KServe: deploy a model as an InferenceService with autoscaling on Kubernetes
domain: ml-ops · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Write an InferenceService manifest (apiVersion serving.kserve.io/v1beta1) with spec.predictor.model.modelFormat.name (e.g. sklearn) and storageUri pointing to your model artifact, plus resources requests/limits.
Add spec.predictor.minReplicas and spec.predictor.maxReplicas to bound the autoscaler; KServe defaults minReplicas to 1, and you can set it to 0 to allow scale-to-zero.
Optionally set scaleTarget and scaleMetric on the predictor to tune the autoscaling threshold — KServe's default autoscaler (Knative Pod Autoscaler / KPA) scales on concurrent request count against scaleTarget.
Apply the manifest with `kubectl apply -n <namespace> -f isvc.yaml`, then check status with `kubectl get inferenceservices <name> -n <namespace>` and confirm the READY column and URL.
Send inference requests to `http://<isvc-name>.<namespace>.<domain>/v1/models/<model>:predict` (via the ingress gateway) to exercise and observe autoscaling under load.
Known gotchas
Do not deploy InferenceServices into namespaces labeled control-plane — the storage-initializer won't be injected and the pod fails with "No such file or directory: '/mnt/models'".
minReplicas=0 (scale-to-zero) means the first request after an idle period pays a cold-start penalty while a new pod starts and loads the model.
Which fields actually control scaling depends on the active autoscalerClass (Knative KPA vs Kubernetes HPA vs KEDA) — scaleTarget/scaleMetric apply to KPA, while HPA/KEDA use different resource- or custom-metric configuration.
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