doc/computing/parallelism.rst - main - githubmirror/scikit-learn

doc/computing/parallelism.rst
200 строк9 KB
Shubhan Chari
DOC Improve jaccard score docstring (#34645)
04 авг 2026, 18:06
04 авг 2026, 18:067cb1868
Parallelism and resource management
===================================

.. _parallelism:

Parallelism
-----------

Some scikit-learn estimators and utilities parallelize costly operations
using multiple CPU cores.

Depending on the type of estimator and sometimes the values of the
constructor parameters, this is either done:

- with higher-level parallelism via `joblib <https://joblib.readthedocs.io/en/latest/>`_.
- with lower-level parallelism via OpenMP, used in C or Cython code.
- with lower-level parallelism via BLAS, used by NumPy and SciPy for generic operations
  on arrays.

The `n_jobs` parameters of estimators always controls the amount of parallelism
managed by joblib (processes or threads depending on the joblib backend).
The thread-level parallelism managed by OpenMP in scikit-learn's own Cython code
or by BLAS & LAPACK libraries used by NumPy and SciPy operations used in scikit-learn
is always controlled by environment variables or `threadpoolctl` as explained below.
Note that some estimators can leverage all three kinds of parallelism at different
points of their training and prediction methods.

We describe these 3 types of parallelism in the following subsections in more detail.

Higher-level parallelism with joblib
....................................

When the underlying implementation uses joblib, the number of workers
(threads or processes) that are spawned in parallel can be controlled via the
``n_jobs`` parameter.

.. note::

    **Startup Overhead**

    When using ``n_jobs > 1`` (or ``n_jobs=-1``), you may observe a delay
    the first time a parallel function is called. This is expected behavior
    caused by the overhead of starting the Python worker processes.
    Subsequent calls will be faster as they reuse the existing pool of workers.

.. note::

    Where (and how) parallelization happens in the estimators using joblib by
    specifying `n_jobs` is currently poorly documented.
    Please help us by improving our docs and tackle `issue 14228
    <https://github.com/scikit-learn/scikit-learn/issues/14228>`_!

Joblib is able to support both multi-processing and multi-threading. Whether
joblib chooses to spawn a thread or a process depends on the **backend**
that it's using.

scikit-learn generally relies on the ``loky`` backend, which is joblib's
default backend. Loky is a multi-processing backend. When doing
multi-processing, in order to avoid duplicating the memory in each process
(which isn't reasonable with big datasets), joblib will create a `memmap
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html>`_
that all processes can share, when the data is bigger than 1MB.

In some specific cases (when the code that is run in parallel releases the
GIL), scikit-learn will indicate to ``joblib`` that a multi-threading
backend is preferable.

As a user, you may control the backend that joblib will use (regardless of
what scikit-learn recommends) by using a context manager::

    from joblib import parallel_backend

    with parallel_backend('threading', n_jobs=2):
        # Your scikit-learn code here

Please refer to the `joblib's docs
<https://joblib.readthedocs.io/en/latest/parallel.html#thread-based-parallelism-vs-process-based-parallelism>`_
for more details.

In practice, whether parallelism is helpful at improving runtime depends on
many factors. It is usually a good idea to experiment rather than assuming
that increasing the number of workers is always a good thing. In some cases
it can be highly detrimental to performance to run multiple copies of some
estimators or functions in parallel (see :ref:`oversubscription<oversubscription>` below).

.. _lower-level-parallelism-with-openmp:

Lower-level parallelism with OpenMP
...................................

OpenMP is used to parallelize code written in Cython or C, relying on
multi-threading exclusively. By default, the implementations using OpenMP
will use as many threads as possible, i.e. as many threads as logical cores.

You can control the exact number of threads that are used either:

- via the ``OMP_NUM_THREADS`` environment variable, for instance when:
  running a python script:

  .. prompt:: bash $

      OMP_NUM_THREADS=4 python my_script.py

- or via `threadpoolctl` as explained by `this piece of documentation
  <https://github.com/joblib/threadpoolctl/#setting-the-maximum-size-of-thread-pools>`_.

:ref:`SKLEARN_PAIRWISE_DIST_CHUNK_SIZE <envvar_SKLEARN_PAIRWISE_DIST_CHUNK_SIZE>`
tunes chunk size for OpenMP-parallel pairwise distance kernels in Cython (e.g.
nearest neighbors). This is an advanced setting.

