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

Inherits:

Struct

Object
Struct
Aws::SageMaker::Types::DescribeTrainingJobResponse

show all

Includes:: Aws::Structure

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

Overview

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 an AutoML job.
#billable_time_in_seconds ⇒ Integer

The billable time in seconds.
#billable_token_count ⇒ Integer

The billable token count for eligible serverless training jobs.
#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>

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
#debug_rule_evaluation_statuses ⇒ Array<Types::DebugRuleEvaluationStatus>

Evaluation status of Amazon SageMaker Debugger rules for debugging on a 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

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

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose 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 collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
#hyper_parameters ⇒ Hash<String,String>

Algorithm-specific parameters.
#infra_check_config ⇒ Types::InfraCheckConfig

Contains information about the infrastructure health check configuration for the training job.
#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 SageMaker Ground Truth labeling job that created the transform or training job.
#last_modified_time ⇒ Time

A timestamp that indicates when the status of the training job was last modified.
#mlflow_config ⇒ Types::MlflowConfig

The MLflow configuration using SageMaker managed MLflow.
#mlflow_details ⇒ Types::MlflowDetails

The MLflow details of this job.
#model_artifacts ⇒ Types::ModelArtifacts

Information about the Amazon S3 location that is configured for storing model artifacts.
#model_package_config ⇒ Types::ModelPackageConfig

The configuration for the model package.
#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 Amazon Resource Name (ARN) of the output model package containing model weights or checkpoints.
#profiler_config ⇒ Types::ProfilerConfig

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
#profiler_rule_configurations ⇒ Array<Types::ProfilerRuleConfiguration>

Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
#profiler_rule_evaluation_statuses ⇒ Array<Types::ProfilerRuleEvaluationStatus>

Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
#profiling_status ⇒ String

Profiling status of a training job.
#progress_info ⇒ Types::TrainingProgressInfo

The Serverless training job progress information.
#remote_debug_config ⇒ Types::RemoteDebugConfig

Configuration for remote debugging.
#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.
#serverless_job_config ⇒ Types::ServerlessJobConfig

The configuration for serverless training jobs.
#stopping_condition ⇒ Types::StoppingCondition

Specifies a limit to how long a model training job can run.
#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

Name of the model 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] 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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_arn ⇒ `String`

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

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#billable_time_in_seconds ⇒ `Integer`

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 bills 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)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#billable_token_count ⇒ `Integer`

The billable token count for eligible serverless training jobs.

Returns:

(Integer)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#checkpoint_config ⇒ `Types::CheckpointConfig`

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

Returns:

(Types::CheckpointConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#creation_time ⇒ `Time`

A timestamp that indicates when the training job was created.

Returns:

(Time)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html

Returns:

(Types::DebugHookConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

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

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

Returns:

(Array<Types::DebugRuleConfiguration>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

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

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

Returns:

(Array<Types::DebugRuleEvaluationStatus>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 algorithms in distributed training.

Returns:

(Boolean)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#enable_managed_spot_training ⇒ `Boolean`

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

Returns:

(Boolean)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#enable_network_isolation ⇒ `Boolean`

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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Returns:

(Boolean)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#environment ⇒ `Hash<String,String>`

The environment variables to set in the Docker container.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

Returns:

(Hash<String,String>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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:

CreateProcessingJob][1
CreateTrainingJob][2
CreateTransformJob][3

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html [3]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html

Returns:

(Types::ExperimentConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#failure_reason ⇒ `String`

If the training job failed, the reason it failed.

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#final_metric_data_list ⇒ `Array<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:

(Array<Types::MetricData>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#hyper_parameters ⇒ `Hash<String,String>`

Algorithm-specific parameters.

Returns:

(Hash<String,String>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#infra_check_config ⇒ `Types::InfraCheckConfig`

Contains information about the infrastructure health check configuration for the training job.

Returns:

(Types::InfraCheckConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

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

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

Returns:

(Array<Types::Channel>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#labeling_job_arn ⇒ `String`

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

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#mlflow_config ⇒ `Types::MlflowConfig`

The MLflow configuration using SageMaker managed MLflow.

Returns:

(Types::MlflowConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#mlflow_details ⇒ `Types::MlflowDetails`

The MLflow details of this job.

Returns:

(Types::MlflowDetails)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#model_package_config ⇒ `Types::ModelPackageConfig`

The configuration for the model package.

Returns:

(Types::ModelPackageConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#output_model_package_arn ⇒ `String`

The Amazon Resource Name (ARN) of the output model package containing model weights or checkpoints.

