761 lines
30 KiB
Python
761 lines
30 KiB
Python
#!/usr/bin/env python3
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"""浏览器 MuJoCo 平台的本地 mjlab 训练桥接服务（仅绑定 loopback）。"""
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from __future__ import annotations
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import argparse
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import hmac
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import json
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import os
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import re
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import secrets
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import shutil
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import signal
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import subprocess
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import sys
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import threading
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import uuid
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from collections import deque
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from contextlib import suppress
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from dataclasses import dataclass, field
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from datetime import UTC, datetime
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from http import HTTPStatus
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from pathlib import Path
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from typing import Any
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from urllib.parse import parse_qs, unquote, urlsplit
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from tuning.manager import TuningError, TuningManager
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from tuning.process import GpuLease, ResourceBusyError
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VERSION = "0.4.0"
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# 浏览器当前 ONNX 运行时只实现 Go2 的 47→12 部署契约；其他任务须由服务启动参数显式放行。
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DEFAULT_TASKS = ("Unitree-Go2-Flat",)
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ACTIVE_STATES = {"queued", "running"}
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MAX_JOBS = 20
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ANSI_ESCAPE = re.compile(r"\x1b\[[0-?]*[ -/]*[@-~]")
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ITERATION_PATTERNS = (
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re.compile(r"(?:learning\s+)?iteration\D+(\d+)\s*/\s*(\d+)", re.I),
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re.compile(r"(?:learning\s+)?iteration\D+(\d+)", re.I),
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)
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RUN_NAME = re.compile(r"^[A-Za-z0-9_.-]{1,64}$")
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LOCAL_ORIGIN = re.compile(r"^https?://(?:localhost|127\.0\.0\.1)(?::\d+)?$")
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LOCAL_HOST = re.compile(r"^(?:localhost|127\.0\.0\.1)(?::\d+)?$")
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def now_iso() -> str:
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return datetime.now(UTC).isoformat()
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def termination_signal_handler(_signum: int, _frame: Any) -> None:
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"""将 SIGTERM 转成受控退出，使 main 的 finally 能清理训练子进程。"""
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raise KeyboardInterrupt
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class ApiError(Exception):
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def __init__(self, status: int, message: str):
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super().__init__(message)
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self.status = status
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@dataclass
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class TrainingConfig:
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task_id: str
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num_envs: int
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max_iterations: int
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seed: int
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run_name: str
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device: str
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gpu_ids: list[int]
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wandb_mode: str
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reward_config: dict[str, Any] | None = None
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@dataclass
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class TrainingJob:
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id: str
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config: TrainingConfig
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state: str = "queued"
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created_at: str = field(default_factory=now_iso)
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started_at: str | None = None
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ended_at: str | None = None
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iteration: int = 0
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message: str = "等待本地训练进程启动"
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logs: deque[str] = field(default_factory=lambda: deque(maxlen=200))
