AWS SDK for Go (PILOT)
API Reference

PREVIEW DOCUMENTATION - This is a preview of a new format for the AWS SDK for Go API Reference documentation. For the current AWS SDK for Go API Reference, see https://docs.aws.amazon.com/sdk-for-go/api/.

We welcome your feedback on this new version of the documentation. Send your comments to aws-sdkdocs-feedback@amazon.com.

CreateHyperParameterTuningJobInput

import "github.com/aws/aws-sdk-go/service/sagemaker"

type CreateHyperParameterTuningJobInput struct { HyperParameterTuningJobConfig *HyperParameterTuningJobConfig `type:"structure" required:"true"` HyperParameterTuningJobName *string `min:"1" type:"string" required:"true"` Tags []*Tag `type:"list"` TrainingJobDefinition *HyperParameterTrainingJobDefinition `type:"structure" required:"true"` WarmStartConfig *HyperParameterTuningJobWarmStartConfig `type:"structure"` }

HyperParameterTuningJobConfig

Configures a hyperparameter tuning job.

HyperParameterTuningJobName

Type: *string

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. The name must have { } to { } characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

HyperParameterTuningJobName is a required field

Tags

Describes a tag.

TrainingJobDefinition

Defines the training jobs launched by a hyperparameter tuning job.

WarmStartConfig

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

Method

GoString

func (s CreateHyperParameterTuningJobInput) GoString() string

GoString returns the string representation

SetHyperParameterTuningJobConfig

func (s *CreateHyperParameterTuningJobInput) SetHyperParameterTuningJobConfig(v *HyperParameterTuningJobConfig) *CreateHyperParameterTuningJobInput

SetHyperParameterTuningJobConfig sets the HyperParameterTuningJobConfig field's value.

SetHyperParameterTuningJobName

func (s *CreateHyperParameterTuningJobInput) SetHyperParameterTuningJobName(v string) *CreateHyperParameterTuningJobInput

SetHyperParameterTuningJobName sets the HyperParameterTuningJobName field's value.

SetTags

func (s *CreateHyperParameterTuningJobInput) SetTags(v []*Tag) *CreateHyperParameterTuningJobInput

SetTags sets the Tags field's value.

SetTrainingJobDefinition

func (s *CreateHyperParameterTuningJobInput) SetTrainingJobDefinition(v *HyperParameterTrainingJobDefinition) *CreateHyperParameterTuningJobInput

SetTrainingJobDefinition sets the TrainingJobDefinition field's value.

SetWarmStartConfig

func (s *CreateHyperParameterTuningJobInput) SetWarmStartConfig(v *HyperParameterTuningJobWarmStartConfig) *CreateHyperParameterTuningJobInput

SetWarmStartConfig sets the WarmStartConfig field's value.

String

func (s CreateHyperParameterTuningJobInput) String() string

String returns the string representation

Validate

func (s *CreateHyperParameterTuningJobInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

On this page: