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PyTorch Deep Learning Hands-On

You're reading from   PyTorch Deep Learning Hands-On Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788834131
Length 250 pages
Edition 1st Edition
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Authors (2):
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Sherin Thomas Sherin Thomas
Author Profile Icon Sherin Thomas
Sherin Thomas
Sudhanshu Passi Sudhanshu Passi
Author Profile Icon Sudhanshu Passi
Sudhanshu Passi
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Toc

ONNX

The ONNX protocol was built to create interoperability between different frameworks. This helps AI developers and organizations to choose the right framework to develop AI models where they spend most of their time. Once the development and training phases are over, they can migrate the model to any framework of their choice to serve it in production.

Different frameworks could be optimized for different purposes, such as mobile deployment, readability and flexibility, production deployment, and others. Converting the model to different frameworks is sometimes inevitable and manual conversion is time-consuming. This is another use case that ONNX is trying to solve with interoperability.

Let's take any framework example to see where ONNX is going to fit in. The framework will have a language API, which is used by developers, then a graph representation of the model developed by them. This IR then goes to the highly optimized runtime for execution. ONNX provides a unified standard...

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