Version 0.1.2

This release includes the necessary machinery to convert a Jupyter Notebook to a Kubeflow Pipelines deployment.

This release provides four main modules:

• nbparser: notebook parse module; tagging-language; generation of code graph
• static_analysis: run static analysis over code blocks to detect data dependencies
• marshal: functions to (de)serialize objects of any type with dynamic dispatchers
• codegen: generate kfp Python code using templates, based on the graph produced by nbparser module

Flask Server

The api module provides a simple Flask app that exposes the /kale API that accepts a JupyterNotebook in raw format and call the Kale core module to create a KFP deployment.

JupyterLab extension

The kale-toolbar-runner provides a deployment button in the JupyterNotebook's toolbar. By clicking the deployment button Jupyter will send a POST request to localhost:5000/kale with the currently active raw notebook.