ksmm 0.1.0

An extension to manage Kernelspecs from JupyterLab

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 Manger and currently ships:

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

Context

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.

Install Kernelspecs Templates

You will need Kernelspec templates.

make install-kernelspecs

This will install the python-template-1 Kernelspec example located in the examples folder.

Install from a Release

pip install --upgrade ksmm

This will install the extension inside the current JupyterLab Environment.

Develop

Use the provided environment.yaml to install the conda environment.

conda deactivate && \
  make env-rm && \
  make env
conda activate ksmm

# Install the server and frontend in dev mode.
make install-dev

# In terminal 1, Start the jupyterlab.
# open http://localhost:8234?token=...
make jlab

# In terminal 2, start the extension building in watch mode.
make watch

When making changes to the extension you will need to issue a jupyter labextension build, or, start jlpm run watch in the root of the repository to rebuild on every changes. You do not need to restart or rebuild JupyterLab for changes on the frontend extensions, but do need to restart the server for changes to the Python code.

About Kernelspec Templates

You system adminstrator can create Kernelspect 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).

{
  "argv": [
    "slurm",
    "run",
    "--mem=1048576000",
    "--cpu=14",
    "python3.8",
    "-m",
    "ipykernel",
    "-f",
    "{connection_file}"
  ],
  "display_name": "Python 3.8 Template 1",
  "language": "python",
  "metadata": {
    "template": {
      "tpl": {
        "argv": [
          "slurm",
          "run",
          "--mem={mem_slurm}",
          "--cpu={cpu}",
          "python3.8",
          "-m",
          "ipykernel",
          "-f",
          "{connection_file}"
        ],
        "display_name": "Python 3.8 RAM:{mem}/ CPU={cpu} / {gpu}"
      },
      "parameters": {
        "cpu": {
          "type": "integer",
          "title": "CPU core",
          "default": 1,
          "maximum": 48,
          "minimum": 1
        },
        "mem": {
          "type": "string",
          "title": "Memory (GB)",
          "enum": [
            "Small",
            "Medium",
            "Big"
          ]
        },
        "gpu": {
          "type": "boolean",
          "title": "Graphic Processor",
          "default": true
        }
      },
      "mapping": {
        "mem_slurm": {
          "mem": {
            "Small": "102400",
            "Medium": "512000",
            "Big": "1048576000"
          }
        }
      }
    }
  }
}

General Settings

Launch Arguments