Class: Aws::SageMaker::Types::HyperParameterTrainingJobDefinition

Inherits:

Struct

Object
Struct
Aws::SageMaker::Types::HyperParameterTrainingJobDefinition

show all

Includes:: Aws::Structure

Defined in:: lib/aws-sdk-sagemaker/types.rb

Overview

Defines the training jobs launched by a hyperparameter tuning job.

Constant Summary collapse

SENSITIVE =

[]

Instance Attribute Summary collapse

#algorithm_specification ⇒ Types::HyperParameterAlgorithmSpecification

The [HyperParameterAlgorithmSpecification] object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
#checkpoint_config ⇒ Types::CheckpointConfig

Contains information about the output location for managed spot training checkpoint data.
#definition_name ⇒ String

The job definition name.
#enable_inter_container_traffic_encryption ⇒ Boolean

To encrypt all communications between ML compute instances in distributed training, choose ‘True`.
#enable_managed_spot_training ⇒ Boolean

A Boolean indicating whether managed spot training is enabled (‘True`) or not (`False`).
#enable_network_isolation ⇒ Boolean

Isolates the training container.
#environment ⇒ Hash<String,String>

An environment variable that you can pass into the SageMaker [CreateTrainingJob] API.
#hyper_parameter_ranges ⇒ Types::ParameterRanges

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches.
#hyper_parameter_tuning_resource_config ⇒ Types::HyperParameterTuningResourceConfig

The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job.
#input_data_config ⇒ Array<Types::Channel>

An array of [Channel] objects that specify the input for the training jobs that the tuning job launches.
#output_data_config ⇒ Types::OutputDataConfig

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
#resource_config ⇒ Types::ResourceConfig

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
#retry_strategy ⇒ Types::RetryStrategy

The number of times to retry the job when the job fails due to an ‘InternalServerError`.
#role_arn ⇒ String

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
#static_hyper_parameters ⇒ Hash<String,String>

Specifies the values of hyperparameters that do not change for the tuning job.
#stopping_condition ⇒ Types::StoppingCondition

Specifies a limit to how long a model hyperparameter training job can run.
#tuning_objective ⇒ Types::HyperParameterTuningJobObjective

Defines the objective metric for a hyperparameter tuning job.
#vpc_config ⇒ Types::VpcConfig

The [VpcConfig] object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to.

Instance Attribute Details

#algorithm_specification ⇒ `Types::HyperParameterAlgorithmSpecification`

The [HyperParameterAlgorithmSpecification] object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterAlgorithmSpecification.html

Returns:

(Types::HyperParameterAlgorithmSpecification)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#checkpoint_config ⇒ `Types::CheckpointConfig`

Contains information about the output location for managed spot training checkpoint data.

Returns:

(Types::CheckpointConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#definition_name ⇒ `String`

The job definition name.

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#enable_inter_container_traffic_encryption ⇒ `Boolean`

To encrypt all communications between ML compute instances in distributed training, choose ‘True`. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

Returns:

(Boolean)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#enable_managed_spot_training ⇒ `Boolean`

A Boolean indicating whether managed spot training is enabled (‘True`) or not (`False`).

Returns:

(Boolean)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#enable_network_isolation ⇒ `Boolean`

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Returns:

(Boolean)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#environment ⇒ `Hash<String,String>`

An environment variable that you can pass into the SageMaker

CreateTrainingJob][1: API. You can use an existing [environment

variable from the training container] or use your own. See

Define metrics and variables][3: for more information.

<note markdown=“1”> The maximum number of items specified for ‘Map Entries` refers to the maximum number of environment variables for each `TrainingJobDefinition` and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can’t exceed the maximum number specified.

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html#sagemaker-CreateTrainingJob-request-Environment [3]: docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html

Returns:

(Hash<String,String>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#hyper_parameter_ranges ⇒ `Types::ParameterRanges`

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

<note markdown=“1”> The maximum number of items specified for ‘Array Members` refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can’t exceed the maximum number specified.

</note>

Returns:

(Types::ParameterRanges)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#hyper_parameter_tuning_resource_config ⇒ `Types::HyperParameterTuningResourceConfig`

The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose ‘File` for `TrainingInputMode` in the `AlgorithmSpecification` parameter to additionally store training data in the storage volume (optional).

