Parallelism, resource management, and configuration
===================================================

.. _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 details.

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::

    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>`_.

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/>`_.

Configuration switches
-----------------------

Python API
..........

:func:`sklearn.set_config` and :func:`sklearn.config_context` can be used to change
parameters of the configuration which control aspect of parallelism.

.. _environment_variable:

Environment variables
.....................

These environment variables should be set before importing scikit-learn.

`SKLEARN_ASSUME_FINITE`
~~~~~~~~~~~~~~~~~~~~~~~

Sets the default value for the `assume_finite` argument of
:func:`sklearn.set_config`.

`SKLEARN_WORKING_MEMORY`
~~~~~~~~~~~~~~~~~~~~~~~~

Sets the default value for the `working_memory` argument of
:func:`sklearn.set_config`.

`SKLEARN_SEED`
~~~~~~~~~~~~~~

Sets the seed of the global random generator when running the tests, for
reproducibility.

Note that scikit-learn tests are expected to run deterministically with
explicit seeding of their own independent RNG instances instead of relying on
the numpy or Python standard library RNG singletons to make sure that test
results are independent of the test execution order. However some tests might
forget to use explicit seeding and this variable is a way to control the initial
state of the aforementioned singletons.

`SKLEARN_TESTS_GLOBAL_RANDOM_SEED`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Controls the seeding of the random number generator used in tests that rely on
the `global_random_seed` fixture.

All tests that use this fixture accept the contract that they should
deterministically pass for any seed value from 0 to 99 included.

In nightly CI builds, the `SKLEARN_TESTS_GLOBAL_RANDOM_SEED` environment
variable is drawn randomly in the above range and all fixtured tests will run
for that specific seed. The goal is to ensure that, over time, our CI will run
all tests with different seeds while keeping the test duration of a single run
of the full test suite limited. This will check that the assertions of tests
written to use this fixture are not dependent on a specific seed value.

The range of admissible seed values is limited to [0, 99] because it is often
not possible to write a test that can work for any possible seed and we want to
avoid having tests that randomly fail on the CI.

Valid values for `SKLEARN_TESTS_GLOBAL_RANDOM_SEED`:

- `SKLEARN_TESTS_GLOBAL_RANDOM_SEED="42"`: run tests with a fixed seed of 42
- `SKLEARN_TESTS_GLOBAL_RANDOM_SEED="40-42"`: run the tests with all seeds
  between 40 and 42 included
- `SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all"`: run the tests with all seeds
  between 0 and 99 included. This can take a long time: only use for individual
  tests, not the full test suite!

If the variable is not set, then 42 is used as the global seed in a
deterministic manner. This ensures that, by default, the scikit-learn test
suite is as deterministic as possible to avoid disrupting our friendly
third-party package maintainers. Similarly, this variable should not be set in
the CI config of pull-requests to make sure that our friendly contributors are
not the first people to encounter a seed-sensitivity regression in a test
unrelated to the changes of their own PR. Only the scikit-learn maintainers who
watch the results of the nightly builds are expected to be annoyed by this.

When writing a new test function that uses this fixture, please use the
following command to make sure that it passes deterministically for all
admissible seeds on your local machine:

.. prompt:: bash $

    SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all" pytest -v -k test_your_test_name

`SKLEARN_SKIP_NETWORK_TESTS`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When this environment variable is set to a non zero value, the tests that need
network access are skipped. When this environment variable is not set then
network tests are skipped.

`SKLEARN_RUN_FLOAT32_TESTS`
~~~~~~~~~~~~~~~~~~~~~~~~~~~

When this environment variable is set to '1', the tests using the
`global_dtype` fixture are also run on float32 data.
When this environment variable is not set, the tests are only run on
float64 data.

`SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When this environment variable is set to a non zero value, the `Cython`
derivative, `boundscheck` is set to `True`. This is useful for finding
segfaults.

`SKLEARN_BUILD_ENABLE_DEBUG_SYMBOLS`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When this environment variable is set to a non zero value, the debug symbols
will be included in the compiled C extensions. Only debug symbols for POSIX
systems are configured.

`SKLEARN_PAIRWISE_DIST_CHUNK_SIZE`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This sets the size of chunk to be used by the underlying `PairwiseDistancesReductions`
implementations. The default value is `256` which has been showed to be adequate on
most machines.

Users looking for the best performance might want to tune this variable using
powers of 2 so as to get the best parallelism behavior for their hardware,
especially with respect to their caches' sizes.

`SKLEARN_WARNINGS_AS_ERRORS`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This environment variable is used to turn warnings into errors in tests and
documentation build.

Some CI (Continuous Integration) builds set `SKLEARN_WARNINGS_AS_ERRORS=1`, for
example to make sure that we catch deprecation warnings from our dependencies
and that we adapt our code.

To locally run with the same "warnings as errors" setting as in these CI builds
you can set `SKLEARN_WARNINGS_AS_ERRORS=1`.

By default, warnings are not turned into errors. This is the case if
`SKLEARN_WARNINGS_AS_ERRORS` is unset, or `SKLEARN_WARNINGS_AS_ERRORS=0`.

This environment variable uses specific warning filters to ignore some warnings,
since sometimes warnings originate from third-party libraries and there is not
much we can do about it. You can see the warning filters in the
`_get_warnings_filters_info_list` function in `sklearn/utils/_testing.py`.

Note that for documentation build, `SKLEARN_WARNING_AS_ERRORS=1` is checking
that the documentation build, in particular running examples, does not produce
any warnings. This is different from the `-W` `sphinx-build` argument that
catches syntax warnings in the rst files.
