Kubernetes Tensorflow服务资源调度

Kubernetes Tensorflow服务资源调度实践

在Kubernetes环境中部署TensorFlow Serving服务时，合理的资源调度至关重要。本文将分享一个完整的资源调度方案，包含Docker容器化和负载均衡配置。

Docker容器构建

首先创建TensorFlow Serving的Dockerfile：

FROM tensorflow/serving:latest-gpu

# 复制模型文件
COPY ./models /models

# 配置启动参数
ENV MODEL_NAME=my_model
ENV TF_SERVING_MODEL_PATH=/models

EXPOSE 8500 8501
CMD ["tensorflow_model_server", "--model_name=${MODEL_NAME}", "--model_base_path=${TF_SERVING_MODEL_PATH}"]

Kubernetes部署配置

创建Deployment资源：

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-gpu
        ports:
        - containerPort: 8500
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
            nvidia.com/gpu: 1
          limits:
            memory: "4Gi"
            cpu: "2000m"
            nvidia.com/gpu: 1

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
  type: LoadBalancer

调度优化

通过资源请求和限制，确保GPU资源合理分配。使用nvidia.com/gpu标签实现GPU调度，避免资源争抢。

部署后可通过kubectl get pods验证容器状态，使用kubectl describe pod查看调度详情。