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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

Modifying the environment layer

The environment layer comprises the machine learning framework and all the software needed to support its execution, such as libraries, compilers, and auxiliary tools.

What can we change in the environment layer?

As we discussed before, we may not have the necessary permission to change anything in the environment layer. This restriction depends on the type of environment we use to train the model. In third-party environments, such as notebook’s online services, we do not have the flexibility to make advanced configurations, such as downloading, compiling, and installing a specialized library. We can upgrade a package or install a new library, but nothing beyond that.

To overcome this restriction, we commonly use containers. Containers allow us to configure anything we need to run our application without requiring the support or permission of everyone else. Obviously, we are talking about the environment layer and not about the execution...

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