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Class: Aws::SageMaker::Types::OutputDataConfig

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

Overview

Note:

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

{
  kms_key_id: "KmsKeyId",
  s3_output_path: "S3Uri", # required
}

Provides information about how to store model training results (model artifacts).

Returned by:

Instance Attribute Summary collapse

Instance Attribute Details

#kms_key_idString

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • // KMS Key Alias

    "alias/ExampleAlias"

  • // Amazon Resource Name (ARN) of a KMS Key Alias

    "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don\'t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role\'s account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

Returns:

  • (String)

    The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

#s3_output_pathString

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

Returns:

  • (String)

    Identifies the S3 path where you want Amazon SageMaker to store the model artifacts.