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In this webinar, researchers and developers will learn about mixed-precision techniques for training Deep Neural Networks with Tensor Core GPUs using PyTorch. First, we’ll describe real-world use cases that have benefited from significant speedups with mixed-precision training, without sacrificing accuracy or stability. We’ll give a conceptual overview of how and why mixed-precision training works. Finally, we’ll walk you through a live example of how to enable mixed-precision training using NVIDIA’s Automatic Mixed-Precision (AMP), which implements the entire recipe automatically in only three lines of user code.

By viewing this recorded webinar, you’ll learn:
  • Benefits of mixed-precision training on NVIDIA Tensor Core GPUs
  • Techniques of mixed precision training: loss scaling, master weights, and preserving accuracy for selected operations
  • Performance guidelines
  • How to use NVIDIA’s toolkit for Automatic Mixed-Precision (AMP)

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Speaker

Michael Carilli

Senior Developer Technology Engineer, NVIDIA

Michael Carilli is a Senior Developer Technology Engineer on the Deep Learning Frameworks team at Nvidia. His focus is making mixed-precision and multi-GPU training in PyTorch fast, numerically stable, and easy to use. Previously, he worked at the Air Force Research Laboratory optimizing CFD code for modern parallel architectures. He holds a PhD in computational physics from the University of California, Santa Barbara.

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Date & Time: Wednesday, April 22, 2018