**TReqs** ([treqs.ai](https://treqs.ai)) is a separate product for queueing and reviewing compute requests on your own infrastructure — think of it as pull requests for compute jobs. When TReqs schedules a run, the lineage gets auto-registered in GLaaS; from any GLaaS DAG, two actions connect you back into TReqs.

This page is relevant if your org uses TReqs (or you're considering it). If you only use `roar` and GLaaS, the GLaaS side works on its own — TReqs is an opt-in upgrade for orchestration and review.

## View Training Request

On a DAG page, click **View Training Request** to open the request in TReqs.

If you have permission to view it on the TReqs side, you can see:

- Peer review and discussion
- Compute target activity, logs, and execution history
- Training-request timeline and updates

This gives you the collaboration and execution view of the same lineage context represented in GLaaS.

## Run in TReqs

The **Run in TReqs** action clones a DAG context into TReqs as a new workflow-backed training request.

In practice: you launch work in your own TReqs org and project while preserving the original context for reproduction. Training requests behave like pull requests for compute — past work can be queued to run again with the exact same data, code, and environment, pending whatever approval gate your org has set.

If your org has no approval gate, the request runs immediately once submitted. If it does have one, the request waits for approval and you (or a reviewer) sees the full lineage context before the compute starts.