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

  1. 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.

  2. Confirm any blockers tagged for the milestone are resolved, and that other issues tagged for the milestone can be postponed.

  3. 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/ can 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.

    • The maint_tools/ script may be used to identify pull requests that were merged but likely missing from What’s New.

  4. 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 scikit-learn projects on and, separately.

  • become a member of the scikit-learn team on conda-forge by editing the recipe/meta.yaml file on

  • maintainer on

Preparing a release PR

Releasing the first RC of e.g. version 0.99 involves creating the release branch 0.99.X directly on the main repo, where X really is the letter X, not a placeholder. This is considered the feature freeze. The development for the major and minor releases of 0.99 should also happen under 0.99.X. Each release (rc, major, or minor) is a tag under that branch.

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 minor documentation changes and bug fixes. Any major enhancement or feature should be excluded.

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. 0.999.3):

$ # assuming master and upstream/master are the same
$ git checkout -b release-0.999.3 master

Then, create a PR to the scikit-learn/0.999.X branch (not to master!) with all the desired changes:

$ git rebase -i upstream/0.999.2

Do not forget to add a commit updating sklearn.__version__.

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

  1. Create the release branch on the main repo, if it does not exist. This is done only once, as the major and minor releases happen on the same branch:

    $ git checkout -b 0.99.X

    Again, X is literal here, and 99 is replaced by the release number. The branches are called 0.19.X, 0.20.X, etc.

  2. Update docs. Note that this is for the final release, not necessarily for the RC releases. These changes should be made in master and cherry-picked into the release branch, only before the final release.

    • Edit the doc/whats_new.rst file to add release title and commit statistics. You can retrieve commit statistics with:

      $ git shortlog -s 0.99.33.. | cut -f2- | sort --ignore-case | tr '\n' ';' | sed 's/;/, /g;s/, $//'
    • Update the release date in whats_new.rst

    • Edit the doc/templates/index.html to change the ‘News’ entry of the front page.

  3. On the branch for releasing, update the version number in sklearn/, the __version__ variable by removing dev* only when ready to release. On master, increment the version in the same place (when branching for release). This means while we’re in the release candidate period, the latest stable is two versions behind the master branch, instead of one.

  4. At this point all relevant PRs should have been merged into the 0.99.X branch. Create the source tarball:

    • Wipe clean your repo:

      $ git clean -xfd
    • Generate the tarball:

      $ python sdist
    • You can also test a binary dist build using:

      $ python bdist_wheel
    • You can test if PyPi is going to accept the package using:

      $ twine check dist/*

    You can run twine check after step 5 (fetching artifacts) as well.

    The result should be in the dist/ folder. We will upload it later with the wheels. Check that you can install it in a new virtualenv and that the tests pass.

  5. 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. You can test upload the sdist to, and test the next step by setting BUILD_COMMIT to the branch name (0.99.X for instance) in a PR to the wheel building repo. Once all works, you can proceed with tagging. Create the tag and push it (if it’s an RC, it can be 0.xxrc1 for instance):

    $ git tag -a 0.99  # in the 0.99.X branch
    $ git push 0.99
  6. Update the dependency versions and set BUILD_COMMIT variable to the release tag at:

    Once the CI has completed successfully, collect the generated binary wheel packages and upload them to PyPI by running the following commands in the scikit-learn source folder (checked out at the release tag):

    $ rm -r dist # only if there's anything other than the sdist tar.gz there
    $ pip install -U wheelhouse_uploader twine
    $ python fetch_artifacts
  7. Check the content of the dist/ folder: 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

    $ twine upload --verbose --repository-url dist/*

    Upload everything at once to

    $ twine upload dist/*
  8. For major/minor (not bug-fix release), update the symlink for stable and the latestStable variable in

    $ cd /tmp
    $ git clone --depth 1 --no-checkout
    $ cd
    $ 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 master and release branch
* [ ] create tag
* [ ] update dependencies and release tag at
* [ ] twine the wheels to PyPI when that's green
* [ ] draft
* [ ] confirm bot detected at and wait for merge
* [ ] publish
* [ ] fix the binder release version in ``.binder/requirement.txt`` (see
* [ ] 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 <> 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 web site

The scikit-learn web site ( is hosted at GitHub, but should rarely be updated manually by pushing to the 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 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 if: type = 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

Experimental features

The sklearn.experimental module was introduced in 0.21 and contains experimental features / estimators that are subject to change without deprecation cycle.

To create an experimental module, you can just copy and modify the content of, or

Note that the public import path must be to a public subpackage (like sklearn/ensemble or sklearn/impute), not just a .py module. Also, the (private) experimental features that are imported must be in a submodule/subpackage of the public subpackage, e.g. sklearn/ensemble/_hist_gradient_boosting/ or sklearn/impute/ 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 experimenal estimators should be done in the parent module, protected by the if typing.TYPE_CHECKING check. See sklearn/ensemble/, or sklearn/impute/ for an example.

Please also write basic tests following those in

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 enable_my_experimental_feature in doc/, else sphinx won’t be able to import the corresponding modules. Note that using from sklearn.experimental import * does not work.

Note that some experimental classes / functions are not included in the sklearn.experimental module: sklearn.datasets.fetch_openml.