Kubernetes Ingress控制器配置TensorFlow服务
在Kubernetes环境中部署TensorFlow Serving微服务时,Ingress控制器是实现外部流量接入的关键组件。本文将详细介绍如何通过Ingress控制器配置TensorFlow服务的访问入口。
基础环境准备
首先确保已部署好TensorFlow Serving服务,使用以下Deployment配置:
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: tensorflow/serving:latest
ports:
- containerPort: 8501
- containerPort: 8500
Ingress配置方案
创建Ingress资源,实现负载均衡和路由转发:
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: tensorflow-ingress
annotations:
nginx.ingress.kubernetes.io/rewrite-target: /
nginx.ingress.kubernetes.io/backend-protocol: "HTTP"
spec:
rules:
- host: tf.example.com
http:
paths:
- path: /predict
pathType: Prefix
backend:
service:
name: tensorflow-serving
port:
number: 8501
高级负载均衡配置
为实现更精细的流量控制,可添加以下配置:
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: tensorflow-ingress
annotations:
nginx.ingress.kubernetes.io/upstream-hash-by: "$remote_addr"
nginx.ingress.kubernetes.io/limit-rps: "100"
nginx.ingress.kubernetes.io/limit-connections: "10"
spec:
rules:
- host: tf.example.com
http:
paths:
- path: /predict
pathType: Prefix
backend:
service:
name: tensorflow-serving
port:
number: 8501
部署验证
通过kubectl命令部署并验证:
kubectl apply -f tensorflow-deployment.yaml
kubectl apply -f tensorflow-ingress.yaml
kubectl get ingress
访问http://tf.example.com/predict即可调用TensorFlow服务,实现Docker容器化后的微服务架构。

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