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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

Part 2: Going Faster

In this part, you will learn about the main techniques and approaches that can be used in PyTorch to accelerate the training process of deep learning models. First, you will learn how to compile a model by using the Compile API. After that, you will learn how to use and configure specialized libraries to optimize the training process on CPUs. Then, you will learn how to build an efficient data pipeline to keep the GPU busy all the time. Also, you will learn how to simplify the model by applying pruning and compression techniques. Finally, you will learn how to adopt automatic mixed precision to reduce computing time and memory consumption.

This part has the following chapters:

Chapter 3, Compiling the Model
Chapter 4, Using Specialized Libraries
Chapter 5, Building an Efficient Data Pipeline
Chapter 6, Simplifying the Model
Chapter 7, Adopting Mixed Precision

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Accelerate Model Training with PyTorch 2.X

Published in: Apr 2024 Publisher: Packt ISBN-13: 9781805120100

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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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