jupyterlab-genv 0.1.1

A JupyterLab extension for managing GPU environments using genv.

jupyterlab_genv

A JupyterLab extension.

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

Requirements

• JupyterLab >= 3.0

Install

Conda

Pip

To install the extension, execute:

pip install jupyterlab_genv

Uninstall

To remove the extension, execute:

pip uninstall jupyterlab_genv

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 setup

Create a virtual environment

conda create -n jupyterlab_genv --override-channels --strict-channel-priority -c conda-forge -c nodefaults jupyterlab=3 cookiecutter nodejs jupyter-packaging git

Activate the virtual environment

conda activate jupyterlab_genv

Install kernel provisioner

First, you can test that the Python package is installed correctly and exposes the provisioner class by running:

python -c "from jupyterlab_genv import genv_provisioner; provisioner = genv_provisioner.GenvProvisioner(); print(provisioner)"

Second, you can test that the kernel provisioner is registered in Jupyter by running:

jupyter kernelspec provisioners

To add a kernel provisioner to a kernel, edit its kernel.json file:

vim $(conda info --base)/envs/jupyterlab_genv/share/jupyter/kernels/python3/kernel.json

And add:

"metadata": {
  "kernel_provisioner": {
    "provisioner_name": "genv-provisioner"
  }
}

List all available kernels

ls -la $(conda info --base)/envs/${CONDA_DEFAULT_ENV}/share/jupyter/kernels/

List all running kernels

ls -la $(jupyter --runtime-dir)/kernel-*.json

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.

# Install package in development mode
pip install -e .
# 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 jupyterlab_genv
# 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

Running SHELL=bash jupyter lab --no-browser is even better

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 jupyterlab_genv
pip uninstall jupyterlab_genv

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 jupyterlab_genv within that folder.

Packaging the extension

See RELEASE

https://github.com/jupyterlab/extension-cookiecutter-ts/pull/230