Amazon SageMaker Deploy Model to Endpoint

arazzo: 1.0.1
info:
  title: Amazon SageMaker Deploy Model to Endpoint
  summary: Create a model, build an endpoint configuration, launch an endpoint, and poll it until it is in service.
  description: >-
    The complete SageMaker hosting pipeline. The workflow registers a model from
    an inference container and model artifacts, defines an endpoint
    configuration that places that model on a production variant, creates the
    endpoint, and then polls the endpoint until its status leaves Creating and
    settles on a terminal state such as InService or Failed. Each step spells
    out its request inline so the flow can be read and executed without opening
    the underlying OpenAPI description.
  version: 1.0.0
sourceDescriptions:
- name: sagemakerApi
  url: ../openapi/amazon-sagemaker-openapi.yml
  type: openapi
workflows:
- workflowId: deploy-model-to-endpoint
  summary: Register a model and deploy it to a hosted real-time endpoint.
  description: >-
    Creates a model, an endpoint configuration referencing that model, and an
    endpoint built from the configuration, then polls the endpoint until it is
    no longer in the Creating state.
  inputs:
    type: object
    required:
    - modelName
    - image
    - modelDataUrl
    - executionRoleArn
    - endpointConfigName
    - variantName
    - initialInstanceCount
    - instanceType
    - endpointName
    properties:
      modelName:
        type: string
        description: A unique name for the model.
      image:
        type: string
        description: The registry path of the Docker image that contains the inference code.
      modelDataUrl:
        type: string
        description: The S3 path where the model artifacts are stored.
      executionRoleArn:
        type: string
        description: The ARN of the IAM role SageMaker can assume to access model artifacts.
      endpointConfigName:
        type: string
        description: A unique name for the endpoint configuration.
      variantName:
        type: string
        description: The name of the production variant.
      initialInstanceCount:
        type: integer
        description: The initial number of instances to launch for the variant.
      instanceType:
        type: string
        description: The ML compute instance type to deploy for the variant.
      endpointName:
        type: string
        description: A unique name for the endpoint.
  steps:
  - stepId: createModel
    description: >-
      Register a model from an inference container image and model artifacts so
      it can be referenced by an endpoint configuration.
    operationId: CreateModel
    parameters:
    - name: X-Amz-Target
      in: header
      value: SageMaker.CreateModel
    requestBody:
      contentType: application/x-amz-json-1.1
      payload:
        ModelName: $inputs.modelName
        PrimaryContainer:
          Image: $inputs.image
          ModelDataUrl: $inputs.modelDataUrl
        ExecutionRoleArn: $inputs.executionRoleArn
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      modelArn: $response.body#/ModelArn
  - stepId: createEndpointConfig
    description: >-
      Define an endpoint configuration that places the newly created model on a
      single production variant with the requested instance type and count.
    operationId: CreateEndpointConfig
    parameters:
    - name: X-Amz-Target
      in: header
      value: SageMaker.CreateEndpointConfig
    requestBody:
      contentType: application/x-amz-json-1.1
      payload:
        EndpointConfigName: $inputs.endpointConfigName
        ProductionVariants:
        - VariantName: $inputs.variantName
          ModelName: $inputs.modelName
          InitialInstanceCount: $inputs.initialInstanceCount
          InstanceType: $inputs.instanceType
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      endpointConfigArn: $response.body#/EndpointConfigArn
  - stepId: createEndpoint
    description: >-
      Create a hosted endpoint from the endpoint configuration so SageMaker
      begins provisioning resources and deploying the model.
    operationId: CreateEndpoint
    parameters:
    - name: X-Amz-Target
      in: header
      value: SageMaker.CreateEndpoint
    requestBody:
      contentType: application/x-amz-json-1.1
      payload:
        EndpointName: $inputs.endpointName
        EndpointConfigName: $inputs.endpointConfigName
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      endpointArn: $response.body#/EndpointArn
  - stepId: pollEndpoint
    description: >-
      Describe the endpoint to read its status. Repeat this step while the
      endpoint remains in the Creating state and continue once it reaches a
      terminal state such as InService or Failed.
    operationId: DescribeEndpoint
    parameters:
    - name: X-Amz-Target
      in: header
      value: SageMaker.DescribeEndpoint
    requestBody:
      contentType: application/x-amz-json-1.1
      payload:
        EndpointName: $inputs.endpointName
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      endpointStatus: $response.body#/EndpointStatus
      failureReason: $response.body#/FailureReason
    onSuccess:
    - name: stillCreating
      type: goto
      stepId: pollEndpoint
      criteria:
      - context: $response.body
        condition: $.EndpointStatus == "Creating"
        type: jsonpath
    - name: endpointTerminal
      type: end
      criteria:
      - context: $response.body
        condition: $.EndpointStatus != "Creating"
        type: jsonpath
  outputs:
    modelArn: $steps.createModel.outputs.modelArn
    endpointConfigArn: $steps.createEndpointConfig.outputs.endpointConfigArn
    endpointArn: $steps.createEndpoint.outputs.endpointArn
    endpointStatus: $steps.pollEndpoint.outputs.endpointStatus