微服务架构模型服务监控告警

在TensorFlow Serving微服务架构中，模型服务监控告警是保障系统稳定运行的关键环节。本文将介绍如何构建完整的监控告警体系。

监控指标收集

首先需要配置Prometheus采集器，通过以下Docker-compose配置实现：

version: '3'
services:
  tensorflow-serving:
    image: tensorflow/serving:latest
    ports:
      - "8501:8501"
      - "8500:8500"
    volumes:
      - ./models:/models
    environment:
      MODEL_NAME: my_model
      MODEL_BASE_PATH: /models
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8500/v1/models/my_model"]
      interval: 30s
      timeout: 10s
      retries: 3

告警规则配置

在Prometheus中添加告警规则：

groups:
- name: tensorflow.rules
  rules:
  - alert: ModelUnhealthy
    expr: tensorflow_serving_model_load_count{job="tensorflow-serving"} < 1
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "模型服务不可用"
      description: "模型加载失败超过2分钟"

负载均衡配置

使用Nginx实现负载均衡：

upstream tensorflow_servers {
    server 172.17.0.2:8501;
    server 172.17.0.3:8501;
    server 172.17.0.4:8501;
}

server {
    listen 80;
    location / {
        proxy_pass http://tensorflow_servers;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

告警通知集成

通过Webhook将告警推送到Slack：

import requests
import json

def send_alert(message):
    webhook_url = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL"
    payload = {
        "text": message,
        "channel": "#model-alerts"
    }
    requests.post(webhook_url, json=payload)

通过以上配置，可以实现从模型服务健康检查到告警通知的完整监控链路。