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::HyperParameterTrainingJobDefinition object:

  $service_obj->Method(Att1 => { AlgorithmSpecification => $value, ..., VpcConfig => $value  });

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(...);


Defines the training jobs launched by a hyperparameter tuning job.


REQUIRED AlgorithmSpecification => Paws::SageMaker::HyperParameterAlgorithmSpecification

  The HyperParameterAlgorithmSpecification object that specifies the
resource algorithm to use for the training jobs that the tuning job

EnableInterContainerTrafficEncryption => Bool

  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.

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.

The Semantic Segmentation built-in algorithm does not support network isolation.

InputDataConfig => ArrayRef[Paws::SageMaker::Channel]

  An array of Channel objects that specify the input for the training
jobs that the tuning job launches.

REQUIRED OutputDataConfig => Paws::SageMaker::OutputDataConfig

  Specifies the path to the Amazon S3 bucket where you store model
artifacts from the training jobs that the tuning job launches.

REQUIRED ResourceConfig => Paws::SageMaker::ResourceConfig

  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.

REQUIRED RoleArn => Str

  The Amazon Resource Name (ARN) of the IAM role associated with the
training jobs that the tuning job launches.

StaticHyperParameters => Paws::SageMaker::HyperParameters

  Specifies the values of hyperparameters that do not change for the
tuning job.

REQUIRED StoppingCondition => Paws::SageMaker::StoppingCondition

  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.

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.

VpcConfig => Paws::SageMaker::VpcConfig

  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


This class forms part of Paws, describing an object used in Paws::SageMaker


The source code is located here:

Please report bugs to: