Runs
A run is a single execution of a workflow. Every time a workflow is triggered — manually, via the API, the UI, or by an event — the platform creates a new run and tracks its progress from start to finish.
Runs are the operational unit of a workflow: they carry the run-time inputs, the outputs of each phase, the execution logs, and the final status.
Run states
A run moves through a defined set of states during its lifetime:
| State | Meaning |
|---|---|
SCHEDULED | Created, waiting for resources, for the time it is due to begin, or to start moving again after a pause |
RUNNING | Actively executing |
PAUSING | A pause is requested; the step in progress is still running (see Pause and resume) |
PAUSED | Held between steps. Resuming continues from that point (see Pause and resume) |
AWAITING_INPUT | Waiting for human/machine input (see Human in the Loop) |
COMPLETED | All phases finished successfully |
FAILED | A phase or step raised an unrecoverable error, or the run crashed |
CANCELLED | Manually cancelled before completion |
What a run tracks
- Inputs: the parameter values provided when the run was started
- Phase results: the output or status of each phase, in order
- Logs: a structured execution log from every step (see Logs)
- Artifacts: any data produced by phases (see Artifacts)
- Workflow version: the exact version of the workflow code that was executed
Starting a run
You can start a run from the platform UI or the REST API. The workflow ID is visible in the platform UI on the workflow's detail page.
curl -X POST https://api.unitelabs.io/v1/workflows/{workflow_id}/runs \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"inputs": {"sample_id": "S001"}}'
Phase-level execution
Operators can run a workflow starting from a specific phase, or run a range of phases. This is useful for:
- Resuming after a manual intervention
- Debugging a specific stage in isolation
- Replaying a phase with different inputs
Campaigns
A campaign is a collection of runs of the same workflow type — for example, running the same ELISA protocol across 50 samples. The platform groups these runs so you can track aggregate results, identify outliers, and compare outcomes across runs.
Related concepts
- Workflow: the process being executed
- Logs: execution output for each run
- Artifacts: data produced by a run
- Human in the Loop: pausing a run for manual input
Last updated