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Accelerate Model Training with PyTorch 2.X

You're reading from  Accelerate Model Training with PyTorch 2.X

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805120100
Pages 230 pages
Edition 1st Edition
Languages
Author (1):
Maicon Melo Alves Maicon Melo Alves
Profile icon Maicon Melo Alves

Table of Contents (17) Chapters

Preface 1. Part 1: Paving the Way
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

Training Models Faster

In the last chapter, we learned the factors that contribute to increasing the computational burden of the training process. Those factors have a direct influence on the complexity of the training phase and, hence, on the execution time.

Now, it is time to learn how to accelerate this process. In general, we can improve performance by changing something in the software stack or increasing the number of computing resources.

In this chapter, we will start to understand both of these options. Next, we will learn what can be modified in the application and environment layers.

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

  • Understanding the approaches to accelerate the training process
  • Knowing the layers of the software stack used to train a model
  • Learning the difference between vertical and horizontal scaling
  • Understanding what can be changed in the application layer to accelerate the training process.
  • Understanding what can...
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