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

Building a CNN model for image recognition

PyTorch Lightning is a versatile framework, which makes training and scaling DL models easy by focusing on building models than writing complex programs. PyTorch Lightning is bundled with many useful features and options for building DL models. Since it is hard to cover all the topics in a single chapter, we will keep exploring different features of PyTorch Lightning in every chapter.

Here are the steps for building an image classifier using a CNN:

  1. Importing the packages
  2. Collecting the data
  3. Preparing the data
  4. Building the model
  5. Training the model
  6. Evaluating the accuracy of the model

Importing the packages

We will get started using the following steps:

  1. First things first—install and load the necessary packages, as follows:
    !pip install torch==1.10.0 torchvision==0.11.1 torchtext==0.11.0 torchaudio==0.10.0 --quiet
    !pip install pytorch-lightning==1.5.2 --quiet
    !pip install opendatasets -...