class Aws::SageMaker::Types::InferenceSpecification

Defines how to perform inference generation after a training job is run.

@note When making an API call, you may pass InferenceSpecification

data as a hash:

    {
      containers: [ # required
        {
          container_hostname: "ContainerHostname",
          image: "ContainerImage", # required
          image_digest: "ImageDigest",
          model_data_url: "Url",
          product_id: "ProductId",
          environment: {
            "EnvironmentKey" => "EnvironmentValue",
          },
        },
      ],
      supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
      supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge
      supported_content_types: ["ContentType"], # required
      supported_response_mime_types: ["ResponseMIMEType"], # required
    }

@!attribute [rw] containers

The Amazon ECR registry path of the Docker image that contains the
inference code.
@return [Array<Types::ModelPackageContainerDefinition>]

@!attribute [rw] supported_transform_instance_types

A list of the instance types on which a transformation job can be
run or on which an endpoint can be deployed.

This parameter is required for unversioned models, and optional for
versioned models.
@return [Array<String>]

@!attribute [rw] supported_realtime_inference_instance_types

A list of the instance types that are used to generate inferences in
real-time.

This parameter is required for unversioned models, and optional for
versioned models.
@return [Array<String>]

@!attribute [rw] supported_content_types

The supported MIME types for the input data.
@return [Array<String>]

@!attribute [rw] supported_response_mime_types

The supported MIME types for the output data.
@return [Array<String>]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/InferenceSpecification AWS API Documentation

Constants

SENSITIVE