pub struct CreateHyperParameterTuningJobRequest {
pub hyper_parameter_tuning_job_config: HyperParameterTuningJobConfig,
pub hyper_parameter_tuning_job_name: String,
pub tags: Option<Vec<Tag>>,
pub training_job_definition: Option<HyperParameterTrainingJobDefinition>,
pub training_job_definitions: Option<Vec<HyperParameterTrainingJobDefinition>>,
pub warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>,
}
Fields
hyper_parameter_tuning_job_config: HyperParameterTuningJobConfig
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.
hyper_parameter_tuning_job_name: String
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
training_job_definition: Option<HyperParameterTrainingJobDefinition>
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
training_job_definitions: Option<Vec<HyperParameterTrainingJobDefinition>>
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>
Specifies the configuration for starting the hyperparameter 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.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICALDATAAND_ALGORITHM
as the WarmStartType
value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
Trait Implementations
sourceimpl Clone for CreateHyperParameterTuningJobRequest
impl Clone for CreateHyperParameterTuningJobRequest
sourcefn clone(&self) -> CreateHyperParameterTuningJobRequest
fn clone(&self) -> CreateHyperParameterTuningJobRequest
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Default for CreateHyperParameterTuningJobRequest
impl Default for CreateHyperParameterTuningJobRequest
sourcefn default() -> CreateHyperParameterTuningJobRequest
fn default() -> CreateHyperParameterTuningJobRequest
Returns the “default value” for a type. Read more
sourceimpl PartialEq<CreateHyperParameterTuningJobRequest> for CreateHyperParameterTuningJobRequest
impl PartialEq<CreateHyperParameterTuningJobRequest> for CreateHyperParameterTuningJobRequest
sourcefn eq(&self, other: &CreateHyperParameterTuningJobRequest) -> bool
fn eq(&self, other: &CreateHyperParameterTuningJobRequest) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &CreateHyperParameterTuningJobRequest) -> bool
fn ne(&self, other: &CreateHyperParameterTuningJobRequest) -> bool
This method tests for !=
.
impl StructuralPartialEq for CreateHyperParameterTuningJobRequest
Auto Trait Implementations
impl RefUnwindSafe for CreateHyperParameterTuningJobRequest
impl Send for CreateHyperParameterTuningJobRequest
impl Sync for CreateHyperParameterTuningJobRequest
impl Unpin for CreateHyperParameterTuningJobRequest
impl UnwindSafe for CreateHyperParameterTuningJobRequest
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more