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GCP extensions for Jupyter and JupyterLab

GoogleCloudPlatform
GoogleCloudPlatform
pushedAt 4 months ago

GoogleCloudPlatform/jupyter-extensions

Google Cloud Platform Extensions for Jupyter and JupyterLab

This repository serves as a common repository for Google-developed extensions for the Jupyter and JupyterLab environments.

Development

The following steps only need to be completed once to setup your initial development environment.

Note: If at any point you want to go through these steps on a fresh JupyterLab environment, you can run pipenv --rm to remove the virtual environment.

  1. Clone this repository.

  2. cd into the repository directory and install the NPM dependencies with npm install.

  3. Run npm run bootstrap to install the dependencies for each of the extension subfolders.

    • You should repeat this command anytime you add a new NPM dependency to one of the subfolder packages.
  4. Run pipenv install.

    • Install pipenv if you don't have it already to assist with managing a clean Python environment.
  5. Run pipenv shell to activate the virtual Python environment with the necessary dependencies installed.

  6. Run cd shared/ and then npm run install-shared to install the shared package.

  7. Run npm run link to link the gcp-jupyterlab-shared package into the JupyterLab environment.

    • This allows local development on the common frontend components library to be used in other extensions without needing to publish and re-install updated versions of the packages.
    • If you are having trouble running this command, try restarting these steps on a new JupyterLab environment (see Note above).
  8. cd into the folder of the extension you plan to develop and run npm run install-extension. This installs the extension in the JupyterLab environment in development mode. Afterwards, cd back to the root of the repository.

    • Run jupyter labextension install @jupyter-widgets/[email protected] if your extension requires ipywidget. Currently, this is a requirement for the jupyterlab_bigquery extension.
  9. Run npm run watch to start the TypeScript compiler in watch mode. This will watch for changes in any of the TypeScript sources.

    • Alternatively, you can run the npm run watch command only in the packages you are working in.
  10. In a seperate terminal, run pipenv shell and then npm run devserver. This will start JupyterLab in watch mode to pick up any changes to either the TypeScript or Python code.

    • You can watch the terminal output to ensure that the TS compiler has successfully recompiled your package. Then, you can refresh the browser tab to see your change.
    • If your change isn't present after a refresh cycle, you may need to stop and restart the watch or devserver tasks. This may occur when adding a new file to the project.

You can then open JupyterLab at one of the links shown in the logging output.

Testing on an AI Platform Notebook

If you would like to test an extension in an AI Platform Notebook, you can do so by following these steps.

  1. Create a new AI Platform Notebook from the Cloud Console.

  2. cd into the directory of the extension you wish to install.

  3. Run ../scripts/deploy.sh passing the instance's name and zone as arguments.

    • ie. ../scripts/deploy.sh test-instance us-east1-b
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