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

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.

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 model's 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.