aws_sdk_personalize/types/

_solution_version.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.

/// <p>An object that provides information about a specific version of a <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_Solution.html">Solution</a> in a Custom dataset group.</p>
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::fmt::Debug)]
pub struct SolutionVersion {
    /// <p>The name of the solution version.</p>
    pub name: ::std::option::Option<::std::string::String>,
    /// <p>The ARN of the solution version.</p>
    pub solution_version_arn: ::std::option::Option<::std::string::String>,
    /// <p>The ARN of the solution.</p>
    pub solution_arn: ::std::option::Option<::std::string::String>,
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub perform_hpo: bool,
    /// <p>When true, Amazon Personalize searches for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses <code>recipeArn</code>.</p>
    pub perform_auto_ml: bool,
    /// <p>Whether the solution version should perform an incremental update. When set to true, the training will process only the data that has changed since the latest training, similar to when trainingMode is set to UPDATE. This can only be used with solution versions that use the User-Personalization recipe.</p>
    pub perform_incremental_update: ::std::option::Option<bool>,
    /// <p>The ARN of the recipe used in the solution.</p>
    pub recipe_arn: ::std::option::Option<::std::string::String>,
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model.</p>
    pub event_type: ::std::option::Option<::std::string::String>,
    /// <p>The Amazon Resource Name (ARN) of the dataset group providing the training data.</p>
    pub dataset_group_arn: ::std::option::Option<::std::string::String>,
    /// <p>Describes the configuration properties for the solution.</p>
    pub solution_config: ::std::option::Option<crate::types::SolutionConfig>,
    /// <p>The time used to train the model. You are billed for the time it takes to train a model. This field is visible only after Amazon Personalize successfully trains a model.</p>
    pub training_hours: ::std::option::Option<f64>,
    /// <p>The scope of training to be performed when creating the solution version. A <code>FULL</code> training considers all of the data in your dataset group. An <code>UPDATE</code> processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use <code>UPDATE</code>.</p>
    pub training_mode: ::std::option::Option<crate::types::TrainingMode>,
    /// <p>If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.</p>
    pub tuned_hpo_params: ::std::option::Option<crate::types::TunedHpoParams>,
    /// <p>The status of the solution version.</p>
    /// <p>A solution version can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING</p></li>
    /// <li>
    /// <p>CREATE IN_PROGRESS</p></li>
    /// <li>
    /// <p>ACTIVE</p></li>
    /// <li>
    /// <p>CREATE FAILED</p></li>
    /// <li>
    /// <p>CREATE STOPPING</p></li>
    /// <li>
    /// <p>CREATE STOPPED</p></li>
    /// </ul>
    pub status: ::std::option::Option<::std::string::String>,
    /// <p>If training a solution version fails, the reason for the failure.</p>
    pub failure_reason: ::std::option::Option<::std::string::String>,
    /// <p>The date and time (in Unix time) that this version of the solution was created.</p>
    pub creation_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub last_updated_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    /// <p>Whether the solution version was created automatically or manually.</p>
    pub training_type: ::std::option::Option<crate::types::TrainingType>,
}
impl SolutionVersion {
    /// <p>The name of the solution version.</p>
    pub fn name(&self) -> ::std::option::Option<&str> {
        self.name.as_deref()
    }
    /// <p>The ARN of the solution version.</p>
    pub fn solution_version_arn(&self) -> ::std::option::Option<&str> {
        self.solution_version_arn.as_deref()
    }
    /// <p>The ARN of the solution.</p>
    pub fn solution_arn(&self) -> ::std::option::Option<&str> {
        self.solution_arn.as_deref()
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn perform_hpo(&self) -> bool {
        self.perform_hpo
    }
    /// <p>When true, Amazon Personalize searches for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses <code>recipeArn</code>.</p>
    pub fn perform_auto_ml(&self) -> bool {
        self.perform_auto_ml
    }
    /// <p>Whether the solution version should perform an incremental update. When set to true, the training will process only the data that has changed since the latest training, similar to when trainingMode is set to UPDATE. This can only be used with solution versions that use the User-Personalization recipe.</p>
    pub fn perform_incremental_update(&self) -> ::std::option::Option<bool> {
        self.perform_incremental_update
    }
