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

Key Features

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

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.

Accelerate Model Training with PyTorch 2.X

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Build more accurate models by boosting the model training process

Dramatically accelerate the building process of complex models using PyTorch to extract the best performance from any computing environmentKey FeaturesReduce the model-building time by applying optimization techniques and approachesHarness the computing power of multiple devices and machines to boos

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Author(s): Alves, Maicon Melo • Drummond, Lúcia Maria De Assumpção

Publisher: Packt Publishing

Pub. Date: 2024

pages: 230

Language: lang_en

ISBN: 978-1-80512-010-0

eISBN: 978-1-80512-191-6

Dramatically accelerate the building process of complex models using PyTorch to extract the best performance from any computing environmentKey FeaturesReduce the model-building time by applying optimization techniques and approachesHarness the computing power of multiple devices and machines to boos

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.

See all description...