TReqs is where machine-learning training work gets planned, reviewed, and run. You describe a training run as a training request — code at a specific commit, a workflow file, and a compute target — put it up for review the way you would a pull request, and queue it once it's approved.
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treqs CLI
Command-line control planetreqs CLI
Command-line control planeAll of these docs describe what you can do in the TReqs Dashboard, but almost all of that can also be done with the TReqs CLI — creating projects, writing and opening training requests, configuring compute targets and their secrets, queueing runs, reading logs. No operation exists only as a button, so a coding agent can drive the whole loop rather than stopping at the parts that were automatable.
Three things make it work unattended:
--jsonon any command, for machine-readable output. It goes before the subcommand:treqs --json whoami.- Token auth with no browser.
TREQS_API_TOKENtakes precedence over a stored login, so an ephemeral environment needs no device flow. - Exit codes that mean something.
treqs jobs waitexits non-zero unless the run reached Completed, so a run that fails fails the script around it.
These docs are agent-readable too. Append .md to any page for its markdown source, or start from llms.txt for the index and llms-full.txt for the whole corpus.