Dramatically accelerate the building process of complex models using PyTorch to extract the best performance from any computing environment

Key Features

• Reduce the model-building time by applying optimization techniques and approaches
• Harness the computing power of multiple devices and machines to boost the training process
• Focus on model quality by quickly evaluating different model configurations
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Penned by an expert in High-Performance Computing (HPC) with over 25 years of experience, this book is your guide to enhancing the performance of model training using PyTorch, one of the most widely adopted machine learning frameworks.

Youll start by understanding how model complexity impacts training time before discovering distinct levels of performance tuning to expedite the training process. Youll also learn how to use a new PyTorch feature to compile the model and train it faster, alongside learning how to benefit from specialized libraries to optimize the training process on the CPU. As you progress, youll gain insights into building an efficient data pipeline to keep accelerators occupied during the entire training execution and explore strategies for reducing model complexity and adopting mixed precision to minimize computing time and memory consumption. The book will get you acquainted with distributed training and show you how to use PyTorch to harness the computing power of multicore systems and multi-GPU environments available on single or multiple machines.

By the end of this book, youll be equipped with a suite of techniques, approaches, and strategies to speed up training, so you can focus on what really matters-building stunning models!

What you will learn

• Compile the model to train it faster
• Use specialized libraries to optimize the training on the CPU
• Build a data pipeline to boost GPU execution
• Simplify the model through pruning and compression techniques
• Adopt automatic mixed precision without penalizing the models accuracy
• Distribute the training step across multiple machines and devices

Who this book is for

This book is for intermediate-level data scientists who want to learn how to leverage PyTorch to speed up the training process of their machine learning models by employing a set of optimization strategies and techniques. To make the most of this book, familiarity with basic concepts of machine learning, PyTorch, and Python is essential. However, there is no obligation to have a prior understanding of distributed computing, accelerators, or multicore processors.

Table of Contents

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