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.