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artifact: Path | None = None
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process: subprocess.Popen[str] | None = None
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cancel_requested: bool = False
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def public(self) -> dict[str, Any]:
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progress = min(1.0, max(0.0, self.iteration / self.config.max_iterations))
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if self.state == "succeeded":
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progress = 1.0
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return {
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"id": self.id,
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"state": self.state,
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"taskId": self.config.task_id,
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"createdAt": self.created_at,
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"startedAt": self.started_at,
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"endedAt": self.ended_at,
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"iteration": self.iteration,
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"maxIterations": self.config.max_iterations,
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"progress": progress,
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"message": self.message,
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"logs": list(self.logs),
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"artifactReady": self.artifact is not None and self.artifact.is_file(),
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"artifactName": self.artifact.name if self.artifact else None,
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}
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class TrainingManager:
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def __init__(
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self,
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trainer_root: Path,
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python: str,
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tasks: tuple[str, ...],
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check_environment: bool = True,
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lease: GpuLease | None = None,
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):
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self.trainer_root = trainer_root.expanduser().resolve()
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self.python = str(Path(python).expanduser()) if os.sep in python else python
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self.tasks = tasks
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self.jobs: dict[str, TrainingJob] = {}
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self.lock = threading.RLock()
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self.check_environment = check_environment
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self._environment_error: str | None | bool = False
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self.lease = lease or GpuLease()
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self.preset_resolver: Any = None
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def readiness_error(self) -> str | None:
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if not self.trainer_root.is_dir():
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return f"训练工程目录不存在：{self.trainer_root}"
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if not (self.trainer_root / "scripts" / "train.py").is_file():
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return f"训练入口不存在：{self.trainer_root / 'scripts/train.py'}"
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executable = Path(self.python)
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if not executable.is_file() and shutil.which(self.python) is None:
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return f"Python 解释器不存在：{self.python}"
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if self.check_environment and self._environment_error is False:
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probe = (
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"import importlib.util,sys; "
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"missing=[m for m in ('mjlab','torch','tyro','wandb') "
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"if importlib.util.find_spec(m) is None]; "
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"print(','.join(missing)); sys.exit(bool(missing))"
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)
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try:
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result = subprocess.run(
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[self.python, "-c", probe],
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cwd=self.trainer_root,
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capture_output=True,
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text=True,
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timeout=15,
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check=False,
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)
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missing = result.stdout.strip()
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self._environment_error = (
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f"训练 Python 缺少依赖：{missing}" if result.returncode else None
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)
