File size: 2,427 Bytes
91ac3cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | 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()
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