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

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Contains information about a training job.

Returned by:

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_specificationTypes::AlgorithmSpecification

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

Returns:

#creation_timeTime

A timestamp that indicates when the training job was created.

Returns:

  • (Time)

    A timestamp that indicates when the training job was created.

#enable_inter_container_traffic_encryptionBoolean

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

Returns:

  • (Boolean)

    To encrypt all communications between ML compute instances in distributed training, choose True.

#enable_network_isolationBoolean

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

Returns:

  • (Boolean)

    If the TrainingJob was created with network isolation, the value is set to true.

#failure_reasonString

If the training job failed, the reason it failed.

Returns:

  • (String)

    If the training job failed, the reason it failed.

#final_metric_data_listArray<Types::MetricData>

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

Returns:

  • (Array<Types::MetricData>)

    A list of final metric values that are set when the training job completes.

#hyper_parametersHash<String,String>

Algorithm-specific parameters.

Returns:

  • (Hash<String,String>)

    Algorithm-specific parameters.

#input_data_configArray<Types::Channel>

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

Returns:

  • (Array<Types::Channel>)

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

#labeling_job_arnString

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

Returns:

  • (String)

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

#last_modified_timeTime

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

Returns:

  • (Time)

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

#model_artifactsTypes::ModelArtifacts

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

Returns:

  • (Types::ModelArtifacts)

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

#output_data_configTypes::OutputDataConfig

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

Returns:

  • (Types::OutputDataConfig)

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

#resource_configTypes::ResourceConfig

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

Returns:

  • (Types::ResourceConfig)

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

#role_arnString

The AWS Identity and Access Management (IAM) role configured for the training job.

Returns:

  • (String)

    The AWS Identity and Access Management (IAM) role configured for the training job.

#secondary_statusString

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 support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

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

Completed
  • Completed - The training job has completed.

^

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

^

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

  • Stopped - The training job has stopped.

Stopping
  • Stopping - Stopping the training job.

^

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTrainingStack

  • DownloadingTrainingImage

    Possible values:

    • Starting
    • LaunchingMLInstances
    • PreparingTrainingStack
    • Downloading
    • DownloadingTrainingImage
    • Training
    • Uploading
    • Stopping
    • Stopped
    • MaxRuntimeExceeded
    • Completed
    • Failed

Returns:

  • (String)

    Provides detailed information about the state of the training job.

#secondary_status_transitionsArray<Types::SecondaryStatusTransition>

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

Returns:

#stopping_conditionTypes::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.

Returns:

#tagsArray<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.

Returns:

  • (Array<Types::Tag>)

    An array of key-value pairs.

#training_end_timeTime

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

Returns:

  • (Time)

    Indicates the time when the training job ends on training instances.

#training_job_arnString

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

Returns:

  • (String)

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

#training_job_nameString

The name of the training job.

Returns:

  • (String)

    The name of the training job.

#training_job_statusString

The status of the training job.

Training job statuses are:

  • InProgress - The training is in progress.

  • Completed - The training job has completed.

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

  • Stopping - The training job is stopping.

  • Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

Possible values:

  • InProgress
  • Completed
  • Failed
  • Stopping
  • Stopped

Returns:

  • (String)

    The status of the training job.

#training_start_timeTime

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

Returns:

  • (Time)

    Indicates the time when the training job starts on training instances.

#tuning_job_arnString

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

Returns:

  • (String)

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

#vpc_configTypes::VpcConfig

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

Returns: