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

Serving PyTorch models in the cloud

Deep learning is computationally expensive and therefore demands powerful and sophisticated computational hardware. Not everyone has access to a local machine that has enough CPUs and GPUs to train gigantic deep learning models in a reasonable amount of time. Furthermore, we cannot guarantee 100 percent availability for a local machine that is serving a trained model for inference.

For reasons such as these, cloud computing platforms are a vital alternative for both training and serving deep learning models.

In this section, we will discuss how to use PyTorch with some of the most popular cloud platforms – AWS, Google Cloud, and Microsoft Azure. We will explore the different ways of serving a trained PyTorch model on each of these platforms. The model-serving exercises we discussed in earlier sections of this chapter were executed on a local machine. The goal of this section is to enable you to perform similar exercises using virtual...