Contribute — JupyterLab 4.4.3 documentation (original) (raw)

If you’re reading this section, you’re probably interested in contributing to JupyterLab. Welcome and thanks for your interest in contributing!

Please take a look at the Contributor documentation, familiarize yourself with using JupyterLab, and introduce yourself to the community (on the chat and/or the forum) and share what area of the project you are interested in working on. Please also see the Jupyter Community Guides.

You can help make it better by:

We have labeled some issues as good first issueor help wantedthat we believe are good examples of small, self-contained changes. We encourage those that are new to the code base to implement and/or ask questions about these issues. You are not required to ask for a permission to work on such issue, but if you do and do not get a reply within 48 hours please assume that no one else is working on it (even if someone previously volunteered) and open a pull request with proposed implementation. If you are not certain about the implementation, using draft pull requests is encouraged.

If you believe you’ve found a security vulnerability in JupyterLab or any Jupyter project, please report it to security@ipython.org. If you prefer to encrypt your security reports, you can use this PGP public key.

Table of contents

General Guidelines for Contributing#

For general documentation about contributing to Jupyter projects, see the Project Jupyter Contributor Documentationand Code of Conduct.

Backwards Compatibility, Versions and Breaking Changes#

New versions of JupyterLab may break backwards compatibility with extensions and other Jupyter customizations. Breaking changes are kept to a minimum where possible. JupyterLab development and release cycles follow semantic versioning, so when breaking changes are necessary, they are communicated via the version numbering scheme. In short, this means that, for a JupyterLab version X.Y.Z:

Contributions to JupyterLab extensions and other customizations should plan for possible breaking changes. Consider documenting your maintenance plans to users in these projects. You may also wish to consider pinning the major version of JupyterLab when developing extensions (in your package metadata).

We maintain a major version of JupyterLab for one year after its successor’s first release. See version lifecycle for details. JupyterLab v4 was released on May 15, 2023, so JupyterLab v3 will be maintained until May 15, 2024. JupyterLab v1 and v2 are no longer maintained. All JupyterLab v2 and v3 users are strongly advised to upgrade as soon as possible.

Languages, Tools and Processes#

All source code is written inTypeScript. See the Style Guide.

All non-python source code is formatted using prettier, and python source code is formatted using ruff. When code is modified and committed, all staged files will be automatically formatted using pre-commit git hooks (with help frompre-commit). The benefit of using a code formatters like prettier and ruff is that it removes the topic of code style from the conversation when reviewing pull requests, thereby speeding up the review process.

As long as your code is valid, the pre-commit hook should take care of how it should look.pre-commit and its associated hooks will automatically be installed when you run pip install -e ".[dev,test]"

To install pre-commit manually, run the following:

pip install pre-commit pre-commit install

You can invoke the pre-commit hook by hand at any time with:

which should run any autoformatting on your code and tell you about any errors it couldn’t fix automatically. You may also install ruff integrationinto your text editor to format code automatically.

If you have already committed files before setting up the pre-commit hook with pre-commit install, you can fix everything up usingpre-commit run --all-files. You need to make the fixing commit yourself after that.

You may also use the prettier npm script (e.g. npm run prettier oryarn prettier or jlpm prettier) to format the entire code base. We recommend installing a prettier extension for your code editor and configuring it to format your code with a keyboard shortcut or automatically on save.

Submitting a Pull Request Contribution#

Generally, an issue should be opened describing a piece of proposed work and the issues it solves before a pull request is opened. A triager will ensure that your issue meets our definition of ready before we can merge any pull requests that relate to it.

Pull requests must target the development branch (= main) even if it aims at addressing an issue seen in a stable release. Once the pull request is merged on the development branch, it will be backported to the stable branch using a bot action (or manually if the bot action failed).

Note

Don’t hesitate to mention the targeted version in a PR description. A maintainer will set the milestone accordingly.

