Class: Aws::SageMaker::Types::TrainingJob

Inherits:

Struct

Object
Struct
Aws::SageMaker::Types::TrainingJob

show all

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

Overview

Contains information about a training job.

Constant Summary collapse

SENSITIVE =

[]

Instance Attribute Summary collapse

#algorithm_specification ⇒ Types::AlgorithmSpecification
Information about the algorithm used for training, and algorithm metadata.
#auto_ml_job_arn ⇒ String
The Amazon Resource Name (ARN) of the job.
#billable_time_in_seconds ⇒ Integer
The billable time in seconds.
#checkpoint_config ⇒ Types::CheckpointConfig
Contains information about the output location for managed spot training checkpoint data.
#creation_time ⇒ Time
A timestamp that indicates when the training job was created.
#debug_hook_config ⇒ Types::DebugHookConfig
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths.
#debug_rule_configurations ⇒ Array<Types::DebugRuleConfiguration>
Information about the debug rule configuration.
#debug_rule_evaluation_statuses ⇒ Array<Types::DebugRuleEvaluationStatus>
Information about the evaluation status of the rules for the training job.
#enable_inter_container_traffic_encryption ⇒ Boolean
To encrypt all communications between ML compute instances in distributed training, choose True.
#enable_managed_spot_training ⇒ Boolean
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances.
#enable_network_isolation ⇒ Boolean
If the TrainingJob was created with network isolation, the value is set to true.
#environment ⇒ Hash<String,String>
The environment variables to set in the Docker container.
#experiment_config ⇒ Types::ExperimentConfig
Associates a SageMaker job as a trial component with an experiment and trial.
#failure_reason ⇒ String
If the training job failed, the reason it failed.
#final_metric_data_list ⇒ Array<Types::MetricData>
A list of final metric values that are set when the training job completes.
#hyper_parameters ⇒ Hash<String,String>
Algorithm-specific parameters.
#input_data_config ⇒ Array<Types::Channel>
An array of Channel objects that describes each data input channel.
#labeling_job_arn ⇒ String
The Amazon Resource Name (ARN) of the labeling job.
#last_modified_time ⇒ Time
A timestamp that indicates when the status of the training job was last modified.
#model_artifacts ⇒ Types::ModelArtifacts
Information about the Amazon S3 location that is configured for storing model artifacts.
#model_package_config ⇒ Types::ModelPackageConfig
The model package configuration.
#output_data_config ⇒ Types::OutputDataConfig
The S3 path where model artifacts that you configured when creating the job are stored.
#output_model_package_arn ⇒ String
The output model package Amazon Resource Name (ARN) that contains model weights or checkpoint.
#profiler_config ⇒ Types::ProfilerConfig
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
#resource_config ⇒ Types::ResourceConfig
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
#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 Web Services Identity and Access Management (IAM) role configured for the training job.
#secondary_status ⇒ String
Provides detailed information about the state of the training job.
#secondary_status_transitions ⇒ Array<Types::SecondaryStatusTransition>
A history of all of the secondary statuses that the training job has transitioned through.
#stopping_condition ⇒ Types::StoppingCondition
Specifies a limit to how long a model training job can run.
#tags ⇒ Array<Types::Tag>
An array of key-value pairs.
#tensor_board_output_config ⇒ Types::TensorBoardOutputConfig
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
#training_end_time ⇒ Time
Indicates the time when the training job ends on training instances.
#training_job_arn ⇒ String
The Amazon Resource Name (ARN) of the training job.
#training_job_name ⇒ String
The name of the training job.
#training_job_status ⇒ String
The status of the training job.
#training_start_time ⇒ Time
Indicates the time when the training job starts on training instances.
#training_time_in_seconds ⇒ Integer
The training time in seconds.
#tuning_job_arn ⇒ String
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
#vpc_config ⇒ Types::VpcConfig
A [VpcConfig][1] object that specifies the VPC that this training job has access to.
#warm_pool_status ⇒ Types::WarmPoolStatus
The status of the warm pool associated with the training job.

Instance Attribute Details

#algorithm_specification ⇒ `Types::AlgorithmSpecification`

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

Returns:

(Types::AlgorithmSpecification)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_arn ⇒ `String`

The Amazon Resource Name (ARN) of the job.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#billable_time_in_seconds ⇒ `Integer`

The billable time in seconds.

