collective decision.
Below is a diagram of an agent that manages a local group of workers.
[image]
Concepts
This section describes the high-level classes and concepts that are
relevant to understanding the role of the "agent" in torchelastic.
class torch.distributed.elastic.agent.server.ElasticAgent
Agent process responsible for managing one or more worker
   processes. The worker processes are assumed to be regular
   distributed PyTorch scripts. When the worker process is created by
   the agent, the agent provides the necessary information for the
   worker processes to properly initialize a torch process group.
The exact deployment topology and ratio of agent-to-worker is
   dependent on the specific implementation of the agent and the
   user's job placement preferences. For instance, to run a
   distributed training job on GPU with 8 trainers (one per GPU) one
   can:

Use 8 x single GPU instances, place an agent per instance,
      managing 1 worker per agent.
