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

Chapter 10: Scaling and Managing Training

So far, we have been on an exciting journey in the realm of Deep Learning (DL). We have learned how to recognize images, how to create new images or generate new texts, and how to train machines without fully labeled sets. It's an open secret that achieving good results for a DL model requires a massive amount of compute power, often requiring the help of a Graphics Processing Unit (GPU). We have come a long way since the early days of DL when data scientists had to manually distribute the training to each node of the GPU. PyTorch Lightning obfuscates most of the complexities associated with managing underlying hardware or pushing down training to the GPU.

In the earlier chapters, we have pushed down training via brute force. However, doing so is not practical when you have to deal with a massive training effort for large-scale data. In this chapter, we will take a nuanced view of the challenges of training a model at scale and managing...