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 the Hugging Face Hub for pre-trained models

In the previous section, we used a pre-trained BERT model as an example to demonstrate the interface between the transformers and torch libraries. In that example, we used the Hugging Face Hub [5] to download and then load a pre-trained BERT model. In this section, we will explore using the Hugging Face Hub to load pre-trained models further and demonstrate how to use the Hugging Face Hub via a Python library and via the Hugging Face website. All the code for this section is available on GitHub [6].

Note

You need to use an API token to access many of the models available on the Hugging Face Hub. You can access your API token from the following page: https://huggingface.co/settings/tokens.

To use the Hugging Face Hub Python library, we need to install it with the following command:

pip install huggingface_hub

Once it is installed, we can run the following command to import the library and fetch all...