Run:AI System Components¶
Run:AI is installed over a Kubernetes Cluster
Researchers submit Machine Learning workloads via the Run:AI Command-Line Interface (CLI), or directly by sending YAML files to Kubernetes.
Administrators monitor and set priorities via the Administrator User Interface
The Run:AI Cluster¶
The Run:AI Cluster contains:
- The Run:AI Scheduler which extends the Kubernetes scheduler. It uses business rules to schedule workloads sent by Researchers.
- Fractional GPU management. Responsible for the Run:AI Virtualization technology which allows Researchers to allocate parts of a GPU rather than a whole GPU
- The Run:AI agent. Responsible for sending Monitoring data to the Run:AI Cloud.
- Clusters require outbound network connectivity to the Run:AI Cloud.
- The Run:AI cluster is installed as a Kubernetes Operator
- Run:AI is installed in its own namesapce runai
- Workloads are run in the context of Projects. Each project is a Kubernetes namespace with its own settings and access control.
The Run:AI Cloud¶
The Run:AI Cloud is the basis of the Administrator User Interface.
- The Run:AI cloud aggregates monitoring information from multiple tenants (customers).
- Each customer may manage multiple Run:AI clusters.