Want to learn how to accelerate fine-tuning models on your CPU using PyTorch? You are invited to virtually attend the University of Houston Hewlett Packard Enterprise Data Science Institute meeting on Thursday, March 30, 2023 at 1 PM CDT/2 PM EDT. Register on Eventbrite (free) and confirm your attendance through that platform, if you plan on attending.
PyTorch delivers great CPU performance, and it can be further accelerated with the Intel® Extension for PyTorch*. In this tutorial, Ben Consolvo, AI Solutions Engineering Manager at Intel, will walk you through a real-world example of training an AI image segmentation model using PyTorch 1.13.1 (with ResNet34 architecture); the model will learn to identify roads and speed limits from satellite images, all on the newly released 4th Gen Intel® Xeon® Scalable Processor (code-named Sapphire Rapids). You can find the code to this example on the Intel® Extension for PyTorch GitHub repository here.
Thanks to Ishita Sharma for the opportunity to present at the Hewlett Packard Enterprise Data Science Institute at the University of Houston meeting!
We encourage you to learn more about and incorporate Intel’s other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow. Also, visit AI & ML page to learn about Intel’s AI software development resources to prepare, build, deploy, and scale AI solutions.
For more details about the new 4th Gen Intel® Xeon® Scalable Processors, visit AI Platform where you can learn about how Intel is empowering developers to run end-to-end AI pipelines with Intel® Xeon® Scalable Processors.
About our expert
Ben Consolvo, AI Solutions Engineering Manager at Intel
Ben has been building a team and a program around Intel’s AI technology paired with Intel’s HW offerings. He brings a background and passion in data science, particularly in deep learning (DL) and computer vision. He has applied his skills in DL in the cybersecurity industry to automatically identify phishing websites, as well as to the oil and gas industry to identify subsurface features for geophysical imaging.
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