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#profiler_rule_configurations ⇒ `Array<Types::ProfilerRuleConfiguration>`

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

Returns:

(Array<Types::ProfilerRuleConfiguration>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#profiler_rule_evaluation_statuses ⇒ `Array<Types::ProfilerRuleEvaluationStatus>`

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

Returns:

(Array<Types::ProfilerRuleEvaluationStatus>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#profiling_status ⇒ `String`

Profiling status of a training job.

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#progress_info ⇒ `Types::TrainingProgressInfo`

The Serverless training job progress information.

Returns:

(Types::TrainingProgressInfo)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#remote_debug_config ⇒ `Types::RemoteDebugConfig`

Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see [Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/train-remote-debugging.html

Returns:

(Types::RemoteDebugConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#retry_strategy ⇒ `Types::RetryStrategy`

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

Returns:

(Types::RetryStrategy)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#secondary_status ⇒ `String`

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].

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

InProgress : * Starting - Starting the training job.

* `Pending` - The training job is waiting for compute capacity or
  compute resource provision.

* `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

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

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#serverless_job_config ⇒ `Types::ServerlessJobConfig`

The configuration for serverless training jobs.

Returns:

(Types::ServerlessJobConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_arn ⇒ `String`

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

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_name ⇒ `String`

Name of the model training job.

Returns:

(String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_status ⇒ `String`

The status of the training job.

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)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

#training_time_in_seconds ⇒ `Integer`

The training time in seconds.

Returns:

(Integer)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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].

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

Returns:

(Types::VpcConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  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 'lib/aws-sdk-sagemaker/types.rb', line 21910

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,
  :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,
  :billable_token_count,
  :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,
  :environment,
  :retry_strategy,
  :remote_debug_config,
  :infra_check_config,
  :serverless_job_config,
  :mlflow_config,
  :model_package_config,
  :mlflow_details,
  :progress_info,
  :output_model_package_arn)
  SENSITIVE = []
  include Aws::Structure
end

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

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

#billable_token_count ⇒ 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>

#infra_check_config ⇒ Types::InfraCheckConfig

#input_data_config ⇒ Array<Types::Channel>

#labeling_job_arn ⇒ String

#last_modified_time ⇒ Time

#mlflow_config ⇒ Types::MlflowConfig

#mlflow_details ⇒ Types::MlflowDetails

#model_artifacts ⇒ Types::ModelArtifacts

#model_package_config ⇒ Types::ModelPackageConfig

#output_data_config ⇒ Types::OutputDataConfig

#output_model_package_arn ⇒ String

#profiler_config ⇒ Types::ProfilerConfig

#profiler_rule_configurations ⇒ Array<Types::ProfilerRuleConfiguration>

#profiler_rule_evaluation_statuses ⇒ Array<Types::ProfilerRuleEvaluationStatus>

#profiling_status ⇒ String

#progress_info ⇒ Types::TrainingProgressInfo

#remote_debug_config ⇒ Types::RemoteDebugConfig

#resource_config ⇒ Types::ResourceConfig

#retry_strategy ⇒ Types::RetryStrategy

#role_arn ⇒ String

#secondary_status ⇒ String

#secondary_status_transitions ⇒ Array<Types::SecondaryStatusTransition>

#serverless_job_config ⇒ Types::ServerlessJobConfig

#stopping_condition ⇒ Types::StoppingCondition

#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`

#billable_token_count ⇒ `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>`

#infra_check_config ⇒ `Types::InfraCheckConfig`

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

#labeling_job_arn ⇒ `String`

#last_modified_time ⇒ `Time`

#mlflow_config ⇒ `Types::MlflowConfig`

#mlflow_details ⇒ `Types::MlflowDetails`

#model_artifacts ⇒ `Types::ModelArtifacts`

#model_package_config ⇒ `Types::ModelPackageConfig`

#output_data_config ⇒ `Types::OutputDataConfig`

#output_model_package_arn ⇒ `String`

#profiler_config ⇒ `Types::ProfilerConfig`

#profiler_rule_configurations ⇒ `Array<Types::ProfilerRuleConfiguration>`

#profiler_rule_evaluation_statuses ⇒ `Array<Types::ProfilerRuleEvaluationStatus>`

#profiling_status ⇒ `String`

#progress_info ⇒ `Types::TrainingProgressInfo`

#remote_debug_config ⇒ `Types::RemoteDebugConfig`

#resource_config ⇒ `Types::ResourceConfig`

#retry_strategy ⇒ `Types::RetryStrategy`

#role_arn ⇒ `String`

#secondary_status ⇒ `String`

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

#serverless_job_config ⇒ `Types::ServerlessJobConfig`

#stopping_condition ⇒ `Types::StoppingCondition`

#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`