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except (OSError, subprocess.TimeoutExpired) as error:
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self._environment_error = f"无法检查训练 Python 环境：{error}"
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return self._environment_error if isinstance(self._environment_error, str) else None
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def active_job_id(self) -> str | None:
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with self.lock:
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return next((job.id for job in self.jobs.values() if job.state in ACTIVE_STATES), None)
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def health(self) -> dict[str, Any]:
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error = self.readiness_error()
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return {
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"version": VERSION,
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"ready": error is None,
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"trainerRoot": str(self.trainer_root),
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"python": self.python,
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"tasks": list(self.tasks),
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"activeJobId": self.active_job_id(),
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"error": error,
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}
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def parse_config(self, payload: Any) -> TrainingConfig:
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if not isinstance(payload, dict):
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raise ApiError(HTTPStatus.BAD_REQUEST, "请求体必须是 JSON 对象")
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task_id = payload.get("taskId")
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if task_id not in self.tasks:
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raise ApiError(HTTPStatus.BAD_REQUEST, f"不允许的训练任务：{task_id}")
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def integer(name: str, minimum: int, maximum: int) -> int:
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value = payload.get(name)
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if (
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isinstance(value, bool)
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or not isinstance(value, int)
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or not minimum <= value <= maximum
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):
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raise ApiError(HTTPStatus.BAD_REQUEST, f"{name} 必须在 {minimum}–{maximum} 之间")
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return value
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run_name = payload.get("runName", "web")
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if not isinstance(run_name, str) or not RUN_NAME.fullmatch(run_name):
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raise ApiError(
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HTTPStatus.BAD_REQUEST,
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"runName 只能包含字母、数字、点、下划线和连字符，最长 64 字符",
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)
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device = payload.get("device")
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if device not in ("cpu", "gpu"):
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raise ApiError(HTTPStatus.BAD_REQUEST, "device 必须是 cpu 或 gpu")
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raw_gpu_ids = payload.get("gpuIds", [])
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if not isinstance(raw_gpu_ids, list) or any(
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isinstance(value, bool) or not isinstance(value, int) or value < 0 or value > 255
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for value in raw_gpu_ids
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):
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raise ApiError(HTTPStatus.BAD_REQUEST, "gpuIds 必须是非负整数数组")
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if device == "gpu" and not raw_gpu_ids:
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raise ApiError(HTTPStatus.BAD_REQUEST, "GPU 训练至少需要一个 GPU 编号")
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wandb_mode = payload.get("wandbMode", "offline")
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if wandb_mode not in ("offline", "disabled", "online"):
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raise ApiError(HTTPStatus.BAD_REQUEST, "wandbMode 必须是 offline、disabled 或 online")
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preset_id = payload.get("rewardPresetId")
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reward_config = None
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if preset_id is not None:
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if not isinstance(preset_id, str) or not re.fullmatch(r"[0-9a-f]{32}", preset_id):
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raise ApiError(HTTPStatus.BAD_REQUEST, "rewardPresetId 格式无效")
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if self.preset_resolver is None:
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raise ApiError(HTTPStatus.BAD_REQUEST, "奖励 preset 服务未就绪")
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try:
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reward_config = self.preset_resolver(preset_id)
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except KeyError as error:
