Class: Aws::Neptunedata::Types::StartMLModelTrainingJobInput

Inherits:

Struct

• Object
• Struct
• Aws::Neptunedata::Types::StartMLModelTrainingJobInput

Includes:

Structure

Defined in:

lib/aws-sdk-neptunedata/types.rb

Overview

See Also:

• AWS API Documentation

Constant Summary

SENSITIVE =

[]

Instance Attribute Summary

• #base_processing_instance_type ⇒ String

  The type of ML instance used in preparing and managing training of ML models.

• #custom_model_training_parameters ⇒ Types::CustomModelTrainingParameters

  The configuration for custom model training.

• #data_processing_job_id ⇒ String

  The job ID of the completed data-processing job that has created the data that the training will work with.

• #enable_managed_spot_training ⇒ Boolean

  Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances.

• #id ⇒ String

  A unique identifier for the new job.

• #max_hpo_number_of_training_jobs ⇒ Integer

  Maximum total number of training jobs to start for the hyperparameter tuning job.

• #max_hpo_parallel_training_jobs ⇒ Integer

  Maximum number of parallel training jobs to start for the hyperparameter tuning job.

• #neptune_iam_role_arn ⇒ String

  The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.

• #previous_model_training_job_id ⇒ String

  The job ID of a completed model-training job that you want to update incrementally based on updated data.

• #s3_output_encryption_kms_key ⇒ String

  The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job.

• #sagemaker_iam_role_arn ⇒ String

  The ARN of an IAM role for SageMaker execution.This must be listed in your DB cluster parameter group or an error will occur.

• #security_group_ids ⇒ Array<String>

  The VPC security group IDs.

• #subnets ⇒ Array<String>

  The IDs of the subnets in the Neptune VPC.

• #train_model_s3_location ⇒ String

  The location in Amazon S3 where the model artifacts are to be stored.

• #training_instance_type ⇒ String

  The type of ML instance used for model training.

• #training_instance_volume_size_in_gb ⇒ Integer

  The disk volume size of the training instance.

• #training_time_out_in_seconds ⇒ Integer

  Timeout in seconds for the training job.

• #volume_encryption_kms_key ⇒ String

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

Instance Attribute Details

#base_processing_instance_type ⇒ String

The type of ML instance used in preparing and managing training of ML models. This is a CPU instance chosen based on memory requirements for processing the training data and model.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#custom_model_training_parameters ⇒ Types::CustomModelTrainingParameters

The configuration for custom model training. This is a JSON object.

Returns:

• (Types::CustomModelTrainingParameters)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#data_processing_job_id ⇒ String

The job ID of the completed data-processing job that has created the data that the training will work with.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#enable_managed_spot_training ⇒ Boolean

Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances. The default is ‘False`.

Returns:

• (Boolean)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#id ⇒ String

A unique identifier for the new job. The default is An autogenerated UUID.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#max_hpo_number_of_training_jobs ⇒ Integer

Maximum total number of training jobs to start for the hyperparameter tuning job. The default is 2. Neptune ML automatically tunes the hyperparameters of the machine learning model. To obtain a model that performs well, use at least 10 jobs (in other words, set ‘maxHPONumberOfTrainingJobs` to 10). In general, the more tuning runs, the better the results.

Returns:

• (Integer)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#max_hpo_parallel_training_jobs ⇒ Integer

Maximum number of parallel training jobs to start for the hyperparameter tuning job. The default is 2. The number of parallel jobs you can run is limited by the available resources on your training instance.

Returns:

• (Integer)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#neptune_iam_role_arn ⇒ String

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

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#previous_model_training_job_id ⇒ String

The job ID of a completed model-training job that you want to update incrementally based on updated data.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#s3_output_encryption_kms_key ⇒ String

The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#sagemaker_iam_role_arn ⇒ String

The ARN of an IAM role for SageMaker execution.This must be listed in your DB cluster parameter group or an error will occur.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#security_group_ids ⇒ Array<String>

The VPC security group IDs. The default is None.

Returns:

• (Array<String>)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#subnets ⇒ Array<String>

The IDs of the subnets in the Neptune VPC. The default is None.

Returns:

• (Array<String>)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#train_model_s3_location ⇒ String

The location in Amazon S3 where the model artifacts are to be stored.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#training_instance_type ⇒ String

The type of ML instance used for model training. All Neptune ML models support CPU, GPU, and multiGPU training. The default is ‘ml.p3.2xlarge`. Choosing the right instance type for training depends on the task type, graph size, and your budget.

Returns:

• (String)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#training_instance_volume_size_in_gb ⇒ Integer

The disk volume size of the training instance. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML selects a disk volume size based on the recommendation generated in the data processing step.

Returns:

• (Integer)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#training_time_out_in_seconds ⇒ Integer

Timeout in seconds for the training job. The default is 86,400 (1 day).

Returns:

• (Integer)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end

#volume_encryption_kms_key ⇒ String

The Amazon Key Management Service (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)

# File 'lib/aws-sdk-neptunedata/types.rb', line 3585

class StartMLModelTrainingJobInput < Struct.new(
  :id,
  :previous_model_training_job_id,
  :data_processing_job_id,
  :train_model_s3_location,
  :sagemaker_iam_role_arn,
  :neptune_iam_role_arn,
  :base_processing_instance_type,
  :training_instance_type,
  :training_instance_volume_size_in_gb,
  :training_time_out_in_seconds,
  :max_hpo_number_of_training_jobs,
  :max_hpo_parallel_training_jobs,
  :subnets,
  :security_group_ids,
  :volume_encryption_kms_key,
  :s3_output_encryption_kms_key,
  :enable_managed_spot_training,
  :custom_model_training_parameters)
  SENSITIVE = []
  include Aws::Structure
end