GPU Cluster Setup

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The NVIDIA Cluster Agent (NVCA) connects GPU clusters to the NVCF control plane, enabling them to act as deployment targets for Cloud Functions. NVCA is a function deployment orchestrator that registers a cluster’s GPU resources, communicates with the control plane, and manages the lifecycle of function deployments on GPU nodes.

For a fresh install, use the Quickstart. The one-click CLI flow can register a GPU cluster as part of the install. Use this section for manual cluster registration, standalone NVCA installation, and day-two cluster configuration.

If you pin NVCA separately from the recommended compute-plane stack, check the current NVCA version before upgrading to NVCA 3.x. Clusters running NVCA 2.51.0 or earlier have version-specific upgrade guidance. See the 0.6.0 upgrade notes.

After installing NVCA on a cluster:

  • The registered cluster will show as a deployment option in the GET /v2/nvcf/clusterGroups API response.
  • Any functions under the cluster’s authorized NCA IDs can now deploy on the cluster.

Authentication and Keys

Key TypeDescription
NVCF API Key (NAK)Used by NVCA to authenticate with the control plane. See self-hosted-api for details on API key generation.

Prerequisites

  • Access to a Kubernetes cluster including GPU-enabled nodes (“GPU cluster”)

  • Registering the cluster requires kubectl and helm installed.

  • The user registering the cluster must have the cluster-admin role privileges to install the NVIDIA Cluster Agent Operator (nvca-operator).

Supported Kubernetes Versions

  • Supported versions are the latest Kubernetes minor release and the two prior minor releases (N-2). See official Kubernetes docs for current supported versions.

Considerations

  • The NVIDIA Cluster Agent currently only supports caching if the cluster is enabled with StorageClass configurations. If the “Caching Support” capability is enabled, the agent will make the best effort by attempting to detect storage during deployments and fall back on non-cached workflows.
  • Each function and task requires several infrastructure containers be deployed alongside workload containers. These infrastructure containers collectively need 6 CPU cores and 8 Gi of system memory to execute. Each GPU node must have at least this many resources, ideally significantly more for workload resource usage.