Struct HyperParameterTrainingJobSummary 

pub struct HyperParameterTrainingJobSummary {
    pub creation_time: f64,
    pub failure_reason: Option<String>,
    pub final_hyper_parameter_tuning_job_objective_metric: Option<FinalHyperParameterTuningJobObjectiveMetric>,
    pub objective_status: Option<String>,
    pub training_end_time: Option<f64>,
    pub training_job_arn: String,
    pub training_job_definition_name: Option<String>,
    pub training_job_name: String,
    pub training_job_status: String,
    pub training_start_time: Option<f64>,
    pub tuned_hyper_parameters: HashMap<String, String>,
    pub tuning_job_name: Option<String>,
}

Specifies summary information about a training job.

Fields

creation_time: f64

The date and time that the training job was created.

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<String>

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.

training_end_time: Option<f64>

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 Amazon SageMaker detects a job failure.

training_job_arn: String

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

training_job_definition_name: Option<String>

The training job definition name.

training_job_name: String

The name of the training job.

training_job_status: String

The status of the training job.

training_start_time: Option<f64>

The date and time that the training job started.

tuned_hyper_parameters: HashMap<String, String>

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

tuning_job_name: Option<String>

The HyperParameter tuning job that launched the training job.

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 Default for HyperParameterTrainingJobSummary

fn default() -> HyperParameterTrainingJobSummary

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for HyperParameterTrainingJobSummary

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

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