Kubernetes节点污点与TensorFlow调度

Kubernetes节点污点与TensorFlow调度

在TensorFlow Serving微服务架构中，合理利用Kubernetes节点污点(Taints)和容忍度(Tolerations)能够实现模型服务的精准调度，提升资源利用率。

污点配置实践

首先为GPU节点添加污点：

kubectl taint nodes gpu-node1 gpu-type=tesla:NoSchedule

然后在TensorFlow Serving部署文件中添加容忍：

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tensorflow-serving
spec:
  replicas: 3
  selector:
    matchLabels:
      app: tensorflow-serving
  template:
    spec:
      tolerations:
      - key: "gpu-type"
        operator: "Equal"
        value: "tesla"
        effect: "NoSchedule"
      containers:
      - name: serving
        image: tensorflow/serving:latest-gpu
        ports:
        - containerPort: 8501

负载均衡配置

通过Service实现负载均衡：

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

调度优化

为避免模型服务抢占，可设置优先级：

kubectl create priorityclass high-priority --value=1000

在部署配置中引用：

spec:
  template:
    spec:
      priorityClassName: high-priority

通过以上配置，能够实现GPU资源的专属调度，确保TensorFlow服务稳定运行。