Returns:

(Integer)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  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 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#creation_time ⇒ `Time`

A timestamp that indicates when the training job was created.

Returns:

(Time)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#debug_hook_config ⇒ `Types::DebugHookConfig`

Configuration information for the Amazon SageMaker 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.

Returns:

(Types::DebugHookConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#debug_rule_configurations ⇒ `Array<Types::DebugRuleConfiguration>`

Information about the debug rule configuration.

Returns:

(Array<Types::DebugRuleConfiguration>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#debug_rule_evaluation_statuses ⇒ `Array<Types::DebugRuleEvaluationStatus>`

Information about the evaluation status of the rules for the training job.

Returns:

(Array<Types::DebugRuleEvaluationStatus>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  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 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#enable_managed_spot_training ⇒ `Boolean`

When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.

Returns:

(Boolean)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#enable_network_isolation ⇒ `Boolean`

If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.

Returns:

(Boolean)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#environment ⇒ `Hash<String,String>`

The environment variables to set in the Docker container.

Returns:

(Hash<String,String>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#experiment_config ⇒ `Types::ExperimentConfig`

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

Returns:

(Types::ExperimentConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#failure_reason ⇒ `String`

If the training job failed, the reason it failed.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#final_metric_data_list ⇒ `Array<Types::MetricData>`

A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.

Returns:

(Array<Types::MetricData>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#hyper_parameters ⇒ `Hash<String,String>`

Algorithm-specific parameters.

Returns:

(Hash<String,String>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

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

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

Your input must be in the same Amazon Web Services region as your training job.

Returns:

(Array<Types::Channel>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#labeling_job_arn ⇒ `String`

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

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#last_modified_time ⇒ `Time`

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

Returns:

(Time)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#model_artifacts ⇒ `Types::ModelArtifacts`

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

Returns:

(Types::ModelArtifacts)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#model_package_config ⇒ `Types::ModelPackageConfig`

The model package configuration.

Returns:

(Types::ModelPackageConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#output_data_config ⇒ `Types::OutputDataConfig`

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

Returns:

(Types::OutputDataConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#output_model_package_arn ⇒ `String`

The output model package Amazon Resource Name (ARN) that contains model weights or checkpoint.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#profiler_config ⇒ `Types::ProfilerConfig`

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

Returns:

(Types::ProfilerConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#resource_config ⇒ `Types::ResourceConfig`

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

Returns:

(Types::ResourceConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  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 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#role_arn ⇒ `String`

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

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#secondary_status ⇒ `String`

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

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.
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.
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
PreparingTrainingStack
DownloadingTrainingImage

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#secondary_status_transitions ⇒ `Array<Types::SecondaryStatusTransition>`

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

Returns:

(Array<Types::SecondaryStatusTransition>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#stopping_condition ⇒ `Types::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, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, 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.

Returns:

(Types::StoppingCondition)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#tags ⇒ `Array<Types::Tag>`

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Returns:

(Array<Types::Tag>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#tensor_board_output_config ⇒ `Types::TensorBoardOutputConfig`

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

Returns:

(Types::TensorBoardOutputConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#training_end_time ⇒ `Time`

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 SageMaker detects a job failure.

Returns:

(Time)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_arn ⇒ `String`

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

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_name ⇒ `String`

The name of the training job.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_status ⇒ `String`

The status of the training job.

Training job statuses are:

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)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#training_start_time ⇒ `Time`

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)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#training_time_in_seconds ⇒ `Integer`

The training time in seconds.

Returns:

(Integer)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#tuning_job_arn ⇒ `String`

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

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#vpc_config ⇒ `Types::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:

(Types::VpcConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

#warm_pool_status ⇒ `Types::WarmPoolStatus`

The status of the warm pool associated with the training job.

