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Contributing to the Jupyter extension for Visual Studio Code

main branch

| Main Build

[For contributing to the Microsoft Python Language Server see its own repo; for Pylance see its own repo; for debugpy see its own repo]

Contributing a pull request

Prerequisites

  1. Node.js 18.15.0
  2. npm 9.5.0
  3. Python 3.6 or later
  4. Windows, macOS, or Linux
  5. Visual Studio Code
  6. The following VS Code extensions:

Setup

git clone https://github.com/microsoft/vscode-jupyter
cd vscode-jupyter
npm ci
# Run this to setup the necessary pre-commit hooks.
npm run setup-precommit-hook
python3 -m venv .venv
# Activate the virtual environment as appropriate for your shell, For example, on bash it's ...
source .venv/bin/activate
# and in Windows cmd or PowerShell
.venv\Scripts\activate
# The Python code in the extension is formatted using Black.
python -m pip install black

Incremental Build

Run the Compile build Tasks from the Run Build Task... command picker (short cut CTRL+SHIFT+B or ⇧⌘B). This will leave build tasks running in the background and which will re-run as files are edited and saved. You can see the output from either task in the Terminal panel (use the selector to choose which output to look at).

You can also compile from the command-line. For a full compile you can use:

npx gulp prePublishNonBundle

For incremental builds you can use the following commands depending on your needs:

# This will compile everthing, but only watch for changes to the desktop bundle.
npm run compile
# This watches changes to all files, webviews, web version of extension, node version of extension, etc
# This can be resource intensive, as there are a number of bundles created, thus requiring monitoring of files for each of these numerous bundles.
npm run compile-watch-all

Sometimes you will need to run npm run clean and even rm -r out dist. This is especially true if you have added or removed files.

Errors and Warnings

TypeScript errors and warnings will be displayed in the Problems window of Visual Studio Code.

Run dev build and validate your changes

To test changes, open the vscode-jupyter folder in VSCode, and select the workspace titled vscode-jupyter. Then, open the debug panel by clicking the Run and Debug icon on the sidebar, select the Extension option from the top menu, and click start. A new window will launch with the title [Extension Development Host].

Running Unit Tests

Note: Unit tests are those in files with extension .unit.test.ts.

  1. Make sure you have compiled all code (done automatically when using incremental building)
  2. Ensure you have disabled breaking into 'Uncaught Exceptions' when running the Unit Tests
  3. For the linters and formatters tests to pass successfully, you will need to have those corresponding Python libraries installed locally
  4. Run the Tests via the Unit Tests launch option.

You can also run them from the command-line (after compiling):

npm run test:unittests  # runs all unit tests
npm run test:unittests -- --grep='<NAME-OF-SUITE>'

To run only a specific test suite for unit tests: Alter the launch.json file in the "Debug Unit Tests" section by setting the grep field:

    "args": [
        "--timeout=60000",
        "--grep", "<suite name>"
    ],

...this will only run the suite with the tests you care about during a test run (be sure to set the debugger to run the Debug Unit Tests launcher).

Running Integration Tests (with VS Code)

Note: Integration tests are those in files with extension *.vscode.test*.ts.

  1. Make sure you have compiled all code (done automatically when using incremental building)
  2. Some of the tests require specific virtual environments. Run the 'src/test/datascience/setupTestEnvs.cmd` (or equivalent) to create them.
  3. For the linters and formatters tests to pass successfully, you will need to have those corresponding Python libraries installed locally by using the build/test-requirements.txt file
  4. Run the tests via npm run or the Debugger launch options (you can "Start Without Debugging").

You can also run the tests from the command-line (after compiling):

npm run testVSCode  # will launch the VSC UI

Customising the Test Run

If you want to change which tests are run or which version of Python is used, you can do this by setting environment variables. The same variables work when running from the command line or launching from within VSCode, though the mechanism used to specify them changes a little.

  • Setting CI_PYTHON_PATH lets you change the version of python the tests are executed with
  • Setting VSC_JUPYTER_CI_TEST_GREP lets you filter the tests by name

CI_PYTHON_PATH

In some tests a Python executable is actually run. The default executable is python (for now). Unless you've run the tests inside a virtual environment, this will almost always mean Python 2 is used, which probably isn't what you want.

By setting the CI_PYTHON_PATH environment variable you can control the exact Python executable that gets used. If the executable you specify isn't on $PATH then be sure to use an absolute path.

This is also the mechanism for testing against other versions of Python.

