Kubernetes Tensorflow服务部署调试

Kubernetes TensorFlow服务部署调试

在Kubernetes环境中部署TensorFlow服务需要考虑容器化和负载均衡配置。首先创建Docker镜像：

FROM tensorflow/serving:latest
COPY model /models/model
EXPOSE 8501
ENTRYPOINT ["tensorflow_model_server"]
CMD ["--model_base_path=/models/model"]

部署到Kubernetes集群：

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: tensorflow-serving
        image: your-registry/tensorflow-serving:latest
        ports:
        - containerPort: 8501
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
---
apiVersion: v1
kind: Service
metadata:
  name: tensorflow-service
spec:
  selector:
    app: tensorflow-serving
  ports:
  - port: 8501
    targetPort: 8501
  type: LoadBalancer

配置负载均衡：

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: tensorflow-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
spec:
  rules:
  - host: model.example.com
    http:
      paths:
      - path: /predict
        backend:
          service:
            name: tensorflow-service
            port:
              number: 8501

调试时可通过kubectl logs查看容器日志，使用kubectl port-forward进行本地测试。建议配置健康检查探针确保服务可用性。