Kubernetes Horizontal Pod Autoscaler与TensorFlow服务

在TensorFlow Serving微服务架构中，Kubernetes HPA与TensorFlow服务的集成是实现弹性伸缩的关键方案。本文将通过实际配置展示如何基于CPU使用率自动调节TensorFlow服务Pod数量。

首先，部署TensorFlow Serving服务并创建HPA配置文件：

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tensorflow-serving
spec:
  replicas: 2
  selector:
    matchLabels:
      app: tensorflow-serving
  template:
    metadata:
      labels:
        app: tensorflow-serving
    spec:
      containers:
      - name: tensorflow-serving
        image: tensorflow/serving:latest
        ports:
        - containerPort: 8501
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: tensorflow-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: tensorflow-serving
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

执行部署命令：

kubectl apply -f tensorflow-deployment.yaml
kubectl apply -f hpa-config.yaml

通过压力测试验证HPA效果，当CPU使用率超过70%时，HPA会自动增加Pod数量。建议配合Ingress Controller实现负载均衡，并配置适当的资源限制避免资源争抢。

在生产环境中，还需要监控HPA的调节频率和Pod的健康状态，确保服务稳定性。