# Generated by default/object.tt package Paws::SageMaker::TrainingSpecification; use Moose; has MetricDefinitions => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::MetricDefinition]'); has SupportedHyperParameters => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::HyperParameterSpecification]'); has SupportedTrainingInstanceTypes => (is => 'ro', isa => 'ArrayRef[Str|Undef]', required => 1); has SupportedTuningJobObjectiveMetrics => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::HyperParameterTuningJobObjective]'); has SupportsDistributedTraining => (is => 'ro', isa => 'Bool'); has TrainingChannels => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::ChannelSpecification]', required => 1); has TrainingImage => (is => 'ro', isa => 'Str', required => 1); has TrainingImageDigest => (is => 'ro', isa => 'Str'); 1; ### main pod documentation begin ### =head1 NAME Paws::SageMaker::TrainingSpecification =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::TrainingSpecification object: $service_obj->Method(Att1 => { MetricDefinitions => $value, ..., TrainingImageDigest => $value }); =head3 Results returned from an API call Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::TrainingSpecification object: $result = $service_obj->Method(...); $result->Att1->MetricDefinitions =head1 DESCRIPTION Defines how the algorithm is used for a training job. =head1 ATTRIBUTES =head2 MetricDefinitions => ArrayRef[L] A list of C objects, which are used for parsing metrics generated by the algorithm. =head2 SupportedHyperParameters => ArrayRef[L] A list of the C objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.E =head2 B SupportedTrainingInstanceTypes => ArrayRef[Str|Undef] A list of the instance types that this algorithm can use for training. =head2 SupportedTuningJobObjectiveMetrics => ArrayRef[L] A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job. =head2 SupportsDistributedTraining => Bool Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training. =head2 B TrainingChannels => ArrayRef[L] A list of C objects, which specify the input sources to be used by the algorithm. =head2 B TrainingImage => Str The Amazon ECR registry path of the Docker image that contains the training algorithm. =head2 TrainingImageDigest => Str An MD5 hash of the training algorithm that identifies the Docker image used for training. =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