Getting Started

Install

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Prerequisites

  • Kubernetes 1.29+ cluster with GPU nodes (the Certification CRD’s CEL rules use the quantity() library, which the API server accepts in new CRD expressions from 1.29)
  • kubectl configured and pointing at the target cluster
  • NVIDIA GPU Operator installed
  • Helm 3.x

Install the CLI

Download and run the installer. It detects your OS and architecture and resolves the newest stable release itself:

$curl -fsSL https://github.com/NVIDIA/cluster-readiness-engine/releases/latest/download/installer | bash

To pin a version, download the installer from that release and pass the tag:

$curl -fsSL https://github.com/NVIDIA/cluster-readiness-engine/releases/download/<tag>/installer | bash -s -- -v <tag>

The installer automatically downloads and verifies a SHA-256 checksum before installing. On air-gapped systems, ensure checksums.txt from the same release is reachable alongside the binary.

Verify the installation:

$nvcrectl --version

Set up the cluster

nvcrectl setup init installs the controller and its dependencies in two phases:

  1. deps — Kubeflow Trainer (required for TrainJob workloads)
  2. helm — NVCRE Helm chart (CRDs, controller deployment, built-in LogProfiles)
$nvcrectl setup init

Registry access

The controller image and Helm chart are pulled anonymously from GHCR; no token is needed. If your cluster pulls from a private mirror or fork instead, pass --image-pull-secret <github-token> and the CLI creates the pull secret for you.

Verify

Check that the controller is running:

$kubectl get pods -n nvcre

Check that the CRDs are installed:

$kubectl get crds | grep nvcre.nvidia.com

Uninstall

$nvcrectl setup reset

This removes all NVCRE custom resources, the controller, CRDs, and Kubeflow Trainer. To keep Kubeflow Trainer, pass --skip-phases=deps:

$nvcrectl setup reset --skip-phases=deps