Issue Management#

Opening an issue lets community members participate in the design discussion, makes others aware of work being done, and sets the stage for a fruitful community interaction. When you open a new bug or enhancement request, please provide all the requested information in the issue template so that a responder will be able to triage your bug without delay.

A pull request should reference the issue it is addressing. Once the pull request is merged, the issue related to it will also be closed. If there is additional discussion around implementation the issue may be re-opened. Once 30 days have passed with no additional discussion, the lock bot will lock the issue. If additional discussion is desired, or if the pull request doesn’t fully address the locked issue, please open a new issue referencing the locked issue.

New issues are subject to triage. A developer with triage permissions (a triager) will do the following:

  1. Read the issue
  2. Search the existing issues and mark it as a duplicate if necessary
  3. If additional information is required, add a comment requesting it
  4. If the issue is ready to be worked on, assign it to a milestone
  5. Apply appropriate labels to the issue (see examples below)

A developer may start to work on an issue as soon as it is filed. Please work with a triager if they have any questions about your issue so that your changes can be merged in without delay.

Definition of Ready#

One of the main goals of triage is to get issues into a state where they are ready for someone to work on. Once a triager is satisfied that an issue meets the definition below, they will remove the status:Needs Triagelabel from it. We will not merge a pull request for an issue that still needs triage.

Triagers should also ensure that the issue has appropriate labels that describe it, such as labels with the pkg: prefix for issues that affect one or more packages.

All requested information, where applicable, is provided. From the templates in JupyterLab’s issues:

For a bug:

For a feature request:

The issue should represent real, relevant, feasible work. In short, if a knowledgeable person were to be assigned this issue, they would be able to complete it with a reasonable amount of effort and assistance, and it furthers the goals of the Jupyter project.

Labels Used by Triagers#

All new bugs and enhancement requests have the status:Needs Triage label.

On a regular basis, Jupyter contributors (triage reviewers or triagers) review JupyterLab issues tagged with status:Needs Triage, starting with the oldest, and determine whether they meet the definition of ready.

Once triaged, if the issue is ready, the reviewer removes thestatus:Needs Triage label; no additional label is required. If there is not enough information in the issue as filed, the triage reviewer applies the status:Needs Info label and leaves status:Needs Triage in place. If an issue has remained in status:Needs Info for more than 14 days without any follow-up communication, the reviewer should applystatus:Blocked. A blocked issue should be closed after another 14 days pass without a reply that unblocks it.

Our expectation is that every new issue should be examined within a week of its creation.

Triagers should label easier/lower complexity issues as good first issue to facilitate beginner contributions. A good first issue should have:

Unless an issue is time-sensitive, such as if it is a release blocker for an imminent release, experienced Jupyter contributors should avoid picking up recent issues with the good first issue label.

Tagging Issues with Labels#

Users without the commit rights to the JupyterLab repository can tag issues with labels using the @meeseeksdev bot. For example: To apply the label foo and bar baz to an issue, comment@meeseeksdev tag foo "bar baz" on the issue.

Contributing from within the browser#

Contributing to JupyterLab codebase is also possible without setting up a local environment, directly from the Web browser:

Using Binder, you can test the current main branch and your changes within the browser as well. We recommend you have at least 8 GB of RAM for this. To build and launch an instance of the latest JupyterLab main, openthis linkin a new tab. The build takes about 7 minutes to complete.

To test your own branch hosted on GitHub, enter it on https://mybinder.org. If everything goes right, filling out the form takes about 2 minutes, and the build should take about 7 minutes again.

Setting up a local development environment#

Note

Look at the automated dev environment section, for some automation ways to set up a local development environment.

This section explains how to set up a local development environment. We assume you use GNU/Linux, macOS, or Windows Subsystem for Linux. If using Windows, we recommend installing Anaconda for windowsand then using the Anaconda command prompt for all installation steps.

Installing Node.js and jlpm#

Building JupyterLab from its GitHub source code requires Node.js. The development version requires Node.js version 20+, as defined in theengines specification indev_mode/package.json.

