Maicon Melo Alves

Accelerate Model Training with PyTorch 2.X

Build more accurate models by boosting the model training process

• Broschiertes Buch

Produktbeschreibung

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 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. You'll start by understanding how model complexity impacts training time before discovering distinct levels of performance tuning to expedite the training process. You'll 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, you'll 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, you'll 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 learnCompile 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 ContentsDeconstructing 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

Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 230
• Erscheinungstermin: 30. April 2024
• Englisch
• Abmessung: 235mm x 191mm x 13mm
• Gewicht: 438g
• ISBN-13: 9781805120100
• ISBN-10: 1805120107
• Artikelnr.: 70454733

Autorenporträt

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.