PyTorch Distributed

Here, message passing semantics is leveraged, which helps any process to communicate with other processes through the messages. Different communication backends are used, and the communication doesn't need to be from the same machine. But, of course, multiple processes should be started in parallel, and coordination tools in the cluster must be enabled. There are three main components in the torch. First, distributed as distributed data-parallel training, RPC-based distributed training, and collective communication.