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

Inherits:

Struct

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

show all

Defined in:: gems/aws-sdk-sagemaker/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][1] 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.

Returns:

(Types::HyperParameterAlgorithmSpecification)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#definition_name ⇒ `String`

The job definition name.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#environment ⇒ `Hash<String,String>`

An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.

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.

Returns:

(Hash<String,String>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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.

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.

Returns:

(Types::ParameterRanges)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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.

Returns:

(Array<Types::Channel>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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.

If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

Returns:

(Types::ResourceConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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.

Returns:

(Types::HyperParameterTuningJobObjective)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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.

Returns:

(Types::VpcConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19504

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,
  :stopping_condition,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :retry_strategy,
  :hyper_parameter_tuning_resource_config,
  :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`