#[non_exhaustive]pub struct DescribeHyperParameterTuningJobOutput {Show 18 fields
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)§
This struct is marked as non-exhaustive
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.
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§
Source§impl DescribeHyperParameterTuningJobOutput
impl DescribeHyperParameterTuningJobOutput
Sourcepub fn hyper_parameter_tuning_job_name(&self) -> Option<&str>
pub fn hyper_parameter_tuning_job_name(&self) -> Option<&str>
The name of the hyperparameter tuning job.
Sourcepub fn hyper_parameter_tuning_job_arn(&self) -> Option<&str>
pub fn hyper_parameter_tuning_job_arn(&self) -> Option<&str>
The Amazon Resource Name (ARN) of the tuning job.
Sourcepub fn hyper_parameter_tuning_job_config(
&self,
) -> Option<&HyperParameterTuningJobConfig>
pub fn hyper_parameter_tuning_job_config( &self, ) -> Option<&HyperParameterTuningJobConfig>
The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
Sourcepub fn training_job_definition(
&self,
) -> Option<&HyperParameterTrainingJobDefinition>
pub fn training_job_definition( &self, ) -> Option<&HyperParameterTrainingJobDefinition>
The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
Sourcepub fn training_job_definitions(&self) -> &[HyperParameterTrainingJobDefinition]
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().
Sourcepub fn hyper_parameter_tuning_job_status(
&self,
) -> Option<&HyperParameterTuningJobStatus>
pub fn hyper_parameter_tuning_job_status( &self, ) -> Option<&HyperParameterTuningJobStatus>
The status of the tuning job.
Sourcepub fn creation_time(&self) -> Option<&DateTime>
pub fn creation_time(&self) -> Option<&DateTime>
The date and time that the tuning job started.
Sourcepub fn hyper_parameter_tuning_end_time(&self) -> Option<&DateTime>
pub fn hyper_parameter_tuning_end_time(&self) -> Option<&DateTime>
The date and time that the tuning job ended.
Sourcepub fn last_modified_time(&self) -> Option<&DateTime>
pub fn last_modified_time(&self) -> Option<&DateTime>
The date and time that the status of the tuning job was modified.
Sourcepub fn training_job_status_counters(&self) -> Option<&TrainingJobStatusCounters>
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.
Sourcepub fn objective_status_counters(&self) -> Option<&ObjectiveStatusCounters>
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.
Sourcepub fn best_training_job(&self) -> Option<&HyperParameterTrainingJobSummary>
pub fn best_training_job(&self) -> Option<&HyperParameterTrainingJobSummary>
A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.
Sourcepub fn overall_best_training_job(
&self,
) -> Option<&HyperParameterTrainingJobSummary>
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.
Sourcepub fn warm_start_config(
&self,
) -> Option<&HyperParameterTuningJobWarmStartConfig>
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.
Sourcepub fn autotune(&self) -> Option<&Autotune>
pub fn autotune(&self) -> Option<&Autotune>
A flag to indicate if autotune is enabled for the hyperparameter tuning job.
Sourcepub fn failure_reason(&self) -> Option<&str>
pub fn failure_reason(&self) -> Option<&str>
If the tuning job failed, the reason it failed.
Sourcepub fn tuning_job_completion_details(
&self,
) -> Option<&HyperParameterTuningJobCompletionDetails>
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.
Sourcepub fn consumed_resources(
&self,
) -> Option<&HyperParameterTuningJobConsumedResources>
pub fn consumed_resources( &self, ) -> Option<&HyperParameterTuningJobConsumedResources>
The total resources consumed by your hyperparameter tuning job.
Source§impl DescribeHyperParameterTuningJobOutput
impl DescribeHyperParameterTuningJobOutput
Sourcepub fn builder() -> DescribeHyperParameterTuningJobOutputBuilder
pub fn builder() -> DescribeHyperParameterTuningJobOutputBuilder
Creates a new builder-style object to manufacture DescribeHyperParameterTuningJobOutput.
Trait Implementations§
Source§impl Clone for DescribeHyperParameterTuningJobOutput
impl Clone for DescribeHyperParameterTuningJobOutput
Source§fn clone(&self) -> DescribeHyperParameterTuningJobOutput
fn clone(&self) -> DescribeHyperParameterTuningJobOutput
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreSource§impl PartialEq for DescribeHyperParameterTuningJobOutput
impl PartialEq for DescribeHyperParameterTuningJobOutput
Source§fn eq(&self, other: &DescribeHyperParameterTuningJobOutput) -> bool
fn eq(&self, other: &DescribeHyperParameterTuningJobOutput) -> bool
self and other values to be equal, and is used by ==.Source§impl RequestId for DescribeHyperParameterTuningJobOutput
impl RequestId for DescribeHyperParameterTuningJobOutput
Source§fn request_id(&self) -> Option<&str>
fn request_id(&self) -> Option<&str>
None if the service could not be reached.impl StructuralPartialEq for DescribeHyperParameterTuningJobOutput
Auto Trait Implementations§
impl Freeze for DescribeHyperParameterTuningJobOutput
impl RefUnwindSafe for DescribeHyperParameterTuningJobOutput
impl Send for DescribeHyperParameterTuningJobOutput
impl Sync for DescribeHyperParameterTuningJobOutput
impl Unpin for DescribeHyperParameterTuningJobOutput
impl UnwindSafe for DescribeHyperParameterTuningJobOutput
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