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An extension to manage Kernelspecs from JupyterLab

Project description

Kernel Specification Manager JupyterLab Extension

This JupyterLab Extension allows users to manage Kernelspecs from within JupyterLab.

ksmm is a temporary name, originally standing for Kernelspec Manager and currently ships:

  • Kernelspec creation based on parametrized templates.
  • Kernelspec Editing: name, attributes.
  • Kernel Duplication.
  • Kernel Deletion.


On large distributed systems, it is common to wish to parametrize kernels and choose parameters for a remote environment, like number of CPU, Memory limit, presence of GPU. Or even set other parameters in environment variables.

This currently requires to create a new Kernelspec for jupyter using the command line which can be a tedious and complicated task.

Modifying existing Kernelspec also does not always works as they are cached on a per notebook.

This is an attempt to provide a UI based on json-schema and templates, for end users to easily create, duplicate and modify kernelspec, without being exposed to too much of the internal details.


  • Jupyterlab >= 4.0


pip install ksmm

Next, you will need to install some Kernelspec templates. As an example, start by adding the python-template-1 kernelspec:

jupyter kernelspec install ./examples/python-template-1/

To list existing kernelspecs, run jupyter kernelspec list .


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_pyflyby 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 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 run 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 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=False

About Kernelspec Templates

You system adminstrator can create Kernelspec templates for you. As a user, if you click on the picker icon of a template card, you will be prompted for the Kernelspec parameters.

When you will click on the Create Kernelspec button, a new Kernespec will be created.

This is an example of such a Kernelspec template. The metadata/template/tpl stanza should contain a Json Schema compliant structure. You can browser the react-jsonschema-form for examples.

You can use the metadata/template/mapping stanza to create visual mappings (e.g. Small will be mapped to 102400). The example/python-template-1 contains an example. To install that example template in your environment, you need to run jupyter kernelspec install ./examples/python-template-1 (add --user to install in your user space).

Click to view the kernelspec example.
  "argv": [
  "display_name": "Python 3.8 Template 1",
  "language": "python",
  "metadata": {
    "template": {
      "tpl": {
        "argv": [
        "display_name": "Python cache_size {cache_size_map} matplotlib {matplotlib}"
      "parameters": {
        "poll_interval": {
          "type": "number",
          "minimum": 0.01,
          "maximum": 1,
          "multipleOf": 0.01,
          "title": "Kernel pool interval in seconds",
          "default": 0.01
        "cache_size": {
          "type": "integer",
          "title": "Set the size of the cache",
          "default": "Medium",
          "enum": ["Small", "Medium", "Big"]
        "matplotlib": {
          "type": "string",
          "title": "Configure matplotlib for interactive use with the default matplotlib backend",
          "default": "widget",
          "enum": [
        "logfile": {
          "type": "string",
          "title": "Set the path for the logfile",
          "default": "/tmp/kernel.out"
      "mapping": {
        "cache_size_map": {
          "cache_size": {
            "Small": "102400",
            "Medium": "512000",
            "Big": "1048576000"

General Settings

Launch Arguments


To publish a release, you need to manually bump the version number of the package.json file, see this diff for example.

   "name": "@deshaw/jupyterlab-ksmm",
-  "version": "0.1.4",
+  "version": "0.1.5",
   "description": "An extension to manage Kernelspecs from JupyterLab",
   "keywords": [

Pleas follow Semantic Versioning rules when bumping the version number.


This was created by the D. E. Shaw group in conjunction with Quansight.

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