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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
Author (1):
Maicon Melo Alves Maicon Melo Alves
Profile icon 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 16. Other Books You May Enjoy

Multithreading with OpenMP

OpenMP is a library used for parallelizing tasks by harnessing all the power of multicore processors by using the multithreading technique. In the context of PyTorch, OpenMP is employed to parallelize operations executed in the training phase and to accelerate preprocessing tasks related to data augmentation, normalization, and so forth.

As multithreading is a key concept here, to see how OpenMP works, follow me to the next section to understand this technique.

What is multithreading?

Multithreading is a technique to parallelize tasks in a multicore system, which, in turn, is a computer system endowed with multicore processors. Nowadays, any computing system has multicore processors; smartphones, notebooks, and even TVs have CPUs with more than one processing core.

As an example, let’s look at the notebook that I’m using right now to write this book. My notebook possesses one Intel i5-8265U processor, which has eight cores, as illustrated...

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