Wow! eBook
Accelerate Model Training with PyTorch 2.X: Build more accurate models by boosting the model training process
Author(s): Maicon Melo Alves (Author)
- Publisher: Packt Publishing
- Publication Date: 30 April 2024
- Language: English
- Print length: 230 pages
- ISBN-10: 1805120107
- ISBN-13: 9781805120100
Book Description
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.
Table of Contents
- Deconstructing the Training Process
- Training Models Faster
- Compiling the Model
- Using Specialized Libraries
- Building an Efficient Data Pipeline
- Simplifying the Model
- Adopting Mixed Precision
- Distributed Training at a Glance
- Training with Multiple CPUs
- Training with Multiple GPUs
- Training with Multiple Machines
Editorial Reviews
Review
“This book is a great resource for all students, researchers, and professionals who intend to learn how to accelerate model training with the latest release of PyTorch in a smooth way.
This very didactic book starts by introducing how the training process works and what kind of modifications can be done at the application and environment layers to accelerate the training process. The book describes methods to accelerate model training, such as the Compile API, specialized libraries like OpenMP and IPEX, and building an efficient data pipeline. It also explains how to distribute training across multiple CPUs and GPUs. This book not only provides current and highly relevant content for the learning and updating of any professional working in the field of computing but also impresses with its extremely didactic presentation of the subject. You will certainly appreciate the quiz at the end of each chapter and the connection made between the chapters in the summary at the end of each chapter.
In all chapters, codes, and examples of use are presented. For all these reasons, I believe that the book could be successfully adopted by undergraduate and graduate courses s a support bibliography for them too.”
Prof. Lúcia Maria de Assumpção Drummond, Titular professor at Fluminense Federal University, Brazil
About the Author
Dr. Maicon Melo Alves is a senior system analyst and academic professor specialized in High Performance Computing (HPC) systems. In the last five years, he got interested in understanding how HPC systems have been used to leverage Artificial Intelligence applications. To better understand this topic, he completed in 2021 the MBA in Data Science of Pontifícia Universidade Católica of Rio de Janeiro (PUC-RIO). He has over 25 years of experience in IT infrastructure and, since 2006, he works with HPC systems at Petrobras, the Brazilian energy state company. He obtained his D.Sc. degree in Computer Science from the Fluminense Federal University (UFF) in 2018 and possesses three published books and publications in international journals of HPC area.
