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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You can use the gpus argument passed to Trainer to specify the number of GPUs."

A block of code is set as follows:

import pytorch_lightning as pl
...
# use only 10% of the training data for each epoch
trainer = pl.Trainer(limit_train_batches=0.1)
# use only 10 batches per epoch
trainer = pl.Trainer(limit_train_batches=10)

Any command-line input or output is written as follows:

pip install pytorch-lightning

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "For example, in Google Colab, you can Change Runtime Type to set Runtime shape to High-RAM instead of Standard so that you can increase the value of the num_workers argument to DataLoader "

Tips or Important Notes

Appear like this.