    /// <p>The ARN of the recipe used in the solution.</p>
    pub fn recipe_arn(&self) -> ::std::option::Option<&str> {
        self.recipe_arn.as_deref()
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model.</p>
    pub fn event_type(&self) -> ::std::option::Option<&str> {
        self.event_type.as_deref()
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group providing the training data.</p>
    pub fn dataset_group_arn(&self) -> ::std::option::Option<&str> {
        self.dataset_group_arn.as_deref()
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn solution_config(&self) -> ::std::option::Option<&crate::types::SolutionConfig> {
        self.solution_config.as_ref()
    }
    /// <p>The time used to train the model. You are billed for the time it takes to train a model. This field is visible only after Amazon Personalize successfully trains a model.</p>
    pub fn training_hours(&self) -> ::std::option::Option<f64> {
        self.training_hours
    }
    /// <p>The scope of training to be performed when creating the solution version. A <code>FULL</code> training considers all of the data in your dataset group. An <code>UPDATE</code> processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use <code>UPDATE</code>.</p>
    pub fn training_mode(&self) -> ::std::option::Option<&crate::types::TrainingMode> {
        self.training_mode.as_ref()
    }
    /// <p>If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.</p>
    pub fn tuned_hpo_params(&self) -> ::std::option::Option<&crate::types::TunedHpoParams> {
        self.tuned_hpo_params.as_ref()
    }
    /// <p>The status of the solution version.</p>
    /// <p>A solution version can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING</p></li>
    /// <li>
    /// <p>CREATE IN_PROGRESS</p></li>
    /// <li>
    /// <p>ACTIVE</p></li>
    /// <li>
    /// <p>CREATE FAILED</p></li>
    /// <li>
    /// <p>CREATE STOPPING</p></li>
    /// <li>
    /// <p>CREATE STOPPED</p></li>
    /// </ul>
    pub fn status(&self) -> ::std::option::Option<&str> {
        self.status.as_deref()
    }
    /// <p>If training a solution version fails, the reason for the failure.</p>
    pub fn failure_reason(&self) -> ::std::option::Option<&str> {
        self.failure_reason.as_deref()
    }
    /// <p>The date and time (in Unix time) that this version of the solution was created.</p>
    pub fn creation_date_time(&self) -> ::std::option::Option<&::aws_smithy_types::DateTime> {
        self.creation_date_time.as_ref()
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn last_updated_date_time(&self) -> ::std::option::Option<&::aws_smithy_types::DateTime> {
        self.last_updated_date_time.as_ref()
    }
    /// <p>Whether the solution version was created automatically or manually.</p>
    pub fn training_type(&self) -> ::std::option::Option<&crate::types::TrainingType> {
        self.training_type.as_ref()
    }
}
impl SolutionVersion {
    /// Creates a new builder-style object to manufacture [`SolutionVersion`](crate::types::SolutionVersion).
    pub fn builder() -> crate::types::builders::SolutionVersionBuilder {
        crate::types::builders::SolutionVersionBuilder::default()
    }
}

/// A builder for [`SolutionVersion`](crate::types::SolutionVersion).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default, ::std::fmt::Debug)]
#[non_exhaustive]
pub struct SolutionVersionBuilder {
    pub(crate) name: ::std::option::Option<::std::string::String>,
    pub(crate) solution_version_arn: ::std::option::Option<::std::string::String>,
    pub(crate) solution_arn: ::std::option::Option<::std::string::String>,
    pub(crate) perform_hpo: ::std::option::Option<bool>,
    pub(crate) perform_auto_ml: ::std::option::Option<bool>,
    pub(crate) perform_incremental_update: ::std::option::Option<bool>,
    pub(crate) recipe_arn: ::std::option::Option<::std::string::String>,
    pub(crate) event_type: ::std::option::Option<::std::string::String>,
    pub(crate) dataset_group_arn: ::std::option::Option<::std::string::String>,
    pub(crate) solution_config: ::std::option::Option<crate::types::SolutionConfig>,
    pub(crate) training_hours: ::std::option::Option<f64>,
    pub(crate) training_mode: ::std::option::Option<crate::types::TrainingMode>,
    pub(crate) tuned_hpo_params: ::std::option::Option<crate::types::TunedHpoParams>,
    pub(crate) status: ::std::option::Option<::std::string::String>,
    pub(crate) failure_reason: ::std::option::Option<::std::string::String>,
    pub(crate) creation_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    pub(crate) last_updated_date_time: ::std::option::Option<::aws_smithy_types::DateTime>,
    pub(crate) training_type: ::std::option::Option<crate::types::TrainingType>,
}
impl SolutionVersionBuilder {
    /// <p>The name of the solution version.</p>
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.name = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The name of the solution version.</p>
    pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.name = input;
        self
    }
    /// <p>The name of the solution version.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        &self.name
    }
    /// <p>The ARN of the solution version.</p>
    pub fn solution_version_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.solution_version_arn = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The ARN of the solution version.</p>
    pub fn set_solution_version_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.solution_version_arn = input;
        self
    }
    /// <p>The ARN of the solution version.</p>
    pub fn get_solution_version_arn(&self) -> &::std::option::Option<::std::string::String> {
        &self.solution_version_arn
    }
    /// <p>The ARN of the solution.</p>
    pub fn solution_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.solution_arn = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The ARN of the solution.</p>
    pub fn set_solution_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.solution_arn = input;
        self
    }
    /// <p>The ARN of the solution.</p>
    pub fn get_solution_arn(&self) -> &::std::option::Option<::std::string::String> {
        &self.solution_arn
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn perform_hpo(mut self, input: bool) -> Self {
        self.perform_hpo = ::std::option::Option::Some(input);
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn set_perform_hpo(mut self, input: ::std::option::Option<bool>) -> Self {
        self.perform_hpo = input;
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is <code>false</code>.</p>
    pub fn get_perform_hpo(&self) -> &::std::option::Option<bool> {
        &self.perform_hpo
    }
    /// <p>When true, Amazon Personalize searches for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses <code>recipeArn</code>.</p>
    pub fn perform_auto_ml(mut self, input: bool) -> Self {
        self.perform_auto_ml = ::std::option::Option::Some(input);
        self
    }
    /// <p>When true, Amazon Personalize searches for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses <code>recipeArn</code>.</p>
    pub fn set_perform_auto_ml(mut self, input: ::std::option::Option<bool>) -> Self {
        self.perform_auto_ml = input;
        self
    }
    /// <p>When true, Amazon Personalize searches for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses <code>recipeArn</code>.</p>
    pub fn get_perform_auto_ml(&self) -> &::std::option::Option<bool> {
        &self.perform_auto_ml
    }
    /// <p>Whether the solution version should perform an incremental update. When set to true, the training will process only the data that has changed since the latest training, similar to when trainingMode is set to UPDATE. This can only be used with solution versions that use the User-Personalization recipe.</p>
    pub fn perform_incremental_update(mut self, input: bool) -> Self {
        self.perform_incremental_update = ::std::option::Option::Some(input);
        self
    }
    /// <p>Whether the solution version should perform an incremental update. When set to true, the training will process only the data that has changed since the latest training, similar to when trainingMode is set to UPDATE. This can only be used with solution versions that use the User-Personalization recipe.</p>
    pub fn set_perform_incremental_update(mut self, input: ::std::option::Option<bool>) -> Self {
        self.perform_incremental_update = input;
        self
    }
    /// <p>Whether the solution version should perform an incremental update. When set to true, the training will process only the data that has changed since the latest training, similar to when trainingMode is set to UPDATE. This can only be used with solution versions that use the User-Personalization recipe.</p>
    pub fn get_perform_incremental_update(&self) -> &::std::option::Option<bool> {
        &self.perform_incremental_update
    }
    /// <p>The ARN of the recipe used in the solution.</p>
    pub fn recipe_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.recipe_arn = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The ARN of the recipe used in the solution.</p>
    pub fn set_recipe_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.recipe_arn = input;
        self
    }
    /// <p>The ARN of the recipe used in the solution.</p>
    pub fn get_recipe_arn(&self) -> &::std::option::Option<::std::string::String> {
        &self.recipe_arn
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model.</p>
    pub fn event_type(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.event_type = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model.</p>
    pub fn set_event_type(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.event_type = input;
        self
    }
