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.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the colour images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: Kickstarting with PyTorch Lightning
Section 1: Kickstarting with PyTorch Lightning
Free Chapter
2
Chapter 1: PyTorch Lightning Adventure
Chapter 1: PyTorch Lightning Adventure
What makes PyTorch Lightning so special?
<pip install> – My Lightning adventure
Understanding the key components of PyTorch Lightning
Crafting AI applications using PyTorch Lightning
Further reading
Summary
3
Chapter 2: Getting off the Ground with the First Deep Learning Model
Chapter 2: Getting off the Ground with the First Deep Learning Model
Technical requirements
Getting started with Neural Networks
Building a Hello World MLP model
Building our first Deep Learning model
Building a CNN model for image recognition
Summary
4
Chapter 3: Transfer Learning Using Pre-Trained Models
Chapter 3: Transfer Learning Using Pre-Trained Models
Technical requirements
Getting started with transfer learning
An image classifier using a pre-trained ResNet-50 architecture
Text classification using BERT transformers
Summary
5
Chapter 4: Ready-to-Cook Models from Lightning Flash
Chapter 4: Ready-to-Cook Models from Lightning Flash
Technical requirements
Getting started with Lightning Flash
Flash is as simple as 1-2-3
Video classification using Flash
Automatic speech recognition using Flash
Further learning
Summary
6
Section 2: Solving using PyTorch Lightning
Section 2: Solving using PyTorch Lightning
7
Chapter 5: Time Series Models
Chapter 5: Time Series Models
Technical requirements
Introduction to time series
Getting started with time series models
Traffic volume forecasting using the LSTM time series model
Summary
8
Chapter 6: Deep Generative Models
Chapter 6: Deep Generative Models
Technical requirements
Getting started with GAN models
Creating new food items using a GAN
Creating new butterfly species using a GAN
GAN training challenges
Creating images using DCGAN
Summary
9
Chapter 7: Semi-Supervised Learning
Chapter 7: Semi-Supervised Learning
Technical requirements
Getting started with semi-supervised learning
Going through the CNN–RNN architecture
Generating captions for images
Summary
10
Chapter 8: Self-Supervised Learning
Chapter 8: Self-Supervised Learning
Technical requirements
Getting started with Self-Supervised Learning
What is Contrastive Learning?
SimCLR architecture
SimCLR model for image recognition
Summary
11
Section 3: Advanced Topics
Section 3: Advanced Topics
12
Chapter 9: Deploying and Scoring Models
Chapter 9: Deploying and Scoring Models
Technical requirements
Deploying and scoring a Deep Learning model natively
Deploying and scoring inter-portable models
Next steps
Further reading
Summary
13
Chapter 10: Scaling and Managing Training
Chapter 10: Scaling and Managing Training
Technical Requirements
Managing training
Scaling up training
Controlling training
Further reading
Summary
Why subscribe?
14
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Deploying and scoring a Deep Learning model natively

Once a Deep Learning model is trained, it basically contains all the information about its structure, that is, its model weights, layers, and so on. For us to be able to use this model later in the production environment on new sets of data, we need to store this model in a suitable format. The process of converting a data object into a format that can be stored in memory is called serialization. Once a model is serialized in such a fashion, it's an autonomous entity and can be transmitted or transferred to a different operating system or a different deployment environment (such as staging or production).

However, once a model is transferred to a production environment, we must reconstruct the model parameters and weights in their original format. This process of recreation from the serialized format is called de-serialization.

There are some other ways to productionalize ML models as well, but the most commonly used method...

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