Kubernetes Pod质量度量指标设定

Kubernetes Pod质量度量指标设定

在TensorFlow Serving微服务架构中，合理设置Pod的质量度量指标对于保障模型服务稳定性至关重要。本文将结合实际部署经验，详细说明如何通过Kubernetes配置资源限制与请求，以及设置健康检查探针。

1. 资源限制配置

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tensorflow-serving
spec:
  replicas: 3
  selector:
    matchLabels:
      app: tensorflow-serving
  template:
    metadata:
      labels:
        app: tensorflow-serving
    spec:
      containers:
      - name: serving
        image: tensorflow/serving:2.13.0
        ports:
        - containerPort: 8501
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"

2. 健康检查探针配置

        livenessProbe:
          httpGet:
            path: /v1/models/tensorflow-serving
            port: 8501
          initialDelaySeconds: 30
          periodSeconds: 10
          timeoutSeconds: 5
        readinessProbe:
          httpGet:
            path: /v1/models/tensorflow-serving
            port: 8501
          initialDelaySeconds: 15
          periodSeconds: 5
          timeoutSeconds: 3

3. 自定义指标监控

通过配置Prometheus监控，可以收集关键指标：

• tensorflow_serving_request_count - 请求计数
• tensorflow_serving_request_duration_seconds - 请求耗时
• container_cpu_usage_seconds_total - CPU使用率

建议将这些指标与Kubernetes Dashboard或Grafana集成，实现自动化告警和扩缩容。