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

Deploying a PyTorch model on Android

In this section, we will create an Android app that allows you to capture an image using the phone camera and make a prediction (image classification) on the captured image. In Chapter 1 of this book, we trained a Modified National Institute of Standards and Technology (MNIST) model to classify handwritten digits. In Chapter 13, we used tracing to convert the trained MNIST model from the original PyTorch format into an intermediate representation (IR). For our Android app, we will first optimize this traced MNIST model using PyTorch Mobile and then use the optimized model to make predictions (handwritten digit classification) on the captured image. All code for this section is available on GitHub [2].

Converting the PyTorch model to a mobile-friendly format

PyTorch Mobile provides a function, optimize_for_mobile, that converts a traced PyTorch model object into a mobile-friendly lightweight format. This can be done with the following lines...