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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

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781805120100

Length 230 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Maicon Melo Alves

View More author details

Table of Contents (17) Chapters

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

Why subscribe?

16. Other Books You May Enjoy

Part 3: Going Distributed

In this part, you will learn how to spread the training process across multiple devices and machines. First, you will learn about the fundamental concepts related to the distributed training process. Then, you will learn how to distribute the training process on multiple CPUs in a single machine. After that, you will learn how to train the model by using multiple GPUs in a single machine. At the end, you will learn how to distribute the training process among multiple devices located in multiple machines.

This part has the following chapters:

Chapter 8, Distributed Training at a Glance,
Chapter 9, Training with Multiple CPUs
Chapter 10, Training with Multiple GPUs
Chapter 11, Training with Multiple Machines

The rest of the chapter is locked

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Authors (1)

Maicon Melo Alves

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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