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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 Part 1: Paving the Way
Chapter 1: Deconstructing the Training Process Chapter 2: Training Models Faster Part 2: Going Faster
Chapter 3: Compiling the Model Chapter 4: Using Specialized Libraries Chapter 5: Building an Efficient Data Pipeline Chapter 6: Simplifying the Model Chapter 7: Adopting Mixed Precision Part 3: Going Distributed
Chapter 8: Distributed Training at a Glance Chapter 9: Training with Multiple CPUs Chapter 10: Training with Multiple GPUs Chapter 11: Training with Multiple Machines Index 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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