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Key benefits

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

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.

Who is this book 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.

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

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

Frequently bought together

Accelerate Model Training with PyTorch 2.X

$44.99

$35.99 ~~$51.99~~

$36.99 ~~$49.99~~

Total $ 117.97 146.97 29.00 saved

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Amazon verified reviews

Amazon Customer Jun 01, 2024

Accelerate model training with PyTorch is a comprehensive guide for data scientists looking to enhance their model training efficiency using PyTorch. With a focus on optimization techniques and performance tuning, this book provides valuable insights and strategies to significantly reduce model-building time and maximize computing resources.The book starts by addressing the impact of model complexity on training time and gradually progresses to advanced topics such as compiling models, utilizing specialized libraries for CPU optimization, and building efficient data pipelines to enhance GPU execution. Readers will benefit from learning about pruning and compression techniques to simplify models, adopting mixed precision for faster computations, and exploring distributed training across multiple machines and devices.What sets this book apart is its practical approach to speeding up model training without compromising quality. The author's expertise shines through in the clear explanations and actionable strategies provided throughout the chapters.Overall, this book serves as a valuable resource for data scientists seeking to optimize their model training process and focus on building exceptional machine learning models. Whether you're looking to harness the computing power of multiple devices or streamline your training workflow.

Amazon Verified review

tt0507 May 26, 2024

The book is written for intermediate—to advanced-level data scientists and engineers who want to harness the power of PyTorch. The chapter on building efficient data pipelines and simplifying models was very useful. The best part about this book was the detailed explanations of using GPU to train models. Many chapters included knowledge about optimizing CPU/GPU and serve as a great reference when training models on GPUs. Overall, the book has a good balance of visualization, code (both GitHub repo and code reference within the book), and explanation, which helps to understand the concepts explained in the book better.

Pratyush Jun 26, 2024

This is a great book for data scientists and engineers. It explains how to speed up training machine learning models using PyTorch 2.0. The book covers various techniques like using specialized libraries, optimizing data pipelines, and distributed training. It includes practical examples and clear explanations, making it easy to understand. This book is perfect for anyone who wants to make their model training faster and more efficient, whether you're a student, researcher, or professional in the field.

Soni Raju Aug 13, 2024

It was a great text about the various use cases. If there was more detail about the software’s limitations, it would have been even better.

Didi Jul 06, 2024

The deep learning (DL) revolution has completely transformed the fields of computer vision and natural language processing in recent years, and DL is becoming more and more important in many other areas of science and engineering. With increasing model sizes and limited hardware resources, the need to accelerate DL model training is becoming a critical part of almost any real-world work in DL.This book, written by a high-performance computing (HPC) expert, is a unique and comprehensive guide to accelerating DL model training using PyTorch. This practical guide begins with an introduction to DL model model training and ways to accelerate it, including topics such as model compilation and building efficient data pipelines. It proceeds with more advanced strategies for acceleration, like model simplification and using mixed precision. The last part of the book describes a variety of useful techniques and strategies for CPU, GPU and multi-node distributed training.To get the most out of this book, readers are expected to have some familiarity with machine learning, PyTorch, and Python. System analysts and system administrators responsible for providing and maintaining infrastructure for AI workloads can also greatly benefit from this book.To summarize, this book is a wonderful, up-to-date resource for researchers, data scientists, software engineers, and system administrators interested in accelerating DL model training using PyTorch and relevant libraries in its ecosystem. Highly recommended!

Accelerate Model Training with PyTorch 2.X: Build more accurate models by boosting the model training process

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