Run:AI System Components¶

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 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.

Kubernetes-Related Details¶

• The Run:AI cluster is installed as a Kubernetes Operator
• Run:AI is installed in its own Kubernetes namespace named 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.