PyTorch provides a rich set of tools, but specific applications frequently demand operations or performance optimizations not available out-of-the-box. This chapter addresses how to extend PyTorch beyond its standard Python application programming interface.

We will construct custom operators using C++ and CUDA for scenarios requiring high computational efficiency or specialized algorithms. You'll gain experience working directly with PyTorch's C++ backend (ATen), managing data transfer between PyTorch Tensors and NumPy arrays, and structuring custom network components and optimization strategies by extending torch.nn.Module and torch.optim.Optimizer. Additionally, techniques for interfacing with existing C libraries using Foreign Function Interfaces (FFI) will be covered. By the end of this chapter, you will be equipped to integrate custom code and external libraries into your PyTorch workflows.