2.3 Administering Run:ai

Key points

1. A project can be a team, a person or an initiative. In Kubernetes, each project becomes a namespace.

   What NVIDIA says (2)

   “NVIDIA Run:ai uses Projects as the primary organization management unit.”

   — NVIDIA Run:ai: Projects

   “Projects are manifested as Kubernetes namespaces.”

   — NVIDIA Run:ai: Projects

2. Departments sit above projects. They let you allocate quota across projects and apply policies at department level.

   What NVIDIA says (1)

   “Departments group multiple projects under a shared organizational scope.”

   — NVIDIA Run:ai: Departments

3. A subject is a user, group or service account. A scope is the part of the organization the role applies to. Run:ai has predefined roles and allows custom ones.

   What NVIDIA says (1)

   “A role defines a set of permissions that can be assigned to a subject in a scope”

   — NVIDIA Run:ai: Roles

4. A node pool groups nodes by a label, such as GPU type. With over quota enabled, projects can still use a new pool before getting quota there.

   What NVIDIA says (1)

   “Once created, the new node pool is automatically assigned to all projects and departments with a quota of zero GPU resources, unlimited CPU resources, and over quota enabled”

   — NVIDIA Run:ai: Node Pools

5. Each node pool has its own scheduler instance. Workloads sent to a pool are scheduled by that instance.

   What NVIDIA says (1)

   “Creating a new node pool creates a new instance of the NVIDIA Run:ai Scheduler .”

   — NVIDIA Run:ai: Node Pools

Key terms

• NVIDIA Run:ai: A Kubernetes platform that schedules AI workloads and shares GPUs between teams by quota.
• Run:ai project: The main Run:ai unit for a team: it holds a GPU quota and maps to a Kubernetes namespace.
• Run:ai department: A group of Run:ai projects under one shared scope.
• Node pool: A Run:ai set of nodes with its own scheduler instance and quotas.

Sample question

What is the primary unit Run:ai uses to give a team GPU quota, scheduling rules and access?