Presentation: From Research to Production With PyTorch

Track: Machine Learning for Developers

Location: Soho Complex, 7th fl.

Duration: 1:40pm - 2:30pm

Day of week: Monday

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Abstract

PyTorch is a powerful, flexible deep learning platform that enables engineers and researchers to move quickly from research to production. Since the 1.0 release a few months ago, researchers and engineers are already seeing success in taking advantage of the new capabilities to take deep learning models from research into production. With the 1.1 release, yet more new features have been released and ecosystem projects launched.    

This talk will cover some of the latest features from PyTorch including the TorchScript JIT compiler, distributed data parallel training, TensorBoard integration, new APIs, and more. We’ll also discuss some of the most exciting projects coming out of the PyTorch ecosystem like BoTorch, Ax, and PyTorch BigGraph. Finally, we’ll dig into some of the use cases and industries where people are successfully taking PyTorch models to production, from cars to cancer treatments.

Speaker: Jeff Smith

Engineering Manager @Facebook AI

Jeff Smith is an engineering manager at Facebook AI where he supports the PyTorch team. He’s the author of Machine Learning Systems and Exploring Deep Learning for Language. While working at the intersection of functional programming, distributed systems, and machine learning, he coined the term reactive machine learning to describe an ideal machine learning architecture and associated set of techniques. Prior to joining Facebook, he built teams and technology for AI products like x.ai and Amelia.

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