HPE Machine Learning Development Environment 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.
HPE System Architecture
The master is the central component of the HPE Machine Learning Development Environment system. It is responsible for
Storing experiment, trial, and workload metadata.
Scheduling and dispatching work to agents.
Managing provisioning and deprovisioning of agents in clouds.
Advancing the experiment, trial, and workload state machines over time.
Hosting the WebUI and the REST API.
An agent manages a number of slots, which are computing devices (typically a GPU or CPU). An agent has no state and only communicates with the master. Each agent is responsible for
Discovering local computing devices (slots) and sending metadata about them to the master.
Running the workloads that are requested by the master.
Monitoring containers and sending information about them to the master.
The task container runs a training task or other task(s) in a containerized environment. Training tasks are expected to have access to the data that will be used in training. The agents are responsible for reporting the status of the task container to the master.