Maintainer / core-developer information¶
This section is about preparing a major release, incrementing the minor version, or a bug fix release incrementing the patch version. Our convention is that we release one or more release candidates (0.RRrcN) before releasing the final distributions. We follow the PEP101 to indicate release candidates, post, and minor releases.
Before a release¶
Update authors table:
cd build_tools; make authors; cd ..
and commit. This is only needed if the authors have changed since the last release. This step is sometimes done independent of the release. This updates the maintainer list and is not the contributor list for the release.
Confirm any blockers tagged for the milestone are resolved, and that other issues tagged for the milestone can be postponed.
Ensure the change log and commits correspond (within reason!), and that the change log is reasonably well curated. Some tools for these tasks include:
maint_tools/sort_whats_new.pycan put what’s new entries into sections. It’s not perfect, and requires manual checking of the changes. If the whats new list is well curated, it may not be necessary.
maint_tools/whats_missing.shscript may be used to identify pull requests that were merged but likely missing from What’s New.
Make sure the deprecations, FIXME and TODOs tagged for the release have been taken care of.
The release manager requires a set of permissions on top of the usual permissions given to maintainers, which includes:
maintainer role on
become a member of the scikit-learn team on conda-forge by editing the
Preparing a release PR¶
Major version release¶
Prior to branching please do not forget to prepare a Release Highlights page as
a runnable example and check that its HTML rendering looks correct. These
release highlights should be linked from the
for the new version of scikit-learn.
Releasing the first RC of e.g. version
0.99.0 involves creating the release
0.99.X directly on the main repo, where
X really is the letter X,
not a placeholder. The development for the major and minor releases of
should also happen under
0.99.X. Each release (rc, major, or minor) is a
tag under that branch.
This is done only once, as the major and minor releases happen on the same branch:
# Assuming upstream is an alias for the main scikit-learn repo: git fetch upstream main git checkout upstream/main git checkout -b 0.99.X git push --set-upstream upstream 0.99.X
Xis literal here, and
99is replaced by the release number. The branches are called
In terms of including changes, the first RC ideally counts as a feature freeze. Each coming release candidate and the final release afterwards will include only minor documentation changes and bug fixes. Any major enhancement or feature should be excluded.
Then you can prepare a local branch for the release itself, for instance:
release-0.99.0rc1, push it to your github fork and open a PR to the
scikit-learn/0.99.X branch. Copy the Release checklist templates
in the description of the Pull Request to track progress.
This PR will be used to push commits related to the release as explained in Making a release.
You can also create a second PR from main and targeting main to increment
__version__ variable in
sklearn/__init__.py to increment the dev
version. This means while we’re in the release candidate period, the latest
stable is two versions behind the main branch, instead of one. In this PR
targeting main you should also include a new file for the matching version
doc/whats_new/ folder so PRs that target the next version can
contribute their changelog entries to this file in parallel to the release
Minor version release¶
The minor releases should include bug fixes and some relevant documentation changes only. Any PR resulting in a behavior change which is not a bug fix should be excluded.
First, create a branch, on your own fork (to release e.g.
# assuming main and upstream/main are the same git checkout -b release-0.99.3 main
Then, create a PR to the
scikit-learn/0.99.X branch (not to
main!) with all the desired changes:
git rebase -i upstream/0.99.2
Copy the Release checklist templates in the description of the Pull Request to track progress.
Do not forget to add a commit updating
It’s nice to have a copy of the
git rebase -i log in the PR to help others
understand what’s included.
Making a release¶
Ensure that you have checked out the branch of the release PR as explained in Preparing a release PR above.
Update docs. Note that this is for the final release, not necessarily for the RC releases. These changes should be made in main and cherry-picked into the release branch, only before the final release.
doc/whats_new/v0.99.rstfile to add release title and list of contributors. You can retrieve the list of contributor names with:
$ git shortlog -s 0.98.33.. | cut -f2- | sort --ignore-case | tr '\n' ';' | sed 's/;/, /g;s/, $//' | fold -s
For major releases, link the release highlights example from the
Update the release date in
doc/templates/index.htmlto change the ‘News’ entry of the front page (with the release month as well).
On the branch for releasing, update the version number in
For major releases, please add a 0 at the end:
For the first release candidate, use the
rc1suffix on the expected final release number:
Trigger the wheel builder with the
[cd build]commit marker using the command:
git commit --allow-empty -m "Trigger wheel builder workflow: [cd build]"
The wheel building workflow is managed by GitHub Actions and the results be browsed at: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22
Before building the wheels, make sure that the
pyproject.toml file is
up to date and using the oldest version of
numpy for each Python version
to avoid ABI
incompatibility issues. Moreover, a new line have to be included in the
pyproject.toml file for each new supported version of Python.
The acronym CD in
[cd build] stands for Continuous Delivery and refers to the
automation used to generate the release artifacts (binary and source
packages). This can be seen as an extension to CI which stands for
Continuous Integration. The CD workflow on
GitHub Actions is also used to automatically create nightly builds and
publish packages for the developement branch of scikit-learn. See
Installing nightly builds.
Once all the CD jobs have completed successfully in the PR, merge it, again with the
[cd build]marker in the commit message. This time the results will be uploaded to the staging area.
You should then be able to upload the generated artifacts (.tar.gz and .whl files) to https://test.pypi.org using the “Run workflow” form for the following GitHub Actions workflow:
If this went fine, you can proceed with tagging. Proceed with caution. Ideally, tags should be created when you’re almost certain that the release is ready, since adding a tag to the main repo can trigger certain automated processes.
