From 6462db49a7c4c58e24eae52ca145231cb8380d6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9=20T=20Greg=C3=B3rio?= Date: Mon, 21 Dec 2020 17:51:19 -0300 Subject: [PATCH] [TASK09] Improve Readme.MD --- README.md | 27 +++++++++------------------ 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 3e772ea..12213c1 100644 --- a/README.md +++ b/README.md @@ -6,26 +6,9 @@ In this project, you will apply the skills you have acquired in this course to o You are given a pre-trained, `sklearn` model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on [the data source site](https://www.kaggle.com/c/boston-housing). This project tests your ability to operationalize a Python flask app—in a provided file, `app.py`—that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling. -### Project Tasks - -Your project goal is to operationalize this working, machine learning microservice using [kubernetes](https://kubernetes.io/), which is an open-source system for automating the management of containerized applications. In this project you will: -* Test your project code using linting -* Complete a Dockerfile to containerize this application -* Deploy your containerized application using Docker and make a prediction -* Improve the log statements in the source code for this application -* Configure Kubernetes and create a Kubernetes cluster -* Deploy a container using Kubernetes and make a prediction -* Upload a complete Github repo with CircleCI to indicate that your code has been tested - -You can find a detailed [project rubric, here](https://review.udacity.com/#!/rubrics/2576/view). - -**The final implementation of the project will showcase your abilities to operationalize production microservices.** - ---- - ## Setup the Environment -* Create a virtualenv and activate it +* Create a virtualenv and activate it. It's possible to create the virtual env with `make setup`. * Run `make install` to install the necessary dependencies ### Running `app.py` @@ -40,3 +23,11 @@ You can find a detailed [project rubric, here](https://review.udacity.com/#!/rub * Setup and Configure Kubernetes locally * Create Flask app in Container * Run via kubectl + +## Files in this repo: + +The project has a few shell script files to help managing the resources. + +* **run_docker.sh**: builds a docker image and runs it, mapping the port 8000 of you computer. +* **uploaddocker.sh**: tags and upload the docker image to a repo in docker hub. You may change the docker hub repository to meet one of your own. +* **run_kubernets.sh**: deploy the application to a Kubernets node.