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  1. Symptoms

    GPU nodes for an AI video upscaling job were created normally and reached Ready, and then about 10 to 11 minutes later Karpenter treated each one as unhealthy and replaced it. Every new node went the same way, so the GPU pods kept restarting and the upscaling work could not make progress.

    Trigger

    Enabling Karpenter's node auto-repair feature gate (nodeRepair: true) and then running GPU pods on nodes from the Bottlerocket NVIDIA AMI.

    Root cause
    Component
    Contributing factor
    Detection

    The team noticed GPU nodes being replaced repeatedly after running GPU pods, then found the DCGMError message in the node events.

    Remediation
    • Disabled Karpenter node auto-repair by setting the nodeRepair feature gate to false
    Lessons

    Automatic node repair acts on every unhealthy node condition, including AcceleratedHardwareReady, so an image defect that only breaks GPU health monitoring is enough to have every GPU node terminated and recreated in a loop. Before turning on auto-repair, check which conditions it watches and whether the node image reports them correctly. Disabling auto-repair stops the loop at once but is only a temporary measure; installing the missing library or changing the AMI address the cause but cost more on an immutable OS such as Bottlerocket.

Check your cluster for this

This case involves GPU driver and Karpenter. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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