[−][src]Struct rusoto_sagemaker::HyperParameterTuningJobConfig
Configures a hyperparameter tuning job.
Fields
hyper_parameter_tuning_job_objective: Option<HyperParameterTuningJobObjective>
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
parameter_ranges: Option<ParameterRanges>
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
resource_limits: ResourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
strategy: String
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search strategy, set this to Bayesian
. To randomly search, set it to Random
. For information about search strategies, see How Hyperparameter Tuning Works.
training_job_early_stopping_type: Option<String>
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is OFF
):
- OFF
-
Training jobs launched by the hyperparameter tuning job do not use early stopping.
- AUTO
-
Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
tuning_job_completion_criteria: Option<TuningJobCompletionCriteria>
The tuning job's completion criteria.
Trait Implementations
impl Clone for HyperParameterTuningJobConfig
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pub fn clone(&self) -> HyperParameterTuningJobConfig
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pub fn clone_from(&mut self, source: &Self)
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impl Debug for HyperParameterTuningJobConfig
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impl Default for HyperParameterTuningJobConfig
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pub fn default() -> HyperParameterTuningJobConfig
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impl<'de> Deserialize<'de> for HyperParameterTuningJobConfig
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pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
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__D: Deserializer<'de>,
impl PartialEq<HyperParameterTuningJobConfig> for HyperParameterTuningJobConfig
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pub fn eq(&self, other: &HyperParameterTuningJobConfig) -> bool
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pub fn ne(&self, other: &HyperParameterTuningJobConfig) -> bool
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impl Serialize for HyperParameterTuningJobConfig
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pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
__S: Serializer,
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__S: Serializer,
impl StructuralPartialEq for HyperParameterTuningJobConfig
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Auto Trait Implementations
impl RefUnwindSafe for HyperParameterTuningJobConfig
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impl Send for HyperParameterTuningJobConfig
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impl Sync for HyperParameterTuningJobConfig
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impl Unpin for HyperParameterTuningJobConfig
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impl UnwindSafe for HyperParameterTuningJobConfig
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Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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T: for<'de> Deserialize<'de>,
impl<T> From<T> for T
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> Same<T> for T
type Output = T
Should always be Self
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
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pub fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,