package Paws::SageMaker::DescribeTrainingJobResponse; use Moose; has AlgorithmSpecification => (is => 'ro', isa => 'Paws::SageMaker::AlgorithmSpecification', required => 1); has AutoMLJobArn => (is => 'ro', isa => 'Str'); has BillableTimeInSeconds => (is => 'ro', isa => 'Int'); has CheckpointConfig => (is => 'ro', isa => 'Paws::SageMaker::CheckpointConfig'); has CreationTime => (is => 'ro', isa => 'Str', required => 1); has DebugHookConfig => (is => 'ro', isa => 'Paws::SageMaker::DebugHookConfig'); has DebugRuleConfigurations => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::DebugRuleConfiguration]'); has DebugRuleEvaluationStatuses => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::DebugRuleEvaluationStatus]'); has EnableInterContainerTrafficEncryption => (is => 'ro', isa => 'Bool'); has EnableManagedSpotTraining => (is => 'ro', isa => 'Bool'); has EnableNetworkIsolation => (is => 'ro', isa => 'Bool'); has Environment => (is => 'ro', isa => 'Paws::SageMaker::TrainingEnvironmentMap'); has ExperimentConfig => (is => 'ro', isa => 'Paws::SageMaker::ExperimentConfig'); has FailureReason => (is => 'ro', isa => 'Str'); has FinalMetricDataList => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::MetricData]'); has HyperParameters => (is => 'ro', isa => 'Paws::SageMaker::HyperParameters'); has InputDataConfig => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::Channel]'); has LabelingJobArn => (is => 'ro', isa => 'Str'); has LastModifiedTime => (is => 'ro', isa => 'Str'); has ModelArtifacts => (is => 'ro', isa => 'Paws::SageMaker::ModelArtifacts', required => 1); has OutputDataConfig => (is => 'ro', isa => 'Paws::SageMaker::OutputDataConfig'); has ProfilerConfig => (is => 'ro', isa => 'Paws::SageMaker::ProfilerConfig'); has ProfilerRuleConfigurations => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::ProfilerRuleConfiguration]'); has ProfilerRuleEvaluationStatuses => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::ProfilerRuleEvaluationStatus]'); has ProfilingStatus => (is => 'ro', isa => 'Str'); has ResourceConfig => (is => 'ro', isa => 'Paws::SageMaker::ResourceConfig', required => 1); has RetryStrategy => (is => 'ro', isa => 'Paws::SageMaker::RetryStrategy'); has RoleArn => (is => 'ro', isa => 'Str'); has SecondaryStatus => (is => 'ro', isa => 'Str', required => 1); has SecondaryStatusTransitions => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::SecondaryStatusTransition]'); has StoppingCondition => (is => 'ro', isa => 'Paws::SageMaker::StoppingCondition', required => 1); has TensorBoardOutputConfig => (is => 'ro', isa => 'Paws::SageMaker::TensorBoardOutputConfig'); has TrainingEndTime => (is => 'ro', isa => 'Str'); has TrainingJobArn => (is => 'ro', isa => 'Str', required => 1); has TrainingJobName => (is => 'ro', isa => 'Str', required => 1); has TrainingJobStatus => (is => 'ro', isa => 'Str', required => 1); has TrainingStartTime => (is => 'ro', isa => 'Str'); has TrainingTimeInSeconds => (is => 'ro', isa => 'Int'); has TuningJobArn => (is => 'ro', isa => 'Str'); has VpcConfig => (is => 'ro', isa => 'Paws::SageMaker::VpcConfig'); has _request_id => (is => 'ro', isa => 'Str'); ### main pod documentation begin ### =head1 NAME Paws::SageMaker::DescribeTrainingJobResponse =head1 ATTRIBUTES =head2 B AlgorithmSpecification => L Information about the algorithm used for training, and algorithm metadata. =head2 AutoMLJobArn => Str The Amazon Resource Name (ARN) of an AutoML job. =head2 BillableTimeInSeconds => Int The billable time in seconds. Billable time refers to the absolute wall-clock time. Multiply C by the number of instances (C) in your training cluster to get the total compute time Amazon SageMaker will bill you if you run distributed training. The formula is as follows: C . You can calculate the savings from using managed spot training using the formula C<(1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100>. For example, if C is 100 and C is 500, the savings is 80%. =head2 CheckpointConfig => L =head2 B CreationTime => Str A timestamp that indicates when the training job was created. =head2 DebugHookConfig => L =head2 DebugRuleConfigurations => ArrayRef[L] Configuration information for Debugger rules for debugging output tensors. =head2 DebugRuleEvaluationStatuses => ArrayRef[L] Evaluation status of Debugger rules for debugging on a training job. =head2 EnableInterContainerTrafficEncryption => Bool To encrypt all communications between ML compute instances in distributed training, choose C. 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. =head2 EnableManagedSpotTraining => Bool A Boolean indicating whether managed spot training is enabled (C) or not (C). =head2 EnableNetworkIsolation => Bool If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose C. