geti-runtime-configuring-inference-pipeline

# Geti Runtime: Configuring Inference Pipeline Use this skill to operate the Geti application pipeline as a runtime workflow. This skill is for configuration and validation, not backend code development. ## When to Use - User asks how to configure source, model, and sink for live inference. - User reports pipeline issues such as no frames, no predictions, or unstable throughput. - User wants a safe checklist to move from trained model to enabled runtime pipeline. - User needs to validate that pipeline configuration changes are effective. ## Scope - In scope: runtime API usage, pipeline configuration, status checks, and operational diagnostics. - Out of scope: implementing new backend endpoints or changing backend internals. ## Procedure 1. Confirm prerequisites. - Get backend endpoint and auth details if needed. - Confirm project exists and a trained model is available. - Confirm source and sink endpoints are reachable. 2. Collect the current state first. - Read project state and current pipeline configuration. - Capture source, selected model, sink, and pipeline status before editing. - Avoid blind overwrite when partial updates are enough. 3. Configure the project pipeline. - Model: Verify the source format is compatible with the selected model (8b vs 16b). - Source: Verify connectivity. - Sink: Verify destination connectivity and configured formats. 4. Enable the pipeline. - Activate the pipeline only after all three components are fully validated. - Prefer a minimal-change rollout to isolate failures. 5. Validate end-to-end. - Verify that frames are successfully ingested from the source and rendered with predicted labels. - Confirm that inference output is delivered to your configured sink in the expected format.