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AI 应用监控与运维：确保系统稳定运行

news 2026/6/10 17:51:19

AI 应用监控与运维：确保系统稳定运行

前言

AI 应用的监控与运维是确保系统稳定运行的关键。一个好的监控系统能够及时发现问题、预警风险、保障服务质量。

我在项目中负责过多个 AI 系统的运维工作，对监控指标和运维流程有深入理解。今天分享一些实用的监控和运维经验。

监控指标

性能指标

class PerformanceMetrics: """性能指标""" def __init__(self): self.request_count = 0 self.total_latency = 0 self.error_count = 0 def record_request(self, latency: float, success: bool): """记录请求""" self.request_count += 1 self.total_latency += latency if not success: self.error_count += 1 def get_metrics(self) -> dict: """获取指标""" return { "requests_total": self.request_count, "avg_latency": self.total_latency / max(self.request_count, 1), "error_rate": self.error_count / max(self.request_count, 1) }

资源指标

class ResourceMetrics: """资源指标""" def __init__(self): self.cpu_usage = 0 self.memory_usage = 0 self.gpu_usage = 0 def update(self): """更新指标""" import psutil self.cpu_usage = psutil.cpu_percent() self.memory_usage = psutil.virtual_memory().percent # GPU 使用率 try: import GPUtil gpus = GPUtil.getGPUs() if gpus: self.gpu_usage = gpus[0].load * 100 except: self.gpu_usage = 0 def get_metrics(self) -> dict: """获取指标""" return { "cpu_usage": self.cpu_usage, "memory_usage": self.memory_usage, "gpu_usage": self.gpu_usage }

日志系统

import logging from pythonjsonlogger import jsonlogger class StructuredLogger: """结构化日志""" def __init__(self, name: str): self.logger = logging.getLogger(name) self.logger.setLevel(logging.INFO) # 控制台处理器 handler = logging.StreamHandler() formatter = jsonlogger.JsonFormatter( '%(asctime)s %(levelname)s %(message)s %(request_id)s' ) handler.setFormatter(formatter) self.logger.addHandler(handler) def log_request(self, request_id: str, prompt: str, latency: float): """记录请求日志""" self.logger.info( "Request processed", extra={ "request_id": request_id, "prompt_length": len(prompt), "latency": latency } ) def log_error(self, request_id: str, error: str): """记录错误日志""" self.logger.error( "Request failed", extra={ "request_id": request_id, "error": error } )

告警系统

class AlertingSystem: """告警系统""" def __init__(self): self.thresholds = { "error_rate": 0.05, "latency": 2.0, "cpu_usage": 90 } def check_alerts(self, metrics: dict) -> list: """检查告警条件""" alerts = [] if metrics.get("error_rate", 0) > self.thresholds["error_rate"]: alerts.append({ "level": "critical", "message": f"错误率过高: {metrics['error_rate']:.2%}" }) if metrics.get("avg_latency", 0) > self.thresholds["latency"]: alerts.append({ "level": "warning", "message": f"延迟过高: {metrics['avg_latency']:.2f}s" }) return alerts def send_alert(self, alert: dict): """发送告警""" # 可以发送到 Slack、邮件等 print(f"ALERT [{alert['level']}]: {alert['message']}")

自动化运维

class AutoScaler: """自动扩缩容""" def __init__(self, min_replicas: int = 1, max_replicas: int = 10): self.min_replicas = min_replicas self.max_replicas = max_replicas self.current_replicas = 1 def scale(self, metrics: dict): """根据指标调整副本数""" error_rate = metrics.get("error_rate", 0) latency = metrics.get("avg_latency", 0) if latency > 3.0 and self.current_replicas < self.max_replicas: self.current_replicas += 1 print(f"扩容到 {self.current_replicas} 个副本") elif error_rate < 0.01 and self.current_replicas > self.min_replicas: self.current_replicas -= 1 print(f"缩容到 {self.current_replicas} 个副本")