Repository: scikit-learn/scikit-learn
File: examples/release_highlights/plot_release_highlights_1_8_0.py (Python)

Code before:
#         FunctionTransformer(
#             lambda x: torch.tensor(x.to_numpy().astype(np.float32), device="cuda"))
#         ,
#         CalibratedClassifierCV(
#             RidgeClassifierCV(alphas=alphas), method="temperature"
#         ),
#     )
#     with sklearn.config_context(array_api_dispatch=True):
#         cv_results = cross_validate(ridge_pipeline_gpu, features, target)
#
#
# See `full notebook on Google Colab
# <https://colab.research.google.com/drive/1ztH8gUPv31hSjEeR_8pw20qShTwViGRx?usp=sharing>`_
# for more details. On this particular example, using the Colab GPU vs using a
# single CPU core leads to a 10x speedup which is quite typical for such
# workloads.

# %%
# Free-threaded CPython 3.14 support
# ----------------------------------
#
# scikit-learn has support for free-threaded CPython, in particular
# free-threaded wheels are available for all of our supported platforms on Python
# 3.14.
#
# We would be very interested by user feed-back, here are a few things you can
# try:
#
# - install free-threaded CPython 3.14, run your favourite
#   scikit-learn script and check that nothing breaks unexpectedly.
#   Note that CPython 3.14 is strongly advised because a number of
#   free-threaded bugs have been fixed since CPython 3.13.
# - if you use some estimators with a `n_jobs` parameter, try changing the
#   default backend to threading with `joblib.parallel_config` as in the
#   snippet below. This could potentially speed-up your code because the
#   default joblib backend is process-based and incurs more overhead than
#   threads.
#
#   .. code-block:: python
#
#       grid_search = GridSearchCV(clf, param_grid=param_grid, n_jobs=4)
#       with joblib.parallel_config(backend="threading"):
#           grid_search.fit(X, y)
#
# - don't hesitate to report any issue or unexpected performance behaviour by
#   opening an `GitHub issue <https://github.com/scikit-learn/scikit-learn/issues/new/choose>`_!
#
# Free-threaded (also known as nogil) CPython is a version of CPython that aims
# to enable efficient multi-threaded use cases by removing the Global
# Interpreter Lock (GIL).
#

Review comment (suggestion):
```suggestion
# We would be very interested by user feedback. Here are a few things you can
```

Code after:
#         CalibratedClassifierCV(
#             RidgeClassifierCV(alphas=alphas), method="temperature"
#         ),
#     )
#     with sklearn.config_context(array_api_dispatch=True):
#         cv_results = cross_validate(ridge_pipeline_gpu, features, target)
#
#
# See the `full notebook on Google Colab
# <https://colab.research.google.com/drive/1ztH8gUPv31hSjEeR_8pw20qShTwViGRx?usp=sharing>`_
# for more details. On this particular example, using the Colab GPU vs using a
# single CPU core leads to a 10x speedup which is quite typical for such workloads.

# %%
# Free-threaded CPython 3.14 support
# ----------------------------------
#
# scikit-learn has support for free-threaded CPython, in particular
# free-threaded wheels are available for all of our supported platforms on Python
# 3.14.
#
# We would be very interested by user feedback. Here are a few things you can
# try:
#
# - install free-threaded CPython 3.14, run your favourite
#   scikit-learn script and check that nothing breaks unexpectedly.
#   Note that CPython 3.14 (rather than 3.13) is strongly advised because a
#   number of free-threaded bugs have been fixed since CPython 3.13.
# - if you use some estimators with a `n_jobs` parameter, try changing the
#   default backend to threading with `joblib.parallel_config` as in the
#   snippet below. This could potentially speed-up your code because the
#   default joblib backend is process-based and incurs more overhead than
#   threads.
#
#   .. code-block:: python
#
#       grid_search = GridSearchCV(clf, param_grid=param_grid, n_jobs=4)
#       with joblib.parallel_config(backend="threading"):
#           grid_search.fit(X, y)
#
# - don't hesitate to report any issue or unexpected performance behaviour by
#   opening a `GitHub issue <https://github.com/scikit-learn/scikit-learn/issues/new/choose>`_!
#
# Free-threaded (also known as nogil) CPython is a version of CPython that aims
# to enable efficient multi-threaded use cases by removing the Global
# Interpreter Lock (GIL).
#
# For more details about free-threaded CPython see `py-free-threading doc
# <https://py-free-threading.github.io>`_, in particular `how to install a
# free-threaded CPython <https://py-free-threading.github.io/installing-cpython/>`_
# and `Ecosystem compatibility tracking <https://py-free-threading.github.io/tracking/>`_.