About this book
PyTorch Lightning lets researchers build their own deep learning (DL) models without having to worry about the boilerplate. This book will help you maximize productivity for DL projects while ensuring full flexibility from model formulation to implementation.
The book provides a hands-on approach to implementing PyTorch Lightning models and associated methodologies that will have you up and running and productive in no time. You'll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your own specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train self-supervised learning, semi-supervised learning, and time series models using PyTorch Lightning. Later, you will gain detailed insights into how generative adversarial networks (GANs) work. Finally, you will get to grips with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging.
By the end of this book, you will be able to build and deploy your own scalable DL applications using PyTorch Lightning.
- Publication date:
- December 2021