jupyterlab-env-kernel 1.1.5

A JupyterLab extension to create env and kernel.

jupyterlab_env_kernel

A JupyterLab extension to create env and kernel.

This extension is composed of a Python package named jupyterlab_env_kernel for the server extension and a NPM package named jupyterlab-env-kernel for the frontend extension.

Requirements

• JupyterLab >= 4.0.0

Install

To install the extension, execute:

pip install jupyterlab-env-kernel

Uninstall

To remove the extension, execute:

pip uninstall jupyterlab_env_kernel

Usage

Opening the Panel

Click the Conda Environments icon in the left sidebar to open the Conda Environment Manager panel.

Creating a Conda Environment: Manual Mode

1. Select the Manual tab
2. Environment Name — Enter a unique name for your environment (e.g. my-analysis)
3. Python Version — Select the Python version from the dropdown (3.9, 3.10, 3.11, 3.12)
4. Extra Packages — Optionally enter comma-separated packages to install (e.g. numpy, pandas, scikit-learn)
5. Click + Create Environment & Kernel

The extension will:

• Create a new conda environment using mamba
• Install ipykernel into the environment
• Register it as a Jupyter kernel automatically

⏳ Creation typically takes 1–2 minutes depending on the number of packages.

Creating a Conda Environment: From Configuration File

You can also create environments from configuration files:

From environment.yml file

1. Select the From File tab
2. Select File Type: environment.yml
3. Upload your environment.yml file
4. Click + Create Environment & Kernel

Example environment.yml:

name: my-scientific-env
channels:
  - conda-forge
dependencies:
  - python=3.11
  - numpy
  - pandas
  - matplotlib
  - scikit-learn

The environment name will be taken from the name field in the YAML file.

From requirements.txt file

1. Select the From File tab
2. Select File Type: requirements.txt
3. Upload your requirements.txt file
4. Environment Name — Enter a name for your new environment (required for requirements.txt)
5. Python Version — Select the Python version to use
6. Click + Create Environment & Kernel

Example requirements.txt:

numpy>=1.20.0
pandas>=1.3.0
matplotlib>=3.4.0
scikit-learn>=0.24.0
jupyter>=1.0.0

Using the New Kernel

Once created, the kernel is immediately available:

1. Open a notebook (or create a new one)
2. Click the kernel selector in the top right
3. Select Python (your-env-name) from the list

💡 If you don't see the kernel yet, refresh the page or restart the kernel picker.

Error Messages

Message	Cause	Fix
⚠️ Environment 'x' already exists	An env with that name already exists	Choose a different name
❌ Please enter an environment name	Name field is empty (manual mode)	Enter a name before clicking create
❌ File parsing error	Invalid YAML/requirements format	Check file format; must be valid YAML or requirements.txt
❌ Error: ...	mamba failed	Check JupyterLab server logs for details

Checking Available Environments

You can verify your environments were created correctly from a terminal:

mamba env list
jupyter kernelspec list

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 jupyterlab_env_kernel directory

# Set up a virtual environment and install package in development mode
python -m venv .venv
source .venv/bin/activate
pip install --editable ".[dev,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 jupyterlab_env_kernel

# Rebuild extension Typescript source after making changes
# IMPORTANT: Unlike the steps above which are performed only once, do this step
# every time you make a change.
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 jupyterlab_env_kernel
pip uninstall jupyterlab_env_kernel

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-env-kernel 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 jupyterlab_env_kernel

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