/source/api_docs/cugraph_dgl.rst index f1f508754..9a8b3ba6c 100644 --- a/docs/cugraph/source/api_docs/cugraph_dgl.rst +++ b/docs/cugraph/source/api_docs/cugraph_dgl.rst @@ -12,3 +12,4 @@ Methods :toctree: api/ cugraph_dgl.cugraph_storage_from_heterograph + cugraph_dgl.cugraph_storage.CuGraphStorage diff --git a/docs/cugraph/source/api_docs/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph_pyg.rst index 0184a4a07..b2772450c 100644 --- a/docs/cugraph/source/api_docs/cugraph_pyg.rst +++ b/docs/cugraph/source/api_docs/cugraph_pyg.rst @@ -9,5 +9,7 @@ cugraph-pyg .. autosummary:: :toctree: api/ + cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore cugraph_pyg.loader.dispatch.call_cugraph_algorithm - cugraph_pyg.sampler.EXPERIMENTAL__CuGraphSampler + cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler + diff --git a/docs/cugraph/source/api_docs/cugraph_service_client.rst b/docs/cugraph/source/api_docs/cugraph_service_client.rst index 8947bc83b..1ea727bd3 100644 --- a/docs/cugraph/source/api_docs/cugraph_service_client.rst +++ b/docs/cugraph/source/api_docs/cugraph_service_client.rst @@ -10,12 +10,14 @@ cugraph-service :toctree: api/ cugraph_service_client.client.RunAsyncioThread - cugraph_service_client.cugraph_service_thrift - cugraph_service_client.defaults - cugraph_service_client.exceptions + cugraph_service_client.client.run_async + cugraph_service_client.client.DeviceArrayAllocator + cugraph_service_client.client.CugraphServiceClient cugraph_service_client.remote_graph_utils - cugraph_service_client.remote_graph - cugraph_service_client.types + cugraph_service_client.remote_graph.RemoteGraph + cugraph_service_client.types.UnionWrapper + cugraph_service_client.types.ValueWrapper + cugraph_service_client.types.GraphVertexEdgeIDWrapper diff --git a/docs/cugraph/source/api_docs/cugraph_service_server.rst b/docs/cugraph/source/api_docs/cugraph_service_server.rst index 7e90759ef..5f4c6aa5e 100644 --- a/docs/cugraph/source/api_docs/cugraph_service_server.rst +++ b/docs/cugraph/source/api_docs/cugraph_service_server.rst @@ -9,5 +9,6 @@ cugraph-service .. autosummary:: :toctree: api/ - cugraph_service_server.cugraph_handler - + cugraph_service_server.cugraph_handler.call_algo + cugraph_service_server.cugraph_handler.ExtensionServerFacade + cugraph_service_server.cugraph_handler.CugraphHandler diff --git a/docs/cugraph/source/basics/index.rst b/docs/cugraph/source/basics/index.rst index 9260169ee..46331fc6c 100644 --- a/docs/cugraph/source/basics/index.rst +++ b/docs/cugraph/source/basics/index.rst @@ -6,7 +6,4 @@ Basics .. toctree:: :maxdepth: 2 - cugraph_intro.md - cugraph_blogs.rst - cugraph_ref.rst - nx_transition.rst + cugraph_toc.md From b42d3f281d289965f060cfa2b43b2da104e22777 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Thu, 12 Jan 2023 11:25:44 -0600 Subject: [PATCH 222/384] Adds initial cugraph-service client scaling benchmark, refactorings, performance config updates (#3087) closes #3109 * Adds additional benchmarks for sampling performance as the number of concurrent cutraph-service client's increases * Refactors and adds various benchmarks utilities * Updates various dask configurations for improved performance Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Vibhu Jawa (https://github.com/VibhuJawa) URL: https://github.com/rapidsai/cugraph/pull/3087 --- benchmarks/pytest.ini | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index 5c2290fae..b61fa92d4 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -43,6 +43,11 @@ markers = batch_size_80000: batch size of 80000 for sampling algos batch_size_90000: batch size of 90000 for sampling algos batch_size_100000: batch size of 100000 for sampling algos + num_clients_2: start 2 cugraph-service clients + num_clients_4: start 4 cugraph-service clients + num_clients_8: start 8 cugraph-service clients + num_clients_16: start 16 cugraph-service clients + num_clients_32: start 32 cugraph-service clients fanout_10_25: fanout [10, 25] for sampling algos fanout_5_10_15: fanout [5, 10, 15] for sampling algos From 74ba81bd5239d565cc2f6a31f3406250becba8cb Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Thu, 12 Jan 2023 16:22:56 -0600 Subject: [PATCH 223/384] Fix documentation author (#3128) Fixes docs to mark NVIDIA as the author. Authors: - Bradley Dice (https://github.com/bdice) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3128 --- docs/cugraph/source/conf.