pub struct CreateHyperParameterTuningJob { /* private fields */ }
Expand description
Fluent builder constructing a request to CreateHyperParameterTuningJob
.
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
Implementations
sourceimpl CreateHyperParameterTuningJob
impl CreateHyperParameterTuningJob
sourcepub async fn send(
self
) -> Result<CreateHyperParameterTuningJobOutput, SdkError<CreateHyperParameterTuningJobError>>
pub async fn send(
self
) -> Result<CreateHyperParameterTuningJobOutput, SdkError<CreateHyperParameterTuningJobError>>
Sends the request and returns the response.
If an error occurs, an SdkError
will be returned with additional details that
can be matched against.
By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.
sourcepub fn hyper_parameter_tuning_job_name(self, input: impl Into<String>) -> Self
pub fn hyper_parameter_tuning_job_name(self, input: impl Into<String>) -> Self
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 Amazon Web Services account and Amazon Web Services 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.
sourcepub fn set_hyper_parameter_tuning_job_name(self, input: Option<String>) -> Self
pub fn set_hyper_parameter_tuning_job_name(self, input: Option<String>) -> Self
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 Amazon Web Services account and Amazon Web Services 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.
sourcepub fn hyper_parameter_tuning_job_config(
self,
input: HyperParameterTuningJobConfig
) -> Self
pub fn hyper_parameter_tuning_job_config(
self,
input: HyperParameterTuningJobConfig
) -> Self
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.
sourcepub fn set_hyper_parameter_tuning_job_config(
self,
input: Option<HyperParameterTuningJobConfig>
) -> Self
pub fn set_hyper_parameter_tuning_job_config(
self,
input: Option<HyperParameterTuningJobConfig>
) -> Self
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.
sourcepub fn training_job_definition(
self,
input: HyperParameterTrainingJobDefinition
) -> Self
pub fn training_job_definition(
self,
input: HyperParameterTrainingJobDefinition
) -> Self
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.
sourcepub fn set_training_job_definition(
self,
input: Option<HyperParameterTrainingJobDefinition>
) -> Self
pub fn set_training_job_definition(
self,
input: Option<HyperParameterTrainingJobDefinition>
) -> Self
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.
sourcepub fn training_job_definitions(
self,
input: HyperParameterTrainingJobDefinition
) -> Self
pub fn training_job_definitions(
self,
input: HyperParameterTrainingJobDefinition
) -> Self
Appends an item to TrainingJobDefinitions
.
To override the contents of this collection use set_training_job_definitions
.
A list of the HyperParameterTrainingJobDefinition
objects launched for this tuning job.
sourcepub fn set_training_job_definitions(
self,
input: Option<Vec<HyperParameterTrainingJobDefinition>>
) -> Self
pub fn set_training_job_definitions(
self,
input: Option<Vec<HyperParameterTrainingJobDefinition>>
) -> Self
A list of the HyperParameterTrainingJobDefinition
objects launched for this tuning job.
sourcepub fn warm_start_config(
self,
input: HyperParameterTuningJobWarmStartConfig
) -> Self
pub fn warm_start_config(
self,
input: HyperParameterTuningJobWarmStartConfig
) -> Self
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 IDENTICAL_DATA_AND_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.
sourcepub fn set_warm_start_config(
self,
input: Option<HyperParameterTuningJobWarmStartConfig>
) -> Self
pub fn set_warm_start_config(
self,
input: Option<HyperParameterTuningJobWarmStartConfig>
) -> Self
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 IDENTICAL_DATA_AND_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.
Appends an item to Tags
.
To override the contents of this collection use set_tags
.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
Trait Implementations
sourceimpl Clone for CreateHyperParameterTuningJob
impl Clone for CreateHyperParameterTuningJob
sourcefn clone(&self) -> CreateHyperParameterTuningJob
fn clone(&self) -> CreateHyperParameterTuningJob
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
Auto Trait Implementations
impl !RefUnwindSafe for CreateHyperParameterTuningJob
impl Send for CreateHyperParameterTuningJob
impl Sync for CreateHyperParameterTuningJob
impl Unpin for CreateHyperParameterTuningJob
impl !UnwindSafe for CreateHyperParameterTuningJob
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcepub fn borrow_mut(&mut self) -> &mut T
pub 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.
sourcepub fn to_owned(&self) -> T
pub fn to_owned(&self) -> T
Creates owned data from borrowed data, usually by cloning. Read more
sourcepub fn clone_into(&self, target: &mut T)
pub 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