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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 Run:ai User Interface

architecture

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.

multi-cluster-architecture

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.

Last update: March 23, 2022