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raise ApiError(HTTPStatus.BAD_REQUEST, "奖励 preset 不存在") from error
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return TrainingConfig(
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task_id=task_id,
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num_envs=integer("numEnvs", 1, 16384),
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max_iterations=integer("maxIterations", 1, 1_000_000),
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seed=integer("seed", 0, 2_147_483_647),
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run_name=run_name,
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device=device,
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gpu_ids=raw_gpu_ids,
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wandb_mode=wandb_mode,
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reward_config=reward_config,
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)
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def start(self, payload: Any) -> dict[str, Any]:
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error = self.readiness_error()
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if error:
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raise ApiError(HTTPStatus.SERVICE_UNAVAILABLE, error)
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config = self.parse_config(payload)
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with self.lock:
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if self.active_job_id():
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raise ApiError(HTTPStatus.CONFLICT, "已有训练任务正在运行，请等待完成或先停止任务")
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while len(self.jobs) >= MAX_JOBS:
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completed = next(
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(job_id for job_id, job in self.jobs.items() if job.state not in ACTIVE_STATES),
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None,
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)
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if completed is None:
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raise ApiError(HTTPStatus.CONFLICT, "训练任务历史已满，请稍后重试")
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del self.jobs[completed]
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job = TrainingJob(id=uuid.uuid4().hex, config=config)
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owner = f"training:{job.id}"
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try:
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self.lease.acquire(owner)
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except ResourceBusyError as error:
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raise ApiError(HTTPStatus.CONFLICT, str(error)) from error
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self.jobs[job.id] = job
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try:
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threading.Thread(
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target=self._run, args=(job,), name=f"training-{job.id[:8]}", daemon=True
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).start()
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except Exception:
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self.jobs.pop(job.id, None)
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self.lease.release(owner)
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raise
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return job.public()
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def get(self, job_id: str) -> dict[str, Any]:
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with self.lock:
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job = self.jobs.get(job_id)
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if not job:
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raise ApiError(HTTPStatus.NOT_FOUND, "训练任务不存在或服务已重启")
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return job.public()
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def artifact(self, job_id: str) -> Path:
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with self.lock:
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job = self.jobs.get(job_id)
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if not job:
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raise ApiError(HTTPStatus.NOT_FOUND, "训练任务不存在或服务已重启")
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if not job.artifact or not job.artifact.is_file():
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raise ApiError(HTTPStatus.NOT_FOUND, "该训练任务尚未生成 policy.onnx")
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return job.artifact
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def cancel(self, job_id: str) -> dict[str, Any]:
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with self.lock:
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job = self.jobs.get(job_id)
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if not job:
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raise ApiError(HTTPStatus.NOT_FOUND, "训练任务不存在或服务已重启")
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if job.state not in ACTIVE_STATES:
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return job.public()
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job.cancel_requested = True
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job.message = "正在停止训练进程"
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process = job.process
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if process and process.poll() is None:
