Ignite Your AI Solutions on CPUs and GPUs
Ignite Your AI Solutions on CPUs and GPUs
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Overview
scikit-learn* is among the most useful and robust libraries for machine learning. It provides a selection of tools for machine learning 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 showcases the Intel® Extension for Scikit-learn*. Learn how to use it to speed up many standard machine learning algorithms for scikit-learn (such as kmeans, dbscan, and pca) on CPUs with only a few lines of code. He also addresses how changing a few lines of code can target these same kernels for use on GPUs.
This video shows:
- Where to get and how to install the extension, which is part of the AI Tools
- An example scikit-learn algorithm sped up over stock scikit-learn
- A demonstration of the single line of code that enumerates all Intel®-optimized scikit-learn functions
- How to apply the functional patch to activate Intel Extension for Scikit-learn
- How to apply the dpctl command to offload data and computation to an Intel® GPU
- Upcoming hands-on workshops for in-depth information
Accelerate data science and AI pipelines-from preprocessing through machine learning-and provide interoperability for efficient model development.
Speed up and scale your scikit-learn* workflows for CPUs and GPUs across single- and multi-node configurations with this Python* module for machine learning.