Struct HyperParameterTrainingJobSummary

#[non_exhaustive]
pub struct HyperParameterTrainingJobSummary {
    pub training_job_definition_name: Option<String>,
    pub training_job_name: Option<String>,
    pub training_job_arn: Option<String>,
    pub tuning_job_name: Option<String>,
    pub creation_time: Option<DateTime>,
    pub training_start_time: Option<DateTime>,
    pub training_end_time: Option<DateTime>,
    pub training_job_status: Option<TrainingJobStatus>,
    pub tuned_hyper_parameters: Option<HashMap<String, String>>,
    pub failure_reason: Option<String>,
    pub final_hyper_parameter_tuning_job_objective_metric: Option<FinalHyperParameterTuningJobObjectiveMetric>,
    pub objective_status: Option<ObjectiveStatus>,
}

The container for the summary information about a training 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.

training_job_definition_name: Option<String>

The training job definition name.

training_job_name: Option<String>

The name of the training job.

training_job_arn: Option<String>

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

tuning_job_name: Option<String>

The HyperParameter tuning job that launched the training job.

creation_time: Option<DateTime>

The date and time that the training job was created.

training_start_time: Option<DateTime>

The date and time that the training job started.

training_end_time: Option<DateTime>

Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.

training_job_status: Option<TrainingJobStatus>

The status of the training job.

tuned_hyper_parameters: Option<HashMap<String, String>>

A list of the hyperparameters for which you specified ranges to search.

failure_reason: Option<String>

The reason that the training job failed.

final_hyper_parameter_tuning_job_objective_metric: Option<FinalHyperParameterTuningJobObjectiveMetric>

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

objective_status: Option<ObjectiveStatus>

The status of the objective metric for the training job:

• Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

• Pending: The training job is in progress and evaluation of its final objective metric is pending.

• Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

Implementations

impl HyperParameterTrainingJobSummary

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

The training job definition name.

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

The name of the training job.

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

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

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

The HyperParameter tuning job that launched the training job.

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

The date and time that the training job was created.

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

The date and time that the training job started.

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

Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.

pub fn training_job_status(&self) -> Option<&TrainingJobStatus>

The status of the training job.

pub fn tuned_hyper_parameters(&self) -> Option<&HashMap<String, String>>

A list of the hyperparameters for which you specified ranges to search.

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

The reason that the training job failed.

pub fn final_hyper_parameter_tuning_job_objective_metric( &self, ) -> Option<&FinalHyperParameterTuningJobObjectiveMetric>

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

pub fn objective_status(&self) -> Option<&ObjectiveStatus>

The status of the objective metric for the training job:

• Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

• Pending: The training job is in progress and evaluation of its final objective metric is pending.

• Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

impl HyperParameterTrainingJobSummary

pub fn builder() -> HyperParameterTrainingJobSummaryBuilder

Creates a new builder-style object to manufacture HyperParameterTrainingJobSummary.

Trait Implementations

impl Clone for HyperParameterTrainingJobSummary

fn clone(&self) -> HyperParameterTrainingJobSummary

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterTrainingJobSummary

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

Formats the value using the given formatter.

impl PartialEq for HyperParameterTrainingJobSummary

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for HyperParameterTrainingJobSummary