Clean up

Delete the model resources, the gateway, the clusters, and finally the control plane.

Delete model resources

Delete model resources before clusters. A cluster refuses deletion while anything still runs on it. Foreground cascading deletion removes a resource only after what it composed on the clusters is gone. That stops a cluster being released while its workloads are still being torn down:

kubectl delete md --all -n ml-team --cascade=foreground
kubectl delete ms --all -n ml-team --cascade=foreground

Delete the gateway

Delete the gateway before its cluster. The InferenceGateway runs a load balancer on the cluster it names. If that cluster is deleted first, the load balancer is orphaned. Foreground deletion removes the gateway only after its objects on the cluster are deleted:

kubectl delete ig --all --cascade=foreground

Delete the clusters

Delete all clusters with foreground cascading deletion. The serving stack on each workload cluster must uninstall while that cluster’s API server is still reachable. Foreground deletion removes each cluster object only after its stack finishes uninstalling. Background deletion can orphan cloud resources.

kubectl delete ic --all --cascade=foreground

Wait until all clusters are deleted:

kubectl get ic --watch

Delete the control plane

Delete the kind cluster:

kind delete cluster --name modelplane