
Static Quantization with Eager Mode in PyTorch

PARALLEL AND DISTRIBUTED TRAINING
PyTorch provides features that can accelerate performance in research and production such as native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++. This section includes tutorials on parallel and distributed training: 
* Single-Machine Model Parallel Best Practices
* Getting started with Distributed Data Parallel
* Getting started with Distributed RPC Framework
* Implementing a Parameter Server Using Distributed RPC Framework