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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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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

Summary

You learned that PyTorch relies on third-party libraries to accelerate the training process. Besides understanding the concept of multithreading, you have learned how to install, configure, and use OpenMP. In addition, you have learned how to install and use IPEX, which is a set of libraries developed by Intel to optimize the training process of PyTorch code executed on Intel-based platforms.

OpenMP can accelerate the training process by employing multiple threads to parallelize the execution of PyTorch code, whereas IPEX is useful for replacing the operations provided by the default PyTorch library by optimizing the operations written specifically for Intel hardware.

In the next chapter, you will learn how to create an efficient data pipeline to keep the GPU working at peak performance during the entire training process.

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