PyTorch Pocket Reference

Chapter 8. The PyTorch Ecosystem and Additional Resources

In the previous chapters, you’ve learned everything you need to design and deploy deep learning models with PyTorch. You have learned how to build, train, test, and accelerate your models across different platforms and how to deploy those models to the cloud and edge devices. As you’ve seen, PyTorch has powerful capabilities in both development and deployment environments and is highly extensible, allowing you to create customizations tailored to your needs.

To conclude this reference guide, we’ll explore the PyTorch Ecosystem, other supporting libraries, and additional resources. The PyTorch Ecosystem is one of the most powerful advantages of PyTorch. It provides a rich set of projects, tools, models, libraries, and platforms to explore AI and accelerate your AI development.

The PyTorch Ecosystem includes projects and libraries created by researchers, third-party vendors, and the PyTorch community. These projects are well maintained and have been vetted by the PyTorch team to ensure their quality and utility.

In addition, the PyTorch project includes other libraries that support specific domains, including Torchvision for computer vision and Torchtext for NLP. PyTorch also supports other packages like TensorBoard for visualization, and there’s an abundance of learning resources for further study, like Papers with Code and PyTorch Academy.

In this chapter, we’ll begin with an overview of the PyTorch Ecosystem and a high-level ...