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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

What is a computing cluster?

A computing cluster is a system of powerful servers interconnected by a high-performance network, as shown in Figure 11.1. This environment can be provisioned on-premises or in the cloud:

Figure 11.1 – A computing cluster

Figure 11.1 – A computing cluster

The computing power provided by these machines is combined to solve complex problems or to execute highly intensive computing tasks. A computing cluster is also known as a high-performance computing (HPC) system.

Each server has powerful computing resources such as multiple CPUs and GPUs, fast memory devices, ultra-fast disks, and special network adapters. Moreover, a computing cluster often has a parallel filesystem, which provides high transfer I/O rates.

Although not formally defined, we conventionally use the term “cluster” to reference environments comprised of four machines at least. Some computing clusters have a half-dozen machines, while others have more than two or three...

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