Use this for any single Autolab experiment that should result in exactly one managed benchmark run.

Workflow

1. Load the local operator env and refresh from local master:
   • . ~/.autolab/credentials
   • uv run scripts/refresh_master.py --fetch-dag
2. Edit only train.py for the single intended hypothesis.
3. Run preflight before launch:
   • uv run scripts/hf_job.py preflight
4. Launch exactly one managed experiment:
   • uv run scripts/hf_job.py launch --mode experiment
5. Stream logs and parse the final metric:
   • uv run scripts/hf_job.py logs <JOB_ID> --follow --output /tmp/autolab-run.log
   • uv run scripts/parse_metric.py /tmp/autolab-run.log
6. Record the run locally:
   • uv run scripts/submit_patch.py --comment "..."
7. Promotion is local and only happens if val_bpb beats current master.

Guardrails

• Treat train_orig.py as the refreshed local-master base. If preflight reports multiple known hypothesis categories, stop and inspect the diff before launching.
• Ignore repo git main and origin/main when judging freshness. In this rig repo those refs describe control-plane history, not the benchmark master. The comparable base is whatever refresh_master.py just wrote into train_orig.py, research/live/master.json, and research/results.tsv.
• Never run uv run scripts/hf_job.py launch --mode prepare from an experiment-scoped worktree. prepare is shared bootstrap work, not per-experiment work.
• Do not launch a second experiment job for the same experiment unless you have a specific reason and intentionally override the duplicate check.
• If the workspace looks stale against the current local master, stop and rewrite the experiment rather than rationalizing the mismatch.

Fast Checks

• uv run scripts/hf_job.py preflight --json Use this when you need to inspect the diff preview, active conflicts, or detected change categories programmatically.
• uv run scripts/trackio_reporter.py summary --max-jobs 25 Use this to confirm the experiment id or hypothesis is not already active.