Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Using ONNX to export PyTorch models

There are scenarios in production systems where most of the already deployed machine learning models are written in a certain deep learning library, say, TensorFlow, with its own sophisticated model-serving infrastructure. However, if a certain model is written using PyTorch, we would like it to be runnable using TensorFlow to conform to the serving strategy. This is one among various other use cases where a framework such as ONNX is useful.

ONNX is a universal format where the essential operations of a deep learning model such as matrix multiplications and activations, written differently in different deep learning libraries, are standardized. It enables us to interchangeably use different deep learning libraries, programming languages, and even operating environments to run the same deep learning model.

Here, we will demonstrate how to run a model, trained using PyTorch, in TensorFlow. We will first export the PyTorch model into ONNX format...