Paws::SageMaker::HyperParameterTrainingJobDefinition
This class represents one of two things:
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 });
Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::HyperParameterTrainingJobDefinition object:
$result = $service_obj->Method(...); $result->Att1->AlgorithmSpecification
Defines the training jobs launched by a hyperparameter tuning job.
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
To encrypt all communications between ML compute instances in distributed training, specify C<True>. Encryption provides greater security for distributed training, but training take longer because of the additional communications between ML compute instances.
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.
The Semantic Segmentation built-in algorithm does not support network isolation.
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
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 File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
File
TrainingInputMode
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
Specifies the values of hyperparameters that do not change for the tuning job.
Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
SIGTERM
When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
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 (http://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).
This class forms part of Paws, describing an object used in Paws::SageMaker
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
To install Paws::SDK::Config, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Paws::SDK::Config
CPAN shell
perl -MCPAN -e shell install Paws::SDK::Config
For more information on module installation, please visit the detailed CPAN module installation guide.