Capture what actually ran. Without changing your code.
If it ran, roar saw it. Prefix your training script with roar run. A runtime
observer attaches to the process and records every file, every
argument, every dependency — exactly as it happened.
Install roar.
works on macOS, Linux · Python 3.10+
Four commands. That's the whole interface.
roar doesn't want to be your framework. It wants to observe what you already do and make it replayable.
Run under observation.
Wraps any command — Python, bash, torchrun — and records everything it reads, writes, and depends on.
Inspect a run.
Prints the inferred DAG, the arguments, the environment, the files touched, and the artifacts produced.
Compare two runs.
See exactly what moved: code, data, hyperparameters, hardware. No more "I think something changed."
Re-run the way it actually ran.
Same data, same environment, same recipe — rebuilt from the captured lineage, not from memory.
Everything that influences the result.
No logger to configure. No decorators to sprinkle. roar watches the process and records what matters.
It works with what you already have.
roar sits in front of your training script, not inside it. Your framework, your orchestrator, your storage — all unchanged.
Pair it with the rest of TReqs.
roar captures lineage locally and that's already useful. For a team source of truth and AI-native coordination, connect it to GLaaS and TReqs.
Make the lineage a team source of truth.
GLaaS stores every run and artifact your team produces, content-addressably. Resolve any hash to the recipe that made it.
Read about GLaaS →Coordinate who's running what — human or agent.
Training requests let the team review and approve runs before compute starts. Works for humans and for AI agents that want to train things.
Why TReqs →Install roar. Point it at a script. See what you've been missing.
Thirty seconds to install. No account needed to start.