If you use conda, you can get it with:

conda install -c conda-forge nodejs=20

If you use Homebrew on macOS:

You can also use the installer from the Node.jswebsite.

To check which version of Node.js is installed:

Using automation to set up a local development environment#

While there is a lot to learn by following the steps above, they can be automated to save time. The main advantages of using automation are: reduced time to get the environment up-and-running, reduced time to re-build the environment, better standardisation (“baseline”, reproducible environments). This section shows how to do that using VS Code dev containers, Docker and Vagrant.

Setup for VS Code#

To start a local development environment for JupyterLab using VS Code dev containers, you need to:

  1. Install the VS Code Dev Containers extension.
  2. Fork the JupyterLab repository.
  3. Clone your fork locally:

git clone https://github.com//jupyterlab.git

  1. Open the local clone with VS Code.
  2. Open the repository in a container. VS Code should prompt you with a pop-up to do so. In case it does not, you can click on the icon >< on the bottom left. Then choose Reopen in container.

Note

It will take quite some times the first time.

Setup using Docker#

To start a JupyterLab development container in a UNIX system with docker installed:

  1. Fork the JupyterLab repository.
  2. Start the container:

git clone https://github.com//jupyterlab.git cd jupyterlab bash docker/start.sh

The above command will build the docker image if it does not exist, then start the container with JupyterLab running in watch mode. The port 8888 is exposed and the current JupyterLab repo is mounted into the container. Then you can start developing JupyterLab with your favorite IDE, JupyterLab will be rebuilt on the fly.

Other available commands:

bash docker/start.sh dev 4567 # Start JupyterLab dev container at port 4567 bash docker/start.sh stop # Stop the running container bash docker/start.sh clean # Remove the docker image bash docker/start.sh build # Rebuild the docker image

Log into the container's shell with the JupyterLab environment activated.

It's useful to run the tests or install dependencies.

bash docker/start.sh shell

To add TypeScript dependencies to the project, you need to log into the container’s shell, install the dependencies to update the package.json and yarn.lock files, and then rebuild the docker image.

bash docker/start.sh shell

In the container shell

jlpm add ... exit

Back to host shell

bash docker/start.sh build

Setup using Vagrant#

A practical example can be found there and includes a Vagrantfile, the bootstrap files and additional documentation.

Installing JupyterLab#

Fork the JupyterLabrepository.

Then use the following steps:

git clone https://github.com//jupyterlab.git cd jupyterlab pip install -e ".[dev,test]" jlpm install jlpm run build # Build the dev mode assets

Build the core mode assets

jlpm run build:core

Build the app dir assets

jupyter lab build

Frequent issues#

Important

On Windows, symbolic links need to be activated on Windows 10 or above for Python version 3.8 or higher by activating the ‘Developer Mode’. That may not be allowed by your administrators. See Activate Developer Mode on Windowsfor instructions.

For installation instructions for contributors who want to write documentation, please seeWriting Documentation

Run JupyterLab#

Start JupyterLab in development mode:

Development mode ensures that you are running the JavaScript assets that are built in the dev-installed Python package. Note that when running in dev mode, extensions will not be activated by default - referdocumentation on extension development to know more.

When running in dev mode, a red stripe will appear at the top of the page; this is to indicate running an unreleased version.

If you want to change the TypeScript code and rebuild on the fly (needs page refresh after each rebuild):

jupyter lab --dev-mode --watch

Build and Run the Tests#

jlpm run build:testutils jlpm test

You can run tests for an individual package by changing to the appropriate package folder:

cd packages/notebook jlpm run build:test jlpm test --runInBand

Note

--runInBand option will run all tests serially in the current process. We advice to use it as some tests are spinning a Jupyter Server that does not like to be executed in parallel.

We use jest for all tests, so standard jest workflows apply. Tests can be debugged in either VSCode or Chrome. It can help to add anit.only to a specific test when debugging. All of the test*scripts in each package accept jest cli options.

