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Accelerate Model Training with PyTorch 2.X

You're reading from   Accelerate Model Training with PyTorch 2.X Build more accurate models by boosting the model training process

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805120100
Length 230 pages
Edition 1st Edition
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Author (1):
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Maicon Melo Alves Maicon Melo Alves
Author Profile Icon Maicon Melo Alves
Maicon Melo Alves
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Paving the Way FREE CHAPTER
2. Chapter 1: Deconstructing the Training Process 3. Chapter 2: Training Models Faster 4. Part 2: Going Faster
5. Chapter 3: Compiling the Model 6. Chapter 4: Using Specialized Libraries 7. Chapter 5: Building an Efficient Data Pipeline 8. Chapter 6: Simplifying the Model 9. Chapter 7: Adopting Mixed Precision 10. Part 3: Going Distributed
11. Chapter 8: Distributed Training at a Glance 12. Chapter 9: Training with Multiple CPUs 13. Chapter 10: Training with Multiple GPUs 14. Chapter 11: Training with Multiple Machines 15. Index 16. Other Books You May Enjoy

Demystifying the multi-GPU environment

A multi-GPU environment is a computing system with more than one GPU device. Although multiple interconnected machines with just one GPU can be considered a multi-GPU environment, we usually employ this term to describe environments with two or more GPUs per machine.

To understand how this environment works under the hood, we need to learn about the connectivity of the devices and technologies that are adopted to provide efficient communication across multiple GPUs.

However, before we dive into these topics, we will answer a disquieting question that has probably come to your mind: will we have access to an expensive environment like that? Yes, we will. But first, let’s briefly discuss the increasing popularity of multi-GPU environments.

The popularity of multi-GPU environments

Going back 10 years ago, it was inconceivable to think of a machine with more than one GPU. Besides the high cost of this device, the applicability of...

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