Learn how to speed up many scikit-learn ML algorithms on CPUs and GPUs with only a few lines of python code.
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, data scientist and AI expert 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 SKlearn 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