Extension for scikit-learn*

Speed up your [scikit-learn](https://scikit-learn.org) applications for CPUs and GPUs across single- and multi-node configurations

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Overview

Extension for scikit-learn is a free software AI accelerator designed to deliver up to 100X acceleration to existing workflows from scikit-learn, which is the most widely-used Python library for machine learning on tabular data. This software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations.

With Extension for scikit-learn, you can:

• Get an average speed up of 8.5x on training and inference with equivalent mathematical accuracy
• Benefit from performance improvements across different hardware configurations, including GPUs and multi-GPU configurations
• Integrate the extension into your existing scikit-learn applications without code modifications
• Continue to use the open-source scikit-learn API
• Enable and disable the extension with a couple of lines of code or at the command line

Acceleration

Benchmarks code

Optimizations

Easiest way to benefit from accelerations from the extension is by patching scikit-learn with it:

• Enable CPU optimizations

  import numpy as np
  from sklearnex import patch_sklearn
  patch_sklearn()
  
  from sklearn.cluster import DBSCAN
  
  X = np.array([[1., 2.], [2., 2.], [2., 3.],
                [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
  clustering = DBSCAN(eps=3, min_samples=2).fit(X)

• Enable GPU optimizations

  Note: executing on GPU has additional system software requirements - see details.

  import numpy as np
  from sklearnex import patch_sklearn, config_context
  patch_sklearn()
  
  from sklearn.cluster import DBSCAN
  
  X = np.array([[1., 2.], [2., 2.], [2., 3.],
                [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
  with config_context(target_offload="gpu:0"):
      clustering = DBSCAN(eps=3, min_samples=2).fit(X)

👀 Read about other ways to patch scikit-learn.

👀 Check out available notebooks for more examples.

Usage without patching

Alternatively, all functionalities are also available under a separate module which can be imported directly, without involving any patching.

• To run on CPU:

  import numpy as np
  from sklearnex.cluster import DBSCAN
  
  X = np.array([[1., 2.], [2., 2.], [2., 3.],
                [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
  clustering = DBSCAN(eps=3, min_samples=2).fit(X)

• To run on GPU:

  import numpy as np
  from sklearnex import config_context
  from sklearnex.cluster import DBSCAN
  
  X = np.array([[1., 2.], [2., 2.], [2., 3.],
                [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
  with config_context(target_offload="gpu:0"):
      clustering = DBSCAN(eps=3, min_samples=2).fit(X)

Installation

To install Extension for scikit-learn, run:

pip install scikit-learn-intelex

Package is also offered through other channels such as conda-forge. See all installation instructions in the Installation Guide.

Documentation

• Quick Start
• Documentation and Tutorials
• Release Notes
• Medium Blog Posts
• Code of Conduct

Extension and oneDAL

Acceleration in patched scikit-learn classes is achieved by replacing calls to scikit-learn with calls to oneDAL (oneAPI Data Analytics Library) behind the scenes:

• oneAPI Data Analytics Library

Samples & Examples

• Examples
• Samples
• Kaggle Kernels

How to Contribute

We welcome community contributions, check our Contributing Guidelines to learn more.

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