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Accelerate Model Training with PyTorch 2.X

Accelerate Model Training with PyTorch 2.X

By : Maicon Melo Alves
4.4 (10)
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Accelerate Model Training with PyTorch 2.X

Accelerate Model Training with PyTorch 2.X

4.4 (10)
By: Maicon Melo Alves

Overview of this book

This book, written by an HPC expert with over 25 years of experience, guides you through enhancing model training performance using PyTorch. Here you’ll learn how model complexity impacts training time and discover performance tuning levels to expedite the process, as well as utilize PyTorch features, specialized libraries, and efficient data pipelines to optimize training on CPUs and accelerators. You’ll also reduce model complexity, adopt mixed precision, and harness the power of multicore systems and multi-GPU environments for distributed training. By the end, you'll be equipped with techniques and strategies to speed up training and focus on building stunning models.
Table of Contents (17 chapters)
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1
Part 1: Paving the Way
4
Part 2: Going Faster
10
Part 3: Going Distributed

Compiling the Model

Paraphrasing one of the famous presenters: “It’s time!” After completing our initial steps toward performance improvement, it is time to learn a new capability of PyTorch 2.0 to accelerate the training and inference of deep learning models.

We are talking about the Compile API, which was presented in PyTorch 2.0 as one of the most exciting capabilities of this new version. In this chapter, we will learn how to use this API to build a faster model to optimize the execution of its training phase.

Here is what you will learn as part of this chapter:

  • The benefits of graph mode over eager mode
  • How to use the API to compile a model
  • The components, workflow, and backends used by the API

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