    /// <p>The event type (for example, 'click' or 'like') that is used for training the model.</p>
    pub fn get_event_type(&self) -> &::std::option::Option<::std::string::String> {
        &self.event_type
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group providing the training data.</p>
    pub fn dataset_group_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.dataset_group_arn = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group providing the training data.</p>
    pub fn set_dataset_group_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.dataset_group_arn = input;
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group providing the training data.</p>
    pub fn get_dataset_group_arn(&self) -> &::std::option::Option<::std::string::String> {
        &self.dataset_group_arn
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn solution_config(mut self, input: crate::types::SolutionConfig) -> Self {
        self.solution_config = ::std::option::Option::Some(input);
        self
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn set_solution_config(mut self, input: ::std::option::Option<crate::types::SolutionConfig>) -> Self {
        self.solution_config = input;
        self
    }
    /// <p>Describes the configuration properties for the solution.</p>
    pub fn get_solution_config(&self) -> &::std::option::Option<crate::types::SolutionConfig> {
        &self.solution_config
    }
    /// <p>The time used to train the model. You are billed for the time it takes to train a model. This field is visible only after Amazon Personalize successfully trains a model.</p>
    pub fn training_hours(mut self, input: f64) -> Self {
        self.training_hours = ::std::option::Option::Some(input);
        self
    }
    /// <p>The time used to train the model. You are billed for the time it takes to train a model. This field is visible only after Amazon Personalize successfully trains a model.</p>
    pub fn set_training_hours(mut self, input: ::std::option::Option<f64>) -> Self {
        self.training_hours = input;
        self
    }
    /// <p>The time used to train the model. You are billed for the time it takes to train a model. This field is visible only after Amazon Personalize successfully trains a model.</p>
    pub fn get_training_hours(&self) -> &::std::option::Option<f64> {
        &self.training_hours
    }
    /// <p>The scope of training to be performed when creating the solution version. A <code>FULL</code> training considers all of the data in your dataset group. An <code>UPDATE</code> processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use <code>UPDATE</code>.</p>
    pub fn training_mode(mut self, input: crate::types::TrainingMode) -> Self {
        self.training_mode = ::std::option::Option::Some(input);
        self
    }
    /// <p>The scope of training to be performed when creating the solution version. A <code>FULL</code> training considers all of the data in your dataset group. An <code>UPDATE</code> processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use <code>UPDATE</code>.</p>
    pub fn set_training_mode(mut self, input: ::std::option::Option<crate::types::TrainingMode>) -> Self {
        self.training_mode = input;
        self
    }
    /// <p>The scope of training to be performed when creating the solution version. A <code>FULL</code> training considers all of the data in your dataset group. An <code>UPDATE</code> processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use <code>UPDATE</code>.</p>
    pub fn get_training_mode(&self) -> &::std::option::Option<crate::types::TrainingMode> {
        &self.training_mode
    }
    /// <p>If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.</p>
    pub fn tuned_hpo_params(mut self, input: crate::types::TunedHpoParams) -> Self {
        self.tuned_hpo_params = ::std::option::Option::Some(input);
        self
    }
    /// <p>If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.</p>
    pub fn set_tuned_hpo_params(mut self, input: ::std::option::Option<crate::types::TunedHpoParams>) -> Self {
        self.tuned_hpo_params = input;
        self
    }
    /// <p>If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.</p>
    pub fn get_tuned_hpo_params(&self) -> &::std::option::Option<crate::types::TunedHpoParams> {
        &self.tuned_hpo_params
    }
    /// <p>The status of the solution version.</p>
    /// <p>A solution version can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING</p></li>
    /// <li>
    /// <p>CREATE IN_PROGRESS</p></li>
    /// <li>
    /// <p>ACTIVE</p></li>
    /// <li>
    /// <p>CREATE FAILED</p></li>
    /// <li>
    /// <p>CREATE STOPPING</p></li>
    /// <li>
    /// <p>CREATE STOPPED</p></li>
    /// </ul>
    pub fn status(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.status = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The status of the solution version.</p>
