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

Inherits:
Struct
  • Object
show all
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.m4.4xlarge, ml.m4.10xlarge, ml.c4.xlarge, ml.c4.2xlarge, 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
  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.

Instance Attribute Summary collapse

Instance Attribute Details

#instance_countInteger

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

Returns:

  • (Integer)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2145

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

#instance_typeString

The ML compute instance type.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2145

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

#volume_kms_key_idString

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

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2145

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

#volume_size_in_gbInteger

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)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2145

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