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28039030267

Pull #2573

github

rajathagasthya 
Add Cluster Autoscaler integration example with NPD and NRC

When the Cluster Autoscaler adds a GPU node, the node reports Ready
long before the driver, container toolkit, and device plugin are
installed. Workloads scheduled in that window fail or occupy the node
so the autoscaler considers the scale-up satisfied, and MIG node pools
can deadlock entirely when scaling from zero.

Add a reference guide and example manifests that gate scheduling on
GPU readiness using upstream components: the node pool template
applies a startup taint, Node Problem Detector publishes a
nvidia.com/GPUReady node condition from an nvidia-smi probe, and the
Node Readiness Controller removes the taint once the condition is
True. The GPU Operator itself is unchanged; its operands only need
tolerations via the existing daemonsets.tolerations and NFD worker
toleration values.

The example includes a kind-based simulation for clusters without
GPUs: workers join pre-tainted via kubelet registration (matching
node pool template semantics), a marker file stands in for the
nvidia-smi probe, and a node-scaler script vendored from the NRC
repo simulates a scale-up by adding a fresh worker to the running
cluster.

Known limitation documented in the guide: NRC requires the
readiness.k8s.io/ taint prefix while managed autoscalers on GKE and
AKS only support the reserved startup-taint key prefix, so the
pattern currently requires a self-managed Cluster Autoscaler;
kubernetes-sigs/node-readiness-controller#279 tracks lifting this.

Signed-off-by: Rajath Agasthya <ragasthya@nvidia.com>
Pull Request #2573: Add Cluster Autoscaler integration example with NPD and NRC

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