UnitML

A JupyterLab extension used to generate pytest unit tests for a machine learning model. The tool relies on the v1.2 TEC Descriptors, specifically the Data Pipeline and Trained Model descriptors. Tests are generated based on the input and output specification contained within these descriptors.

This extension is composed of a Python package named unitml for the server extension and a NPM package named unitml for the frontend extension.

Requirements

• JupyterLab >= 3.0
• pytest >= 7.4.2
• Pillow >= 10.0.1

Install

To install the extension, download .whl file and execute:

conda create -n unitml-env jupyterlab
conda activate unitml-env
pip install unitml-0.1.0-py3-none-any.whl
jupyter lab

Usage

Once installed, the extension will be added to Jupyter Lab and can be ran by using the command UnitML in the Command Palette. The descriptors must be located in the directory that Jupyter Lab is started in, or the extension will be unable to find them.

Once the tool has been ran, it will generate the file test_generated.py in the directory with the descriptors. This file contains a framework for unit testing the model specified.

While most of the file is ready to go, there are a couple sections that require input from the user in order to work. These sections are marked with comments starting with # USER INPUT and give an explanation of what needs to be added.

Once these sections have been updated, the tests are ready to be ran. Ensure that the data pipeline and model files are available to be imported by the test file and run the tests. Pytest can be run with the command, which will run all the tests contained in the file.

pytest

If desired, this can also be done within a jupyter notebook to preserve the output:

import subprocess
subprocess.run["pytest"]

Uninstall

To remove the extension, execute:

pip uninstall unitml

Troubleshoot

If you are seeing the frontend extension, but it is not working, check that the server extension is enabled:

jupyter server extension list

If the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:

jupyter labextension list

Contributing

Development install

Note: 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 unitml directory
# Install package in development mode
pip install -e ".[test]"
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Server extension must be manually installed in develop mode
jupyter server extension enable unitml
# Rebuild extension Typescript source after making changes
jlpm build

You 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 watch
# Run JupyterLab in another terminal
jupyter lab

With 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 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=False

Development uninstall

# Server extension must be manually disabled in develop mode
jupyter server extension disable unitml
pip uninstall unitml

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named unitml within that folder.

Testing the extension

Server tests

This extension is using Pytest for Python code testing.

Install test dependencies (needed only once):

pip install -e ".[test]"
# Each time you install the Python package, you need to restore the front-end extension link
jupyter labextension develop . --overwrite

To execute them, run:

pytest -vv -r ap --cov unitml

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

jlpm
jlpm test

Integration tests

This extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.

More information are provided within the ui-tests README.

Packaging the extension

See RELEASE