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Accelerate Model Training with PyTorch 2.X
By :
Accelerate Model Training with PyTorch 2.X
By:
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)
Preface
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
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