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Class: Aws::SageMaker::Types::CreateModelInput

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing CreateModelInput as input to an Aws::Client method, you can use a vanilla Hash:

{
  model_name: "ModelName", # required
  primary_container: {
    container_hostname: "ContainerHostname",
    image: "Image",
    model_data_url: "Url",
    environment: {
      "EnvironmentKey" => "EnvironmentValue",
    },
    model_package_name: "ArnOrName",
  },
  containers: [
    {
      container_hostname: "ContainerHostname",
      image: "Image",
      model_data_url: "Url",
      environment: {
        "EnvironmentKey" => "EnvironmentValue",
      },
      model_package_name: "ArnOrName",
    },
  ],
  execution_role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  enable_network_isolation: false,
}

Instance Attribute Summary collapse

Instance Attribute Details

#containersArray<Types::ContainerDefinition>

Specifies the containers in the inference pipeline.

Returns:

#enable_network_isolationBoolean

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

The Semantic Segmentation built-in algorithm does not support network isolation.

Returns:

  • (Boolean)

    Isolates the model container.

#execution_role_arnString

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Returns:

  • (String)

    The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs.

#model_nameString

The name of the new model.

Returns:

  • (String)

    The name of the new model.

#primary_containerTypes::ContainerDefinition

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

Returns:

  • (Types::ContainerDefinition)

    The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

#tagsArray<Types::Tag>

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Returns:

  • (Array<Types::Tag>)

    An array of key-value pairs.

#vpc_configTypes::VpcConfig

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

Returns:

  • (Types::VpcConfig)

    A [VpcConfig][1] object that specifies the VPC that you want your model to connect to.