Extension for Scikit-learn*

Speed up your scikit-learn 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 over 10-100X acceleration to your existing scikit-learn code. The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations.

With Extension for Scikit-learn, you can:

• Speed up training and inference by up to 100x with equivalent mathematical accuracy
• Benefit from performance improvements across different CPU 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)

👀 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.

Integration

The easiest way of accelerating scikit-learn workflows with the extension is through through patching, which replaces the stock scikit-learn algorithms with their optimized versions provided by the extension using the same namespaces in the same modules as scikit-learn.

The patching only affects supported algorithms and their parameters. You can still use not supported ones in your code, the package simply fallbacks into the stock version of scikit-learn.

TIP: Enable verbose mode to see which implementation of the algorithm is currently used.

To patch scikit-learn, you can:

• Use the following command-line flag:

  python -m sklearnex my_application.py

• Add the following lines to the script:

  from sklearnex import patch_sklearn
  patch_sklearn()

👀 Read about other ways to patch scikit-learn.

As an alternative, accelerated classes from the extension can also be imported directly without patching, thereby allowing to keep them separate from stock scikit-learn ones - for example:

from sklearnex.cluster import DBSCAN as exDBSCAN
from sklearn.cluster import DBSCAN as stockDBSCAN

# ...

Documentation

• Quick Start
• Documentation and Tutorials
• Release Notes
• Medium Blogs
• 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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