ACCELERATE MODEL TRAINING WITH PYTORCH 2.X: 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 environment Key Features Reduce the model-building time by applying optimization techniques and approaches Harness the computing power of multiple devices and machines to b...
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| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing Ltd.
2024
|
| Edition: | 1st edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781805120100/?ar |
| Summary: | 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 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 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. |
| Physical Description: | 1 Online-Ressource |
| ISBN: | 9781805121916 180512191X 9781805120100 |
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| spelling | Alves, Maicon Melo VerfasserIn aut ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process Maicon Melo Alves ; foreword by Prof. Lúcia Maria de Assumpação Drummond Titular 1st edition. Birmingham, UK Packt Publishing Ltd. 2024 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 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 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. Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) Titular, Lúcia Maria de Assumpação Drummond MitwirkendeR ctb 1805120107 Erscheint auch als Druck-Ausgabe 1805120107 |
| spellingShingle | Alves, Maicon Melo ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) |
| title | ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process |
| title_auth | ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process |
| title_exact_search | ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process |
| title_full | ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process Maicon Melo Alves ; foreword by Prof. Lúcia Maria de Assumpação Drummond Titular |
| title_fullStr | ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process Maicon Melo Alves ; foreword by Prof. Lúcia Maria de Assumpação Drummond Titular |
| title_full_unstemmed | ACCELERATE MODEL TRAINING WITH PYTORCH 2.X build more accurate models by boosting the model training process Maicon Melo Alves ; foreword by Prof. Lúcia Maria de Assumpação Drummond Titular |
| title_short | ACCELERATE MODEL TRAINING WITH PYTORCH 2.X |
| title_sort | accelerate model training with pytorch 2 x build more accurate models by boosting the model training process |
| title_sub | build more accurate models by boosting the model training process |
| topic | Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) |
| topic_facet | Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) |
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