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