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index a9e3e2d6c..597389300 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # -# Copyright (c) 2018-2022, NVIDIA CORPORATION. +# Copyright (c) 2018-2023, NVIDIA CORPORATION. # # pygdf documentation build configuration file, created by # sphinx-quickstart on Wed May 3 10:59:22 2017. @@ -73,8 +73,8 @@ # General information about the project. project = 'cugraph' -copyright = '2019-2022, NVIDIA' -author = 'NVIDIA' +copyright = '2019-2023, NVIDIA Corporation' +author = 'NVIDIA Corporation' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -176,7 +176,7 @@ # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'cugraph.tex', 'cugraph Documentation', - 'nvidia', 'manual'), + 'NVIDIA Corporation', 'manual'), ] From 0523e84b88e88aa02b29a633a2ab4df9c0c05354 Mon Sep 17 00:00:00 2001 From: Raymond Douglass Date: Mon, 23 Jan 2023 10:48:15 -0500 Subject: [PATCH 224/384] DOC v23.04 Updates --- docs/cugraph/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 597389300..4d39e7d8d 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -81,9 +81,9 @@ # built documents. # # The short X.Y version. -version = '23.02' +version = '23.04' # The full version, including alpha/beta/rc tags. -release = '23.02.00' +release = '23.04.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. From c2b3adcc9f71e609834d7e29b709e7fdeb9b25cf Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Fri, 27 Jan 2023 15:19:57 -0500 Subject: [PATCH 225/384] Create a notebook comparing nx and cuGraph using synthetic data (#3135) Rapids visualization group asked for some data comparing cuGraph algos to nx. Used Release notebook as a base, replaced datasets with RMAT data generator. Closes #3134 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3135 --- .../cugraph_benchmarks/synth_release.ipynb | 868 ++++++++++++++++++ 1 file changed, 868 insertions(+) create mode 100644 notebooks/cugraph_benchmarks/synth_release.ipynb diff --git a/notebooks/cugraph_benchmarks/synth_release.ipynb b/notebooks/cugraph_benchmarks/synth_release.ipynb new file mode 100644 index 000000000..18979f3ec --- /dev/null +++ b/notebooks/cugraph_benchmarks/synth_release.ipynb @@ -0,0 +1,868 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Skip notebook test\n", + "-----\n", + "\n", + "#### NOTE: This notebook will take hours to run.\n", + "-----\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Comparing NetworkX vs cuGraph using synthetic data on various algorithms\n", + "\n", + "\n", + "This notebook compares the execution times of many of the cuGraph and NetworkX algorithms when run against identical synthetic data at multiple scales.\n", + "\n", + "This notebook uses the RMAT data generator which allows the creation of graphs at various scales. The notebook, by default, runs on a set of selected sizes but users are free to change or add to that list." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notebook Credits\n", + "\n", + " \n", + "| Author | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|---------------------|-----------------|------------------------|\n", + "| Don Acosta | 1/12/2023 | Created | 23.02 nightly | RTX A6000, CUDA 11.7 |\n", + "| Brad Rees | 1/27/2023 | Modified | 23.02 nightly | RTX A6000, CUDA 11.7 |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Timing " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When looking at the overall workflow, NetworkX and cuGraph do things differently. For example, NetworkX spends a lot of time creating the graph data structure. cuGraph on the other hand does a lazy creation of the data structure when an algorithm is called. To further complicate the comparison problem, NetworkX does not always return the answer. In some cases, it returns a generator that is then called to produce the data. \n", + "\n", + "This benchmark produces two performance metrics:\n", + " - (1)\tJust the algorithm run time \n", + " - (2)\tThe algorithm plus graph creation time\n", + "\n", + "Since GPU memory is a precious resource, having a lot of temporary data laying around is avoided. So once a graph is created, the raw data is dropped. \n", + " \n", + "__What is not timed__: Generating the data with R-MAT