Intro to Intel Extensions of Scikit-learn to Accelerate Machine Learning Frameworks

Help Desk


Scikit-learn is among the most useful and robust libraries for machine learning, providing a selection of tools for ML and statistical modeling via a consistent interface in Python, including classification, regression, clustering, and dimensionality reduction. In this session, Solution Architect Bob Chesebrough, will showcase the Intel® Extension for Scikit-learn < > and how to use it to speed up on CPUs, with only a few lines of code, many Scikit-learn standard ML algorithms such as kmeans, dbscan, and pca. He’ll also address how changing a few lines of code can target these same kernels for use on GPUs.

Key takeaways:

• Where to get and how to install the Intel extension, part of the Intel® oneAPI AI Analytics Toolkit <>

• Example scikit-learn algorithm speed up over stock scikit-learn

• Demonstration of the single line of code that enumerates all the Intel-optimized scikit-learn functions

• How to apply the functional patch to turn on Intel Extensions for Scikit-learn

• How to apply the dpctl command to offload data and computation to an Intel GPU

• Describe upcoming hands-on workshops for deeper dives

Robert Chesebrough, Solution Architect, Intel Developer Academy