You are viewing documentation for version 2 of the AWS SDK for Ruby. Version 3 documentation can be found here.
Class: Aws::SageMaker::Types::TrainingJob
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::TrainingJob
- Defined in:
- (unknown)
Overview
Contains information about a training job.
Returned by:
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 debug hook parameters, collection configuration, 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 totrue
. -
#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.
-
#output_data_config ⇒ Types::OutputDataConfig
The S3 path where model artifacts that you configured when creating the job are stored.
-
#resource_config ⇒ Types::ResourceConfig
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
-
#role_arn ⇒ String
The AWS 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 TensorBoard output.
.
-
#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 object that specifies the VPC that this training job has access to.
Instance Attribute Details
#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 debug hook parameters, collection configuration, 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
. 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.
#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.
#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.
#experiment_config ⇒ Types::ExperimentConfig
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
#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. Used only if the training job was configured to use metrics.
#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.
#output_data_config ⇒ Types::OutputDataConfig
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
#resource_config ⇒ Types::ResourceConfig
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
#role_arn ⇒ String
The AWS Identity and Access Management (IAM) role configured for the training job.
#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.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
- InProgress
Starting
- Starting the training job.Downloading
- An optional stage for algorithms that supportFile
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 theFailureReason
field ofDescribeTrainingJobResponse
.
^
- Stopped
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime.Stopped
- The training job has stopped.
- Stopping
Stopping
- Stopping the training job.
^
SecondaryStatus
are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
Possible values:
- Starting
- LaunchingMLInstances
- PreparingTrainingStack
- Downloading
- DownloadingTrainingImage
- Training
- Uploading
- Stopping
- Stopped
- MaxRuntimeExceeded
- Completed
- Failed
- Interrupted
- MaxWaitTimeExceeded
#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. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use
this 120-second window to save the model artifacts, so the results of
training are not lost.
#tags ⇒ Array<Types::Tag>
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
#tensor_board_output_config ⇒ Types::TensorBoardOutputConfig
Configuration of storage locations for TensorBoard output.
#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 Amazon SageMaker detects a job failure.
#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 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 theFailureReason
field in the response to aDescribeTrainingJobResponse
call.Stopping
- The training job is stopping.Stopped
- The training job has stopped.
For more detailed information, see SecondaryStatus
.
Possible values:
- InProgress
- Completed
- Failed
- Stopping
- Stopped
#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.
#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. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.