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

Język publikacji: angielskim
Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process Maicon Melo Alves, Lúcia Maria de Assumpçao Drummond - okladka książki

Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process Maicon Melo Alves, Lúcia Maria de Assumpçao Drummond - okladka książki

Serie wydawnicze:
Learning
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
230
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     ePub

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Do przechowalni

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.

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O autorze książki

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.

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