| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import math |
| from pathlib import Path |
| from typing import Any |
|
|
|
|
| def load_result(path: str | Path) -> dict[str, Any]: |
| value = json.loads(Path(path).read_text(encoding="utf-8")) |
| if not isinstance(value, dict): |
| raise ValueError(f"Evaluation root must be an object: {path}") |
| return value |
|
|
|
|
| def gate_result( |
| candidate: dict[str, Any], |
| *, |
| baseline: dict[str, Any] | None = None, |
| max_loss: float | None = None, |
| min_improvement: float = 0.0, |
| ) -> dict[str, Any]: |
| loss = float(candidate["validation_loss"]) |
| if not math.isfinite(loss): |
| raise ValueError("Candidate validation loss is not finite") |
| if max_loss is not None and loss > max_loss: |
| raise ValueError( |
| f"Candidate loss {loss:.6f} exceeds maximum {max_loss:.6f}" |
| ) |
| baseline_loss = None |
| improvement = None |
| if baseline is not None: |
| baseline_loss = float(baseline["validation_loss"]) |
| if not math.isfinite(baseline_loss): |
| raise ValueError("Baseline validation loss is not finite") |
| improvement = baseline_loss - loss |
| if improvement < min_improvement: |
| raise ValueError( |
| f"Loss improvement {improvement:.6f} is below required " |
| f"{min_improvement:.6f}" |
| ) |
| samples = candidate.get("samples") |
| if not isinstance(samples, list) or not samples: |
| raise ValueError("Candidate evaluation has no generation samples") |
| return { |
| "passed": True, |
| "candidate_loss": loss, |
| "baseline_loss": baseline_loss, |
| "loss_improvement": improvement, |
| "sample_count": len(samples), |
| } |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser( |
| description="Stop a staged training pipeline when evaluation regresses." |
| ) |
| parser.add_argument("--candidate", required=True) |
| parser.add_argument("--baseline") |
| parser.add_argument("--max-loss", type=float) |
| parser.add_argument("--min-improvement", type=float, default=0.0) |
| args = parser.parse_args() |
| result = gate_result( |
| load_result(args.candidate), |
| baseline=load_result(args.baseline) if args.baseline else None, |
| max_loss=args.max_loss, |
| min_improvement=args.min_improvement, |
| ) |
| print(json.dumps(result, ensure_ascii=False, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