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with suppress(ProcessLookupError):
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os.killpg(process.pid, signal.SIGTERM)
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threading.Thread(target=self._kill_later, args=(process,), daemon=True).start()
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return self.get(job_id)
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@staticmethod
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def _kill_later(process: subprocess.Popen[str]) -> None:
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try:
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process.wait(timeout=5)
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except subprocess.TimeoutExpired:
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with suppress(ProcessLookupError):
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os.killpg(process.pid, signal.SIGKILL)
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def shutdown(self) -> None:
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"""同步停止活动训练，避免服务退出后遗留子进程。"""
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active = self.active_job_id()
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if not active:
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return
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self.cancel(active)
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with self.lock:
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process = self.jobs[active].process
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if process and process.poll() is None:
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try:
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process.wait(timeout=6)
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except subprocess.TimeoutExpired:
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with suppress(ProcessLookupError):
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os.killpg(process.pid, signal.SIGKILL)
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process.wait(timeout=2)
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def command_for(self, config: TrainingConfig) -> list[str]:
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command = [
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self.python,
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"-u",
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"scripts/train.py",
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config.task_id,
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f"--env.scene.num-envs={config.num_envs}",
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f"--agent.max-iterations={config.max_iterations}",
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f"--agent.seed={config.seed}",
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f"--agent.run-name={config.run_name}",
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]
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if config.reward_config is not None:
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command.extend(
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(
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"--reward-config-json",
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json.dumps(config.reward_config, ensure_ascii=False, separators=(",", ":")),
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)
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)
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if config.device == "cpu":
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command.extend(("--gpu-ids", "None"))
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else:
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# mjlab.TYRO_FLAGS 对 Union[list[int], Literal["all"], None] 使用 JSON 风格
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# list token；传成多个独立参数会被解析为错误的 Union 分支。
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command.extend(("--gpu-ids", json.dumps(config.gpu_ids, separators=(",", ":"))))
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return command
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def _update_from_log(self, job: TrainingJob, raw_line: str) -> None:
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line = ANSI_ESCAPE.sub("", raw_line).strip()
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if not line:
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return
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with self.lock:
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job.logs.append(line[-4000:])
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for pattern in ITERATION_PATTERNS:
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match = pattern.search(line)
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if match:
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job.iteration = min(
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job.config.max_iterations, max(job.iteration, int(match.group(1)))
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)
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break
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job.message = line[-240:]
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def _artifact_snapshot(self) -> dict[Path, int]:
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root = self.trainer_root / "logs" / "rsl_rl"
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if not root.is_dir():
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return {}
