Quick Start

Get ready to elevate your scikit-learn code with Extension for Scikit-learn* and experience the benefits of accelerated performance in just a few simple steps.

Compatibility with Scikit-learn*

Extension for Scikit-learn* is compatible with the latest stable releases of scikit-learn - see Software Requirements for more details.

Integrate Extension for Scikit-learn*

Patching

Once you install the Extension for Scikit-learn*, you can replace estimator classes (algorithms) that exist in the sklearn module from scikit-learn with their optimized versions from the extension. This action is called patching. This is not a permanent change so you can always undo the patching if necessary.

To patch scikit-learn with the Extension for Scikit-learn*, the following methods can be used:

Method

Action

Use a flag in the command line

Run this command:

python -m sklearnex my_application.py

Modify your script

Add the following lines:

from sklearnex import patch_sklearn
patch_sklearn()

Import an estimator from the sklearnex module

Run this command:

from sklearnex.neighbors import NearestNeighbors

These patching methods are interchangeable. They support different enabling scenarios while producing the same result.

Example

This example shows how to patch scikit-learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import module sklearn after these lines.

Example: Drop-In Patching
  import numpy as np
  from sklearnex import patch_sklearn
  patch_sklearn()

  # You need to re-import scikit-learn algorithms after the patch
  from sklearn.cluster import KMeans

  # The use of the original Scikit-learn is not changed
  X = np.array([[1,  2], [1,  4], [1,  0],
                [10, 2], [10, 4], [10, 0]])
  kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
  print(f"kmeans.labels_ = {kmeans.labels_}")

Global Patching

You can also use global patching to patch all your scikit-learn applications without any additional actions.

Before you begin, make sure that you have read and write permissions for Scikit-learn files.

With global patching, you can:

Task

Action

Note

Patch all supported algorithms

Run this command:

python -m sklearnex.glob patch_sklearn

If you run the global patching command several times with different parameters, then only the last configuration is applied.

Patch selected algorithms

Use --algorithm or -a keys with a list of algorithms to patch. For example, to patch only SVC and RandomForestClassifier estimators, run

python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier

Enable global patching via code

Use the patch_sklearn function with the global_patch argument:

from sklearnex import patch_sklearn
patch_sklearn(global_patch=True)
import sklearn

After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.

Disable patching notifications

Use --no-verbose or -nv keys:

python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv

Disable global patching

Run this command:

python -m sklearnex.glob unpatch_sklearn

Disable global patching via code

Use the global_patch argument in the unpatch_sklearn function

from sklearnex import unpatch_sklearn
unpatch_sklearn(global_patch=True)

Tip

If you clone an environment with enabled global patching, it will already be applied in the new environment.

Unpatching

To undo the patch (also called unpatching) is to return the sklearn module to the original implementation and replace patched estimators with the stock scikit-learn estimators.

To unpatch successfully, you must reimport the sklearn module(s):

sklearnex.unpatch_sklearn()
# Re-import scikit-learn algorithms after the unpatch
from sklearn.cluster import KMeans

Installation

Tip

To prevent version conflicts, we recommend creating and activating a new environment for Extension for Scikit-learn*.

Install from PyPI

Recommended by default.

To install Extension for Scikit-learn*, run:

pip install scikit-learn-intelex

Supported Configurations

Operating systems

Windows*, Linux*

Python versions

3.9, 3.10, 3.11, 3.12, 3.13

Devices

CPU, GPU

Modes

Single, SPMD

Tip

Running on GPU involves additional dependencies, see oneAPI and GPU support in Extension for Scikit-learn*. SPMD mode has additional requirements on top of GPU ones, see Distributed Mode (SPMD) for details.

Note

Wheels are only available for x86-64 architecture.

Install from Anaconda* Cloud

To prevent version conflicts, we recommend installing scikit-learn-intelex into a new conda environment.

Note: the main Anaconda channel also provides distributions of scikit-learn-intelex, but it does not provide the latest versions, nor does it provide GPU-enabled builds. It is highly recommended to install it from either Intel’s channel or conda-forge instead.

Recommended for the Intel® Distribution for Python users.

To install, run:

conda install -c https://software.repos.intel.com/python/conda/ scikit-learn-intelex
Supported Configurations

Operating systems

Windows*, Linux*

Python versions

3.9, 3.10, 3.11, 3.12, 3.13

Devices

CPU, GPU

Modes

Single, SPMD

Tip

Running on GPU involves additional dependencies, see oneAPI and GPU support in Extension for Scikit-learn*. SPMD mode has additional requirements on top of GPU ones, see Distributed Mode (SPMD) for details.

Note

Packages are only available for x86-64 architecture.

Build from Sources

See Installation instructions to build Extension for Scikit-learn* from the sources.

Install Intel*(R) AI Tools

Download the Intel AI Tools here. The extension is already included.

Release Notes

See the Release Notes for each version of Extension for Scikit-learn*.

System Requirements

Hardware Requirements

Any processor with x86-64 architecture with at least one of the following instruction sets:

  • SSE2

  • SSE4.2

  • AVX2

  • AVX512

Note

Note: pre-built packages are not provided for other CPU architectures. See Build from Sources for ARM.

Tip

Read more about hardware comparison in our blogs.

Software Requirements

  • Linux* OS: Ubuntu* 18.04 or newer

  • Windows* OS 10 or newer

  • Windows* Server 2019 or newer

Extension for Scikit-learn* is compatible with the latest stable releases of scikit-learn:

  • 1.0.X

  • 1.1.X

  • 1.2.X

  • 1.3.X

  • 1.4.X

  • 1.5.X

  • 1.6.X

Memory Requirements

By default, algorithms in Extension for Scikit-learn* run in the multi-thread mode. This mode uses all available threads. Optimized scikit-learn estimators can consume more RAM than their corresponding unoptimized versions.

Algorithm

Single-thread mode

Multi-thread mode

SVM

Both scikit-learn and Extension for Scikit-learn* consume approximately the same amount of RAM.

In Extension for Scikit-learn*, an algorithm with N threads consumes N times more RAM.

In all Extension for Scikit-learn* algorithms with GPU support, computations run on device memory. The device memory must be large enough to store a copy of the entire dataset. You may also require additional device memory for internal arrays that are used in computation.

See also

Samples