Struct aws_sdk_sagemaker::model::hyper_parameter_tuning_job_search_entity::Builder

pub struct Builder { /* private fields */ }

A builder for HyperParameterTuningJobSearchEntity.

Implementations

impl Builder

pub fn hyper_parameter_tuning_job_name(self, input: impl Into<String>) -> Self

The name of a hyperparameter tuning job.

pub fn set_hyper_parameter_tuning_job_name(self, input: Option<String>) -> Self

The name of a hyperparameter tuning job.

pub fn hyper_parameter_tuning_job_arn(self, input: impl Into<String>) -> Self

The Amazon Resource Name (ARN) of a hyperparameter tuning job.

pub fn set_hyper_parameter_tuning_job_arn(self, input: Option<String>) -> Self

The Amazon Resource Name (ARN) of a hyperparameter tuning job.

pub fn hyper_parameter_tuning_job_config(
    self,
    input: HyperParameterTuningJobConfig
) -> Self

Configures a hyperparameter tuning job.

pub fn set_hyper_parameter_tuning_job_config(
    self,
    input: Option<HyperParameterTuningJobConfig>
) -> Self

Configures a hyperparameter tuning job.

pub fn training_job_definition(
    self,
    input: HyperParameterTrainingJobDefinition
) -> Self

Defines the training jobs launched by a hyperparameter tuning job.

pub fn set_training_job_definition(
    self,
    input: Option<HyperParameterTrainingJobDefinition>
) -> Self

Defines the training jobs launched by a hyperparameter tuning job.

pub fn training_job_definitions(
    self,
    input: HyperParameterTrainingJobDefinition
) -> Self

Appends an item to training_job_definitions.

To override the contents of this collection use set_training_job_definitions.

The job definitions included in a hyperparameter tuning job.

pub fn set_training_job_definitions(
    self,
    input: Option<Vec<HyperParameterTrainingJobDefinition>>
) -> Self

The job definitions included in a hyperparameter tuning job.

pub fn hyper_parameter_tuning_job_status(
    self,
    input: HyperParameterTuningJobStatus
) -> Self

The status of a hyperparameter tuning job.

pub fn set_hyper_parameter_tuning_job_status(
    self,
    input: Option<HyperParameterTuningJobStatus>
) -> Self

The status of a hyperparameter tuning job.

pub fn creation_time(self, input: DateTime) -> Self

The time that a hyperparameter tuning job was created.

pub fn set_creation_time(self, input: Option<DateTime>) -> Self

The time that a hyperparameter tuning job was created.

pub fn hyper_parameter_tuning_end_time(self, input: DateTime) -> Self

The time that a hyperparameter tuning job ended.

pub fn set_hyper_parameter_tuning_end_time(self, input: Option<DateTime>) -> Self

The time that a hyperparameter tuning job ended.

pub fn last_modified_time(self, input: DateTime) -> Self

The time that a hyperparameter tuning job was last modified.

pub fn set_last_modified_time(self, input: Option<DateTime>) -> Self

The time that a hyperparameter tuning job was last modified.

pub fn training_job_status_counters(
    self,
    input: TrainingJobStatusCounters
) -> Self

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

pub fn set_training_job_status_counters(
    self,
    input: Option<TrainingJobStatusCounters>
) -> Self

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

pub fn objective_status_counters(self, input: ObjectiveStatusCounters) -> Self

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

pub fn set_objective_status_counters(
    self,
    input: Option<ObjectiveStatusCounters>
) -> Self

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

pub fn best_training_job(self, input: HyperParameterTrainingJobSummary) -> Self

The container for the summary information about a training job.

pub fn set_best_training_job(
    self,
    input: Option<HyperParameterTrainingJobSummary>
) -> Self

The container for the summary information about a training job.

pub fn overall_best_training_job(
    self,
    input: HyperParameterTrainingJobSummary
) -> Self

The container for the summary information about a training job.

pub fn set_overall_best_training_job(
    self,
    input: Option<HyperParameterTrainingJobSummary>
) -> Self

The container for the summary information about a training job.

pub fn warm_start_config(
    self,
    input: HyperParameterTuningJobWarmStartConfig
) -> Self

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.

pub fn set_warm_start_config(
    self,
    input: Option<HyperParameterTuningJobWarmStartConfig>
) -> Self

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.

pub fn failure_reason(self, input: impl Into<String>) -> Self

The error that was created when a hyperparameter tuning job failed.

pub fn set_failure_reason(self, input: Option<String>) -> Self

The error that was created when a hyperparameter tuning job failed.

pub fn tags(self, input: Tag) -> Self

Appends an item to tags.

To override the contents of this collection use set_tags.

The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources.

pub fn build(self) -> HyperParameterTuningJobSearchEntity

Consumes the builder and constructs a HyperParameterTuningJobSearchEntity.

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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

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

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for Builder