# Generated by default/object.tt package Paws::SageMaker::HyperParameterTrainingJobDefinition; use Moose; has AlgorithmSpecification => (is => 'ro', isa => 'Paws::SageMaker::HyperParameterAlgorithmSpecification', required => 1); has CheckpointConfig => (is => 'ro', isa => 'Paws::SageMaker::CheckpointConfig'); has DefinitionName => (is => 'ro', isa => 'Str'); has EnableInterContainerTrafficEncryption => (is => 'ro', isa => 'Bool'); has EnableManagedSpotTraining => (is => 'ro', isa => 'Bool'); has EnableNetworkIsolation => (is => 'ro', isa => 'Bool'); has HyperParameterRanges => (is => 'ro', isa => 'Paws::SageMaker::ParameterRanges'); has InputDataConfig => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::Channel]'); has OutputDataConfig => (is => 'ro', isa => 'Paws::SageMaker::OutputDataConfig', required => 1); has ResourceConfig => (is => 'ro', isa => 'Paws::SageMaker::ResourceConfig', required => 1); has RetryStrategy => (is => 'ro', isa => 'Paws::SageMaker::RetryStrategy'); has RoleArn => (is => 'ro', isa => 'Str', required => 1); has StaticHyperParameters => (is => 'ro', isa => 'Paws::SageMaker::HyperParameters'); has StoppingCondition => (is => 'ro', isa => 'Paws::SageMaker::StoppingCondition', required => 1); has TuningObjective => (is => 'ro', isa => 'Paws::SageMaker::HyperParameterTuningJobObjective'); has VpcConfig => (is => 'ro', isa => 'Paws::SageMaker::VpcConfig'); 1; ### main pod documentation begin ### =head1 NAME Paws::SageMaker::HyperParameterTrainingJobDefinition =head1 USAGE This class represents one of two things: =head3 Arguments in a call to a service Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object. As an example, if Att1 is expected to be a Paws::SageMaker::HyperParameterTrainingJobDefinition object: $service_obj->Method(Att1 => { AlgorithmSpecification => $value, ..., VpcConfig => $value }); =head3 Results returned from an API call Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::HyperParameterTrainingJobDefinition object: $result = $service_obj->Method(...); $result->Att1->AlgorithmSpecification =head1 DESCRIPTION Defines the training jobs launched by a hyperparameter tuning job. =head1 ATTRIBUTES =head2 B AlgorithmSpecification => L The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches. =head2 CheckpointConfig => L =head2 DefinitionName => Str The job definition name. =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 algorithm in distributed training. =head2 EnableManagedSpotTraining => Bool A Boolean indicating whether managed spot training is enabled (C) or not (C). =head2 EnableNetworkIsolation => Bool Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used 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 HyperParameterRanges => L =head2 InputDataConfig => ArrayRef[L] An array of Channel objects that specify the input for the training jobs that the tuning job launches. =head2 B OutputDataConfig => L Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches. =head2 B ResourceConfig => L The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches. Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose C as the C in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1. =head2 RetryStrategy => L The number of times to retry the job when the job fails due to an C. =head2 B RoleArn => Str The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches. =head2 StaticHyperParameters => L Specifies the values of hyperparameters that do not change for the tuning job. =head2 B StoppingCondition => L Specifies a limit to how long a model hyperparameter 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. =head2 TuningObjective => L =head2 VpcConfig => L The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. 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). =head1 SEE ALSO This class forms part of L, describing an object used in L =head1 BUGS and CONTRIBUTIONS The source code is located here: L Please report bugs to: L =cut