MLProvLab: Provenance Management for Data Science Notebooks
MLProvLab is a JupyterLab extension to track, manage, compare, and visualize the provenance of machine learning notebooks. It can track, at runtime, the datasets, variables, libraries, and functions used in the notebook and their dependencies between cells. It provides users the facility to compare different runs of computational experiments, thereby ensuring a way to help them make their decisions. The tool helps researchers and data scientists to collect more information on their experimentation and interact with them.
This extension is composed of a Python package named mlprovlab for the server extension and a NPM package named mlprovlab
for the frontend extension.
Towards Tracking Provenance from Machine Learning Notebooks, Dominik Kerzel, Sheeba Samuel, Birgitta König-Ries. KDIR 2021
- JupyterLab >= 3.0
pip install mlprovlabIf you are seeing the frontend extension, but it is not working, check that the server extension is enabled:
jupyter server extension listIf the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:
jupyter labextension listNote: You will need NodeJS to build the extension package.
The jlpm command is JupyterLab's pinned version of
yarn that is installed with JupyterLab. You may use
yarn or npm in lieu of jlpm below.
# Clone the repo to your local environment
# Change directory to the mlprovlab directory
# Install package in development mode
pip install -e .
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm run buildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.
# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm run watch
# Run JupyterLab in another terminal
jupyter labWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).
By default, the jlpm run build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:
jupyter lab build --minimize=Falsepip uninstall mlprovlab