000			02975nam a2200277uu 4500
005			20250710181506.0
008			250616s||||||||||||||||o||||||||||| |d
024	8	0	_a9781805121916
040			_aPACKT _cPACKT
041			_aen
044			_aGB
100	0		_aMaicon Melo Alves _eauthor.
700	0		_aLúcia Maria de Assumpção Drummond _eauthor.
710	2		_aPACKT
773	0		_tAccelerate Model Training with PyTorch 2.X _dGB,Packt,2024-04-30 _h230
245	0	0	_aAccelerate Model Training with PyTorch 2.X.
300			_a230.
377			_aen
260			_aGB: _bPackt, _c2024-04-30.
263			_a2024-04-30
264		1	_aGB: _bPackt,
520			_a<p><b>Dramatically accelerate the building process of complex models using PyTorch to extract the best performance from any computing environment</b></p><h4>Key Features</h4><ul><li>Reduce the model-building time by applying optimization techniques and approaches</li><li>Harness the computing power of multiple devices and machines to boost the training process</li><li>Focus on model quality by quickly evaluating different model configurations </li><li>Purchase of the print or Kindle book includes a free PDF eBook </li></ul><h4>Book Description</h4>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.<h4>What you will learn</h4><ul><li>Compile the model to train it faster</li><li>Use specialized libraries to optimize the training on the CPU</li><li>Build a data pipeline to boost GPU execution</li><li>Simplify the model through pruning and compression techniques</li><li>Adopt automatic mixed precision without penalizing the model's accuracy</li><li>Distribute the training step across multiple machines and devices</li></ul><h4>Who this book is for</h4>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.
538			_aData in extended ASCII character set.
538			_aMode of access: Internet.
856	4	0	_uhttps://learning.packt.com/product/471531
999			_c15168 _d15168