Book Image

Deep Learning with PyTorch Lightning

By : Kunal Sawarkar
3.5 (2)
Book Image

Deep Learning with PyTorch Lightning

3.5 (2)
By: Kunal Sawarkar

Overview of this book

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming. Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation. Throughout this book, 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. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning. By the end of this book, you’ll be able to build and deploy DL models with confidence.
Table of Contents (15 chapters)
1
Section 1: Kickstarting with PyTorch Lightning
6
Section 2: Solving using PyTorch Lightning
11
Section 3: Advanced Topics

Controlling training

There is often a need to have an audit, balance, and control mechanism during the training process. Imagine you are training a model for 1,000 epochs and a network failure causes an interruption after 500 epochs. How do you resume training from a certain point while ensuring that you won't lose all your progress, or save a model checkpoint from a cloud environment? Let's see how to deal with these practical challenges that are often part and parcel of an engineer's life.

Saving model checkpoints when using the cloud

Notebooks hosted in cloud environments such as Google Colab have resource limits and idle timeout periods. If these limits are exceeded during the development of a model, then the notebook is deactivated. Owing to the inherently elastic nature of the cloud environment, (which is one of the value propositions of the cloud) the underlying compute and storage resources are decommissioned when a notebook is deactivated. If you refresh...