8.3. Parallelism, resource management, and configuration¶

8.3.1. Parallelism¶

Some scikit-learn estimators and utilities can parallelize costly operations using multiple CPU cores, thanks to the following components:

via the joblib library. In this case the number of threads or processes can be controlled with the n_jobs parameter.
via OpenMP, used in C or Cython code.

In addition, some of the numpy routines that are used internally by scikit-learn may also be parallelized if numpy is installed with specific numerical libraries such as MKL, OpenBLAS, or BLIS.

We describe these 3 scenarios in the following subsections.

8.3.1.1. Joblib-based parallelism¶

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 is currently poorly documented. Please help us by improving our docs and tackle issue 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 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 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 oversubscription below).

8.3.1.2. OpenMP-based parallelism¶

OpenMP is used to parallelize code written in Cython or C, relying on multi-threading exclusively. By default (and unless joblib is trying to avoid oversubscription), the implementation will use as many threads as possible.

You can control the exact number of threads that are used via the OMP_NUM_THREADS environment variable:

OMP_NUM_THREADS=4 python my_script.py

8.3.1.3. Parallel Numpy routines from numerical libraries¶

Scikit-learn relies heavily on NumPy and SciPy, which internally call multi-threaded linear algebra routines implemented in libraries such as MKL, OpenBLAS or BLIS.

The number of threads used by the OpenBLAS, MKL or BLIS libraries can be set via the MKL_NUM_THREADS, OPENBLAS_NUM_THREADS, and BLIS_NUM_THREADS environment variables.

Please note that scikit-learn has no direct control over these implementations. Scikit-learn solely relies on Numpy and Scipy.

Note

At the time of writing (2019), NumPy and SciPy packages distributed on pypi.org (used by pip) and on the conda-forge channel are linked with OpenBLAS, while conda packages shipped on the “defaults” channel from anaconda.org are linked by default with MKL.

8.3.1.4. 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 GridSearchCV (parallelized with joblib) with n_jobs=8 over a HistGradientBoostingClassifier (parallelized with OpenMP). Each instance of HistGradientBoostingClassifier will spawn 8 threads (since you have 8 CPUs). That’s a total of 8 * 8 = 64 threads, which leads to oversubscription of physical CPU resources and 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 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).
Joblib is currently unable to avoid oversubscription in a multi-threading context. It can only do so with the loky backend (which spawns processes).

You will find additional details about joblib mitigation of oversubscription in joblib documentation.

8.3.2. Configuration switches¶

8.3.2.1. Python runtime¶

sklearn.set_config controls the following behaviors:

assume_finite: used to skip validation, which enables faster computations but may lead to segmentation faults if the data contains NaNs.
working_memory: the optimal size of temporary arrays used by some algorithms.

8.3.2.2. Environment variables¶

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

SKLEARN_SITE_JOBLIB: When this environment variable is set to a non zero value, scikit-learn uses the site joblib rather than its vendored version. Consequently, joblib must be installed for scikit-learn to run. Note that using the site joblib is at your own risks: the versions of scikit-learn and joblib need to be compatible. Currently, joblib 0.11+ is supported. In addition, dumps from joblib.Memory might be incompatible, and you might loose some caches and have to redownload some datasets.

Deprecated since version 0.21: As of version 0.21 this parameter has no effect, vendored joblib was removed and site joblib is always used.
SKLEARN_ASSUME_FINITE: Sets the default value for the assume_finite argument of sklearn.set_config.
SKLEARN_WORKING_MEMORY: Sets the default value for the working_memory argument of sklearn.set_config.
SKLEARN_SEED: Sets the seed of the global random generator when running the tests, for reproducibility.
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_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.