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access. =head2 Environment => L The environment variables to set in the Docker container. =head2 ExperimentConfig => L =head2 FailureReason => Str If the training job failed, the reason it failed. =head2 FinalMetricDataList => ArrayRef[L] A collection of C objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch. =head2 HyperParameters => L Algorithm-specific parameters. =head2 InputDataConfig => ArrayRef[L] An array of C objects that describes each data input channel. =head2 LabelingJobArn => Str The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job. =head2 LastModifiedTime => Str A timestamp that indicates when the status of the training job was last modified. =head2 B ModelArtifacts => L Information about the Amazon S3 location that is configured for storing model artifacts. =head2 OutputDataConfig => L The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts. =head2 ProfilerConfig => L =head2 ProfilerRuleConfigurations => ArrayRef[L] Configuration information for Debugger rules for profiling system and framework metrics. =head2 ProfilerRuleEvaluationStatuses => ArrayRef[L] Evaluation status of Debugger rules for profiling on a training job. =head2 ProfilingStatus => Str Profiling status of a training job. Valid values are: C<"Enabled">, C<"Disabled"> =head2 B ResourceConfig => L Resources, including ML compute instances and ML storage volumes, that are configured for model training. =head2 RetryStrategy => L The number of times to retry the job when the job fails due to an C. =head2 RoleArn => Str The Amazon Web Services Identity and Access Management (IAM) role configured for the training job. =head2 B SecondaryStatus => Str Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see C under SecondaryStatusTransition. Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them: =over =item InProgress =over =item * C - Starting the training job. =item * C - An optional stage for algorithms that support C training input mode. It indicates that data is being downloaded to the ML storage volumes. =item * C - Training is in progress. =item * C - The job stopped because the managed spot training instances were interrupted. =item * C - Training is complete and the model artifacts are being uploaded to the S3 location. =back =item Completed =over =item * C - The training job has completed. =back =item Failed =over =item * C - The training job has failed. The reason for the failure is returned in the C field of C. =back =item Stopped =over =item * C - The job stopped because it exceeded the maximum allowed runtime. =item * C - The job stopped because it exceeded the maximum allowed wait time. =item * C - The training job has stopped. =back =item Stopping =over =item * C - Stopping the training job. =back =back Valid values for C are subject to change. We no longer support the following secondary statuses: =over =item * C =item * C =item * C =back Valid values are: C<"Starting">, C<"LaunchingMLInstances">, C<"PreparingTrainingStack">, C<"Downloading">, C<"DownloadingTrainingImage">, C<"Training">, C<"Uploading">, C<"Stopping">, C<"Stopped">, C<"MaxRuntimeExceeded">, C<"Completed">, C<"Failed">, C<"Interrupted">, C<"MaxWaitTimeExceeded">, C<"Updating">, C<"Restarting"> =head2 SecondaryStatusTransitions => ArrayRef[L] A history of all of the secondary statuses that the training job has transitioned through. =head2 B StoppingCondition => L 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, Amazon SageMaker ends the training job. Use this API to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the C 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. =head2 TensorBoardOutputConfig => L =head2 TrainingEndTime => Str Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of C 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. =head2 B TrainingJobArn => Str The Amazon Resource Name (ARN) of the training job. =head2 B TrainingJobName => Str Name of the model training job. =head2 B TrainingJobStatus => Str The status of the training job. Amazon SageMaker provides the following training job statuses: =over =item * C - The training is in progress. =item * C - The training job has completed. =item * C - The training job has failed. To see the reason for the failure, see the C field in the response to a C call. =item * C - The training job is stopping. =item * C - The training job has stopped. =back For more detailed information, see C. Valid values are: C<"InProgress">, C<"Completed">, C<"Failed">, C<"Stopping">, C<"Stopped"> =head2 TrainingStartTime => Str 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 C. 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. =head2 TrainingTimeInSeconds => Int The training time in seconds. =head2 TuningJobArn => Str The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job. =head2 VpcConfig => L 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 (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html). =head2 _request_id => Str =cut 1;