Rapid Prototyping with PyTorch
In the preceding chapters, we saw multiple facets of PyTorch as a Python library. We saw its use to train vision and text models. We learned about its extensive application programming interfaces (APIs) to load and process datasets. We explored the model inference support provided by PyTorch. We 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 when 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 aimed at providing intuitive and easy-to-use APIs, helping...
