347 lines
13 KiB
Python
347 lines
13 KiB
Python
import psutil
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import GPUtil
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import platform
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from datetime import datetime
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from typing import Dict, List, Optional
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import logging
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from pathlib import Path
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import time
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import mlflow
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from mlflow.tracking import MlflowClient
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import pandas as pd
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class SystemMonitor:
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"""系统资源监控类"""
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def __init__(self):
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"""初始化系统监控器"""
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self.logger = logging.getLogger(__name__)
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self._setup_logging()
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self.mlflow_client = MlflowClient()
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def _setup_logging(self):
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"""设置日志"""
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log_dir = Path('.log')
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log_dir.mkdir(exist_ok=True)
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file_handler = logging.FileHandler(
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log_dir / f'system_monitor_{datetime.now():%Y%m%d_%H%M%S}.log'
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)
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file_handler.setFormatter(
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logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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)
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self.logger.addHandler(file_handler)
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self.logger.setLevel(logging.INFO)
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def _get_gpu_info(self) -> List[Dict]:
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"""获取GPU信息"""
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try:
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gpus = GPUtil.getGPUs()
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gpu_info = []
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for gpu in gpus:
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# 获取GPU进程信息
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processes = []
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# if gpu.processes:
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# for proc in gpu.processes:
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# process = psutil.Process(proc.pid)
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# processes.append({
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# 'pid': proc.pid,
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# 'name': process.name(),
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# 'memory': proc.gpu_memory # MB
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# })
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gpu_info.append({
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'id': gpu.id,
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'name': gpu.name,
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'memory': {
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'total': gpu.memoryTotal, # MB
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'used': gpu.memoryUsed, # MB
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'free': gpu.memoryFree # MB
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},
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'utilization': {
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'gpu': gpu.load * 100, # %
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'memory': gpu.memoryUtil * 100 # %
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},
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'temperature': gpu.temperature, # °C
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'power': {
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'draw': gpu.powerDraw if hasattr(gpu, 'powerDraw') else None, # W
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'limit': gpu.powerLimit if hasattr(gpu, 'powerLimit') else None # W
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},
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'processes': processes
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})
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return gpu_info
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except Exception as e:
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self.logger.error(f"获取GPU信息失败: {str(e)}")
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return []
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def _get_cpu_info(self) -> Dict:
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"""获取CPU信息"""
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try:
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cpu_freq = psutil.cpu_freq()
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cpu_temp = psutil.sensors_temperatures().get('coretemp', [None])[0]
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return {
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'count': {
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'physical': psutil.cpu_count(logical=False),
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'logical': psutil.cpu_count(logical=True)
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},
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'utilization': psutil.cpu_percent(interval=1), # %
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'frequency': {
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'current': cpu_freq.current / 1000 if cpu_freq else None, # GHz
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'min': cpu_freq.min / 1000 if cpu_freq else None, # GHz
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'max': cpu_freq.max / 1000 if cpu_freq else None # GHz
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},
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'temperature': cpu_temp.current if cpu_temp else None, # °C
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'memory': self._get_memory_info(),
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'swap': self._get_swap_info()
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}
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except Exception as e:
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self.logger.error(f"获取CPU信息失败: {str(e)}")
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return {}
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def _get_memory_info(self) -> Dict:
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"""获取内存信息"""
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try:
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mem = psutil.virtual_memory()
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return {
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'total': mem.total // (1024 * 1024), # MB
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'used': mem.used // (1024 * 1024), # MB
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'free': mem.free // (1024 * 1024), # MB
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'percent': mem.percent # %
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}
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except Exception as e:
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self.logger.error(f"获取内存信息失败: {str(e)}")
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return {}
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def _get_swap_info(self) -> Dict:
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"""获取交换内存信息"""
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try:
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swap = psutil.swap_memory()
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return {
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'total': swap.total // (1024 * 1024), # MB
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'used': swap.used // (1024 * 1024), # MB
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'free': swap.free // (1024 * 1024), # MB
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'percent': swap.percent # %
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}
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except Exception as e:
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self.logger.error(f"获取交换内存信息失败: {str(e)}")
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return {}
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def _get_disk_info(self) -> Dict:
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"""获取磁盘信息"""
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try:
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disk_info = {}
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for partition in psutil.disk_partitions():
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try:
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usage = psutil.disk_usage(partition.mountpoint)
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disk_info[partition.mountpoint] = {
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'total': usage.total // (1024 * 1024), # MB
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'used': usage.used // (1024 * 1024), # MB
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'free': usage.free // (1024 * 1024), # MB
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'percent': usage.percent # %
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}
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except (PermissionError, OSError):
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continue
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return disk_info
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except Exception as e:
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self.logger.error(f"获取磁盘信息失败: {str(e)}")
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return {}
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def _get_process_info(self) -> Dict:
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"""获取进程信息"""
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try:
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processes = {
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'total': len(psutil.pids()),
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'running': 0,
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'sleeping': 0
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}
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for proc in psutil.process_iter(['status']):
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try:
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status = proc.info['status']
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if status == 'running':
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processes['running'] += 1
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elif status == 'sleeping':
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processes['sleeping'] += 1
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except (psutil.NoSuchProcess, psutil.AccessDenied):
