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https://github.com/datalayer/jupyter-server-nbmodel
Jupyter Server NbModel. - Execute code cell from the server.
https://github.com/datalayer/jupyter-server-nbmodel
jupyter nbmodel rtc server
Last synced: about 8 hours ago
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Jupyter Server NbModel. - Execute code cell from the server.
- Host: GitHub
- URL: https://github.com/datalayer/jupyter-server-nbmodel
- Owner: datalayer
- License: bsd-3-clause
- Created: 2024-05-06T07:24:15.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-09-14T07:37:08.000Z (2 months ago)
- Last Synced: 2024-11-18T04:17:42.034Z (1 day ago)
- Topics: jupyter, nbmodel, rtc, server
- Language: Python
- Homepage: https://datalayer.tech
- Size: 215 KB
- Stars: 14
- Watchers: 3
- Forks: 3
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# jupyter_server_nbmodel
[](https://github.com/datalayer/jupyter-server-nbmodel/actions/workflows/build.yml)
[](https://mybinder.org/v2/gh/datalayer/jupyter-server-nbmodel/main?urlpath=lab)
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:
```bash
pip install jupyter_server_nbmodel[lab]
```
For API-only use:
```bash
pip install jupyter_server_nbmodel
```
Or with recommendations:
```bash
pip install jupyter_server_nbmodel[rtc]
```
## Uninstall
To remove the extension, execute:
```bash
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:
```bash
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:
```bash
jupyter labextension list
```
## How does it works
### Generic case
Execution of a Python code snippet: `print("hello")`
```mermaid
sequenceDiagram
Frontend->>+Server: POST /api/kernels//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//requests/
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//requests/
Server->>ExecutionStack: Get task result
ExecutionStack-->>Server: Result
Server-->>-Frontend: Status 200 & result
```
### With input case
Execution of a Python code snippet: `input("Age:")`
```mermaid
sequenceDiagram
Frontend->>+Server: POST /api/kernels//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//requests/
Server->>ExecutionStack: Get task result
ExecutionStack-->>Server: null
Server-->>-Frontend: Request status 202
end
Kernel->>ExecutionStack: Set pending input
Frontend->>+Server: GET /api/kernels//requests/
Server->>ExecutionStack: Get task result
ExecutionStack-->>Server: Pending input
Server-->>-Frontend: Status 300 & Pending input
Frontend->>+Server: POST /api/kernels//input
Server->>Kernel: Send input msg
Server-->>-Frontend: Returns
loop While status is 202
Frontend->>+Server: GET /api/kernels//requests/
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//requests/
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//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](https://yarnpkg.com/) that is installed with JupyterLab. You may use
`yarn` or `npm` in lieu of `jlpm` below.
```bash
# 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.
```bash
# 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:
```bash
jupyter lab build --minimize=False
```
### Development uninstall
```bash
# 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](https://docs.pytest.org/) for Python code testing.
Install test dependencies (needed only once):
```sh
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:
```sh
pytest -vv -r ap --cov jupyter_server_nbmodel
```
#### Frontend tests
This extension is using [Jest](https://jestjs.io/) for JavaScript code testing.
To execute them, execute:
```sh
jlpm
jlpm test
```
#### Integration tests
This extension uses [Playwright](https://playwright.dev/docs/intro) for the integration tests (aka user level tests).
More precisely, the JupyterLab helper [Galata](https://github.com/jupyterlab/jupyterlab/tree/master/galata) is used to handle testing the extension in JupyterLab.
More information are provided within the [ui-tests](./ui-tests/README.md) README.
### Manual testing
```bash
# 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:
```bash
pip install -e ".[test]"
```
To run the python tests, use:
```bash
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
```bash
pip uninstall jupyter_server_nbmodel
```
### Packaging the extension
See [RELEASE](RELEASE.md)