multi_agent_lab/cli.py

from __future__ import annotations

import argparse
from pathlib import Path

from .config import TrainConfig
from .orchestrator import Orchestrator


def build_parser() -> argparse.ArgumentParser:
    p = argparse.ArgumentParser(prog="multi_agent_lab", description="多 agent 训练与调试闭环示例")
    p.add_argument("--scenario", choices=["stable", "unstable"], default="stable")
    p.add_argument("--seed", type=int, default=42)
    p.add_argument("--epochs", type=int, default=20)
    p.add_argument("--lr", type=float, default=0.2)
    p.add_argument("--loss-eps", type=float, default=1e-12)
    p.add_argument("--n-samples", type=int, default=2000)
    p.add_argument("--n-features", type=int, default=16)
    p.add_argument("--train-ratio", type=float, default=0.8)
    p.add_argument("--l2", type=float, default=0.0)
    p.add_argument("--grad-clip", type=float, default=None)
    p.add_argument("--runs-dir", type=str, default=str(Path(__file__).resolve().parent.parent / "runs"))
    return p


def main(argv: list[str] | None = None) -> int:
    args = build_parser().parse_args(argv)

    cfg = TrainConfig(
        seed=args.seed,
        epochs=args.epochs,
        lr=args.lr,
        loss_eps=args.loss_eps,
        n_samples=args.n_samples,
        n_features=args.n_features,
        train_ratio=args.train_ratio,
        l2=args.l2,
        grad_clip=args.grad_clip,
    )

    orch = Orchestrator(base_runs_dir=Path(args.runs_dir))
    store, summary = orch.run(cfg=cfg, scenario=args.scenario)

    report_path = store.path("report.md")
    print(f"完成：{store.run_dir}")
    print(f"报告：{report_path}")
    print(f"尝试次数：{summary.get('attempts')}")
    return 0