Struct rusoto_sagemaker::CreateHyperParameterTuningJobRequest

pub struct CreateHyperParameterTuningJobRequest {
    pub hyper_parameter_tuning_job_config: HyperParameterTuningJobConfig,
    pub hyper_parameter_tuning_job_name: String,
    pub tags: Option<Vec<Tag>>,
    pub training_job_definition: Option<HyperParameterTrainingJobDefinition>,
    pub warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>,
}

Fields

hyper_parameter_tuning_job_config: HyperParameterTuningJobConfig

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see automatic-model-tuning

hyper_parameter_tuning_job_name: 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.

tags: Option<Vec<Tag>>

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see AWS Tagging Strategies.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

training_job_definition: Option<HyperParameterTrainingJobDefinition>

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>

Specifies the configuration for starting the hyperparameter tuning job using one or more previous 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. If you specify IDENTICALDATAAND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job 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.

Trait Implementations

impl PartialEq<CreateHyperParameterTuningJobRequest> for CreateHyperParameterTuningJobRequest

fn eq(&self, other: &CreateHyperParameterTuningJobRequest) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &CreateHyperParameterTuningJobRequest) -> bool

This method tests for !=.

impl Default for CreateHyperParameterTuningJobRequest

fn default() -> CreateHyperParameterTuningJobRequest

Returns the "default value" for a type.

impl Clone for CreateHyperParameterTuningJobRequest

fn clone(&self) -> CreateHyperParameterTuningJobRequest

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateHyperParameterTuningJobRequest

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Serialize for CreateHyperParameterTuningJobRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.