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.

debugRuleConfigurations

var debugRuleConfigurations: List<DebugRuleConfiguration>?

Information about the debug rule configuration.

debugRuleEvaluationStatuses

var debugRuleEvaluationStatuses: List<DebugRuleEvaluationStatus>?

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

enableInterContainerTrafficEncryption

var enableInterContainerTrafficEncryption: 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.

enableManagedSpotTraining

var enableManagedSpotTraining: 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.

enableNetworkIsolation

var enableNetworkIsolation: 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.

environment

var environment: Map<String, String>?

The environment variables to set in the Docker container.

experimentConfig

var experimentConfig: ExperimentConfig?

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

failureReason

var failureReason: String?

If the training job failed, the reason it failed.

finalMetricDataList

var finalMetricDataList: List<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.

hyperParameters

var hyperParameters: Map<String, String>?

Algorithm-specific parameters.

inputDataConfig

var inputDataConfig: List<Channel>?

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

labelingJobArn

var labelingJobArn: String?

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

lastModifiedTime

var lastModifiedTime: Instant?

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

modelArtifacts

var modelArtifacts: ModelArtifacts?

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

outputDataConfig

var outputDataConfig: OutputDataConfig?

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

profilerConfig

var profilerConfig: ProfilerConfig?

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

resourceConfig

var resourceConfig: ResourceConfig?

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

retryStrategy

var retryStrategy: RetryStrategy?

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

roleArn

var roleArn: String?

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

secondaryStatus

var secondaryStatus: SecondaryStatus?

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.

secondaryStatusTransitions

var secondaryStatusTransitions: List<SecondaryStatusTransition>?

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

stoppingCondition

var stoppingCondition: 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.