Class: Aws::SageMaker::Types::HyperParameterTrainingJobDefinition
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::HyperParameterTrainingJobDefinition
- 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][1] 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][1] 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][1] 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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
).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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 API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
HyperParameterTuningResourceConfig
instead.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 24109 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 |