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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

Why distribute the training on multiple CPUs?

At first sight, thinking about distributing the training process among multiple CPUs in a single machine sounds slightly confusing. After all, we could increase the number of threads used in the training process to allocate all available CPUs (computing cores).

However, as said by Carlos Drummond de Andrade, a famous Brazilian poet, “In the middle of the road there was a stone. There was a stone in the middle of the road.” Let’s see what happens to the training process when we just increase the number of threads in a machine with multiple cores.

Why not increase the number of threads?

In Chapter 4, Using Specialized Libraries, we learned that PyTorch relies on OpenMP to accelerate the training process by employing the multithreading technique. OpenMP assigns threads to physical cores intending to improve the performance of the training process.

So, if we have a certain number of available computing cores...

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