Struct aws_sdk_sagemaker::types::HyperParameterTuningJobSearchEntity

#[non_exhaustive]
pub struct HyperParameterTuningJobSearchEntity {

    pub hyper_parameter_tuning_job_name: Option<String>,
    pub hyper_parameter_tuning_job_arn: Option<String>,
    pub hyper_parameter_tuning_job_config: Option<HyperParameterTuningJobConfig>,
    pub training_job_definition: Option<HyperParameterTrainingJobDefinition>,
    pub training_job_definitions: Option<Vec<HyperParameterTrainingJobDefinition>>,
    pub hyper_parameter_tuning_job_status: Option<HyperParameterTuningJobStatus>,
    pub creation_time: Option<DateTime>,
    pub hyper_parameter_tuning_end_time: Option<DateTime>,
    pub last_modified_time: Option<DateTime>,
    pub training_job_status_counters: Option<TrainingJobStatusCounters>,
    pub objective_status_counters: Option<ObjectiveStatusCounters>,
    pub best_training_job: Option<HyperParameterTrainingJobSummary>,
    pub overall_best_training_job: Option<HyperParameterTrainingJobSummary>,
    pub warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>,
    pub failure_reason: Option<String>,
    pub tuning_job_completion_details: Option<HyperParameterTuningJobCompletionDetails>,
    pub consumed_resources: Option<HyperParameterTuningJobConsumedResources>,
    pub tags: Option<Vec<Tag>>,
}

An entity returned by the SearchRecord API containing the properties of a hyperparameter tuning job.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

hyper_parameter_tuning_job_name: Option<String>

The name of a hyperparameter tuning job.

hyper_parameter_tuning_job_arn: Option<String>

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

hyper_parameter_tuning_job_config: Option<HyperParameterTuningJobConfig>

Configures a hyperparameter tuning job.

training_job_definition: Option<HyperParameterTrainingJobDefinition>

Defines the training jobs launched by a hyperparameter tuning job.

training_job_definitions: Option<Vec<HyperParameterTrainingJobDefinition>>

The job definitions included in a hyperparameter tuning job.

hyper_parameter_tuning_job_status: Option<HyperParameterTuningJobStatus>

The status of a hyperparameter tuning job.

creation_time: Option<DateTime>

The time that a hyperparameter tuning job was created.

hyper_parameter_tuning_end_time: Option<DateTime>

The time that a hyperparameter tuning job ended.

last_modified_time: Option<DateTime>

The time that a hyperparameter tuning job was last modified.

training_job_status_counters: Option<TrainingJobStatusCounters>

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

objective_status_counters: Option<ObjectiveStatusCounters>

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.

best_training_job: Option<HyperParameterTrainingJobSummary>

The container for the summary information about a training job.

overall_best_training_job: Option<HyperParameterTrainingJobSummary>

The container for the summary information about a training job.

warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>

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.

failure_reason: Option<String>

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

tuning_job_completion_details: Option<HyperParameterTuningJobCompletionDetails>

Information about either a current or completed hyperparameter tuning job.

consumed_resources: Option<HyperParameterTuningJobConsumedResources>

The total amount of resources consumed by a hyperparameter tuning job.

tags: Option<Vec<Tag>>

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

Implementations

impl HyperParameterTuningJobSearchEntity

pub fn hyper_parameter_tuning_job_name(&self) -> Option<&str>

The name of a hyperparameter tuning job.

pub fn hyper_parameter_tuning_job_arn(&self) -> Option<&str>

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

pub fn hyper_parameter_tuning_job_config( &self ) -> Option<&HyperParameterTuningJobConfig>

Configures a hyperparameter tuning job.

pub fn training_job_definition( &self ) -> Option<&HyperParameterTrainingJobDefinition>

Defines the training jobs launched by a hyperparameter tuning job.

pub fn training_job_definitions(&self) -> &[HyperParameterTrainingJobDefinition]

The job definitions included in a hyperparameter tuning job.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .training_job_definitions.is_none().

pub fn hyper_parameter_tuning_job_status( &self ) -> Option<&HyperParameterTuningJobStatus>

The status of a hyperparameter tuning job.

pub fn creation_time(&self) -> Option<&DateTime>

The time that a hyperparameter tuning job was created.

pub fn hyper_parameter_tuning_end_time(&self) -> Option<&DateTime>

The time that a hyperparameter tuning job ended.

pub fn last_modified_time(&self) -> Option<&DateTime>

The time that a hyperparameter tuning job was last modified.

pub fn training_job_status_counters(&self) -> Option<&TrainingJobStatusCounters>

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

pub fn objective_status_counters(&self) -> Option<&ObjectiveStatusCounters>

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) -> Option<&HyperParameterTrainingJobSummary>

The container for the summary information about a training job.

pub fn overall_best_training_job( &self ) -> Option<&HyperParameterTrainingJobSummary>

The container for the summary information about a training job.

pub fn warm_start_config( &self ) -> Option<&HyperParameterTuningJobWarmStartConfig>

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) -> Option<&str>

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

pub fn tuning_job_completion_details( &self ) -> Option<&HyperParameterTuningJobCompletionDetails>

Information about either a current or completed hyperparameter tuning job.

pub fn consumed_resources( &self ) -> Option<&HyperParameterTuningJobConsumedResources>

The total amount of resources consumed by a hyperparameter tuning job.

pub fn tags(&self) -> &[Tag]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

impl HyperParameterTuningJobSearchEntity

pub fn builder() -> HyperParameterTuningJobSearchEntityBuilder

Creates a new builder-style object to manufacture HyperParameterTuningJobSearchEntity.

Trait Implementations

impl Clone for HyperParameterTuningJobSearchEntity

fn clone(&self) -> HyperParameterTuningJobSearchEntity

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterTuningJobSearchEntity

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

Formats the value using the given formatter.

impl PartialEq for HyperParameterTuningJobSearchEntity

fn eq(&self, other: &HyperParameterTuningJobSearchEntity) -> 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 HyperParameterTuningJobSearchEntity