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

Summary

In this chapter, we have explored the world of deploying trained PyTorch deep learning models in production systems. We began with building a local model inference pipeline to be able to make predictions using a pre-trained model with a few lines of Python code. We then utilized the model inference logic of this pipeline to build our own model server using Python's Flask library. We went further with the model server to build a self-contained model microservice using Docker that can be deployed and scaled with a one-line command.

Next, we explored TorchServe, which is a recently developed dedicated model-serving framework for PyTorch. We learned how to use this tool to serve PyTorch models with a few lines of code and discussed the advanced capabilities it offers, such as model versioning and metrics monitoring. Thereafter, we elaborated on how to export PyTorch models.

We first learned the two different ways of doing so using TorchScript: tracing and scripting....