Kubernetes Tensorflow服务自动部署

Kubernetes Tensorflow服务自动部署：从Docker到负载均衡的完整实践

在现代AI应用架构中，TensorFlow Serving已成为模型部署的标准方案。本文将通过Kubernetes环境，演示如何实现TensorFlow服务的自动化部署，并集成Docker容器化和负载均衡配置。

1. Docker容器化构建

首先创建TensorFlow Serving的Dockerfile：

FROM tensorflow/serving:latest

# 复制模型文件到容器
COPY model /models/model
RUN mkdir -p /models/model && \
    cp -r /model/* /models/model/

# 暴露端口
EXPOSE 8500 8501

构建镜像：

sudo docker build -t tensorflow-serving:latest .

2. Kubernetes部署配置

创建Deployment配置文件：

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:latest
        ports:
        - containerPort: 8500
        - containerPort: 8501
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"

3. 负载均衡配置

通过Service实现负载均衡：

apiVersion: v1
kind: Service
metadata:
  name: tensorflow-serving-service
spec:
  selector:
    app: tensorflow-serving
  ports:
  - port: 8500
    targetPort: 8500
    protocol: TCP
    name: grpc
  - port: 8501
    targetPort: 8501
    protocol: TCP
    name: http
  type: LoadBalancer

4. 部署验证

应用配置并验证：

kubectl apply -f deployment.yaml
kubectl apply -f service.yaml
kubectl get svc tensorflow-serving-service

最终，通过负载均衡器的外部IP即可访问TensorFlow服务，实现高可用部署。