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continue
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return processes
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except Exception as e:
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self.logger.error(f"获取进程信息失败: {str(e)}")
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return {}
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def get_system_resources(self) -> Dict:
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"""
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获取系统资源使用情况
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Returns:
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系统资源信息
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"""
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try:
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resources = {
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'gpu': self._get_gpu_info(),
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'cpu': self._get_cpu_info(),
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'disk': self._get_disk_info(),
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'processes': self._get_process_info()
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}
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self.logger.info("成功获取系统资源信息")
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return {
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'status': 'success',
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'resources': resources,
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'timestamp': datetime.now().strftime('%Y-%m-%dT%H:%M:%S')
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}
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except Exception as e:
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error_msg = f"获取系统资源信息失败: {str(e)}"
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self.logger.error(error_msg)
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return {
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'status': 'error',
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'message': '获取资源信息失败',
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'details': {
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'error_type': type(e).__name__,
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'error_message': str(e)
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}
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}
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def get_training_history(
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self,
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page: int = 1,
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page_size: int = 10,
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start_time: Optional[str] = None,
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end_time: Optional[str] = None,
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status: Optional[str] = None,
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experiment_name: Optional[str] = None
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) -> Dict:
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"""
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获取训练历史记录
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Args:
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page: 页码
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page_size: 每页数量
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start_time: 开始时间 (YYYY-MM-DD)
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end_time: 结束时间 (YYYY-MM-DD)
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status: 运行状态过滤
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experiment_name: 实验名称过滤
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Returns:
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训练历史记录
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"""
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try:
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# 构建过滤条件
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filter_string = []
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if status:
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filter_string.append(f"status = '{status.upper()}'")
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if start_time:
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start_timestamp = int(pd.Timestamp(start_time).timestamp() * 1000)
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filter_string.append(f"start_time >= {start_timestamp}")
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if end_time:
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end_timestamp = int(pd.Timestamp(end_time).timestamp() * 1000)
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filter_string.append(f"start_time <= {end_timestamp}")
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filter_string = " and ".join(filter_string) if filter_string else None
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# 获取实验ID
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if experiment_name:
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experiment = mlflow.get_experiment_by_name(experiment_name)
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if experiment is None:
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return {
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'status': 'error',
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'message': f'实验 {experiment_name} 不存在'
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}
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experiment_ids = [experiment.experiment_id]
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else:
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experiments = mlflow.search_experiments()
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experiment_ids = [exp.experiment_id for exp in experiments]
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# 获取所有运行记录
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all_runs = []
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for exp_id in experiment_ids:
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runs = mlflow.search_runs(
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experiment_ids=[exp_id],
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filter_string=filter_string,
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order_by=['start_time DESC']
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)
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if not runs.empty:
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all_runs.extend(runs.to_dict('records'))
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# 计算分页
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total_items = len(all_runs)
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total_pages = (total_items + page_size - 1) // page_size
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start_idx = (page - 1) * page_size
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end_idx = min(start_idx + page_size, total_items)
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# 格式化运行记录
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history = []
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for run in all_runs[start_idx:end_idx]:
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# 获取实验信息
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experiment = mlflow.get_experiment(run['experiment_id'])
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# 计算持续时间
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start_time = pd.to_datetime(run['start_time'])
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end_time = pd.to_datetime(run['end_time']) if run['end_time'] else pd.Timestamp.now()
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duration = end_time - start_time
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# 收集参数和指标
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params = {}
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metrics = {}
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tags = {}
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for key, value in run.items():
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if key.startswith('params.'):
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params[key.replace('params.', '')] = value
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elif key.startswith('metrics.'):
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metrics[key.replace('metrics.', '')] = value
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elif key.startswith('tags.'):
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tags[key.replace('tags.', '')] = value
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# 格式化记录
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history_item = {
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'run_id': run['run_id'],
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'experiment_id': run['experiment_id'],
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'experiment_name': experiment.name,
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'model_name': params.get('algorithm', 'Unknown'),
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'dataset': params.get('dataset', 'Unknown'),
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'start_time': start_time.strftime('%Y-%m-%dT%H:%M:%S'),
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'end_time': end_time.strftime('%Y-%m-%dT%H:%M:%S'),
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'duration': str(duration).split('.')[0], # 去除微秒部分
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'status': run['status'],
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'parameters': params,
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'metrics': metrics,
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'tags': tags
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}
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history.append(history_item)
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self.logger.info(f"成功获取训练历史记录, 共{total_items}条记录")
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return {
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'status': 'success',
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'history': history,
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'pagination': {
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'current_page': page,
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'page_size': page_size,
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'total_pages': total_pages,
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'total_items': total_items
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}
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}
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except Exception as e:
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error_msg = f"获取训练历史失败: {str(e)}"
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self.logger.error(error_msg)
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return {
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'status': 'error',
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'message': '获取训练历史失败',
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'details': {
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'error_type': type(e).__name__,
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'error_message': str(e)
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}
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} |