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 a PyTorch model using TorchServe

TorchServe, released in April 2020, is a dedicated PyTorch model-serving framework. Using the functionalities offered by TorchServe, we can serve multiple models at the same time with low prediction latency and without having to write much custom code. Furthermore, TorchServe offers features such as model versioning, metrics monitoring, and data preprocessing and post-processing.

This clearly makes TorchServe a more advanced model-serving alternative than the model microservice we developed in the previous section. However, making custom model microservices still proves to be a powerful solution for complicated machine learning pipelines (which is more common than we might think).

In this section, we will continue working with our handwritten digit classification model and demonstrate how to serve it using TorchServe. After reading this section, you should be able to get started with TorchServe and go further with utilizing its full...