VSCode Debugging#

To debug in VSCode, open a package folder in VSCode. We provide a launch configuration in each package folder. In a terminal, runjlpm test:debug:watch. In VSCode, select “Attach to Jest” from the “Run” sidebar to begin debugging. See VSCode docs on debugging for more details.

Chrome Debugging#

To debug in Chrome, run jlpm test:debug:watch in the terminal. Open Chrome and go to chrome://inspect/. Select the remote device and begin debugging.

Testing Utilities#

There are some helper functions in testutils (which is a public npm package called @jupyterlab/testutils) that are used by many of the tests.

For tests that rely on @jupyterlab/services (starting kernels, interacting with files, etc.), there are two options. If a simple interaction is needed, the Mock namespace exposed by testutilshas a number of mock implementations (see testutils/src/mock.ts). If a full server interaction is required, use the JupyterServer class.

We have a helper function called testEmission to help with writing tests that use Lumino signals, as well as a framePromisefunction to get a Promise for a requestAnimationFrame. We sometimes have to set a sentinel value inside a Promise and then check that the sentinel was set if we need a promise to run without blocking.

Internationalization#

Translatable strings update#

The translatable strings update cannot occur on patch release. They must be delayed on minor or major versions.

Performance Testing#

Benchmark of JupyterLab is done using Playwright. The actions measured are:

Two files are tested: a notebook with many code cells and another with many markdown cells.

The test is run on the CI by comparing the result in the commit at which a PR branch started and the PR branch head on the same CI job to ensure using the same hardware. The benchmark job is triggered on:

The tests are located in the subfolder galata/test/benchmark. And they can be executed with the following command:

A special report will be generated in the folder benchmark-results that will contain 4 files:

The reference, tagged expected, is stored in lab-benchmark-expected.json. It can be created using the -u option of Playwright; i.e. jlpm run test:benchmark -u.

Benchmark parameters#

The benchmark can be customized using the following environment variables:

More tests can be carried out manually on JupyterLab branches and run weekly on the default branch injupyterlab/benchmarks repository.

Visual Regression and UI Tests#

As part of JupyterLab CI workflows, UI tests are run with visual regression checks.Galata is used for UI testing. Galata provides Playwright helpers to control and inspect JupyterLab UI programmatically.

UI tests are run for each commit into JupyterLab project in PRs or direct commits. Code changes can sometimes cause UI tests to fail for various reasons. After each test run, Galata generates a user friendly test result report which can be used to inspect failing UI tests. Result report shows the failure reason, call-stack up to the failure and detailed information on visual regression issues. For visual regression errors, reference image and test capture image, along with diff image generated during comparison are provided in the report. You can use these information to debug failing tests. Galata test report can be downloaded from GitHub Actions page for a UI test run. Test artifact is named galata-report and once you extract it, you can access the report by launching a server to serve the files python -m http.server -d <path-to-extracted-report>. Then open http://localhost:8000/ with your web browser.

Main reasons for UI test failures are:

  1. A visual regression caused by code changes:
    Sometimes unintentional UI changes are introduced by modifications to project source code. Goal of visual regression testing is to detect this kind of UI changes. If your PR / commit is causing visual regression, then debug and fix the regression caused. You can locally run and debug the UI tests to fix the visual regression. To debug your test, you may run PWDEBUG=1 jlpm playwright test <path-to-test-file>. Once you have a fix, you can push the change to your GitHub branch and test with GitHub actions.
  2. An intended update to user interface:
    If your code change is introducing an update to UI which causes existing UI Tests to fail, then you will need to update reference image(s) for the failing tests. In order to do that, you can post a comment on your PR with the following content:
    • please update galata snapshots: A bot will push a new commit to your PR updating galata test snapshots.
    • please update documentation snapshots: A bot will push a new commit to your PR updating documentation test snapshots.
    • please update snapshots: Combine the two previous comments effects.

      The bot will react with +1 emoji to indicate that the run started and then comment back once it concluded. This feature is restricted to a subset of users with higher privileges due to security concerns.

For more information on UI Testing, please read the UI Testing developer documentationand Playwright documentation.