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return {
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path: path.stat().st_mtime_ns for path in root.glob("**/policy.onnx") if path.is_file()
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}
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def _find_artifact(self, before: dict[Path, int]) -> Path | None:
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root = self.trainer_root / "logs" / "rsl_rl"
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if not root.is_dir():
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return None
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changed = [
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path
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for path in root.glob("**/policy.onnx")
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if path.is_file() and before.get(path) != path.stat().st_mtime_ns
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]
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return max(changed, key=lambda path: path.stat().st_mtime_ns) if changed else None
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def _run(self, job: TrainingJob) -> None:
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before = self._artifact_snapshot()
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command = self.command_for(job.config)
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environment = os.environ.copy()
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# 默认离线记录，保留本地 W&B 指标但不要求 API Key；只有前端明确选择
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# online 时才允许 wandb 发起登录/联网。
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environment["WANDB_MODE"] = job.config.wandb_mode
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environment["WANDB_SILENT"] = "true"
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try:
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# Popen 与 process 登记必须和取消检查处于同一个临界区：cancel() 要么在
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# 创建前标记取消，要么在创建后取得进程并终止，不能落入二者之间。
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with self.lock:
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if job.cancel_requested:
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job.state, job.ended_at, job.message = "cancelled", now_iso(), "训练已取消"
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return
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process = subprocess.Popen(
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command,
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cwd=self.trainer_root,
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env=environment,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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text=True,
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encoding="utf-8",
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errors="replace",
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bufsize=1,
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start_new_session=True,
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)
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job.process = process
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job.state, job.started_at, job.message = (
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"running",
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now_iso(),
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"本地训练进程已启动",
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)
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assert process.stdout is not None
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try:
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for line in process.stdout:
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self._update_from_log(job, line)
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finally:
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process.stdout.close()
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return_code = process.wait()
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artifact = self._find_artifact(before)
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with self.lock:
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job.process = None
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job.ended_at = now_iso()
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if job.cancel_requested:
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job.state, job.message = "cancelled", "训练已由用户取消"
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elif return_code != 0:
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job.state, job.message = "failed", f"训练进程退出，返回码 {return_code}"
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elif artifact is None:
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job.state, job.message = "failed", "训练结束，但没有找到本次生成的 policy.onnx"
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else:
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job.state, job.artifact = "succeeded", artifact
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job.iteration = job.config.max_iterations
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job.message = f"训练完成：{artifact.relative_to(self.trainer_root)}"
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except Exception as error: # 服务必须保留错误供前端诊断。
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with self.lock:
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job.process = None
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job.ended_at = now_iso()
