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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

Part 1: Paving the Way

Chapter 1: Deconstructing the Training Process

Chapter 2: Training Models Faster

Part 2: Going Faster

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

Part 3: Going Distributed

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

Index

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Remembering numeric precision

Before diving into the benefits of adopting a mixed precision strategy, it is essential to ground you on numeric representation and common data types. Let’s start by remembering how computers represent numbers.

How do computers represent numbers?

A computer is a machine – endowed with finite resources – that’s designed to work on bits, the smallest unit of information it can manage. As numbers are infinite, computer designers had to put a lot of effort into finding a solution to represent this theoretical concept in a real machine.

To get the work done, computer designers needed to deal with three key factors regarding numeric representation:

Sign: Whether the number is positive or negative
Range: The interval of the represented numbers.
Precision: The number of decimal places.

Considering these elements, computer architects successfully defined numeric data types to represent not only integer...

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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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