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Class: Aws::SageMaker::Types::ResourceConfig

Inherits:

Struct

• Object
• Struct
• Aws::SageMaker::Types::ResourceConfig

Defined in:

gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

Note:

When making an API call, you may pass ResourceConfig data as a hash:

{
  instance_type: "ml.m4.xlarge", # required, accepts 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.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.p3dn.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
  instance_count: 1, # required
  volume_size_in_gb: 1, # required
  volume_kms_key_id: "KmsKeyId",
}

Describes the resources, including ML compute instances and ML storage volumes, to use for model training.

See Also:

• AWS API Documentation

Instance Attribute Summary

• #instance_count ⇒ Integer

  The number of ML compute instances to use.

• #instance_type ⇒ String

  The ML compute instance type.

• #volume_kms_key_id ⇒ String

  The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

• #volume_size_in_gb ⇒ Integer

  The size of the ML storage volume that you want to provision.

Instance Attribute Details

#instance_count ⇒ Integer

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

Returns:

• (Integer)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 10046

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id)
  include Aws::Structure
end

#instance_type ⇒ String

The ML compute instance type.

Returns:

• (String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 10046

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id)
  include Aws::Structure
end

#volume_kms_key_id ⇒ String

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. The VolumeKmsKeyId 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"

Returns:

• (String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 10046

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id)
  include Aws::Structure
end

#volume_size_in_gb ⇒ Integer

The size of the ML storage volume that you want to provision.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

You must specify sufficient ML storage for your scenario.

Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.

Returns:

• (Integer)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 10046

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id)
  include Aws::Structure
end