Kubernetes集群资源分配策略优化

在Kubernetes集群中优化TensorFlow Serving微服务的资源分配策略，需要从容器化部署和负载均衡配置两方面入手。

首先，在Dockerfile构建阶段，建议使用多阶段构建来减小镜像体积。以下是关键配置示例：

FROM tensorflow/tensorflow:2.13.0-gpu-py3-jupyter AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

FROM tensorflow/serving:2.13.0-gpu as runtime
COPY --from=builder /app /app
EXPOSE 8501

其次，通过Kubernetes资源限制配置实现精准分配。创建资源配置文件：

apiVersion: v1
kind: ResourceQuota
metadata:
  name: tf-serving-quota
spec:
  hard:
    requests.cpu: "2"
    requests.memory: 4Gi
    limits.cpu: "4"
    limits.memory: 8Gi
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: tensorflow-serving
spec:
  replicas: 3
  selector:
    matchLabels:
      app: tf-serving
  template:
    metadata:
      labels:
        app: tf-serving
    spec:
      containers:
      - name: tensorflow-serving
        image: tensorflow/serving:2.13.0-gpu
        ports:
        - containerPort: 8501
        resources:
          requests:
            memory: "2Gi"
            cpu: "1"
          limits:
            memory: "4Gi"
            cpu: "2"

最后，配置Ingress负载均衡：

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

通过以上配置，可实现TensorFlow Serving服务的资源高效利用和负载均衡。