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One workflow TOML now parameterises a whole experiment and `giant workflow run <spec.toml>` turns it into a b2luigi DAG whose targets are files on /ceph: nothing already produced is recomputed, every step waits for its inputs, and HTCondor submission/polling is b2luigi's job. - spec.py: workflow TOML -> frozen dataclasses with name-uniqueness and cross-reference validation, unknown keys rejected the way giant.config rejects them, and a short spec_hash per task that folds in its transitive parents — so an edited spec re-runs exactly the affected subtree. - htcondor.py: the CPU/GPU submit settings. The GPU requirement strings (ProvidesEtpCeph + optional device/memory pins) are ported from the condor-gpu-train-rollout branch rather than rewritten. - tasks.py: DatasetTask, WarmCacheTask, GeometryOracleTask, TrainEpochTask (one short GPU job per epoch, chained via --resume, which the training loop already supports unchanged), TrainTask (publishes best.pt/last.pt and a concatenated metrics.csv so downstream never sees the epoch fan-out), RolloutTask, AnalysisPrepTask, AnalysisComputeTask (one job per plot x chunk, walltime sized from run_meta.json at submit time), AnalysisRenderTask (always local — the only step importing plotstyle/LaTeX), WorkflowTask. Task bodies call the existing entry points; none of them reimplement anything. - run.py + `giant workflow run`: settings wiring and the script b2luigi re-executes on workers. add_filename_to_cmd is off because b2luigi passes only the script's basename, and --spec is forwarded via task_cmd_additional_args so a worker resolves the identical task graph. configs/workflow_example.toml is the documented starting point. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>