System Architecture

Determined consists of a single master and one or more agents. There is typically one agent per compute server; a single machine can serve as both a master and an agent.

The master is responsible for

  • Storing experiment, trial, and workload metadata.

  • Scheduling and dispatching work to agents.

  • Advancing the experiment, trial, workload state machines over time.

Each agent manages a number of slots, which represent computing devices, typically one slot per CPU or GPU. On startup the agent sends the master the devices it has available. It then waits for messages from the master and runs the requested workloads; agents have no state and otherwise do not communicate with the master.

A slot executes its workload in a containerized environment called the trial runner. We provide standard Docker containers to run typical deep learning workloads; containers can be customized for specific needs. Trial runners are expected to have access to the training data.

The trial runner runs workloads, which may be training a trial, evaluating a trial on a validation dataset, or other operations like checkpointing model state. The master may then send more workloads or terminate the trial runner (freeing the slot). When sending a workload to the trial runner, the master consults with the searcher to determine which workload to run next.