Returns:

(Types::WarmPoolStatus)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 55048

class TrainingJob < 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,
  :warm_pool_status,
  :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,
  :output_model_package_arn,
  :model_package_config,
  :profiler_config,
  :environment,
  :retry_strategy,
  :tags)
  SENSITIVE = []
  include Aws::Structure
end

Class: Aws::SageMaker::Types::TrainingJob

Overview

Constant Summary collapse

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_specification ⇒ Types::AlgorithmSpecification

#auto_ml_job_arn ⇒ String

#billable_time_in_seconds ⇒ Integer

#checkpoint_config ⇒ Types::CheckpointConfig

#creation_time ⇒ Time

#debug_hook_config ⇒ Types::DebugHookConfig

#debug_rule_configurations ⇒ Array<Types::DebugRuleConfiguration>

#debug_rule_evaluation_statuses ⇒ Array<Types::DebugRuleEvaluationStatus>

#enable_inter_container_traffic_encryption ⇒ Boolean

#enable_managed_spot_training ⇒ Boolean

#enable_network_isolation ⇒ Boolean

#environment ⇒ Hash<String,String>

#experiment_config ⇒ Types::ExperimentConfig

#failure_reason ⇒ String

#final_metric_data_list ⇒ Array<Types::MetricData>

#hyper_parameters ⇒ Hash<String,String>

#input_data_config ⇒ Array<Types::Channel>

#labeling_job_arn ⇒ String

#last_modified_time ⇒ Time

#model_artifacts ⇒ Types::ModelArtifacts

#model_package_config ⇒ Types::ModelPackageConfig

#output_data_config ⇒ Types::OutputDataConfig

#output_model_package_arn ⇒ String

#profiler_config ⇒ Types::ProfilerConfig

#resource_config ⇒ Types::ResourceConfig

#retry_strategy ⇒ Types::RetryStrategy

#role_arn ⇒ String

#secondary_status ⇒ String

#secondary_status_transitions ⇒ Array<Types::SecondaryStatusTransition>

#stopping_condition ⇒ Types::StoppingCondition

#tags ⇒ Array<Types::Tag>

#tensor_board_output_config ⇒ Types::TensorBoardOutputConfig

#training_end_time ⇒ Time

#training_job_arn ⇒ String

#training_job_name ⇒ String

#training_job_status ⇒ String

#training_start_time ⇒ Time

#training_time_in_seconds ⇒ Integer

#tuning_job_arn ⇒ String

#vpc_config ⇒ Types::VpcConfig

#warm_pool_status ⇒ Types::WarmPoolStatus

#algorithm_specification ⇒ `Types::AlgorithmSpecification`

#auto_ml_job_arn ⇒ `String`

#billable_time_in_seconds ⇒ `Integer`

#checkpoint_config ⇒ `Types::CheckpointConfig`

#creation_time ⇒ `Time`

#debug_hook_config ⇒ `Types::DebugHookConfig`

#debug_rule_configurations ⇒ `Array<Types::DebugRuleConfiguration>`

#debug_rule_evaluation_statuses ⇒ `Array<Types::DebugRuleEvaluationStatus>`

#enable_inter_container_traffic_encryption ⇒ `Boolean`

#enable_managed_spot_training ⇒ `Boolean`

#enable_network_isolation ⇒ `Boolean`

#environment ⇒ `Hash<String,String>`

#experiment_config ⇒ `Types::ExperimentConfig`

#failure_reason ⇒ `String`

#final_metric_data_list ⇒ `Array<Types::MetricData>`

#hyper_parameters ⇒ `Hash<String,String>`

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

#labeling_job_arn ⇒ `String`

#last_modified_time ⇒ `Time`

#model_artifacts ⇒ `Types::ModelArtifacts`

#model_package_config ⇒ `Types::ModelPackageConfig`

#output_data_config ⇒ `Types::OutputDataConfig`

#output_model_package_arn ⇒ `String`

#profiler_config ⇒ `Types::ProfilerConfig`

#resource_config ⇒ `Types::ResourceConfig`

#retry_strategy ⇒ `Types::RetryStrategy`

#role_arn ⇒ `String`

#secondary_status ⇒ `String`

#secondary_status_transitions ⇒ `Array<Types::SecondaryStatusTransition>`

#stopping_condition ⇒ `Types::StoppingCondition`

#tags ⇒ `Array<Types::Tag>`

#tensor_board_output_config ⇒ `Types::TensorBoardOutputConfig`

#training_end_time ⇒ `Time`

#training_job_arn ⇒ `String`

#training_job_name ⇒ `String`

#training_job_status ⇒ `String`

#training_start_time ⇒ `Time`

#training_time_in_seconds ⇒ `Integer`

#tuning_job_arn ⇒ `String`

#vpc_config ⇒ `Types::VpcConfig`

#warm_pool_status ⇒ `Types::WarmPoolStatus`