    /// <p>A solution version can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING</p></li>
    /// <li>
    /// <p>CREATE IN_PROGRESS</p></li>
    /// <li>
    /// <p>ACTIVE</p></li>
    /// <li>
    /// <p>CREATE FAILED</p></li>
    /// <li>
    /// <p>CREATE STOPPING</p></li>
    /// <li>
    /// <p>CREATE STOPPED</p></li>
    /// </ul>
    pub fn set_status(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.status = input;
        self
    }
    /// <p>The status of the solution version.</p>
    /// <p>A solution version can be in one of the following states:</p>
    /// <ul>
    /// <li>
    /// <p>CREATE PENDING</p></li>
    /// <li>
    /// <p>CREATE IN_PROGRESS</p></li>
    /// <li>
    /// <p>ACTIVE</p></li>
    /// <li>
    /// <p>CREATE FAILED</p></li>
    /// <li>
    /// <p>CREATE STOPPING</p></li>
    /// <li>
    /// <p>CREATE STOPPED</p></li>
    /// </ul>
    pub fn get_status(&self) -> &::std::option::Option<::std::string::String> {
        &self.status
    }
    /// <p>If training a solution version fails, the reason for the failure.</p>
    pub fn failure_reason(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.failure_reason = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>If training a solution version fails, the reason for the failure.</p>
    pub fn set_failure_reason(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.failure_reason = input;
        self
    }
    /// <p>If training a solution version fails, the reason for the failure.</p>
    pub fn get_failure_reason(&self) -> &::std::option::Option<::std::string::String> {
        &self.failure_reason
    }
    /// <p>The date and time (in Unix time) that this version of the solution was created.</p>
    pub fn creation_date_time(mut self, input: ::aws_smithy_types::DateTime) -> Self {
        self.creation_date_time = ::std::option::Option::Some(input);
        self
    }
    /// <p>The date and time (in Unix time) that this version of the solution was created.</p>
    pub fn set_creation_date_time(mut self, input: ::std::option::Option<::aws_smithy_types::DateTime>) -> Self {
        self.creation_date_time = input;
        self
    }
    /// <p>The date and time (in Unix time) that this version of the solution was created.</p>
    pub fn get_creation_date_time(&self) -> &::std::option::Option<::aws_smithy_types::DateTime> {
        &self.creation_date_time
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn last_updated_date_time(mut self, input: ::aws_smithy_types::DateTime) -> Self {
        self.last_updated_date_time = ::std::option::Option::Some(input);
        self
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn set_last_updated_date_time(mut self, input: ::std::option::Option<::aws_smithy_types::DateTime>) -> Self {
        self.last_updated_date_time = input;
        self
    }
    /// <p>The date and time (in Unix time) that the solution was last updated.</p>
    pub fn get_last_updated_date_time(&self) -> &::std::option::Option<::aws_smithy_types::DateTime> {
        &self.last_updated_date_time
    }
    /// <p>Whether the solution version was created automatically or manually.</p>
    pub fn training_type(mut self, input: crate::types::TrainingType) -> Self {
        self.training_type = ::std::option::Option::Some(input);
        self
    }
    /// <p>Whether the solution version was created automatically or manually.</p>
    pub fn set_training_type(mut self, input: ::std::option::Option<crate::types::TrainingType>) -> Self {
        self.training_type = input;
        self
    }
    /// <p>Whether the solution version was created automatically or manually.</p>
    pub fn get_training_type(&self) -> &::std::option::Option<crate::types::TrainingType> {
        &self.training_type
    }
    /// Consumes the builder and constructs a [`SolutionVersion`](crate::types::SolutionVersion).
    pub fn build(self) -> crate::types::SolutionVersion {
        crate::types::SolutionVersion {
            name: self.name,
            solution_version_arn: self.solution_version_arn,
            solution_arn: self.solution_arn,
            perform_hpo: self.perform_hpo.unwrap_or_default(),
            perform_auto_ml: self.perform_auto_ml.unwrap_or_default(),
            perform_incremental_update: self.perform_incremental_update,
            recipe_arn: self.recipe_arn,
            event_type: self.event_type,
            dataset_group_arn: self.dataset_group_arn,
            solution_config: self.solution_config,
            training_hours: self.training_hours,
            training_mode: self.training_mode,
            tuned_hpo_params: self.tuned_hpo_params,
            status: self.status,
            failure_reason: self.failure_reason,
            creation_date_time: self.creation_date_time,
            last_updated_date_time: self.last_updated_date_time,
            training_type: self.training_type,
        }
    }
}