training-serving.yaml

apiVersion: core.oam.dev/v1beta1
kind: Application
metadata:
  name: training-serving
  namespace: default
spec:
  components:
  # 训练模型
  - name: demo-training
    type: model-training
    properties:
      # 训练模型的镜像
      image: fogdong/train-color:v1
      # 模型训练的框架
      framework: tensorflow
      # 声明存储，将模型持久化。此处会使用集群内的默认 storage class 来创建 PVC
      storage:
        - name: "my-pvc"
          mountPath: "/model"
  
  # 启动模型服务
  - name: demo-serving
    type: model-serving
    # 模型服务会在模型训练完成后启动
    dependsOn:
      - demo-training
    properties:
      protocol: tensorflow
      predictors:
        - name: model
          replicas: 1
          graph:
            # 模型名
            name: my-model
            # 模型框架
            implementation: tensorflow
            # 模型地址，上一步会将训练完的模型保存到 my-pvc 这个 pvc 当中，所以通过 pvc://my-pvc 指定模型的地址
            modelUri: pvc://my-pvc

  # 测试模型服务
  - name: demo-rest-serving
    type: webservice
    dependsOn:
      - demo-serving
    properties:
      image: fogdong/color-serving:v1
      # 使用 LoadBalancer 暴露对外地址，方便调用
      exposeType: LoadBalancer
      env:
        - name: URL
          # 模型服务的地址
          value: http://ambassador.vela-system.svc.cluster.local/seldon/default/demo-serving/v1/models/my-model:predict
      ports:
        - port: 3333
          expose: true