Joe Papa - PyTorch Pocket Reference: Building and Deploying Deep Learning Models
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PyTorch Pocket Reference: Building and Deploying Deep Learning Models: summary, description and annotation
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This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers.
Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices.
- Learn basic PyTorch syntax and design patterns
- Create custom models and data transforms
- Train and deploy models using a GPU and TPU
- Train and test a deep learning classifier
- Accelerate training using optimization and distributed training
- Access useful PyTorch libraries and the PyTorch ecosystem
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by Joe Papa
Copyright 2021 Mobile Insights Technology Group, LLC. All rights reserved.
Printed in the United States of America. Published by OReilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- Interior Designer: David Futato
- Cover Designer: Karen Montgomery
- Illustrator: Kate Dullea
- May 2021: First Edition
- 2021-05-11: First Release
See https://oreil.ly/9781492090007 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. PyTorch Pocket Reference, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-492-09000-7
[LSI]
We are living in exciting times! Some of us have been fortunate to have lived through huge advances in technologythe invention of the personal computer, the dawn of the internet, the proliferation of cell phones, and the advent of social media. And now, major breakthroughs are happening in AI!
Its exciting to watch and be a part of this change. I think were just getting started, and its amazing to think of how the world might change over the next decade. How great it is that were living during these times and can participate in the expansion of AI?
PyTorch has, no doubt, enabled some of the finest advances in deep learning and AI. Its free to download and use, and with it anyone with a computer or internet connection can run AI experiments. In addition to more comprehensive references like this one, there are many free and inexpensive training courses, blog articles, and tutorials that can help you. Anyone can get started using PyTorch for machine learning and AI.
This book is written for both beginners and advanced users interested in machine learning and AI. It will help to have some experience writing Python code and a basic understanding of data science and machine learning.
If youre just getting started in machine learning, this book will help you learn the basics of PyTorch and provide some simple examples. If youve been using another framework, such as TensorFlow, Caffe2, or MXNet, the book with help you become familiar with the PyTorch API and its programming mindset so you can expand your skillset.
If youve been using PyTorch for a while, this book will help you expand your knowledge on advanced topics like acceleration and optimization and provide a quick-reference resource while you use PyTorch for your day-to-day development.
Learning and mastering PyTorch can be very exciting. Theres so much to explore! When I first started learning PyTorch, I wished I had a single resource that would teach me everything. I wanted something that would give me a good high-level look at what PyTorch had to offer, but also would provide examples and enough details when I needed to dig deeper.
There are some really good books and courses on PyTorch, but they often focus on tensors and training for deep learning models. The PyTorch online documentation is really good, too, and provides a lot of details and examples; however, I found using it was often cumbersome. I kept having to click around to learn or Google what I needed to know. I needed a book on my desk that I could earmark and reference as I was coding.
My goal is that this will be the ultimate PyTorch reference for you. In addition to reading through it to get a high-level understanding of the PyTorch resources available to you, I hope that you earmark the key sections for your development work and keep it on your desk. That way if you forget something, you can get the answer right away. If you prefer ebooks or online books, You can bookmark this book online. However you may use it, I hope the book helps you create some amazing new technology with PyTorch!
If youre just beginning to learn PyTorch, you should start at on the PyTorch Ecosystem. Youre bound to discover something new!
This book is roughly organized as follows:
gives a brief introduction to PyTorch, helps you set up your development environment, and provides a fun example for you to try yourself.
covers the tensor, PyTorchs fundamental building block. Its the foundation for everything in PyTorch.
provides example reference designs so you can see PyTorch in action.
Chapters shows you how to accelerate training and optimize your models.
shows you how you can deploy PyTorch to production via local machines, cloud servers, and mobile or edge devices.
guides you in where to go next by introducing the PyTorch Ecosystem, describing popular packages, and listing additional training resources.
The following typographical conventions are used in this book:
ItalicIndicates new terms, URLs, email addresses, filenames, and file extensions.
Constant widthUsed for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
Constant width boldShows commands or other text that should be typed literally by the user. Additionally, bold is used for emphasis in functions in tables.
Constant width italicShows text that should be replaced with user-supplied values or by values determined by context. Additionally, italic transforms listed in tables are currently not supported by TorchScript.
Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/joe-papa/pytorch-book.
If you have a technical question or a problem using the code examples, please email .
This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.
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