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.
Sign in
TReqs uses your GitHub account, so there's no separate password to create. Go to the sign-in page to get started.
Everything in TReqs belongs to an organization. Create one if you're the first person here, or accept an invitation if a colleague has already added you.
Built for agents
All 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 (read docs) — 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.