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 Run:ai 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.
- 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 Control Plane (Backend)¶
The Run:ai control plane is the basis of the Run:ai User Interface.
- The Run:ai cloud aggregates monitoring information from multiple tenants (customers).
- Each customer may manage multiple Run:ai clusters.
The Run:ai control plane resides on the cloud but can also be locally installed. To understand the various installation options see the installation types document.