Chapter 14: Rapid Prototyping with PyTorch

In the preceding chapters, we have seen multiple facets of PyTorch as a Python library. We have seen its use for training vision and text models. We have learned about its extensive application programming interfaces (APIs) for loading and processing datasets. We have explored the model inference support provided by PyTorch. We have also noticed the interoperability of PyTorch across programming languages (such as C++) as well as with other deep learning libraries (such as TensorFlow).

To accommodate all of these features, PyTorch provides a rich and extensive family of APIs, which makes it one of the best deep learning libraries of all time. However, the vast expanse of those features also makes PyTorch a heavy library, and this can sometimes intimidate users about performing streamlined or simple model training and testing tasks.

This chapter is focused on introducing some of the libraries that are built on top of PyTorch and that...