jupyter-server-nbmodel 0.1.1a1

A Jupyter Server extension to execute code cell from the server.

jupyter_server_nbmodel

A Jupyter Server extension to execute code cell from the server.

This extension is composed of a Python package named jupyter_server_nbmodel for the server extension and a NPM package named jupyter-server-nbmodel for the frontend extension.

Requirements

• Jupyter Server
• [recommended] Real-time collaboration for JupyterLab/Notebook: This will push the kernels results in the notebook from the server.

Install

To install the extension for use in JupyterLab or Notebook 7, execute:

pip install jupyter_server_nbmodel[lab]

For API-only use:

pip install jupyter_server_nbmodel

Or with recommendations:

pip install jupyter_server_nbmodel[rtc]

Uninstall

To remove the extension, execute:

pip uninstall jupyter_server_nbmodel

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

How does it works

Generic case

Execution of a Python code snippet: print("hello")

sequenceDiagram
    Frontend->>+Server: POST /api/kernels/<id>/execute
    Server->>+ExecutionStack: Queue request
    ExecutionStack->>Kernel: Execute request msg
    activate Kernel
    ExecutionStack-->>Server: Task uid
    Server-->>-Frontend: Returns task uid
    loop Running
        Kernel->>Shared Document: Add output
        Shared Document->>Frontend: Document update
    end
    loop While status is 202
        Frontend->>+Server: GET /api/kernels/<id>/requests/<uid>
        Server->>ExecutionStack: Get task result
        ExecutionStack-->>Server: null
        Server-->>-Frontend: Request status 202
    end
    Kernel-->>ExecutionStack: Execution reply
    deactivate Kernel
    Frontend->>+Server: GET /api/kernels/<id>/requests/<uid>
    Server->>ExecutionStack: Get task result
    ExecutionStack-->>Server: Result
    Server-->>-Frontend: Status 200 & result

With input case

Execution of a Python code snippet: input("Age:")

sequenceDiagram
    Frontend->>+Server: POST /api/kernels/<id>/execute
    Server->>+ExecutionStack: Queue request
    ExecutionStack->>Kernel: Execute request msg
    activate Kernel
    ExecutionStack-->>Server: Task uid
    Server-->>-Frontend: Returns task uid
    loop Running
        Kernel->>Shared Document: Add output
        Shared Document->>Frontend: Document update
    end
    loop While status is 202
        Frontend->>+Server: GET /api/kernels/<id>/requests/<uid>
        Server->>ExecutionStack: Get task result
        ExecutionStack-->>Server: null
        Server-->>-Frontend: Request status 202
    end
    Kernel->>ExecutionStack: Set pending input
    Frontend->>+Server: GET /api/kernels/<id>/requests/<uid>
    Server->>ExecutionStack: Get task result
    ExecutionStack-->>Server: Pending input
    Server-->>-Frontend: Status 300 & Pending input
    Frontend->>+Server: POST /api/kernels/<id>/input
    Server->>Kernel: Send input msg
    Server-->>-Frontend: Returns
    loop While status is 202
        Frontend->>+Server: GET /api/kernels/<id>/requests/<uid>
        Server->>ExecutionStack: Get task result
        ExecutionStack-->>Server: null
        Server-->>-Frontend: Request status 202
    end
    Kernel-->>ExecutionStack: Execution reply
    deactivate Kernel
    Frontend->>+Server: GET /api/kernels/<id>/requests/<uid>
    Server->>ExecutionStack: Get task result
    ExecutionStack-->>Server: Result
    Server-->>-Frontend: Status 200 & result

[!NOTE] The code snippet is always send in the body of the POST /api/kernels/<id>/execute request to avoid document model discrepancy; the document on the backend is only eventually identical with the frontends (document updates are not instantaneous).

The ExecutionStack maintains an execution queue per kernels to ensure execution order.

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 jupyter_server_nbmodel directory
# Install package in development mode
pip install -e ".[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 jupyter_server_nbmodel
# 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 --autoreload

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 jupyter_server_nbmodel
pip uninstall jupyter_server_nbmodel

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 jupyter-server-nbmodel 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 jupyter_server_nbmodel

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.

Manual testing

# Terminal 1.
jupyter server --port 8888 --autoreload --ServerApp.disable_check_xsrf=True --IdentityProvider.token= --ServerApp.port_retries=0

# Terminal 2.
KERNEL=$(curl -X POST http://localhost:8888/api/kernels)
echo $KERNEL
KERNEL_ID=$(echo $KERNEL | jq --raw-output '.id')
echo $KERNEL_ID
REQUEST=$(curl --include http://localhost:8888/api/kernels/$KERNEL_ID/execute -d "{ \"code\": \"print('1+1')\" }")
RESULT=$(echo $REQUEST | grep -i ^Location: | cut -d' ' -f2 | tr -d '\r')
echo $RESULT

curl http://localhost:8888$RESULT
{"status": "ok", "execution_count": 1, "outputs": "[{\"output_type\": \"stream\", \"name\": \"stdout\", \"text\": \"1+1\\n\"}]"}

Running Tests

Install dependencies:

pip install -e ".[test]"

To run the python tests, use:

pytest

# To test a specific file
pytest jupyter_server_nbmodel/tests/test_handlers.py

# To run a specific test
pytest jupyter_server_nbmodel/tests/test_handlers.py -k "test_post_execute"

Development uninstall

pip uninstall jupyter_server_nbmodel

Packaging the extension

See RELEASE