Book Image

Mastering PyTorch

By : Ashish Ranjan Jha
Book Image

Mastering PyTorch

By: Ashish Ranjan Jha

Overview of this book

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures 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 and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (20 chapters)
1
Section 1: PyTorch Overview
4
Section 2: Working with Advanced Neural Network Architectures
8
Section 3: Generative Models and Deep Reinforcement Learning
13
Section 4: PyTorch in Production Systems

Chapter 10: Operationalizing PyTorch Models into Production

So far in this book, we have covered how to train and test different kinds of machine learning models using PyTorch. We started by reviewing the basic elements of PyTorch that enable us to work on deep learning tasks efficiently. Then, we explored a wide range of deep learning model architectures and applications that can be written using PyTorch.

In this chapter, we will be focusing on taking these models into production. But what does that mean? Basically, we will be discussing the different ways of taking a trained and tested model (object) into a separate environment where it can be used to make predictions or inferences on incoming data. This is what is referred to as the productionization of a model, as the model is being deployed into a production system.

We will begin by discussing some common approaches you can take to serve PyTorch models in production environments, starting from defining a simple model inference...