Description:
  This template is built and deployed by the infrastructure pipeline in various stages (staging/production) as required.
  It specifies the resources that need to be created. In this case, an EventBridge Rule, a SageMaker Pipelines and a
  SageMake Model.
Parameters:
  SageMakerProjectName:
    Type: String
    Description: Name of the project (lower case)
    MinLength: 1
    MaxLength: 32
    AllowedPattern: ^[a-z](-*[a-z0-9])*
  SageMakerProjectId:
    Type: String
    Description: Id of the project
    MinLength: 1
    MaxLength: 32
  ModelExecutionRoleArn:
    Type: String
    Description: Execution role used for deploying the model.
  ModelPackageName:
    Type: String
    Description: Name of the model to use
  InputPath:
    Type: String
    Description: The S3 path where data is stored on which to run inference.
  OutputPath:
    Type: String
    Description: The S3 path where predictions will be output.
  BatchInstanceType:
    Type: String
    Description: Which kind of instance you want to use for Batch inference
    Default: ml.m5.xlarge
  BatchInstanceCount:
    Type: Number
    Description: How many instances you want to use for inference.
    Default: 1
  ScheduleExpressionforPipeline:
    Type: String
    Description: The rate of execution of your pipeline (default 30 minutes)
    Default: 30 minutes
  StageName:
    Type: String
    Description:
      The name for a project pipeline stage, such as dev or prod, for
      which resources are provisioned and deployed.
  PipelineDefinitionBody:
    Type: String
    Description:
      The body of the SageMaker Pipeline.

Resources:
  ModelToDeploy:
    Type: AWS::SageMaker::Model
    Properties:
      ExecutionRoleArn: !Ref ModelExecutionRoleArn
      Containers: 
        - ModelPackageName: !Ref ModelPackageName
      Tags:
        - Key: sagemaker:project-name
          Value: !Sub ${SageMakerProjectName}
        - Key: sagemaker:project-id
          Value: !Sub ${SageMakerProjectId}
  BatchPipeline:
    Type: AWS::SageMaker::Pipeline
    Properties: 
      PipelineDescription: The SM Pipeline that executes the batch inference
      PipelineName: !Sub ${SageMakerProjectName}-${StageName}-BatchPipeline
      RoleArn: !Ref ModelExecutionRoleArn
      PipelineDefinition: 
        PipelineDefinitionBody: !Ref PipelineDefinitionBody
      Tags:
        - Key: sagemaker:project-name
          Value: !Ref SageMakerProjectName
        - Key: sagemaker:project-id
          Value: !Ref SageMakerProjectId
  EventRule:
    Type: AWS::Events::Rule
    Properties:
      Name: !Sub sagemaker-${SageMakerProjectName}-${StageName}-SchedExecRule
      ScheduleExpression: !Sub rate(${ScheduleExpressionforPipeline})
      Targets:
        - Arn: !Sub arn:aws:sagemaker:${AWS::Region}:${AWS::AccountId}:pipeline/${SageMakerProjectName}-${StageName}-batch-pipeline
          Id: MyBatchInferenceTarget
          RoleArn: !Ref ModelExecutionRoleArn
          SageMakerPipelineParameters:
            PipelineParameterList:
              - Name: InputPath
                Value: !Ref InputPath
              - Name: OutputPath
                Value: !Ref OutputPath
              - Name: BatchInstanceType
                Value: !Ref BatchInstanceType
              - Name: BatchInstanceCount
                Value: !Ref BatchInstanceCount
              - Name: ModelName
                Value: !GetAtt ModelToDeploy.ModelName
Outputs:
  ModelName:
    Description: SageMaker model name 
    Value: !GetAtt ModelToDeploy.ModelName