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

Using Accelerate to speed up PyTorch model training

Accelerate is a powerful tool developed by Hugging Face that’s designed to manage distributed training across multiple CPUs, GPUs, and TPUs, or even cloud services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. It abstracts data and model parallelism and efficiently distributes computations across multiple CPUs, GPUs, or TPUs, reducing overhead and streamlining execution, thereby making scaling effortless. The best part is that you need to add just five lines of accelerate code into the existing PyTorch code to optimally utilize the hardware, as shown in Figure 19.6:

Figure 19.6: Schematic representation of accelerating PyTorch model training code with just five lines of accelerate code

To illustrate the usage of accelerate, we will continue the previous example of fine-tuning a BERT model for text classification and use accelerate inside the training code to optimize the...