troubleshoot-ml-k8s

To troubleshoot a stuck or crashing KServe InferenceService, gather kubectl evidence including pod status, conditions, and events to isolate whether the failure is at the application or platform layer. This process narrows observed symptoms to the responsible layer for targeted resolution.

A Knative revision not ready indicates a deployment failure requiring checks of ksvc, revision, and route states alongside targeted logs. Diagnosing the failure involves mapping these specific conditions to underlying platform or application layer issues.

Fixing KServe inference endpoint unreachable errors requires collecting evidence on route conditions and ingress states to identify routing failures. The workflow checks scaling and timeout issues to pinpoint the exact network layer responsible for the unavailability.

Yes, you can diagnose Kubernetes autoscaling and timeout issues for GPU inference workloads by analyzing revision states and pod events. The workflow applies evidence-driven branching to determine if scaling failures stem from resource constraints or application errors.

The best way to collect diagnostic evidence for a failing InferenceService deployment is running targeted kubectl commands to retrieve conditions, events, and logs after setting the correct cluster context. This cluster-safe approach avoids broad sweeps and grounds the analysis in specific facts.