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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

Concepts

Machine Learning

Author (1):

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

Why subscribe?

16. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

all-reduce synchronization 133

Amazon Web Services (AWS) 162

AMP activation, on GPU 114

backend flags, enabling 115

gradient scaler 116, 117

training loop, wrapping with torch.autocast 115, 116

application layer, software stack 25

batch size, modifying 28-30

modifying 25, 26

practical example 26-28

application programming interface (API) 184

Apptainer 22

artificial intelligence (AI) 162

automatic mixed precision (AMP) 112

activating, on GPU 114

benefits 117

enabling 114

B

backend compiler 49

basic workflow, distributed training on PyTorch 135

checkpoint 138

communication group, destroying 137

communication group initialization 136

distributed data loader, instantiating 137

distributed model, instantiating 138

distributed...

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Authors (1)

Maicon Melo Alves

Dr. Maicon Melo Alves is a senior system analyst and academic professor specialized in High Performance Computing (HPC) systems. In the last five years, he got interested in understanding how HPC systems have been used to leverage Artificial Intelligence applications. To better understand this topic, he completed in 2021 the MBA in Data Science of Pontifícia Universidade Católica of Rio de Janeiro (PUC-RIO). He has over 25 years of experience in IT infrastructure and, since 2006, he works with HPC systems at Petrobras, the Brazilian energy state company. He obtained his D.Sc. degree in Computer Science from the Fluminense Federal University (UFF) in 2018 and possesses three published books and publications in international journals of HPC area.

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