aws_sdk_personalize/operation/create_solution/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_solution::_create_solution_output::CreateSolutionOutputBuilder;

pub use crate::operation::create_solution::_create_solution_input::CreateSolutionInputBuilder;

impl crate::operation::create_solution::builders::CreateSolutionInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::create_solution::CreateSolutionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_solution::CreateSolutionError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_solution();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateSolution`.
///
/// <important>
/// <p>By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateSolution.html">update the solution</a> to turn off automatic training. For information about training costs, see <a href="https://aws.amazon.com/personalize/pricing/">Amazon Personalize pricing</a>.</p>
/// </important>
/// <p>Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html">Creating and configuring a solution</a>.</p>
/// <p>By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html">Configuring automatic training</a>.</p>
/// <p>To turn off automatic training, set <code>performAutoTraining</code> to false. If you turn off automatic training, you must manually create a solution version by calling the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html">CreateSolutionVersion</a> operation.</p>
/// <p>After training starts, you can get the solution version's Amazon Resource Name (ARN) with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> API operation. To get its status, use the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>.</p>
/// <p>After training completes you can evaluate model accuracy by calling <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_GetSolutionMetrics.html">GetSolutionMetrics</a>. When you are satisfied with the solution version, you deploy it using <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html">CreateCampaign</a>. The campaign provides recommendations to a client through the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html">GetRecommendations</a> API.</p><note>
/// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> for solution hyperparameter optimization at this time.</p>
/// </note>
/// <p><b>Status</b></p>
/// <p>A solution can be in one of the following states:</p>
/// <ul>
/// <li>
/// <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p></li>
/// <li>
/// <p>DELETE PENDING &gt; DELETE IN_PROGRESS</p></li>
/// </ul>
/// <p>To get the status of the solution, call <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html">DescribeSolution</a>. If you use manual training, the status must be ACTIVE before you call <code>CreateSolutionVersion</code>.</p>
/// <p class="title"><b>Related APIs</b></p>
/// <ul>
/// <li>
/// <p><a href="https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateSolution.html">UpdateSolution</a></p></li>
/// <li>
/// <p><a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutions.html">ListSolutions</a></p></li>
/// <li>
/// <p><a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html">CreateSolutionVersion</a></p></li>
/// <li>
/// <p><a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html">DescribeSolution</a></p></li>
/// <li>
/// <p><a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteSolution.html">DeleteSolution</a></p></li>
/// </ul>
/// <ul>
/// <li>
/// <p><a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a></p></li>
/// <li>
/// <p><a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a></p></li>
/// </ul>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateSolutionFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_solution::builders::CreateSolutionInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_solution::CreateSolutionOutput,
        crate::operation::create_solution::CreateSolutionError,
    > for CreateSolutionFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_solution::CreateSolutionOutput,
            crate::operation::create_solution::CreateSolutionError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateSolutionFluentBuilder {
    /// Creates a new `CreateSolutionFluentBuilder`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the CreateSolution as a reference.
    pub fn as_input(&self) -> &crate::operation::create_solution::builders::CreateSolutionInputBuilder {
        &self.inner
    }
    /// 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](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> ::std::result::Result<
        crate::operation::create_solution::CreateSolutionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_solution::CreateSolutionError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::create_solution::CreateSolution::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_solution::CreateSolution::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::create_solution::CreateSolutionOutput,
        crate::operation::create_solution::CreateSolutionError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
        self.set_config_override(::std::option::Option::Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>The name for the solution.</p>
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.name(input.into());
        self
    }
    /// <p>The name for the solution.</p>
    pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_name(input);
        self
    }
    /// <p>The name for the solution.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_name()
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn perform_hpo(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_hpo(input);
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn set_perform_hpo(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_hpo(input);
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn get_perform_hpo(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_hpo()
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html">Choosing a recipe</a>.</p>
    /// </important>
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn perform_auto_ml(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_auto_ml(input);
        self
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html">Choosing a recipe</a>.</p>
    /// </important>
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn set_perform_auto_ml(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_auto_ml(input);
        self
    }
    /// <important>
    /// <p>We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html">Choosing a recipe</a>.</p>
    /// </important>
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn get_perform_auto_ml(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_auto_ml()
    }
    /// <p>Whether the solution uses automatic training to create new solution versions (trained models). The default is <code>True</code> and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a <code>schedulingExpression</code> in the <code>AutoTrainingConfig</code> as part of solution configuration. For more information about automatic training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html">Configuring automatic training</a>.</p>
    /// <p>Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.</p>
    /// <p>After training starts, you can get the solution version's Amazon Resource Name (ARN) with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> API operation. To get its status, use the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>.</p>
    pub fn perform_auto_training(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_auto_training(input);
        self
    }
    /// <p>Whether the solution uses automatic training to create new solution versions (trained models). The default is <code>True</code> and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a <code>schedulingExpression</code> in the <code>AutoTrainingConfig</code> as part of solution configuration. For more information about automatic training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html">Configuring automatic training</a>.</p>
    /// <p>Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.</p>
    /// <p>After training starts, you can get the solution version's Amazon Resource Name (ARN) with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> API operation. To get its status, use the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>.</p>
    pub fn set_perform_auto_training(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_auto_training(input);
        self
    }
    /// <p>Whether the solution uses automatic training to create new solution versions (trained models). The default is <code>True</code> and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a <code>schedulingExpression</code> in the <code>AutoTrainingConfig</code> as part of solution configuration. For more information about automatic training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html">Configuring automatic training</a>.</p>
    /// <p>Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.</p>
    /// <p>After training starts, you can get the solution version's Amazon Resource Name (ARN) with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> API operation. To get its status, use the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>.</p>
    pub fn get_perform_auto_training(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_auto_training()
    }
    /// <p>The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when <code>performAutoML</code> is false. For information about different Amazon Personalize recipes and their ARNs, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html">Choosing a recipe</a>.</p>
    pub fn recipe_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.recipe_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when <code>performAutoML</code> is false. For information about different Amazon Personalize recipes and their ARNs, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html">Choosing a recipe</a>.</p>
    pub fn set_recipe_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_recipe_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when <code>performAutoML</code> is false. For information about different Amazon Personalize recipes and their ARNs, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html">Choosing a recipe</a>.</p>
    pub fn get_recipe_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_recipe_arn()
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn dataset_group_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.dataset_group_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn set_dataset_group_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_dataset_group_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn get_dataset_group_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_dataset_group_arn()
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn event_type(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.event_type(input.into());
        self
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn set_event_type(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_event_type(input);
        self
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn get_event_type(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_event_type()
    }
    /// <p>The configuration properties for the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p><note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn solution_config(mut self, input: crate::types::SolutionConfig) -> Self {
        self.inner = self.inner.solution_config(input);
        self
    }
    /// <p>The configuration properties for the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p><note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn set_solution_config(mut self, input: ::std::option::Option<crate::types::SolutionConfig>) -> Self {
        self.inner = self.inner.set_solution_config(input);
        self
    }
    /// <p>The configuration properties for the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p><note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn get_solution_config(&self) -> &::std::option::Option<crate::types::SolutionConfig> {
        self.inner.get_solution_config()
    }
    ///
    /// Appends an item to `tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
}