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.
• 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
Attendance is free, but you must register for the event. Links to the webinar will be provided upon registration.
Intel’s DevCloud will be used during the Learning Path Series. If you do not already have a DevCloud account, please visit this link to sign up prior to the first session.