| |
| """Write FINAL_STATUS and a training report from artifacts after jobs finish.""" |
| from __future__ import annotations |
|
|
| import json |
| from datetime import datetime, timezone |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[2] |
|
|
|
|
| def load(path: Path): |
| if not path.exists(): |
| return None |
| return json.loads(path.read_text(encoding="utf-8")) |
|
|
|
|
| def status_of(block, pass_key="status"): |
| if not block: |
| return "NOT_RUN" |
| if isinstance(block, str): |
| return block |
| return block.get(pass_key) or block.get("matrix_status") or "UNKNOWN" |
|
|
|
|
| def main() -> int: |
| lattice = load(ROOT / "artifacts" / "training" / "lattice_summary.json") or {} |
| obs = load(ROOT / "artifacts" / "training" / "observational_and_vision_baselines.json") or {} |
| trans = load(ROOT / "artifacts" / "training" / "transition_sft.json") or {} |
| dec = load(ROOT / "artifacts" / "training" / "decision_sft.json") or {} |
| rlvr = load(ROOT / "artifacts" / "training" / "rlvr.json") or {} |
| prev = load(ROOT / "artifacts" / "FINAL_STATUS.json") or {} |
| matrix = dict(prev.get("matrix") or {}) |
| matrix["Classical baseline"] = "PASS" if obs.get("B0") and obs.get("B1") else matrix.get("Classical baseline", "NOT_RUN") |
| b2 = obs.get("B2_generated_pattern_cnn") or {} |
| b3 = obs.get("B3_generated_pattern_multimodal") or {} |
| matrix["Vision baseline"] = "PASS" if b2.get("status") == "PASS" else ("NOT_RUN" if not b2 else b2.get("status", "FAIL")) |
| matrix["Multimodal baseline"] = "PASS" if b3.get("status") == "PASS" else ("NOT_RUN" if not b3 else b3.get("status", "FAIL")) |
| matrix["Transition SFT"] = "PASS" if (trans.get("mlp") or {}).get("status") == "PASS" else "NOT_RUN" |
| matrix["Decision SFT"] = "PASS" if (dec.get("mlp") or {}).get("status") == "PASS" else "NOT_RUN" |
| if rlvr.get("matrix_status") == "COMPLETED_NOT_JUSTIFIED": |
| matrix["RLVR"] = "COMPLETED_NOT_JUSTIFIED" |
| elif rlvr.get("status") == "PASS": |
| matrix["RLVR"] = rlvr.get("matrix_status") or "PASS" |
| else: |
| matrix["RLVR"] = rlvr.get("status") or "NOT_RUN" |
| notes = dict(prev.get("notes") or {}) |
| notes["SFT/RLVR"] = ( |
| f"Measured lattice n_transitions={lattice.get('n_transitions')} " |
| f"n_decision={lattice.get('n_decision_cases')} " |
| f"transition_mae={(trans.get('mlp') or {}).get('test_realized_mae_cm')} " |
| f"decision_acc={(dec.get('mlp') or {}).get('test_action_accuracy')} " |
| f"rlvr={matrix['RLVR']}. " |
| "Vision B2/B3 used generated pattern PNGs, not FIT-100K." |
| ) |
| payload = { |
| "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), |
| "host": prev.get("host", "daka5svhri0c73etu4ug-fitground"), |
| "gpu": prev.get("gpu", "NVIDIA GeForce RTX 4090 24GB"), |
| "matrix": matrix, |
| "notes": notes, |
| "training": { |
| "lattice": lattice, |
| "B0_test_mae_cm": (obs.get("B0") or {}).get("test_mae_cm"), |
| "B1_test_mae_cm": (obs.get("B1") or {}).get("test_mae_cm"), |
| "B1_xgboost_test_mae_cm": (obs.get("B1_xgboost") or {}).get("test_mae_cm"), |
| "B2_cnn_test_mae_cm": b2.get("test_mae_cm"), |
| "B3_cnn_test_mae_cm": b3.get("test_mae_cm"), |
| "transition_sft_realized_mae_cm": (trans.get("mlp") or {}).get("test_realized_mae_cm"), |
| "decision_sft_accuracy": (dec.get("mlp") or {}).get("test_action_accuracy"), |
| "rlvr": { |
| "status": rlvr.get("matrix_status") or rlvr.get("status"), |
| "before_test": rlvr.get("before_test"), |
| "after_test": rlvr.get("after_test"), |
| }, |
| }, |
| } |
| (ROOT / "artifacts" / "FINAL_STATUS.json").write_text(json.dumps(payload, indent=2), encoding="utf-8") |
| md = ROOT / "reports" / "TRAINING_RUN.md" |
| md.write_text( |
| "\n".join( |
| [ |
| "# FitGround training run", |
| "", |
| "This run trains B0–B3, Transition SFT, Decision SFT, and RLVR on **measured** GarmentCode panel geometry.", |
| "FIT-100K images were not downloaded. Vision models use rasterized pattern drawings.", |
| "No `intended_delta_cm` was copied into `realized_delta_cm`.", |
| "", |
| "```json", |
| json.dumps(payload["training"], indent=2, default=str), |
| "```", |
| "", |
| "## Matrix", |
| "", |
| *[f"- {k}: {v}" for k, v in matrix.items()], |
| "", |
| "## Honesty", |
| "", |
| "- Observational B0/B1 remain FIT-Clean measurement prediction, not intervention GT.", |
| "- B2/B3 FIT-100K: NOT_RUN. B2/B3 generated-pattern: trained.", |
| "- Tiny LMs are from-scratch character transformers, not pretrained MLLMs.", |
| "- RLVR reward is cached measured utility from the lattice, not a live Warp loop.", |
| "", |
| ] |
| ), |
| encoding="utf-8", |
| ) |
| print(json.dumps(payload, indent=2, default=str)[:4000]) |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