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||
job.state = "cancelled" if job.cancel_requested else "failed"
|
||
job.message = f"启动训练失败：{error}"
|
||
job.logs.append(job.message)
|
||
finally:
|
||
self.lease.release(f"training:{job.id}")
|
||
|
||
|
||
class TrainingRequestHandler(BaseHTTPRequestHandler):
|
||
manager: TrainingManager
|
||
tuning_manager: TuningManager
|
||
allowed_origins: tuple[str, ...] = ()
|
||
access_token = ""
|
||
server_version = "MuJoCoLocalTraining/0.4"
|
||
|
||
def log_message(self, format: str, *args: Any) -> None:
|
||
sys.stderr.write(f"[{self.log_date_time_string()}] {format % args}\n")
|
||
|
||
def _origin_allowed(self) -> bool:
|
||
origin = self.headers.get("Origin")
|
||
return (
|
||
origin is None or bool(LOCAL_ORIGIN.fullmatch(origin)) or origin in self.allowed_origins
|
||
)
|
||
|
||
def _host_allowed(self) -> bool:
|
||
host = self.headers.get("Host", "")
|
||
return bool(LOCAL_HOST.fullmatch(host))
|
||
|
||
def _authorized(self) -> bool:
|
||
authorization = self.headers.get("Authorization", "")
|
||
prefix = "Bearer "
|
||
return authorization.startswith(prefix) and hmac.compare_digest(
|
||
authorization[len(prefix) :], self.access_token
|
||
)
|
||
|
||
def _cors(self) -> None:
|
||
origin = self.headers.get("Origin")
|
||
if origin and self._origin_allowed():
|
||
self.send_header("Access-Control-Allow-Origin", origin)
|
||
self.send_header("Vary", "Origin")
|
||
|
||
def _json(self, status: int, payload: Any) -> None:
|
||
body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
|
||
self.send_response(status)
|
||
self._cors()
|
||
self.send_header("Content-Type", "application/json; charset=utf-8")
|
||
self.send_header("Content-Length", str(len(body)))
|
||
self.send_header("Cache-Control", "no-store")
|
||
self.end_headers()
|
||
self.wfile.write(body)
|
||
|
||
def _error(self, error: Exception) -> None:
|
||
if isinstance(error, ApiError):
|
||
self._json(error.status, {"error": str(error)})
|
||
elif isinstance(error, KeyError):
|
||
self._json(HTTPStatus.NOT_FOUND, {"error": "调参 session、trial 或 proposal 不存在"})
|
||
elif isinstance(error, ResourceBusyError):
|
||
self._json(HTTPStatus.CONFLICT, {"error": str(error)})
|
||
elif isinstance(error, TuningError):
|
||
self._json(HTTPStatus.BAD_REQUEST, {"error": str(error)})
|
||
else:
|
||
self._json(
|
||
HTTPStatus.INTERNAL_SERVER_ERROR, {"error": f"本地训练服务内部错误：{error}"}
|
||
)
|
||
|
||
def _ensure_origin(self) -> None:
|
||
if not self._host_allowed():
|
||
raise ApiError(HTTPStatus.FORBIDDEN, "不允许的 Host")
|
||
if not self._origin_allowed():
|
||
raise ApiError(HTTPStatus.FORBIDDEN, "不允许的浏览器来源")
|
||
|
||
def _ensure_request(self) -> None:
|
||
self._ensure_origin()
|
||
if not self._authorized():
|
||
raise ApiError(HTTPStatus.UNAUTHORIZED, "训练服务访问令牌无效")
|
||
|
||
def _payload(self) -> Any:
|
||
try:
|
||
length = int(self.headers.get("Content-Length", "0"))
|
||
except ValueError as error:
|
||
raise ApiError(HTTPStatus.BAD_REQUEST, "Content-Length 无效") from error
|
||
if length <= 0 or length > 32 * 1024:
|
||
raise ApiError(
|
||
HTTPStatus.REQUEST_ENTITY_TOO_LARGE, "训练请求体不能为空且不能超过 32 KiB"
|
||
)
|
||
try:
|
||
return json.loads(self.rfile.read(length))
|
||
except (UnicodeDecodeError, json.JSONDecodeError) as error:
|
||
raise ApiError(HTTPStatus.BAD_REQUEST, "训练请求不是有效 JSON") from error
|
||
|
||
@staticmethod
|
||
def _route(path: str) -> tuple[str | None, bool]:
|
||
match = re.fullmatch(r"/api/training/jobs/([0-9a-f]{32})(/artifacts/policy\.onnx)?", path)
|
||
return (unquote(match.group(1)), bool(match.group(2))) if match else (None, False)
|
||
|
||
def _send_file(self, file_path: Path, filename: str) -> None:
|
||
size = file_path.stat().st_size
|
||
self.send_response(HTTPStatus.OK)
|
||
self._cors()
|
||
self.send_header("Content-Type", "application/octet-stream")
|
||
self.send_header("Content-Disposition", f'attachment; filename="{filename}"')
|
||
self.send_header("Content-Length", str(size))
|
||
self.send_header("Cache-Control", "no-store")
|
||
self.end_headers()
|
||
with file_path.open("rb") as source:
|
||
shutil.copyfileobj(source, self.wfile)
|
||
|
||
def do_OPTIONS(self) -> None:
|
||
try:
|
||
self._ensure_origin()
|
||
self.send_response(HTTPStatus.NO_CONTENT)
|
||
self._cors()
|
||
self.send_header("Access-Control-Allow-Methods", "GET, POST, DELETE, OPTIONS")
|
||
self.send_header("Access-Control-Allow-Headers", "Authorization, Content-Type")
|
||
self.send_header("Access-Control-Max-Age", "600")
|
||
self.end_headers()
|
||
except Exception as error:
|
||
self._error(error)
|
||
|
||
def do_GET(self) -> None:
|
||
try:
|
||
self._ensure_request()
|
||
parsed = urlsplit(self.path)
|
||
path = parsed.path
|
||
if path == "/api/training/health":
|
||
health = self.manager.health()
|
||
health["tuning"] = self.tuning_manager.capability()
|
||
health["resourceOwner"] = self.manager.lease.public()
|
||
self._json(HTTPStatus.OK, health)
|
||
return
|
||
if path == "/api/tuning/capabilities":
|
||
self._json(HTTPStatus.OK, self.tuning_manager.capability())
|
||
return
|
||
if path == "/api/tuning/sessions":
|
||
self._json(HTTPStatus.OK, {"sessions": self.tuning_manager.list()})
|
||
return
|
||
if path == "/api/tuning/presets":
|
||
self._json(HTTPStatus.OK, {"presets": self.tuning_manager.storage.list_presets()})
|
||
return
|
||
match = re.fullmatch(r"/api/tuning/sessions/([0-9a-f]{32})", path)
|
||
if match:
|
||
self._json(HTTPStatus.OK, self.tuning_manager.detail(match.group(1)))
|
||
return
|
||
match = re.fullmatch(
|
||
r"/api/tuning/sessions/([0-9a-f]{32})/trials/([0-9a-f]{32})/metrics", path
|
||
)
|
||
if match:
|
||
query = parse_qs(parsed.query)
|
||
