# Generated by default/object.tt package Paws::SageMaker::TrainingJobDefinition; use Moose; has HyperParameters => (is => 'ro', isa => 'Paws::SageMaker::HyperParameters'); has InputDataConfig => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::Channel]', required => 1); has OutputDataConfig => (is => 'ro', isa => 'Paws::SageMaker::OutputDataConfig', required => 1); has ResourceConfig => (is => 'ro', isa => 'Paws::SageMaker::ResourceConfig', required => 1); has StoppingCondition => (is => 'ro', isa => 'Paws::SageMaker::StoppingCondition', required => 1); has TrainingInputMode => (is => 'ro', isa => 'Str', required => 1); 1; ### main pod documentation begin ### =head1 NAME Paws::SageMaker::TrainingJobDefinition =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::TrainingJobDefinition object: $service_obj->Method(Att1 => { HyperParameters => $value, ..., TrainingInputMode => $value }); =head3 Results returned from an API call Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::TrainingJobDefinition object: $result = $service_obj->Method(...); $result->Att1->HyperParameters =head1 DESCRIPTION Defines the input needed to run a training job using the algorithm. =head1 ATTRIBUTES =head2 HyperParameters => L The hyperparameters used for the training job. =head2 B InputDataConfig => ArrayRef[L] An array of C objects, each of which specifies an input source. =head2 B OutputDataConfig => L the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts. =head2 B ResourceConfig => L The resources, including the ML compute instances and ML storage volumes, to use for model training. =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 SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts. =head2 B TrainingInputMode => Str The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html). If an algorithm supports the C input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the C input mode, Amazon SageMaker streams data directly from S3 to the container. =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