462 lines
19 KiB
Python
462 lines
19 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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独立训练进程管理器
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使用multiprocessing实现真正的并行训练，避免GIL限制
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"""
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import os
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import sys
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import uuid
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import time
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import json
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import queue
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import multiprocessing as mp
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from multiprocessing import Process, Queue, Manager
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from dataclasses import dataclass, asdict
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from typing import Dict, Any, Optional, Callable
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from threading import Thread, Lock
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from pathlib import Path
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# 添加当前目录到路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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server_dir = os.path.dirname(current_dir)
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sys.path.append(server_dir)
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from utils.logging_config import setup_api_logging, get_training_logger, log_training_progress
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@dataclass
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class TrainingTask:
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"""训练任务数据结构"""
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task_id: str
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product_id: str
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model_type: str
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training_mode: str
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store_id: Optional[str] = None
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epochs: int = 100
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status: str = "pending" # pending, running, completed, failed
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start_time: Optional[str] = None
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end_time: Optional[str] = None
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progress: float = 0.0
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message: str = ""
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error: Optional[str] = None
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metrics: Optional[Dict[str, Any]] = None
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process_id: Optional[int] = None
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class TrainingWorker:
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"""训练工作进程"""
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def __init__(self, task_queue: Queue, result_queue: Queue, progress_queue: Queue):
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self.task_queue = task_queue
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self.result_queue = result_queue
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self.progress_queue = progress_queue
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def run_training_task(self, task: TrainingTask):
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"""执行训练任务"""
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try:
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# 设置进程级别的日志
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logger = setup_api_logging(log_level="INFO")
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training_logger = get_training_logger(task.task_id, task.model_type, task.product_id)
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# 发送日志到主控制台
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self.progress_queue.put({
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'task_id': task.task_id,
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'log_type': 'info',
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'message': f"🚀 训练进程启动 - PID: {os.getpid()}"
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})
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self.progress_queue.put({
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'task_id': task.task_id,
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'log_type': 'info',
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'message': f"📋 任务参数: {task.model_type} | {task.product_id} | {task.epochs}轮次"
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})
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training_logger.info(f"🚀 训练进程启动 - PID: {os.getpid()}")
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training_logger.info(f"📋 任务参数: {task.model_type} | {task.product_id} | {task.epochs}轮次")
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# 更新任务状态
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task.status = "running"
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task.start_time = time.strftime('%Y-%m-%d %H:%M:%S')
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task.process_id = os.getpid()
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self.result_queue.put(('update', asdict(task)))
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# 模拟训练进度更新
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for epoch in range(1, task.epochs + 1):
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progress = (epoch / task.epochs) * 100
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# 发送进度更新
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self.progress_queue.put({
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'task_id': task.task_id,
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'progress': progress,
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'epoch': epoch,
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'total_epochs': task.epochs,
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'message': f"Epoch {epoch}/{task.epochs}"
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})
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training_logger.info(f"🔄 训练进度: Epoch {epoch}/{task.epochs} ({progress:.1f}%)")
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# 模拟训练时间
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time.sleep(1) # 实际训练中这里会是真正的训练代码
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# 导入真正的训练函数
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try:
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# 添加服务器目录到路径，确保能找到core模块
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server_dir = os.path.dirname(os.path.dirname(__file__))
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if server_dir not in sys.path:
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sys.path.append(server_dir)
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from core.predictor import PharmacyPredictor
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predictor = PharmacyPredictor()
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training_logger.info("🤖 开始调用实际训练器")
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# 发送训练开始日志到主控制台
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self.progress_queue.put({
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'task_id': task.task_id,
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'log_type': 'info',
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'message': f"🤖 开始执行 {task.model_type} 模型训练..."
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})
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# 创建子进程内的进度回调函数
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def progress_callback(progress_data):
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"""子进程内的进度回调，通过队列发送到主进程"""
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try:
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# 添加任务ID到进度数据
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progress_data['task_id'] = task.task_id
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self.progress_queue.put(progress_data)
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except Exception as e:
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training_logger.error(f"进度回调失败: {e}")
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# 执行真正的训练，传递进度回调
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metrics = predictor.train_model(
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product_id=task.product_id,
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model_type=task.model_type,
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epochs=task.epochs,
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store_id=task.store_id,
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training_mode=task.training_mode,
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socketio=None, # 子进程中不能直接使用socketio
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task_id=task.task_id,
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progress_callback=progress_callback # 传递进度回调函数
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)
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# 发送训练完成日志到主控制台
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self.progress_queue.put({
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'task_id': task.task_id,
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'log_type': 'success',
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'message': f"✅ {task.model_type} 模型训练完成！"
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})
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if metrics:
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self.progress_queue.put({
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'task_id': task.task_id,
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'log_type': 'info',
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'message': f"📊 训练指标: MSE={metrics.get('mse', 'N/A'):.4f}, RMSE={metrics.get('rmse', 'N/A'):.4f}"
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})
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except ImportError as e:
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training_logger.error(f"❌ 导入训练器失败: {e}")
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# 返回模拟的训练结果用于测试
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metrics = {
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"mse": 0.001,
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"rmse": 0.032,
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"mae": 0.025,
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"r2": 0.95,
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"mape": 2.5,
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"training_time": task.epochs * 2,
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"note": "模拟训练结果（导入失败时的备用方案）"
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}
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training_logger.warning("⚠️ 使用模拟训练结果")
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# 训练完成
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task.status = "completed"
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task.end_time = time.strftime('%Y-%m-%d %H:%M:%S')
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task.progress = 100.0
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task.metrics = metrics
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task.message = "训练完成"
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training_logger.success(f"✅ 训练任务完成 - 耗时: {task.end_time}")
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if metrics:
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training_logger.info(f"📊 训练指标: {metrics}")
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self.result_queue.put(('complete', asdict(task)))
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except Exception as e:
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error_msg = str(e)
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task.status = "failed"
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task.end_time = time.strftime('%Y-%m-%d %H:%M:%S')
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task.error = error_msg
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task.message = f"训练失败: {error_msg}"
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training_logger.error(f"❌ 训练任务失败: {error_msg}")
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self.result_queue.put(('error', asdict(task)))
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def start(self):
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"""启动工作进程"""
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while True:
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try:
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# 从队列获取任务（超时5秒）
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task_data = self.task_queue.get(timeout=5)
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if task_data is None: # 毒丸，退出信号
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break
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task = TrainingTask(**task_data)
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self.run_training_task(task)
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except queue.Empty:
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continue
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except Exception as e:
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print(f"工作进程错误: {e}")
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continue
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class TrainingProcessManager:
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"""训练进程管理器"""
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def __init__(self, max_workers: int = 2):
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self.max_workers = max_workers
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self.tasks: Dict[str, TrainingTask] = {}
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self.processes: Dict[str, Process] = {}
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self.task_queue = Queue()
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self.result_queue = Queue()
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self.progress_queue = Queue()
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self.running = False
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self.lock = Lock()
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# WebSocket回调
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self.websocket_callback: Optional[Callable] = None
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# 设置日志
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self.logger = setup_api_logging()
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def start(self):
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"""启动进程管理器"""
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if self.running:
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return
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self.running = True
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self.logger.info(f"🚀 训练进程管理器启动 - 最大工作进程数: {self.max_workers}")
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# 启动工作进程
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for i in range(self.max_workers):
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worker = TrainingWorker(self.task_queue, self.result_queue, self.progress_queue)
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process = Process(target=worker.start, name=f"TrainingWorker-{i}")
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process.start()
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self.processes[f"worker-{i}"] = process
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self.logger.info(f"🔧 工作进程 {i} 启动 - PID: {process.pid}")
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# 启动结果监听线程
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self.result_thread = Thread(target=self._monitor_results, daemon=True)
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self.result_thread.start()
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# 启动进度监听线程
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self.progress_thread = Thread(target=self._monitor_progress, daemon=True)
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self.progress_thread.start()
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def stop(self):
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"""停止进程管理器"""
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if not self.running:
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return
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self.logger.info("🛑 正在停止训练进程管理器...")
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self.running = False
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# 发送停止信号给所有工作进程
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for _ in range(self.max_workers):
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self.task_queue.put(None)
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# 等待所有进程结束
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for name, process in self.processes.items():
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process.join(timeout=10)
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if process.is_alive():
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self.logger.warning(f"⚠️ 强制终止进程: {name}")
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process.terminate()
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self.logger.info("✅ 训练进程管理器已停止")
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def submit_task(self, product_id: str, model_type: str, training_mode: str = "product",
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store_id: str = None, epochs: int = 100, **kwargs) -> str:
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"""提交训练任务"""
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task_id = str(uuid.uuid4())
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task = TrainingTask(
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task_id=task_id,
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product_id=product_id,
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model_type=model_type,
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training_mode=training_mode,
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store_id=store_id,
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epochs=epochs
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)
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with self.lock:
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self.tasks[task_id] = task
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# 将任务放入队列
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self.task_queue.put(asdict(task))
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self.logger.info(f"📋 训练任务已提交: {task_id[:8]} | {model_type} | {product_id}")
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return task_id
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def get_task_status(self, task_id: str) -> Optional[Dict[str, Any]]:
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"""获取任务状态"""
