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 < https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html > 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 < https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html>
• 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 is currently a Solution Architect in the Intel Developer Academy. His education background is physics. His industry experience has been software development and application/performance engineering for fortune 100 companies and national laboratories for over three decades. He is a data scientist, using machine learning/ deep learning for nine years while working for Intel and other high tech companies.