pub struct CreateHyperParameterTuningJobFluentBuilder { /* 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.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..
Implementations§
Source§impl CreateHyperParameterTuningJobFluentBuilder
impl CreateHyperParameterTuningJobFluentBuilder
Sourcepub fn as_input(&self) -> &CreateHyperParameterTuningJobInputBuilder
pub fn as_input(&self) -> &CreateHyperParameterTuningJobInputBuilder
Access the CreateHyperParameterTuningJob as a reference.
Sourcepub async fn send(
self,
) -> Result<CreateHyperParameterTuningJobOutput, SdkError<CreateHyperParameterTuningJobError, HttpResponse>>
pub async fn send( self, ) -> Result<CreateHyperParameterTuningJobOutput, SdkError<CreateHyperParameterTuningJobError, HttpResponse>>
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 customize(
self,
) -> CustomizableOperation<CreateHyperParameterTuningJobOutput, CreateHyperParameterTuningJobError, Self>
pub fn customize( self, ) -> CustomizableOperation<CreateHyperParameterTuningJobOutput, CreateHyperParameterTuningJobError, Self>
Consumes this builder, creating a customizable operation that can be modified before being sent.
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 get_hyper_parameter_tuning_job_name(&self) -> &Option<String>
pub fn get_hyper_parameter_tuning_job_name(&self) -> &Option<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 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 get_hyper_parameter_tuning_job_config(
&self,
) -> &Option<HyperParameterTuningJobConfig>
pub fn get_hyper_parameter_tuning_job_config( &self, ) -> &Option<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.
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 get_training_job_definition(
&self,
) -> &Option<HyperParameterTrainingJobDefinition>
pub fn get_training_job_definition( &self, ) -> &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.
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 get_training_job_definitions(
&self,
) -> &Option<Vec<HyperParameterTrainingJobDefinition>>
pub fn get_training_job_definitions( &self, ) -> &Option<Vec<HyperParameterTrainingJobDefinition>>
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.
Sourcepub fn get_warm_start_config(
&self,
) -> &Option<HyperParameterTuningJobWarmStartConfig>
pub fn get_warm_start_config( &self, ) -> &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 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.
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.
Sourcepub fn autotune(self, input: Autotune) -> Self
pub fn autotune(self, input: Autotune) -> Self
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
-
ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
-
ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
-
TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
-
RetryStrategy: The number of times to retry a training job.
-
Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
-
ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
Sourcepub fn set_autotune(self, input: Option<Autotune>) -> Self
pub fn set_autotune(self, input: Option<Autotune>) -> Self
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
-
ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
-
ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
-
TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
-
RetryStrategy: The number of times to retry a training job.
-
Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
-
ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
Sourcepub fn get_autotune(&self) -> &Option<Autotune>
pub fn get_autotune(&self) -> &Option<Autotune>
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
-
ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
-
ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
-
TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
-
RetryStrategy: The number of times to retry a training job.
-
Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
-
ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
Trait Implementations§
Source§impl Clone for CreateHyperParameterTuningJobFluentBuilder
impl Clone for CreateHyperParameterTuningJobFluentBuilder
Source§fn clone(&self) -> CreateHyperParameterTuningJobFluentBuilder
fn clone(&self) -> CreateHyperParameterTuningJobFluentBuilder
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreAuto Trait Implementations§
impl Freeze for CreateHyperParameterTuningJobFluentBuilder
impl !RefUnwindSafe for CreateHyperParameterTuningJobFluentBuilder
impl Send for CreateHyperParameterTuningJobFluentBuilder
impl Sync for CreateHyperParameterTuningJobFluentBuilder
impl Unpin for CreateHyperParameterTuningJobFluentBuilder
impl !UnwindSafe for CreateHyperParameterTuningJobFluentBuilder
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