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 Optimum to optimize PyTorch model deployment

A crucial aspect of the machine learning life cycle is model deployment. Hugging Face’s Optimum aims to reduce the complexity involved in deploying AI models across diverse platforms, languages, frameworks, and devices. As the name indicates, Optimum also helps optimize the model before deployment.

In this section, we will take a pre-trained model (trained using PyTorch) from the Hugging Face Hub, and convert that PyTorch model into an Open Neural Network Exchange (ONNX) model to use it for inference with ONNX Runtime, as shown in Figure 19.7.

Note

ONNX Runtime is an open-source, high-performance inference engine developed by Microsoft, designed to efficiently execute models that are compliant with the ONNX format across various hardware platforms, such as Intel CPUs, NVIDIA GPUs, Jetson Nano, Android phones, and so on.

We discussed ONNX in Chapter 13, Operationalizing PyTorch Models into Production...