Accelerate scikit-learn for Data Analytics & Machine Learning

Scikit-learn* (often referred to as sklearn) is a Python* module for machine learning. Intel® Extension for Scikit-learn* seamlessly speeds up your scikit-learn applications for Intel CPUs and GPUs across single- and multi-node configurations. This extension package dynamically patches scikit-learn estimators while improving performance for your machine learning algorithms. The extension is part of the AI Tools that provide flexibility to use machine learning tools with your existing AI packages. Using scikit-learn with this extension, you can:

• Speed up training and inference by up to 100x with the equivalent mathematical accuracy.
• Continue to use the open source scikit-learn API.
• Enable and disable the extension with a couple lines of code or at the command line. Both scikit-learn and Intel Extension for Scikit-learn are part of the end-to-end suite of Intel® AI and machine learning development tools and resources.

Features

Drop-in Acceleration

• Speed up scikit-learn (sklearn) algorithms by replacing existing estimators with mathematically-equivalent accelerated versions. Supported Algorithms
• Run on your choice of an x86-compatible CPU or Intel GPU because the accelerations are powered by Intel® oneAPI Data Analytics Library (oneDAL).
• Choose how to apply the accelerations:
  • Patch all compatible algorithms from the command line with no code changes.
  • Add two lines of code to patch all compatible algorithms in your Python script.
  • Specify in your script to patch only selected algorithms.
  • Globally patch and unpatch your environment for all uses of scikit-learn.