Returns:

(Types::HyperParameterTuningResourceConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#input_data_config ⇒ `Array<Types::Channel>`

An array of [Channel] objects that specify the input for the training jobs that the tuning job launches.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Channel.html

Returns:

(Array<Types::Channel>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#output_data_config ⇒ `Types::OutputDataConfig`

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

Returns:

(Types::OutputDataConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#resource_config ⇒ `Types::ResourceConfig`

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose ‘File` as the `TrainingInputMode` in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

<note markdown=“1”> If you want to use hyperparameter optimization with instance type flexibility, use ‘HyperParameterTuningResourceConfig` instead.

</note>

Returns:

(Types::ResourceConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#retry_strategy ⇒ `Types::RetryStrategy`

The number of times to retry the job when the job fails due to an ‘InternalServerError`.

Returns:

(Types::RetryStrategy)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#role_arn ⇒ `String`

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#static_hyper_parameters ⇒ `Hash<String,String>`

Specifies the values of hyperparameters that do not change for the tuning job.

Returns:

(Hash<String,String>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#stopping_condition ⇒ `Types::StoppingCondition`

Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

Returns:

(Types::StoppingCondition)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#tuning_objective ⇒ `Types::HyperParameterTuningJobObjective`

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the ‘Type` parameter. If you want to define a custom objective metric, see [Define metrics and environment variables].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html

Returns:

(Types::HyperParameterTuningJobObjective)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#vpc_config ⇒ `Types::VpcConfig`

The [VpcConfig] object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see [Protect Training Jobs by Using an Amazon Virtual Private Cloud].

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html

Returns:

(Types::VpcConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23023

class HyperParameterTrainingJobDefinition < Struct.new(
  :definition_name,
  :tuning_objective,
  :hyper_parameter_ranges,
  :static_hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :vpc_config,
  :output_data_config,
  :resource_config,
  :hyper_parameter_tuning_resource_config,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

Class: Aws::SageMaker::Types::HyperParameterTrainingJobDefinition

Overview

Constant Summary collapse

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_specification ⇒ Types::HyperParameterAlgorithmSpecification

#checkpoint_config ⇒ Types::CheckpointConfig

#definition_name ⇒ String

#enable_inter_container_traffic_encryption ⇒ Boolean

#enable_managed_spot_training ⇒ Boolean

#enable_network_isolation ⇒ Boolean

#environment ⇒ Hash<String,String>

#hyper_parameter_ranges ⇒ Types::ParameterRanges

#hyper_parameter_tuning_resource_config ⇒ Types::HyperParameterTuningResourceConfig

#input_data_config ⇒ Array<Types::Channel>

#output_data_config ⇒ Types::OutputDataConfig

#resource_config ⇒ Types::ResourceConfig

#retry_strategy ⇒ Types::RetryStrategy

#role_arn ⇒ String

#static_hyper_parameters ⇒ Hash<String,String>

#stopping_condition ⇒ Types::StoppingCondition

#tuning_objective ⇒ Types::HyperParameterTuningJobObjective

#vpc_config ⇒ Types::VpcConfig

#algorithm_specification ⇒ `Types::HyperParameterAlgorithmSpecification`

#checkpoint_config ⇒ `Types::CheckpointConfig`

#definition_name ⇒ `String`

#enable_inter_container_traffic_encryption ⇒ `Boolean`

#enable_managed_spot_training ⇒ `Boolean`

#enable_network_isolation ⇒ `Boolean`

#environment ⇒ `Hash<String,String>`

#hyper_parameter_ranges ⇒ `Types::ParameterRanges`

#hyper_parameter_tuning_resource_config ⇒ `Types::HyperParameterTuningResourceConfig`

#input_data_config ⇒ `Array<Types::Channel>`

#output_data_config ⇒ `Types::OutputDataConfig`

#resource_config ⇒ `Types::ResourceConfig`

#retry_strategy ⇒ `Types::RetryStrategy`

#role_arn ⇒ `String`

#static_hyper_parameters ⇒ `Hash<String,String>`

#stopping_condition ⇒ `Types::StoppingCondition`

#tuning_objective ⇒ `Types::HyperParameterTuningJobObjective`

#vpc_config ⇒ `Types::VpcConfig`