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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 1. Part 1: Paving the Way
2. Chapter 1: Deconstructing the Training Process 3. Chapter 2: Training Models Faster 4. Part 2: Going Faster
5. Chapter 3: Compiling the Model 6. Chapter 4: Using Specialized Libraries 7. Chapter 5: Building an Efficient Data Pipeline 8. Chapter 6: Simplifying the Model 9. Chapter 7: Adopting Mixed Precision 10. Part 3: Going Distributed
11. Chapter 8: Distributed Training at a Glance 12. Chapter 9: Training with Multiple CPUs 13. Chapter 10: Training with Multiple GPUs 14. Chapter 11: Training with Multiple Machines 15. Index 16. Other Books You May Enjoy

Using Microsoft NNI to simplify a model

Neural Network Intelligence (NNI) is an open-source project created by Microsoft to help deep learning practitioners automate tasks such as hyperparameter automatization and neural architecture searches.

NNI also has a set of tools to deal with model simplification in a simpler and straightforward manner. So, we can easily simplify a model by adding a couple of lines to our original code. NNI supports PyTorch and other well-known deep learning frameworks such as TensorFlow.

Note

PyTorch has its own API to prune models, namely torch.prune. Unfortunately, at the time of writing this book, this API does not provide a mechanism to compress a model. Therefore, we have decided to introduce NNI as the solution to accomplish this task. More information about NNI can be found at https://github.com/microsoft/nni.

Let’s start by getting an overview of NNI in the next section.

Overview of NNI

Because NNI is not a native component...

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