Class: Aws::SageMaker::Types::DescribeTrainingJobResponse

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

Overview

Constant Summary collapse

SENSITIVE =
[]

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_specificationTypes::AlgorithmSpecification

Information about the algorithm used for training, and algorithm metadata.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_arnString

The Amazon Resource Name (ARN) of an AutoML job.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#billable_time_in_secondsInteger

The billable time in seconds. Billable time refers to the absolute wall-clock time.

Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

Returns:

  • (Integer)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#checkpoint_configTypes::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 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#creation_timeTime

A timestamp that indicates when the training job was created.

Returns:

  • (Time)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#debug_hook_configTypes::DebugHookConfig

Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#debug_rule_configurationsArray<Types::DebugRuleConfiguration>

Configuration information for Debugger rules for debugging output tensors.

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#debug_rule_evaluation_statusesArray<Types::DebugRuleEvaluationStatus>

Evaluation status of Debugger rules for debugging on a training job.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#enable_inter_container_traffic_encryptionBoolean

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 algorithms in distributed training.

Returns:

  • (Boolean)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#enable_managed_spot_trainingBoolean

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

Returns:

  • (Boolean)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#enable_network_isolationBoolean

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Returns:

  • (Boolean)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#environmentHash<String,String>

The environment variables to set in the Docker container.

Returns:

  • (Hash<String,String>)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#experiment_configTypes::ExperimentConfig

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

  • CreateProcessingJob

  • CreateTrainingJob

  • CreateTransformJob



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#failure_reasonString

If the training job failed, the reason it failed.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#final_metric_data_listArray<Types::MetricData>

A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#hyper_parametersHash<String,String>

Algorithm-specific parameters.

Returns:

  • (Hash<String,String>)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#input_data_configArray<Types::Channel>

An array of Channel objects that describes each data input channel.

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#labeling_job_arnString

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#last_modified_timeTime

A timestamp that indicates when the status of the training job was last modified.

Returns:

  • (Time)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#model_artifactsTypes::ModelArtifacts

Information about the Amazon S3 location that is configured for storing model artifacts.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#output_data_configTypes::OutputDataConfig

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#profiler_configTypes::ProfilerConfig

Configuration information for Debugger system monitoring, framework profiling, and storage paths.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#profiler_rule_configurationsArray<Types::ProfilerRuleConfiguration>

Configuration information for Debugger rules for profiling system and framework metrics.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#profiler_rule_evaluation_statusesArray<Types::ProfilerRuleEvaluationStatus>

Evaluation status of Debugger rules for profiling on a training job.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#profiling_statusString

Profiling status of a training job.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#resource_configTypes::ResourceConfig

Resources, including ML compute instances and ML storage volumes, that are configured for model training.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#retry_strategyTypes::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 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#role_arnString

The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#secondary_statusString

Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress
  • Starting - Starting the training job.

  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

  • Interrupted - The job stopped because the managed spot training instances were interrupted.

  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed
  • Completed - The training job has completed.

^

Failed
  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

^

Stopped
  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

  • MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.

  • Stopped - The training job has stopped.

Stopping
  • Stopping - Stopping the training job.

^

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTraining

  • DownloadingTrainingImage

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#secondary_status_transitionsArray<Types::SecondaryStatusTransition>

A history of all of the secondary statuses that the training job has transitioned through.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#stopping_conditionTypes::StoppingCondition

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

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#tensor_board_output_configTypes::TensorBoardOutputConfig

Configuration of storage locations for the Debugger TensorBoard output data.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#training_end_timeTime

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

Returns:

  • (Time)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_arnString

The Amazon Resource Name (ARN) of the training job.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_nameString

Name of the model training job.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_statusString

The status of the training job.

Amazon SageMaker provides the following training job statuses:

  • InProgress - The training is in progress.

  • Completed - The training job has completed.

  • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

  • Stopping - The training job is stopping.

  • Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#training_start_timeTime

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

Returns:

  • (Time)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#training_time_in_secondsInteger

The training time in seconds.

Returns:

  • (Integer)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#tuning_job_arnString

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end

#vpc_configTypes::VpcConfig

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 15888

class DescribeTrainingJobResponse < Struct.new(
  :training_job_name,
  :training_job_arn,
  :tuning_job_arn,
  :labeling_job_arn,
  :auto_ml_job_arn,
  :model_artifacts,
  :training_job_status,
  :secondary_status,
  :failure_reason,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :creation_time,
  :training_start_time,
  :training_end_time,
  :last_modified_time,
  :secondary_status_transitions,
  :final_metric_data_list,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :training_time_in_seconds,
  :billable_time_in_seconds,
  :debug_hook_config,
  :experiment_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :debug_rule_evaluation_statuses,
  :profiler_config,
  :profiler_rule_configurations,
  :profiler_rule_evaluation_statuses,
  :profiling_status,
  :retry_strategy,
  :environment)
  SENSITIVE = []
  include Aws::Structure
end