Scaling AI experiences at Facebook with PyTorch

Joseph Spisak (Facebook), Hao Lu (Facebook)

2:35pm–3:15pm Wednesday, September 11, 2019

Location: Expo Hall 3

Implementing AI

Secondary topics: Deep Learning tools, Hardware, Machine Learning, Text, Language, and Speech

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Joseph Spisak and Hao Lu lead a deep dive into how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook. You’ll explore the latest product updates and new resources for getting started with PyTorch and discover AI compilers and how you can extend PyTorch to improve performance and support the latest AI accelerator hardware.

What you'll learn

Learn how to use PyTorch for large-scale production deployment in computer vision, natural language processing, and machine translation

Joseph Spisak

Facebook

Joseph Spisak is the product manager for Facebook’s AI open source platform, including PyTorch and ONNX. Previously, he led AI partnerships and deep learning products at Amazon Web Services, where he and his team were dedicated to building tools and solutions to help democratize deep learning for the developer community and ultimately accelerate the development of deep learning-based applications. Joseph holds a bachelor’s degree in electrical engineering from Michigan State University and an MBA and MS in finance from the University of Denver. He’s a proud graduate of the Entrepreneurial and Innovation Program at Stanford University Graduate School of Business.

Website

Hao Lu

Facebook

Hao Lu is a research scientist on Facebook’s AI platform team. She specializes in AI compilers and neural network performance optimization on ARM, x86, and mobile GPU. She holds a bachelor’s degree in microelectronics from Peking University and an MA and PhD in electrical engineering from University of Notre Dame. She’s contributed optimizations and improvements to Caffe2, PyTorch, TVM, and Quantized Neural Networks Package (QNNPACK).