VSC_JUPYTER_CI_TEST_GREP

This environment variable allows providing a regular expression which will be matched against suite and test "names" to be run. By default all tests are run.

For example, to run only the tests in the DataScience - Kernels Finder suite (from src/test/datascience/kernel-launcher/kernelFinder.vscode.test.ts) you would set the value to Kernels Finder.

Be sure to escape any grep-sensitive characters in your suite name.

In some rare cases in the "system" tests the VSC_JUPYTER_CI_TEST_GREP environment variable is ignored. If that happens then you will need to temporarily modify the const defaultGrep = line in src/test/index.ts.

Launching from VSCode

In order to set environment variables when launching the tests from VSCode you should edit the launch.json file. For example you can add the following to the appropriate configuration you want to run to change the interpreter used during testing:

    "env": {
        "CI_PYTHON_PATH": "/absolute/path/to/interpreter/of/choice/python"
    }

On the command line

The mechanism to set environment variables on the command line will vary based on your system, however most systems support a syntax like the following for setting a single variable for a subprocess:

VSC_JUPYTER_CI_TEST_GREP=Sorting npm run testVSCode

Testing Python Scripts

The extension has a number of scripts in ./pythonFiles. Tests for these scripts are found in ./pythonFiles/tests. To run those tests:

  • python2.7 pythonFiles/tests/run_all.py
  • python3 -m pythonFiles.tests

By default, functional tests are included. To exclude them:

python3 -m pythonFiles.tests --no-functional

To run only the functional tests:

python3 -m pythonFiles.tests --functional

Standard Debugging

Clone the repo into any directory, open that directory in VSCode, and use the Extension launch option within VSCode.

Coding Standards

Information on our coding standards can be found here. We have CI tests to ensure the code committed will adhere to the above coding standards.

Messages displayed to the user must be localized using/created constants from/in the localize.ts file.

Development process

To effectively contribute to this extension, it helps to know how its development process works. That way you know not only why the project maintainers do what they do to keep this project running smoothly, but it allows you to help out by noticing when a step is missed or to learn in case someday you become a project maintainer as well!

Folder Structure

At a high level we have a bunch of folders. Each high level is described in this wiki page

Typical workflow

Here's an example of a typical workflow:

  1. Sync to main (get your fork's main to match vscode-jupyter's main)
  2. Create branch
  3. npm ci
  4. npm run clean
  5. Start VS code Insiders root
  6. CTRL+SHIFT+B and build Compile Web Views and Compile
  7. Make code changes
  8. Write and run unit tests if appropriate
  9. Test with Extension launch task
  10. Repeat until works in normal extension
  11. Test with Extension (web) launch task
  12. Run jupyter notebook server to use in web testing
  13. Repeat until works in web extension
  14. Write integration tests and run locally.
  15. Submit PR
  16. Check PR output to make sure tests don't fail.
  17. Debug CI test failures

Helping others

First and foremost, we try to be helpful to users of the extension. We monitor Stack Overflow questions to see where people might need help. We also try to respond to all issues in some way in a timely manner (typically in less than one business day, definitely no more than a week). We also answer questions that reach us in other ways, e.g. Twitter.

For pull requests, we aim to review any externally contributed PR no later than the next sprint from when it was submitted (see Release Cycle below for our sprint schedule).

Release cycle

Planning is done as monthly releases.

The extension aims to do a new release once a month. A release plan is created for each release to help track anything that requires a person to do (long-term this project aims to automate as much of the development process as possible).

All development is actively done in the main branch of the repository. This allows us to have a development build which is expected to be stable at all times. Once we reach a release candidate, it becomes our release branch. At that point only what is in the release branch will make it into the next release.

Pull requests

Key details that all pull requests are expected to handle should be in the pull request template. We do expect CI to be passing for a pull request before we will consider merging it.

Versioning

The extension sets the major version be the year of release, the minor version the release count for that year, and the build number is a number that increments for every build. For example the first release in 2021 is 2021.1.<build number>.

Releasing

Overall steps for releasing are covered in the release plan (template).

Building a release

To create a release build, follow the steps outlined in the release plan (which has a template).

Local Build

Steps to build the extension on your machine once you've cloned the repo:

> npm install -g @vscode/vsce
# Perform the next steps in the vscode-jupyter folder.
> npm ci
> npm run clean
> npm run package # This step takes around 10 minutes.

Resulting in a ms-toolsai-jupyter-insiders.vsix file in your vscode-jupyter folder.

⚠️ If you made changes to package.json, run npm install (instead of npm ci) to update package-lock.json and install dependencies all at once.