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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

What do you mean by compiling?

As a programmer, you will immediately assign the term “compiling” to the process of building a program or application from the source code. Although the complete building process comprises additional phases, such as generating assembly code and linking it to libraries and other objects, it is reasonable to think that way. However, at first glance, it may be a bit confusing to think about the compiling process in the context of this book since we are talking about Python. After all, Python is not a compiled language; it is an interpreted language, and thus, no compiling is involved.

Note

It is important to clarify that Python uses compiled functions for performance purposes, though it is primarily an interpreted language.

That said, what is the meaning of compiling a model? Before answering this question, we must understand the two execution modes of machine learning frameworks. Follow me to the next section.

Execution modes

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