NAME
Paws::SageMaker::TrainingJobDefinition
USAGE
This class represents one of two things:
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 });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->HyperParametersDESCRIPTION
Defines the input needed to run a training job using the algorithm.
ATTRIBUTES
HyperParameters => Paws::SageMaker::HyperParameters
The hyperparameters used for the training job.
REQUIRED InputDataConfig => ArrayRef[Paws::SageMaker::Channel]
An array of Channel objects, each of which specifies an input source.
REQUIRED OutputDataConfig => Paws::SageMaker::OutputDataConfig
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
REQUIRED ResourceConfig => Paws::SageMaker::ResourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
REQUIRED StoppingCondition => Paws::SageMaker::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, 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.
REQUIRED 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 File 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 Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
SEE ALSO
This class forms part of Paws, describing an object used in Paws::SageMaker
BUGS and CONTRIBUTIONS
The source code is located here: https://github.com/pplu/aws-sdk-perl
Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues