Maintainer / core-developer information

Before a release

  1. Update authors table:

    $ cd build_tools; make authors; cd ..

    and commit.

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

Preparing a bug-fix-release

Since any commits to a released branch (e.g. 0.999.X) will automatically update the web site documentation, it is best to develop a bug-fix release with a pull request in which 0.999.X is the base. It also allows you to keep track of any tasks towards release with a TO DO list.

Most development of the bug fix release, and its documentation, should happen in master to avoid asynchrony. To select commits from master for use in the bug fix (version 0.999.3), you can use:

$ git checkout -b release-0.999.3 master
$ git rebase -i 0.999.X

Then pick the commits for release and resolve any issues, and create a pull request with 0.999.X as base. Add a commit updating sklearn.__version__. Additional commits can be cherry-picked into the release-0.999.3 branch while preparing the release.

Making a release

  1. Update docs:

    • 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/index.rst to change the ‘News’ entry of the front page.

    • Note that these changes should be made in master and cherry-picked into the release branch.

  2. 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 verson in the same place (when branching for release).

  3. Create the tag and push it:

    $ git tag -a 0.999
    $ git push --tags
  4. Create the source tarball:

    • Wipe clean your repo:

      $ git clean -xfd
    • Generate the tarball:

      $ python sdist

    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. 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):

    $ pip install -U wheelhouse_uploader twine
    $ python fetch_artifacts
  6. Check the content of the dist/ folder: it should contain all the wheels along with the source tarball (“scikit-learn-XXX.tar.gz”).

    Make sure that you do not have developer versions or older versions of the scikit-learn package in that folder.

    Upload everything at once to

    $ twine upload dist/
  7. For major/minor (not bug-fix release), update the symlink for stable in

    $ cd /tmp
    $ git clone --depth 1 --no-checkout
    $ cd
    $ echo stable > .git/info/sparse-checkout
    $ git checkout master
    $ ln -sf 0.999 stable
    $ 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
* [ ] announce on mailing list
* [ ] (regenerate Dash docs:

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

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.