Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Training a diffusion model for image generation

In this section, we’ll implement a diffusion model from scratch using PyTorch. By the end, this model will be able to generate realistic, high-quality images. Besides PyTorch, we’ll use Hugging Face (an open-source platform that offers diverse AI tools and a collaborative hub for sharing and accessing pre-trained AI models and datasets) to load an image dataset. Besides the dataset, we’ll use the diffusers library [3] from Hugging Face, which provides implementations for models such as UNet and DDPM. We’ll also use Hugging Face’s accelerate library [4] to speed up the diffusion training process by utilizing the Graphical Processing Unit (GPU). We’ll learn more about Hugging Face in Chapter 19, PyTorch and Hugging Face.

Note

GPUs might not be readily available to you. In that case, you can access GPUs via Google Colab: https://colab.google/.

All code for this section...