Good Practices for Integration tests#

Here are some good practices to follow when writing integration tests:

Contributing to the debugger front-end#

To make changes to the debugger extension, a kernel with support for debugging is required.

Check out the user documentation to learn how to install such kernel: Debugger.

Then refresh the page and the debugger sidebar should appear in the right area.

The Debugger Adapter Protocol#

The following diagram illustrates the types of messages sent between the JupyterLab extension and the kernel.

UML sequence diagram illustrating the interaction between a user, JupyterLab, and the kernel. From top to bottom, the timeline starts with opening the notebook and includes annotations where the debugger is started and stopped. Specific interactions and message types are discussed in the subsequent text.

Inspecting Debug Messages in VS Code#

Inspecting the debug messages in VS Code can be useful to understand when debug requests are made (for example triggered by a UI action), and to compare the behavior of the JupyterLab debugger with the Python debugger in VS Code.

The first step is to create a test file and a debug configuration (launch.json):

An editor showing the menu for creating a debug configuration.

{ "version": "0.2.0", "configurations": [ { "name": "Python: Current File", "type": "python", "request": "launch", "program": "${file}", "console": "integratedTerminal", "env": { "DEBUGPY_LOG_DIR": "/path/to/logs/folder" } } ] }

Then start the debugger:

A started debugging session in the editor. There are additional buttons in the upper right for navigating the session.

The content of the log file looks like this:

...

D00000.032: IDE --> { "command": "initialize", "arguments": { "clientID": "vscode", "clientName": "Visual Studio Code", "adapterID": "python", "pathFormat": "path", "linesStartAt1": true, "columnsStartAt1": true, "supportsVariableType": true, "supportsVariablePaging": true, "supportsRunInTerminalRequest": true, "locale": "en-us" }, "type": "request", "seq": 1 }

...

With:

References#

Build and run the stand-alone examples#

To install and build the examples in the examples directory:

To run a specific example, change to the examples directory (i.e.examples/filebrowser) and enter:

Debugging in the Browser#

All methods of building JupyterLab produce source maps. The source maps should be available in the source files view of your browser’s development tools under the webpack:// header.

When running JupyterLab normally, expand the ~ header to see the source maps for individual packages.

When running in --dev-mode, the core packages are available underpackages/, while the third party libraries are available under~. Note: it is recommended to use jupyter lab --watch --dev-modewhile debugging.

When running a test, the packages will be available at the top level (e.g. application/src), and the current set of test files available under /src. Note: it is recommended to use jlpm run watch in the test folder while debugging test options. Seeabove for more info.

High level Architecture#

The JupyterLab application is made up of two major parts:

Each part is named jupyterlab. The developer tutorial documentationprovides additional architecture information.

The NPM Packages#

The repository consists of many npm packages that are managed using the lerna build tool. The npm package source files are in the packages/subdirectory.

Build the NPM Packages from Source#

git clone https://github.com/jupyterlab/jupyterlab.git cd jupyterlab pip install -e . jlpm jlpm run build:packages

Rebuild

jlpm run clean jlpm run build:packages

Writing Documentation#

Documentation is written in Markdown and reStructuredText. In particular, the documentation on our Read the Docs page is written in reStructuredText. To ensure that the Read the Docs page builds, you’ll need to install the documentation dependencies with pip:

To test the docs run:

python -m pytest --check-links -k .md . || python -m pytest --check-links -k .md --lf .

The Read the Docs pages can be built using make:

The JupyterLab API reference documentation is also included in the previous step. To access the documentation, first launch a server to serve the generated files:

And then go to http://localhost:8000/ in your browser.

The JupyterLab API reference documentation can be built separately using jlpm:

Writing Style#

User Interface Naming Conventions#

Documents, Files, and Activities#

Refer to files as either files or documents, depending on the context.

Documents are more human centered. If human viewing, interpretation, or interaction is an important part of the experience, use the term “document”. For example, notebooks and Markdown files will often be referred to as documents except in the context of a file system (e.g., the notebook filename).

