Install roar and register to GLaaS
~/your-model
% uv tool install roar-cli % roar init # wrap any script — no code changes % roar run python train.py % roar register model.pt
What roar captures
Commands, file reads and writes, git state, timing, and runtime context with no pipeline rewrite.
What GLaaS stores
Hash-addressable DAGs, jobs, and artifacts so lineage stays queryable after the run is over.
What you can answer
Which code, data, and environment produced a model, and where that artifact flows next.
Samples in the Registry
Sample
DAG
DAG from a Nanochat training run.
Sample
Job
A step in the execution of a Nanochat training DAG.
Sample
Artifact
Model output artifact from a Nanochat training DAG.
Example Workflows
End-to-End Introduction
This guided example walks through a complete roar + GLaaS workflow.
More examples
coming soon
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GLaaS helps you understand what happened.
TReqs helps you safely re-run, review, and operationalize it.