Class: Aws::Neptunedata::Types::CreateMLEndpointInput

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb

Overview

See Also:

Constant Summary collapse

SENSITIVE = 
[]

Instance Attribute Summary collapse

Instance Attribute Details

#id ⇒ String

A unique identifier for the new inference endpoint. The default is an autogenerated timestamped name.

Returns:

  • (String) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#instance_count ⇒ Integer

The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1

Returns:

  • (Integer) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#instance_type ⇒ String

The type of Neptune ML instance to use for online servicing. The default is ml.m5.xlarge. Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget.

Returns:

  • (String) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#ml_model_training_job_id ⇒ String

The job Id of the completed model-training job that has created the model that the inference endpoint will point to. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

Returns:

  • (String) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#ml_model_transform_job_id ⇒ String

The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

Returns:

  • (String) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#model_name ⇒ String

Model type for training. By default the Neptune ML model is automatically based on the modelType used in data processing, but you can specify a different model type here. The default is rgcn for heterogeneous graphs and kge for knowledge graphs. The only valid value for heterogeneous graphs is rgcn. Valid values for knowledge graphs are: kge, transe, distmult, and rotate.

Returns:

  • (String) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#neptune_iam_role_arn ⇒ String

The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.

Returns:

  • (String) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#update ⇒ Boolean

If set to true, update indicates that this is an update request. The default is false. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

Returns:

  • (Boolean) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end

#volume_encryption_kms_key ⇒ String

The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

Returns:

  • (String) 


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# File 'gems/aws-sdk-neptunedata/lib/aws-sdk-neptunedata/types.rb', line 434

class CreateMLEndpointInput < Struct.new(
  :id,
  :ml_model_training_job_id,
  :ml_model_transform_job_id,
  :update,
  :neptune_iam_role_arn,
  :model_name,
  :instance_type,
  :instance_count,
  :volume_encryption_kms_key)
  SENSITIVE = []
  include Aws::Structure
end