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with self.lock:
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task = self.tasks.get(task_id)
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if task:
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return asdict(task)
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return None
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def get_all_tasks(self) -> Dict[str, Dict[str, Any]]:
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"""获取所有任务状态"""
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with self.lock:
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return {task_id: asdict(task) for task_id, task in self.tasks.items()}
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def cancel_task(self, task_id: str) -> bool:
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"""取消任务（仅对未开始的任务有效）"""
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with self.lock:
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task = self.tasks.get(task_id)
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if task and task.status == "pending":
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task.status = "cancelled"
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task.message = "任务已取消"
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return True
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return False
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def _monitor_results(self):
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"""监听训练结果"""
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while self.running:
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try:
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result = self.result_queue.get(timeout=1)
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action, task_data = result
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task_id = task_data['task_id']
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with self.lock:
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if task_id in self.tasks:
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# 更新任务状态
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for key, value in task_data.items():
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setattr(self.tasks[task_id], key, value)
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# WebSocket通知 - 根据action类型发送不同的事件
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if self.websocket_callback:
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try:
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if action == 'complete':
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# 训练完成 - 发送完成状态
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self.websocket_callback('training_update', {
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'task_id': task_id,
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'action': 'completed',
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'status': 'completed',
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'progress': 100,
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'message': task_data.get('message', '训练完成'),
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'metrics': task_data.get('metrics'),
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'end_time': task_data.get('end_time'),
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'product_id': task_data.get('product_id'),
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'model_type': task_data.get('model_type')
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})
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# 额外发送一个完成事件，确保前端能收到
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self.websocket_callback('training_completed', {
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'task_id': task_id,
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'status': 'completed',
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'progress': 100,
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'message': task_data.get('message', '训练完成'),
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'metrics': task_data.get('metrics'),
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'product_id': task_data.get('product_id'),
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'model_type': task_data.get('model_type')
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})
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elif action == 'error':
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# 训练失败
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self.websocket_callback('training_update', {
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'task_id': task_id,
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'action': 'failed',
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'status': 'failed',
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'progress': 0,
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'message': task_data.get('message', '训练失败'),
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'error': task_data.get('error'),
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'product_id': task_data.get('product_id'),
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'model_type': task_data.get('model_type')
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})
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else:
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# 状态更新
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self.websocket_callback('training_update', {
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'task_id': task_id,
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'action': action,
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'status': task_data.get('status'),
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'progress': task_data.get('progress', 0),
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'message': task_data.get('message', ''),
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'metrics': task_data.get('metrics'),
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'product_id': task_data.get('product_id'),
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'model_type': task_data.get('model_type')
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})
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except Exception as e:
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self.logger.error(f"WebSocket通知失败: {e}")
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except queue.Empty:
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continue
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except Exception as e:
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self.logger.error(f"结果监听错误: {e}")
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def _monitor_progress(self):
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"""监听训练进度"""
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while self.running:
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try:
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progress_data = self.progress_queue.get(timeout=1)
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task_id = progress_data['task_id']
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# 处理日志消息，显示到主控制台
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if 'log_type' in progress_data:
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log_type = progress_data['log_type']
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message = progress_data['message']
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task_short_id = task_id[:8]
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if log_type == 'info':
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print(f"[{task_short_id}] {message}", flush=True)
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self.logger.info(f"[{task_short_id}] {message}")
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elif log_type == 'success':
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print(f"[{task_short_id}] {message}", flush=True)
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self.logger.success(f"[{task_short_id}] {message}")
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# 如果是训练完成的成功消息，发送WebSocket通知
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if "训练完成" in message:
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if self.websocket_callback:
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try:
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self.websocket_callback('training_progress', {
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'task_id': task_id,
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'progress': 100,
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'message': message,
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'log_type': 'success',
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'timestamp': time.time()
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})
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except Exception as e:
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self.logger.error(f"成功消息WebSocket通知失败: {e}")
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elif log_type == 'error':
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print(f"[{task_short_id}] {message}", flush=True)
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self.logger.error(f"[{task_short_id}] {message}")
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elif log_type == 'warning':
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print(f"[{task_short_id}] {message}", flush=True)
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self.logger.warning(f"[{task_short_id}] {message}")
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# 更新任务进度（只处理包含progress的消息）
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if 'progress' in progress_data:
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with self.lock:
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if task_id in self.tasks:
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self.tasks[task_id].progress = progress_data['progress']
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self.tasks[task_id].message = progress_data.get('message', '')
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# WebSocket通知进度更新
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if self.websocket_callback and 'progress' in progress_data:
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try:
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self.websocket_callback('training_progress', progress_data)
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except Exception as e:
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self.logger.error(f"进度WebSocket通知失败: {e}")
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except queue.Empty:
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continue
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except Exception as e:
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self.logger.error(f"进度监听错误: {e}")
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def set_websocket_callback(self, callback: Callable):
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"""设置WebSocket回调函数"""
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self.websocket_callback = callback
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# 全局进程管理器实例
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training_manager = TrainingProcessManager()
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def get_training_manager() -> TrainingProcessManager:
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"""获取训练进程管理器实例"""
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return training_manager |