Kubernetes Service类型选择与TensorFlow服务

在TensorFlow Serving微服务架构中，Kubernetes Service类型的选择直接影响模型服务的可用性和性能。本文将深入探讨不同Service类型在TensorFlow部署中的应用。

Service类型对比

ClusterIP: 默认类型，适用于Pod间通信。对于TensorFlow Serving，可配置如下：

apiVersion: v1
kind: Service
metadata:
  name: tensorflow-serving
spec:
  selector:
    app: tensorflow-serving
  ports:
    - port: 8501
      targetPort: 8501
  type: ClusterIP

NodePort: 适用于外部访问，提供固定端口：

apiVersion: v1
kind: Service
metadata:
  name: tensorflow-nodeport
spec:
  selector:
    app: tensorflow-serving
  ports:
    - port: 8501
      targetPort: 8501
      nodePort: 30001
  type: NodePort

LoadBalancer: 云环境推荐，自动分配外部IP：

apiVersion: v1
kind: Service
metadata:
  name: tensorflow-lb
spec:
  selector:
    app: tensorflow-serving
  ports:
    - port: 8501
      targetPort: 8501
  type: LoadBalancer

Docker容器化配置

TensorFlow Serving容器需暴露以下端口：

FROM tensorflow/serving:latest
COPY model /models/model
ENV MODEL_NAME=model
EXPOSE 8500 8501

负载均衡配置

为实现高可用，建议采用Ingress控制器：

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: tensorflow-ingress
spec:
  rules:
  - host: tf.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: tensorflow-serving
            port:
              number: 8501

通过合理选择Service类型，可有效支撑TensorFlow模型的稳定服务化部署。