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Accelerate Model Training with PyTorch 2.X

Accelerate Model Training with PyTorch 2.X

By : Maicon Melo Alves
4.4 (10)
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Accelerate Model Training with PyTorch 2.X

Accelerate Model Training with PyTorch 2.X

4.4 (10)
By: Maicon Melo Alves

Overview of this book

This book, written by an HPC expert with over 25 years of experience, guides you through enhancing model training performance using PyTorch. Here you’ll learn how model complexity impacts training time and discover performance tuning levels to expedite the process, as well as utilize PyTorch features, specialized libraries, and efficient data pipelines to optimize training on CPUs and accelerators. You’ll also reduce model complexity, adopt mixed precision, and harness the power of multicore systems and multi-GPU environments for distributed training. By the end, you'll be equipped with techniques and strategies to speed up training and focus on building stunning models.
Table of Contents (17 chapters)
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1
Part 1: Paving the Way
4
Part 2: Going Faster
10
Part 3: Going Distributed

Demystifying the multi-GPU environment

A multi-GPU environment is a computing system with more than one GPU device. Although multiple interconnected machines with just one GPU can be considered a multi-GPU environment, we usually employ this term to describe environments with two or more GPUs per machine.

To understand how this environment works under the hood, we need to learn about the connectivity of the devices and technologies that are adopted to provide efficient communication across multiple GPUs.

However, before we dive into these topics, we will answer a disquieting question that has probably come to your mind: will we have access to an expensive environment like that? Yes, we will. But first, let’s briefly discuss the increasing popularity of multi-GPU environments.

The popularity of multi-GPU environments

Going back 10 years ago, it was inconceivable to think of a machine with more than one GPU. Besides the high cost of this device, the applicability of...

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