Create the tag and push it (if it’s an RC, it can be
git tag -a 0.99.0 # in the 0.99.X branch git push email@example.com:scikit-learn/scikit-learn.git 0.99.0
Trigger the GitHub Actions workflow again but this time to upload the artifacts to the real https://pypi.org (replace “testpypi” by “pypi” in the “Run workflow” form).
Alternatively, it’s possible to collect locally the generated binary wheel packages and source tarball and upload them all to PyPI by running the following commands in the scikit-learn source folder (checked out at the release tag):
rm -r dist pip install -U wheelhouse_uploader twine python setup.py fetch_artifacts
This command will download all the binary packages accumulated in the staging area on the anaconda.org hosting service and put them in your local
Check the content of the
./distfolder: it should contain all the wheels along with the source tarball (“scikit-learn-RRR.tar.gz”).
Make sure that you do not have developer versions or older versions of the scikit-learn package in that folder.
Before uploading to pypi, you can test upload to test.pypi.org:
twine upload --verbose --repository-url https://test.pypi.org/legacy/ dist/*
Upload everything at once to https://pypi.org:
twine upload dist/*
For major/minor (not bug-fix release), update the symlink for
latestStablevariable in https://github.com/scikit-learn/scikit-learn.github.io:
cd /tmp git clone --depth 1 --no-checkout firstname.lastname@example.org:scikit-learn/scikit-learn.github.io.git cd scikit-learn.github.io echo stable > .git/info/sparse-checkout git checkout master rm stable ln -s 0.999 stable sed -i "s/latestStable = '.*/latestStable = '0.999';/" versionwarning.js git add stable/ versionwarning.js git commit -m "Update stable to point to 0.999" git push origin master
The following GitHub checklist might be helpful in a release PR:
* [ ] update news and what's new date in release branch * [ ] update news and what's new date and sklearn dev0 version in main branch * [ ] check that the for the release wheels can be built successfully * [ ] merge the PR with `[cd build]` commit message to upload wheels to the staging repo * [ ] upload the wheels and source tarball to https://test.pypi.org * [ ] create tag on the main github repo * [ ] upload the wheels and source tarball to PyPI * [ ] https://github.com/scikit-learn/scikit-learn/releases draft * [ ] confirm bot detected at https://github.com/conda-forge/scikit-learn-feedstock and wait for merge * [ ] https://github.com/scikit-learn/scikit-learn/releases publish * [ ] fix the binder release version in ``.binder/requirement.txt`` (see #15847) * [ ] announce on mailing list and on twitter
Merging Pull Requests¶
Individual commits are squashed when a Pull Request (PR) is merged on Github. Before merging,
the resulting commit title can be edited if necessary. Note that this will rename the PR title by default.
the detailed description, containing the titles of all the commits, can be edited or deleted.
for PRs with multiple code contributors care must be taken to keep the
Co-authored-by: name <email@example.com>tags in the detailed description. This will mark the PR as having multiple co-authors. Whether code contributions are significanly enough to merit co-authorship is left to the maintainer’s discretion, same as for the “what’s new” entry.
The scikit-learn.org web site¶
The scikit-learn web site (http://scikit-learn.org) is hosted at GitHub, but should rarely be updated manually by pushing to the https://github.com/scikit-learn/scikit-learn.github.io repository. Most updates can be made by pushing to master (for /dev) or a release branch like 0.99.X, from which Circle CI builds and uploads the documentation automatically.
Travis Cron jobs¶
From https://docs.travis-ci.com/user/cron-jobs: Travis CI cron jobs work similarly to the cron utility, they run builds at regular scheduled intervals independently of whether any commits were pushed to the repository. Cron jobs always fetch the most recent commit on a particular branch and build the project at that state. Cron jobs can run daily, weekly or monthly, which in practice means up to an hour after the selected time span, and you cannot set them to run at a specific time.
For scikit-learn, Cron jobs are used for builds that we do not want to run in each PR. As an example the build with the dev versions of numpy and scipy is run as a Cron job. Most of the time when this numpy-dev build fail, it is related to a numpy change and not a scikit-learn one, so it would not make sense to blame the PR author for the Travis failure.
The definition of what gets run in the Cron job is done in the .travis.yml
config file, exactly the same way as the other Travis jobs. We use a
= cron filter in order for the build to be run only in Cron jobs.
The branch targeted by the Cron job and the frequency of the Cron job is set via the web UI at https://www.travis-ci.org/scikit-learn/scikit-learn/settings.
sklearn.experimental module was introduced in 0.21 and contains
experimental features / estimators that are subject to change without
Note that the public import path must be to a public subpackage (like
sklearn/impute), not just a
Also, the (private) experimental features that are imported must be in a
submodule/subpackage of the public subpackage, e.g.
sklearn/impute/_iterative.py. This is needed so that pickles still work
in the future when the features aren’t experimental anymore.
To avoid type checker (e.g. mypy) errors a direct import of experimental
estimators should be done in the parent module, protected by the
if typing.TYPE_CHECKING check. See sklearn/ensemble/__init__.py,
for an example.
Please also write basic tests following those in test_enable_hist_gradient_boosting.py.
Make sure every user-facing code you write explicitly mentions that the feature
is experimental, and add a
# noqa comment to avoid pep8-related warnings:
# To use this experimental feature, we need to explicitly ask for it: from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingRegressor
For the docs to render properly, please also import
doc/conf.py, else sphinx won’t be
able to import the corresponding modules. Note that using
sklearn.experimental import * does not work.
Note that some experimental classes / functions are not included in the