Parallel NumPy and SciPy routines from numerical libraries
..........................................................

scikit-learn relies heavily on NumPy and SciPy, which internally call
multi-threaded linear algebra routines (BLAS & LAPACK) implemented in libraries
such as MKL, OpenBLAS or BLIS.

You can control the exact number of threads used by BLAS for each library
using environment variables, namely:

- ``MKL_NUM_THREADS`` sets the number of threads MKL uses,
- ``OPENBLAS_NUM_THREADS`` sets the number of threads OpenBLAS uses
- ``BLIS_NUM_THREADS`` sets the number of threads BLIS uses

Note that BLAS & LAPACK implementations can also be impacted by
`OMP_NUM_THREADS`. To check whether this is the case in your environment,
you can inspect how the number of threads effectively used by those libraries
is affected when running the following command in a bash or zsh terminal
for different values of `OMP_NUM_THREADS`:

.. prompt:: bash $

    OMP_NUM_THREADS=2 python -m threadpoolctl -i numpy scipy

.. note::
    At the time of writing (2022), NumPy and SciPy packages which are
    distributed on pypi.org (i.e. the ones installed via ``pip install``)
    and on the conda-forge channel (i.e. the ones installed via
    ``conda install --channel conda-forge``) are linked with OpenBLAS, while
    NumPy and SciPy packages shipped on the ``defaults`` conda
    channel from Anaconda.org (i.e. the ones installed via ``conda install``)
    are linked by default with MKL.


.. _oversubscription:

Oversubscription: spawning too many threads
...........................................

It is generally recommended to avoid using significantly more processes or
threads than the number of CPUs on a machine. Over-subscription happens when
a program is running too many threads at the same time.

Suppose you have a machine with 8 CPUs. Consider a case where you're running
a :class:`~sklearn.model_selection.GridSearchCV` (parallelized with joblib)
with ``n_jobs=8`` over a
:class:`~sklearn.ensemble.HistGradientBoostingClassifier` (parallelized with
OpenMP). Each instance of
:class:`~sklearn.ensemble.HistGradientBoostingClassifier` will spawn 8 threads
(since you have 8 CPUs). That's a total of ``8 * 8 = 64`` threads, which
leads to oversubscription of threads for physical CPU resources and thus
to scheduling overhead.

Oversubscription can arise in the exact same fashion with parallelized
routines from MKL, OpenBLAS or BLIS that are nested in joblib calls.

Starting from ``joblib >= 0.14``, when the ``loky`` backend is used (which
is the default), joblib will tell its child **processes** to limit the
number of threads they can use, so as to avoid oversubscription. In practice
the heuristic that joblib uses is to tell the processes to use ``max_threads
= n_cpus // n_jobs``, via their corresponding environment variable. Back to
our example from above, since the joblib backend of
:class:`~sklearn.model_selection.GridSearchCV` is ``loky``, each process will
only be able to use 1 thread instead of 8, thus mitigating the
oversubscription issue.

Note that:

- Manually setting one of the environment variables (``OMP_NUM_THREADS``,
  ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, or ``BLIS_NUM_THREADS``)
  will take precedence over what joblib tries to do. The total number of
  threads will be ``n_jobs * <LIB>_NUM_THREADS``. Note that setting this
  limit will also impact your computations in the main process, which will
  only use ``<LIB>_NUM_THREADS``. Joblib exposes a context manager for
  finer control over the number of threads in its workers (see joblib docs
  linked below).
- When joblib is configured to use the ``threading`` backend, there is no
  mechanism to avoid oversubscriptions when calling into parallel native
  libraries in the joblib-managed threads.
- All scikit-learn estimators that explicitly rely on OpenMP in their Cython code
  always use `threadpoolctl` internally to automatically adapt the numbers of
  threads used by OpenMP and potentially nested BLAS calls so as to avoid
  oversubscription.

You will find additional details about joblib mitigation of oversubscription
in `joblib documentation
<https://joblib.readthedocs.io/en/latest/parallel.html#avoiding-over-subscription-of-cpu-resources>`_.

You will find additional details about parallelism in numerical python libraries
in `this document from Thomas J. Fan <https://thomasjpfan.github.io/parallelism-python-libraries-design/>`_.