Intel® Extension for Scikit-learn*
Scale your scikit-learn* (sklearn) workflows by changing a couple of lines of code.
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 Intel® AI Analytics Toolkit (AI Kit) that provides 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.
Download as Part of the Toolkit
Intel Extension for Scikit-learn is included as part of the AI Kit that provides accelerated machine learning and data analytics pipelines with optimized deep learning frameworks and high-performing Python libraries.
Develop in the Cloud
Build and optimize oneAPI multiarchitecture applications using the latest optimized Intel® oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary. Free for 120 days with extensions possible.
Download the Stand-Alone Version
A stand-alone version Intel Extension for Scikit-learn is available. You can install from the PIP* package manager for Python or Anaconda*, or download and build from the source.
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.
Example Patch Using Two Lines of Code
import numpy as np
# Turn on scikit-learn optimizations with these 2 simple lines:
from sklearnex import patch_sklearn
patch_sklearn()
# Import scikit-learn algorithms after the patch is enabled
from sklearn.cluster import KMeans
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_}")Benchmarks
Documentation & Code Samples
Documentation
Training & Tutorials
- Introduction to scikit-learn Essentials for Machine Learning
- Advanced scikit-learn Essentials for Machine Learning
- Develop Efficient AI Solutions with Accelerated Machine Learning
- Get Started with Machine Learning Frameworks Using the CoLaboratory* Data Analysis Tool from Google*
- Accelerate Machine Learning Workloads: K-means and GPairs Algorithms
Demos
Benchmarking Intel Extension for Scikit-learn: How Much Faster Is It?
Walk through an example that compares the performance of the K-means fitting algorithm in open source scikit-learn to the accelerated version in Intel Extension for Scikit-learn.
Optimize Utility Maintenance Prediction for Better Service
Get started using a reference kit to predict the health of utility assets and the probability of failure using scikit-learn SVC. Compare its effectiveness with an XGBoost algorithm.
Drive 2x Performance into Your scikit-learn Machine Learning Tasks
This video demonstrates how to use Intel Extension for Scikit-learn, how it accelerates scikit-learn algorithms, and shows examples of end-to-end census and distributed linear regression.
Save Time and Money with Intel Extension for Scikit-learn
Accelerating scikit-learn algorithms can deliver performance improvements up to and sometimes exceeding 100x. If you are running in a commercial cloud environment, this can deliver significant compute cost savings.
Case Studies
Greener Machine Learning Computing with Intel® AI Acceleration
A set of experiments run by Anaconda showed that Intel Extension for Scikit-learn reduced CPU energy by up to 8.5x and DRAM energy by up to 7x while accelerating CPU compute time.
Supply-Chain Optimization at an Enterprise Scale
Architects from Red Hat* and Intel walk through the process of building an application to predict late deliveries using Red Hat OpenShift* Data Science and AI Kit.
HippoScreen Improves AI Performance by 2.4x
The Taiwan-based neurotechnology startup combined its proprietary algorithms with accelerated scikit-learn algorithms to improve machine learning efficiency and training times for its Brain Waves AI system.
Specifications
Processors:
- All CPUs with x86 architecture
- All integrated and discrete GPUs from Intel
Operating systems:
- Linux*
- Windows* and Windows Server*
Language:
- Python
Get Help
Your success is our success. Access these support resources when you need assistance.
For additional help, see our general oneAPI Support.
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