Struct aws_sdk_sagemaker::operation::describe_hyper_parameter_tuning_job::DescribeHyperParameterTuningJobOutput

#[non_exhaustive]
pub struct DescribeHyperParameterTuningJobOutput {

    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 autotune: Option<Autotune>,
    pub failure_reason: Option<String>,
    pub tuning_job_completion_details: Option<HyperParameterTuningJobCompletionDetails>,
    pub consumed_resources: Option<HyperParameterTuningJobConsumedResources>,
    /* private fields */
}

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 the hyperparameter tuning job.

hyper_parameter_tuning_job_arn: Option<String>

The Amazon Resource Name (ARN) of the tuning job.

hyper_parameter_tuning_job_config: Option<HyperParameterTuningJobConfig>

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

training_job_definition: Option<HyperParameterTrainingJobDefinition>

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

training_job_definitions: Option<Vec<HyperParameterTrainingJobDefinition>>

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

hyper_parameter_tuning_job_status: Option<HyperParameterTuningJobStatus>

The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.

creation_time: Option<DateTime>

The date and time that the tuning job started.

hyper_parameter_tuning_end_time: Option<DateTime>

The date and time that the tuning job ended.

last_modified_time: Option<DateTime>

The date and time that the status of the tuning job was modified.

training_job_status_counters: Option<TrainingJobStatusCounters>

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

objective_status_counters: Option<ObjectiveStatusCounters>

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

best_training_job: Option<HyperParameterTrainingJobSummary>

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

overall_best_training_job: Option<HyperParameterTrainingJobSummary>

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>

The configuration for starting the hyperparameter parameter 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.

autotune: Option<Autotune>

A flag to indicate if autotune is enabled for the hyperparameter tuning job.

failure_reason: Option<String>

If the tuning job failed, the reason it failed.

tuning_job_completion_details: Option<HyperParameterTuningJobCompletionDetails>

Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

consumed_resources: Option<HyperParameterTuningJobConsumedResources>

The total resources consumed by your hyperparameter tuning job.

Implementations

impl DescribeHyperParameterTuningJobOutput

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

The name of the hyperparameter tuning job.

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

The Amazon Resource Name (ARN) of the tuning job.

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

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

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

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

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

A list of the HyperParameterTrainingJobDefinition objects launched for this 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 the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.

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

The date and time that the tuning job started.

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

The date and time that the tuning job ended.

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

The date and time that the status of the tuning job was modified.

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

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

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

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

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

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

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

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

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

The configuration for starting the hyperparameter parameter 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.

pub fn autotune(&self) -> Option<&Autotune>

A flag to indicate if autotune is enabled for the hyperparameter tuning job.

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

If the tuning job failed, the reason it failed.

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

Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

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

The total resources consumed by your hyperparameter tuning job.

impl DescribeHyperParameterTuningJobOutput

pub fn builder() -> DescribeHyperParameterTuningJobOutputBuilder

Creates a new builder-style object to manufacture DescribeHyperParameterTuningJobOutput.

Trait Implementations

impl Clone for DescribeHyperParameterTuningJobOutput

fn clone(&self) -> DescribeHyperParameterTuningJobOutput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribeHyperParameterTuningJobOutput

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

Formats the value using the given formatter.

impl PartialEq for DescribeHyperParameterTuningJobOutput

fn eq(&self, other: &DescribeHyperParameterTuningJobOutput) -> 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 RequestId for DescribeHyperParameterTuningJobOutput

fn request_id(&self) -> Option<&str>

Returns the request ID, or None if the service could not be reached.

impl StructuralPartialEq for DescribeHyperParameterTuningJobOutput