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Making a JupyterLab release

This document guides a contributor through creating a release of JupyterLab.

Check installed tools

Review CONTRIBUTING.md. Make sure all the tools needed to generate the built JavaScript files are properly installed.

Creating a full release

We publish the npm packages, a Python source package, and a Python universal binary wheel. We also publish a conda package on conda-forge (see below). See the Python docs on package uploading for twine setup instructions and for why twine is the recommended method.

Getting a clean environment

For convenience, here are commands for getting a completely clean repo. This makes sure that we don't have any extra tags or commits in our repo (especially since we will push our tags later in the process), and that we are on the master branch.

Note that right now, we pin tornado to version 5 because there are some incompatibilities with tornado 6. See jupyterlab#6131.

cd release
conda deactivate
conda remove --all -y -n jlabrelease
rm -rf jupyterlab

conda create -c conda-forge -y -n jlabrelease notebook nodejs twine 'tornado<6'
conda activate jlabrelease
git clone git@github.com:jupyterlab/jupyterlab.git
cd jupyterlab
pip install -ve .

Publish the npm packages

The commands below ensure the latest dependencies and built files, then change package versions. When one package has an effective major release, the packages that depend on it should also get a major release, to prevent consumers that are using the ^ semver requirement from getting a conflict.

This command prompts you for new version numbers. It then updates version numbers, commits, and tags the commit. It does not publish or push the commit to GitHub.

jlpm run version:choose:all

Updating each version to the next prerelease is tedious. This command, run instead of the one above, automatically updates each package to the next prerelease for that package.

jlpm run version:prerelease:all

Once the version is updated, you can inspect the HEAD commit to see the version bumps and tags (i.e., from-git). The next step is to publish to npm, based on these tags. Note that we publish the JavaScript packages using the next tag until we are ready for the final release.

jlpm run publish:next:from-git

And finally, we push this HEAD commit and tags to GitHub:

git push origin master --tags

Publish the Python package

  • Update jupyterlab/_version.py with an rc version
  • Prep the static assets for release:
jlpm run build:update
  • Commit and tag and push the tag
  • Create the Python release artifacts:
rm -rf dist build
python setup.py sdist
python setup.py bdist_wheel --universal
twine upload dist/*

Post prerelease checklist

Now do the actual final release:

  • Update jupyterlab/_version.py with a final version
  • Make a final Python release
  • Create a branch for the release and push to GitHub
  • Merge the PRs on the other repos and set the default branch of the xckd repo
  • Update the latest npm tags by running jlpm run update:dist-tags and running the commands it prints out
  • Publish to conda-forge.

After a few days (to allow for possible patch releases), set up development for the next release:

  • Update jupyterlab/_version.py with a dev version
  • Run jlpm integrity to update the dev_mode version
  • Commit and push the version update to master
  • Release the other repos as appropriate
  • Update version for binder

Updating the xkcd tutorial

  • Clone the repo if you don't have it
git clone git@github.com:jupyterlab/jupyterlab_xkcd.git

Simple updates by rebasing

If the updates are simple, it may be enough to check out a new branch based on the current base branch, then rebase from the root commit, editing the root commit and other commits that involve installing packages to update to the new versions:

git checkout -b 0.XX # whatever the new version is
git rebase -i --root

"Edit" the commits that involve installing packages, so you can update the package.json. Amend the last commit to bump the version number in package.json in preparation for publishing to npm. Then skip down to the step below about publishing the xkcd tutorial. If the edits are more substantial than just updating package versions, then do the next steps instead.

Creating the tutorial from scratch

  • Create a new empty branch in the xkcd repo.
git checkout --orphan name-of-branch
git rm -rf .
git clean -dfx
cookiecutter path-to-local-extension-cookiecutter-ts
# Fill in the values from the previous branch package.json initial commit
cp -r jupyterlab_xkcd/ .
rm -rf jupyterlab_xkcd
  • Create a new PR in JupyterLab.
  • Run through the tutorial in the PR, making commits and updating the tutorial as appropriate.
  • For the publish section of the readme, use the README file from the previous branch, as well as the package.json fields up to license. Bump the version number in preparation for publishing to npm.

Publishing xkcd tutorial changes

  • Replace the tag references in the tutorial with the new branch number, e.g. replace 0.28- with 0.29-. Prefix the new tags with the branch name, e.g. 0.28-01-show-a-panel 
    git tag 0.XX-01-show-a-panel HEAD~5
git tag 0.XX-02-show-a-comic HEAD~4
git tag 0.XX-03-style-and-attribute HEAD~3
git tag 0.XX-04-refactor-and-refresh HEAD~2
git tag 0.XX-05-restore-panel-state HEAD~1
git tag 0.XX-06-prepare-to-publish HEAD
  • Push the branch with the new tags 
    git push origin 0.XX --tags
    Set the branch as the default branch (see github.com/jupyterlab/jupyterlab_xkcd/settings/branches).
  • If there were changes to the example in the documentation, submit a PR to JupyterLab
  • Publish the new @jupyterlab/xkcd npm package. Make sure to update the version number in the last commit of the branch. 
    npm publish

If you make a mistake and need to start over, clear the tags using the following pattern:

git tag | grep 0.XX | xargs git tag -d

Publishing to conda-forge

  • If no requirements have changed, wait for the conda-forge autotick-bot.
  • Otherwise:
  • Get the sha256 hash for conda-forge release:
shasum -a 256 dist/*.tar.gz

Making a patch release JavaScript package(s)

  • Backport the change to the previous release branch
  • Make a new PR against the previous branch
  • Run the following script, where the package is in /packages/package-folder-name (note that multiple packages can be given):
jlpm run patch:release package-folder-name
  • Push the resulting commit and tag.
  • Create a new Python release on the previous branch
  • Cherry pick the patch commit to the master branch
  • Update the dev version of the master branch in _version.py
  • Update the package.json file in dev_mode with the new JupyterLab version in the jupyterlab metadata section.

Update version for binder

Each time we release JupyterLab, we should update the version of JupyterLab used in binder and repo2docker. Here is an example PR that updates the relevant files:

https://github.com/jupyter/repo2docker/pull/169/files

This needs to be done in both the conda and pip buildpacks in both the frozen and non-frozen version of the files.