基于Prometheus的TensorFlow服务监控告警

在TensorFlow Serving微服务架构中，基于Prometheus的监控告警体系是保障模型服务稳定运行的关键。本文将通过实际部署方案，展示如何构建完整的监控告警系统。

Prometheus集成配置 首先，在Docker容器化部署中添加Prometheus客户端依赖：

# requirements.txt
tensorflow-serving-api==2.13.0
prometheus-client==0.17.1

然后在TensorFlow服务代码中集成指标收集：

from prometheus_client import Histogram, Counter, Gauge
import tensorflow as tf

request_duration = Histogram('tensorflow_request_duration_seconds', 'Request duration')
request_count = Counter('tensorflow_requests_total', 'Total requests')
model_gauge = Gauge('tensorflow_model_loaded', 'Model loading status')

@request_duration.time()
def predict(request):
    request_count.inc()
    # 模型推理逻辑

负载均衡配置 采用Nginx作为反向代理，配置负载均衡：

upstream tensorflow_servers {
    server tensorflow-serving-1:8501;
    server tensorflow-serving-2:8501;
    server tensorflow-serving-3:8501;
}

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

告警规则设置 创建prometheus.yml配置：

rule_files:
  - "alert.rules.yml"

scrape_configs:
  - job_name: 'tensorflow-serving'
    static_configs:
      - targets: ['localhost:9090']

告警规则示例：

# alert.rules.yml
groups:
- name: tensorflow-alerts
  rules:
  - alert: HighErrorRate
    expr: rate(tensorflow_requests_total[5m]) > 10
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"

通过以上配置，可实现对TensorFlow服务的实时监控与自动告警，确保服务稳定性。