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Introduction to Getting Faster PyTorch* Programs with TorchDynamo

@IntelDevTools


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Overview

Learn the principles and techniques for making your PyTorch* programs faster, more useable, and performant with the framework’s easy-to-use, just-in-time (JIT) compiler, TorchDynamo.

Introduced in PyTorch 2.0, TorchDynamo transforms a general Python* program into a computational graph. It works by hooking into the Python frame-evaluation process, and then fundamentally changing and supercharging PyTorch operations at the compiler level in the back end.

This session presents this JIT graph compiler and includes:

  • An overview of its design and use, including flexible support for graph acquisition with better performance and usability.
  • The novel TorchDynamo technique principles in PyTorch 2.0, including debugging.
  • How to use Intel® Extension for PyTorch* for TorchDynamo use.
  • Intel’s support for and contributions to the tools.

This session includes demos.

Skill level: Intermediate

 

Featured Software

Get the following stand-alone versions of the tools:

  • Intel Extension for PyTorch
  • TorchDynamo (GitHub*)

 

Download Code Samples

  • Intel Extension for PyTorch (GitHub)
  • TorchDynamo Benchmarks (GitHub)

See All Code Samples

 

More Resources

  • Go Deeper with TorchDynamo
  • What’s Behind PyTorch 2.0: TorchDynamo and TorchInductor (Especially for Developers)

 

 

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PyTorch* Optimizations from Intel

Intel is one of the largest contributors to PyTorch*, providing regular upstream optimizations to the PyTorch deep learning framework that provide superior performance on Intel® architectures. The AI Tools includes the latest binary version of PyTorch tested to work with the rest of the kit, along with Intel® Extension for PyTorch*, which adds the newest Intel optimizations and usability features.

 

Get as Part of the AI Tools

 

PyTorch

  

Intel Extension for PyTorch

 

See All Tools

 

   

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