Use the term files in a less human-focused context. For example, refer to files in relation to a file system or file name.

Activities are either an opened document or another UI panel that is not related to a file, such as terminals, consoles or the inspector.

Notebook Cells#

A notebook contains cells, each of which have input and one or more_outputs_. When the user runs a cell, the kernel reads and executes the input and generates outputs. The notebook then displays the cell’s output. The term output describes one of possibly multiple results of running a cell. Cell output describes the collective output of one cell. Use_outputs of all cells_ to describe all outputs from all cells.

Command Names#

Command names appear in menus, in the Command Palette, and in toolbar buttons (where the name typically appears on hover).

Element Names#

Write most element names in lowercase. These names include:

Write the following sections of the user interface with one or more initial capitals, mirroring their use in the UI:

See The JupyterLab Interface for descriptions of elements in the UI.

The Jupyter Server Extension#

The Jupyter server extension source files are in the jupyterlab/ subdirectory. To use this extension, make sure the Jupyter Notebook server version 4.3 or later is installed.

Build the JupyterLab server extension#

When you make a change to JupyterLab npm package source files, run:

to build the changes, and then refresh your browser to see the changes.

To have the system build after each source file change, run:

jupyter lab --dev-mode --watch

Build Utilities#

There is a range of build utilities for maintaining the repository. To get a suggested version for a library usejlpm run get:dependency foo. To update the version of a library across the repo use jlpm run update:dependency foo ^latest. To remove an unwanted dependency use jlpm run remove:dependency foo.

The key utility is jlpm run integrity, which ensures the integrity of the packages in the repo. It will:

The packages/metapackage package is used to build all of the TypeScript in the repository at once, instead of 50+ individual builds.

The integrity script also allows you to automatically add a dependency for a package by importing from it in the TypeScript file, and then running: jlpm run integrity from the repo root.

We also have scripts for creating and removing packages inpackages/, jlpm run create:package andjlpm run remove:package. When creating a package, if it is meant to be included in the core bundle, add thejupyterlab: { coreDependency: true } metadata to thepackage.json. Packages with extension or mimeExtensionmetadata are considered to be a core dependency unless they are explicitly marked otherwise.

Testing Changes to External Packages#

Linking/Unlinking Packages to JupyterLab#

If you want to make changes to one of JupyterLab’s external packages (for example, Lumino) and test them out against your copy of JupyterLab, you can easily do so using thelink command:

  1. Make your changes and then build the external package
  2. Link JupyterLab to modded package
    • navigate to top level of your JupyterLab repo, then runjlpm link <path-to-external-repo> --all

3. You can then (re)build JupyterLab (eg jlpm run build) and your changes should be picked up by the build.

To restore JupyterLab to its original state, you use the unlinkcommand:

  1. Unlink JupyterLab and modded package
    • navigate to top level of your JupyterLab repo, then runjlpm unlink <path-to-external-repo> --all
  2. Reinstall original version of the external package in JupyterLab
    • run jlpm install --check-files

3. You can then (re)build JupyterLab and everything should be back to default.

Possible Linking Pitfalls#

If you’re working on an external project with more than one package, you’ll probably have to link in your copies of every package in the project, including those you made no changes to. Failing to do so may cause issues relating to duplication of shared state.

Specifically, when working with Lumino, you’ll probably have to link your copy of the "@lumino/messaging" package (in addition to whatever packages you actually made changes to). This is due to potential duplication of objects contained in the MessageLoopnamespace provided by the messaging package.

Keyboard Shortcuts#

Typeset keyboard shortcuts as follows:

Screenshots and Animations#

Our documentation should contain screenshots and animations that illustrate and demonstrate the software. Here are some guidelines for preparing them:

To help us organize screenshots and animations, please name the files with a prefix that matches the names of the source file in which they are used:

sourcefile.rst sourcefile_filebrowser.png sourcefile_editmenu.png

This will help us to keep track of the images as documentation content evolves.

Notes#