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.

• 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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