Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper

Key Features:

• Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domains
• Speed up your research using PyTorch Lightning by creating new loss functions, networks, and architectures
• Train and build new algorithms for massive data using distributed training

Book Description:

PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time.

You'll start by learning 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 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. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work 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 PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning.

What You Will Learn:

• Customize models that are built for different datasets, model architectures, and optimizers
• Understand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be built
• Use out-of-the-box model architectures and pre-trained models using transfer learning
• Run and tune DL models in a multi-GPU environment using mixed-mode precisions
• Explore techniques for model scoring on massive workloads
• Discover troubleshooting techniques while debugging DL models

Who this book is for:

This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.