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Accelerate Model Training with PyTorch 2.X

You're reading from   Accelerate Model Training with PyTorch 2.X Build more accurate models by boosting the model training process

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805120100
Length 230 pages
Edition 1st Edition
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Maicon Melo Alves Maicon Melo Alves
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Maicon Melo Alves
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Paving the Way FREE CHAPTER
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

Accelerating data loading

Accelerating data loading is crucial to get an efficient data pipeline. In general, the following two changes are enough to get the work done:

  • Optimizing a data transfer between the CPU and GPU
  • Increasing the number of workers in the data pipeline

Putting it that way, these changes may sound tougher to implement than they are. Making these changes is quite simple – we just need to add a couple of parameters when creating the DataLoader instance for the data pipeline. We will cover this in the following subsections.

Optimizing a data transfer to the GPU

To transfer data from main memory to the GPU, and vice versa, the device driver must ask the operating system to pin or lock a portion of memory. After receiving access to that pinned memory, the device driver starts to copy data from the original memory location to the GPU, but using the pinned memory as a staging area:

Figure 5.6 – Data transfer between main memory and GPU

Figure 5.6 – Data transfer...

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