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Accelerate Model Training with PyTorch 2.X

You're reading from  Accelerate Model Training with PyTorch 2.X

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805120100
Pages 230 pages
Edition 1st Edition
Languages
Author (1):
Maicon Melo Alves Maicon Melo Alves
Profile icon Maicon Melo Alves

Table of Contents (17) Chapters

Preface Part 1: Paving the Way
Chapter 1: Deconstructing the Training Process Chapter 2: Training Models Faster Part 2: Going Faster
Chapter 3: Compiling the Model Chapter 4: Using Specialized Libraries Chapter 5: Building an Efficient Data Pipeline Chapter 6: Simplifying the Model Chapter 7: Adopting Mixed Precision Part 3: Going Distributed
Chapter 8: Distributed Training at a Glance Chapter 9: Training with Multiple CPUs Chapter 10: Training with Multiple GPUs Chapter 11: Training with Multiple Machines Index Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “The ipex.optimize function returns an optimized version of the model.”

A block of code is set as follows:

config_list = [{    'op_types': ['Linear'],
    'exclude_op_names': ['layer4'],
    'sparse_ratio': 0.3
}]

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

def forward(self, x):    out = self.layer1(x)
    out = self.layer2(out)
    out = out.reshape(out.size(0), -1)
    out = self.fc1(out)
    out = self.fc2(out)
    return out

Any command-line input or output is written as follows:

maicon@packt:~$ nvidia-smi topo -p -i 0,1Device 0 is connected to device 1 by way of multiple PCIe

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “OpenMP is a library used for parallelizing tasks by harnessing all the power of multicore processors by using the multithreading technique.”

Tips or important notes

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