tags = [tag for value in query.get("tags", []) for tag in value.split(",") if tag]
|
||
try:
|
||
max_points = int(query.get("maxPoints", ["1000"])[0])
|
||
except ValueError as error:
|
||
raise TuningError("maxPoints 必须是整数") from error
|
||
if not 10 <= max_points <= 5000:
|
||
raise TuningError("maxPoints 必须在 10–5000 之间")
|
||
self._json(
|
||
HTTPStatus.OK,
|
||
self.tuning_manager.metrics(
|
||
match.group(1), match.group(2), tags or None, max_points
|
||
),
|
||
)
|
||
return
|
||
match = re.fullmatch(
|
||
r"/api/tuning/sessions/([0-9a-f]{32})/artifacts/best/policy\.onnx", path
|
||
)
|
||
if match:
|
||
self._send_file(self.tuning_manager.best_artifact(match.group(1)), "policy.onnx")
|
||
return
|
||
job_id, artifact = self._route(path)
|
||
if not job_id:
|
||
raise ApiError(HTTPStatus.NOT_FOUND, "接口不存在")
|
||
if artifact:
|
||
self._send_file(self.manager.artifact(job_id), "policy.onnx")
|
||
else:
|
||
self._json(HTTPStatus.OK, self.manager.get(job_id))
|
||
except Exception as error:
|
||
self._error(error)
|
||
|
||
def do_POST(self) -> None:
|
||
try:
|
||
self._ensure_request()
|
||
path = urlsplit(self.path).path
|
||
if path == "/api/training/jobs":
|
||
self._json(HTTPStatus.ACCEPTED, self.manager.start(self._payload()))
|
||
return
|
||
if path == "/api/tuning/agent/test":
|
||
self._json(HTTPStatus.OK, self.tuning_manager.test_agent())
|
||
return
|
||
if path == "/api/tuning/sessions":
|
||
self._json(HTTPStatus.ACCEPTED, self.tuning_manager.create(self._payload()))
|
||
return
|
||
match = re.fullmatch(r"/api/tuning/sessions/([0-9a-f]{32})/(pause|resume)", path)
|
||
if match:
|
||
action = (
|
||
self.tuning_manager.pause
|
||
if match.group(2) == "pause"
|
||
else self.tuning_manager.resume
|
||
)
|
||
self._json(HTTPStatus.ACCEPTED, action(match.group(1)))
|
||
return
|
||
match = re.fullmatch(
|
||
r"/api/tuning/sessions/([0-9a-f]{32})/proposals/([0-9a-f]{32})/(approve|reject)",
|
||
path,
|
||
)
|
||
if match:
|
||
action = (
|
||
self.tuning_manager.approve
|
||
if match.group(3) == "approve"
|
||
else self.tuning_manager.reject
|
||
)
|
||
self._json(
|
||
HTTPStatus.ACCEPTED, action(match.group(1), match.group(2), self._payload())
|
||
)
|
||
return
|
||
raise ApiError(HTTPStatus.NOT_FOUND, "接口不存在")
|
||
except Exception as error:
|
||
self._error(error)
|
||
|
||
def do_DELETE(self) -> None:
|
||
try:
|
||
self._ensure_request()
|
||
path = urlsplit(self.path).path
|
||
match = re.fullmatch(r"/api/tuning/sessions/([0-9a-f]{32})", path)
|
||
if match:
|
||
self._json(HTTPStatus.ACCEPTED, self.tuning_manager.cancel(match.group(1)))
|
||
return
|
||
job_id, artifact = self._route(path)
|
||
if not job_id or artifact:
|
||
raise ApiError(HTTPStatus.NOT_FOUND, "接口不存在")
|
||
self._json(HTTPStatus.ACCEPTED, self.manager.cancel(job_id))
|
||
except Exception as error:
|
||
self._error(error)
|
||
|
||
|
||
def default_trainer_root() -> Path:
|
||
configured = os.environ.get("UNITREE_RL_MJLAB_ROOT")
|
||
if configured:
|
||
return Path(configured)
|
||
return Path(__file__).resolve().parent / "rl"
|
||
|
||
|
||
def parse_args() -> argparse.Namespace:
|
||
parser = argparse.ArgumentParser(description="MuJoCo Web 平台本地强化学习训练服务")
|
||
parser.add_argument(
|
||
"--host",
|
||
default="127.0.0.1",
|
||
choices=("127.0.0.1", "localhost"),
|
||
help="仅允许绑定本机回环地址",
|
||
)
|
||
parser.add_argument("--port", type=int, default=8765)
|
||
parser.add_argument(
|
||
"--trainer-root",
|
||
type=Path,
|
||
default=default_trainer_root(),
|
||
help="训练工程目录；默认使用仓库内置的 Go2 训练器",
|
||
)
|
||
parser.add_argument(
|
||
"--trainer-python", default=sys.executable, help="已安装 mjlab/torch 的 Python 解释器"
|
||
)
|
||
parser.add_argument(
|
||
"--tuning-data-root",
|
||
type=Path,
|
||
default=None,
|
||
help="调参 SQLite 与 trial 产物目录；默认位于训练工程 logs/auto_tuning",
|
||
)
|
||
parser.add_argument(
|
||
"--task", action="append", dest="tasks", help="允许前端启动的任务 ID；可重复"
|
||
)
|
||
parser.add_argument(
|
||
"--allow-origin", action="append", default=[], help="额外允许的前端 Origin；可重复"
|
||
)
|
||
parser.add_argument(
|
||
"--token",
|
||
default=os.environ.get("MUJOCO_TRAINING_TOKEN"),
|
||
help="访问令牌；默认随机生成，也可通过 MUJOCO_TRAINING_TOKEN 设置",
|
||
)
|
||
return parser.parse_args()
|
||
|
||
|
||
def main() -> None:
|
||
args = parse_args()
|
||
token = args.token or secrets.token_urlsafe(24)
|
||
if len(token) < 16:
|
||
raise SystemExit("训练服务访问令牌至少需要 16 个字符")
|
||
lease = GpuLease()
|
||
manager = TrainingManager(
|
||
args.trainer_root,
|
||
args.trainer_python,
|
||
tuple(args.tasks or DEFAULT_TASKS),
|
||
lease=lease,
|
||
)
|
||
tuning_root = args.tuning_data_root or (Path(args.trainer_root) / "logs" / "auto_tuning")
|
||
tuning_manager = TuningManager(args.trainer_root, args.trainer_python, tuning_root, lease)
|
||
manager.preset_resolver = tuning_manager.preset_config
|
||
TrainingRequestHandler.manager = manager
|
||
TrainingRequestHandler.tuning_manager = tuning_manager
|
||
TrainingRequestHandler.allowed_origins = tuple(args.allow_origin)
|
||
TrainingRequestHandler.access_token = token
|
||
server = ThreadingHTTPServer((args.host, args.port), TrainingRequestHandler)
|
||
print(f"本地训练服务：http://{args.host}:{args.port}")
|
||
print(f"访问令牌：{token}")
|
||
print(f"训练工程：{manager.trainer_root}")
|
||
print(f"Python：{manager.python}")
|
||
if manager.readiness_error():
|
||
print(f"警告：{manager.readiness_error()}", file=sys.stderr)
|
||
signal.signal(signal.SIGTERM, termination_signal_handler)
|
||
try:
|
||
server.serve_forever()
|
||
except KeyboardInterrupt:
|
||
print("\n正在停止本地训练服务…")
|
||
finally:
|
||
tuning_manager.shutdown()
|
||
manager.shutdown()
|
||
server.server_close()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|