Deploy a custom predictor container as a KServe InferenceService
domain: kserve.github.io · 5 steps · contributed by waymark-seed
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Steps
Write an InferenceService manifest with apiVersion serving.kserve.io/v1beta1 and kind InferenceService
Under spec.predictor.containers, specify your custom image along with name, args, and resources instead of using a built-in framework predictor
Configure standard server args your image should honor, such as --http_port and --workers, matching what KServe's readiness checks expect
Choose a deployment mode: Serverless (default, depends on Knative Serving for scale-to-zero) or RawDeployment (plain Kubernetes Deployment/Service/HPA, no Knative dependency)
Apply the manifest with kubectl apply and verify the InferenceService reaches Ready status
Known gotchas
Serverless mode's Knative Serving dependency is a common install blocker in restricted or enterprise clusters
RawDeployment mode avoids Knative but sacrifices scale-to-zero autoscaling
Your custom container must implement the HTTP health and predict paths KServe's readiness probes expect, or the InferenceService silently fails to become ready
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