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Accelerate Model Training with PyTorch 2.X

Accelerate Model Training with PyTorch 2.X

By : Maicon Melo Alves
4.4 (10)
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Accelerate Model Training with PyTorch 2.X

Accelerate Model Training with PyTorch 2.X

4.4 (10)
By: Maicon Melo Alves

Overview of this book

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.
Table of Contents (17 chapters)
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1
Part 1: Paving the Way
4
Part 2: Going Faster
10
Part 3: Going Distributed

Optimizing Intel CPU with IPEX

IPEX stands for Intel extension for PyTorch and is a set of libraries and tools provided by Intel to accelerate the training and inference of machine learning models.

IPEX is a clear sign by Intel of highlighting the relevance of PyTorch among machine learning frameworks. After all, Intel has invested a lot of energy and resources in designing and maintaining an API specially created for PyTorch.

It is interesting to say that IPEX strongly relies on libraries provided by the Intel oneAPI toolset. oneAPI contains libraries and tools specific for machine learning applications, such as oneDNN, and other ones to accelerate applications, such as oneTBB, in general.

Important note

The complete code shown in this section is available at https://github.com/PacktPublishing/Accelerate-Model-Training-with-PyTorch-2.X/blob/main/code/chapter04/baseline-densenet121_cifar10.ipynb and https://github.com/PacktPublishing/Accelerate-Model-Training-with-PyTorch...

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