TensorFlow Serving架构对比评测报告

TensorFlow Serving架构对比评测报告

架构方案对比

方案一：单节点TensorFlow Serving + Nginx反向代理 使用Docker容器化部署，通过Nginx实现负载均衡。

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

方案二：Kubernetes集群部署 使用Deployment + Service实现自动扩缩容和负载均衡。

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tensorflow-serving
spec:
  replicas: 3
  selector:
    matchLabels:
      app: tensorflow-serving
  template:
    spec:
      containers:
      - image: tensorflow/serving:latest
        ports:
        - containerPort: 8501
        env:
        - name: MODEL_NAME
          value: "model"

负载均衡配置方案

Nginx配置示例：

upstream tensorflow_servers {
    server 172.18.0.2:8501;
    server 172.18.0.3:8501;
    server 172.18.0.4:8501;
}

Kubernetes Service配置：

apiVersion: v1
kind: Service
metadata:
  name: tensorflow-serving-svc
spec:
  selector:
    app: tensorflow-serving
  ports:
    - port: 80
      targetPort: 8501

实施建议

对于生产环境推荐Kubernetes方案，具备更好的弹性扩展能力。单节点方案适合测试环境快速部署。