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scripts/musebench/select.py and fetch.py (unit-tested) have placed the deterministic eval cohort and the budgeted train_ - #9
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…ifier select.py classifies all 351,183 audio members (0 unclassified), picks a sealed eval cohort (3k Jamendo, 3k FMA-OOD, 1k clean per generator from the transformed 'test split', 1k co-created, 200 per transform x generator, paired with the clean eval tracks) and a train pool in priority tiers under a byte budget; train and eval never share a source-track family. fetch.py downloads members by byte range (sha256 ledger, atomic, resumable, free-space floor). verify.py acquire re-plans from the index and checks the manifest, files, hashes, cohort shape and disk policy. Assumptions: budget 170 GB (not 180) to keep ~17 GB margin above the 80 GB floor on /; license_ccurl left null because MuseBench ships no license CSV; magenta_generated_music treated as Lyria RealTime.
Counts taken from selection.json after the 170 GB plan; full SHA-256 recheck of all 64,756 files matched the fetch ledger.
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Milestone of an auto-claude run.
Check:
systemd-run --user --scope -q -p MemoryMax=6G ~/RTX_ENV/bin/python -m pytest -m "not slow" -q -p no:cacheprovider && ~/RTX_ENV/bin/python -I scripts/musebench/verify.py acquirePlan: .claude/auto-plan.md (local to the run).