geti-runtime-running-live-inference

# Geti Runtime: Running Live Inference Use this skill for operational control of live inference in Geti. This skill is for runtime execution and monitoring, not code changes. ## When to Use - User asks how to start or stop live inference for a configured pipeline. - User needs runbook-style checks for runtime health and correctness. - User reports stalls, dropped outputs, or latency spikes during live execution. - User needs incident-style recovery steps with minimal disruption. ## Scope - In scope: runtime start and stop flow, status monitoring, health verification, rollback and recovery. - Out of scope: backend implementation changes and model retraining. ## Procedure 1. Pre-flight checks. - Confirm pipeline configuration is complete (source, model, sink). - Confirm model is loaded and selected. - Confirm endpoints for source and sink are reachable. 2. Start live inference. - Enable runtime execution through the application pipeline workflow. - Capture execution identifiers and current status. 3. Monitor execution. - Check the pipeline status (Idle vs Running). - Verify frames are rendered with predictions, inference output is generated as per sink configuration. - Record key runtime metrics if available (throughput & latency). 4. Validate output quality. - Spot-check predictions against known scenes or samples. - Confirm class distribution and confidence values are reasonable. 5. Handle degradation. - If source degrades: reconnect or switch to a known-good source. - If model output degrades: verify loaded model version and thresholds. - If sink fails: apply fallback sink.