// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::fmt::Debug)]
pub struct CreateSolutionVersionInput {
/// <p>The name of the solution version.</p>
pub name: ::std::option::Option<::std::string::String>,
/// <p>The Amazon Resource Name (ARN) of the solution containing the training configuration information.</p>
pub solution_arn: ::std::option::Option<::std::string::String>,
/// <p>The scope of training to be performed when creating the solution version. The default is <code>FULL</code>. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.</p>
/// <p>If you use <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a>, you can specify a training mode of <code>UPDATE</code>. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify <code>UPDATE</code>, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to <code>FULL</code> and deploy it in a campaign. For more information about automatic updates, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/use-case-recipe-features.html#maintaining-with-automatic-updates">Automatic updates</a>.</p>
/// <p>The <code>UPDATE</code> option can only be used when you already have an active solution version created from the input solution using the <code>FULL</code> option and the input solution was trained with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a> recipe or the legacy <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-hrnn-coldstart.html">HRNN-Coldstart</a> recipe.</p>
pub training_mode: ::std::option::Option<crate::types::TrainingMode>,
/// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution version.</p>
pub tags: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>,
}
impl CreateSolutionVersionInput {
/// <p>The name of the solution version.</p>
pub fn name(&self) -> ::std::option::Option<&str> {
self.name.as_deref()
}
/// <p>The Amazon Resource Name (ARN) of the solution containing the training configuration information.</p>
pub fn solution_arn(&self) -> ::std::option::Option<&str> {
self.solution_arn.as_deref()
}
/// <p>The scope of training to be performed when creating the solution version. The default is <code>FULL</code>. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.</p>
/// <p>If you use <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a>, you can specify a training mode of <code>UPDATE</code>. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify <code>UPDATE</code>, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to <code>FULL</code> and deploy it in a campaign. For more information about automatic updates, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/use-case-recipe-features.html#maintaining-with-automatic-updates">Automatic updates</a>.</p>
/// <p>The <code>UPDATE</code> option can only be used when you already have an active solution version created from the input solution using the <code>FULL</code> option and the input solution was trained with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a> recipe or the legacy <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-hrnn-coldstart.html">HRNN-Coldstart</a> recipe.</p>
pub fn training_mode(&self) -> ::std::option::Option<&crate::types::TrainingMode> {
self.training_mode.as_ref()
}
/// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution version.</p>
///
/// If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use `.tags.is_none()`.
pub fn tags(&self) -> &[crate::types::Tag] {
self.tags.as_deref().unwrap_or_default()
}
}
impl CreateSolutionVersionInput {
/// Creates a new builder-style object to manufacture [`CreateSolutionVersionInput`](crate::operation::create_solution_version::CreateSolutionVersionInput).
pub fn builder() -> crate::operation::create_solution_version::builders::CreateSolutionVersionInputBuilder {
crate::operation::create_solution_version::builders::CreateSolutionVersionInputBuilder::default()
}
}
/// A builder for [`CreateSolutionVersionInput`](crate::operation::create_solution_version::CreateSolutionVersionInput).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default, ::std::fmt::Debug)]
#[non_exhaustive]
pub struct CreateSolutionVersionInputBuilder {
pub(crate) name: ::std::option::Option<::std::string::String>,
pub(crate) solution_arn: ::std::option::Option<::std::string::String>,
pub(crate) training_mode: ::std::option::Option<crate::types::TrainingMode>,
pub(crate) tags: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>,
}
impl CreateSolutionVersionInputBuilder {
/// <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 Amazon Resource Name (ARN) of the solution containing the training configuration information.</p>
/// This field is required.
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 Amazon Resource Name (ARN) of the solution containing the training configuration information.</p>
pub fn set_solution_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.solution_arn = input;
self
}
/// <p>The Amazon Resource Name (ARN) of the solution containing the training configuration information.</p>
pub fn get_solution_arn(&self) -> &::std::option::Option<::std::string::String> {
&self.solution_arn
}
/// <p>The scope of training to be performed when creating the solution version. The default is <code>FULL</code>. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.</p>
/// <p>If you use <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a>, you can specify a training mode of <code>UPDATE</code>. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify <code>UPDATE</code>, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to <code>FULL</code> and deploy it in a campaign. For more information about automatic updates, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/use-case-recipe-features.html#maintaining-with-automatic-updates">Automatic updates</a>.</p>
/// <p>The <code>UPDATE</code> option can only be used when you already have an active solution version created from the input solution using the <code>FULL</code> option and the input solution was trained with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a> recipe or the legacy <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-hrnn-coldstart.html">HRNN-Coldstart</a> recipe.</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. The default is <code>FULL</code>. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.</p>
/// <p>If you use <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a>, you can specify a training mode of <code>UPDATE</code>. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify <code>UPDATE</code>, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to <code>FULL</code> and deploy it in a campaign. For more information about automatic updates, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/use-case-recipe-features.html#maintaining-with-automatic-updates">Automatic updates</a>.</p>
/// <p>The <code>UPDATE</code> option can only be used when you already have an active solution version created from the input solution using the <code>FULL</code> option and the input solution was trained with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a> recipe or the legacy <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-hrnn-coldstart.html">HRNN-Coldstart</a> recipe.</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. The default is <code>FULL</code>. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.</p>
/// <p>If you use <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a>, you can specify a training mode of <code>UPDATE</code>. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify <code>UPDATE</code>, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to <code>FULL</code> and deploy it in a campaign. For more information about automatic updates, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/use-case-recipe-features.html#maintaining-with-automatic-updates">Automatic updates</a>.</p>
/// <p>The <code>UPDATE</code> option can only be used when you already have an active solution version created from the input solution using the <code>FULL</code> option and the input solution was trained with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a> recipe or the legacy <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-hrnn-coldstart.html">HRNN-Coldstart</a> recipe.</p>
pub fn get_training_mode(&self) -> &::std::option::Option<crate::types::TrainingMode> {
&self.training_mode
}
/// 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 version.</p>
pub fn tags(mut self, input: crate::types::Tag) -> Self {
let mut v = self.tags.unwrap_or_default();
v.push(input);
self.tags = ::std::option::Option::Some(v);
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 version.</p>
pub fn set_tags(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>) -> Self {
self.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 version.</p>
pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
&self.tags
}
/// Consumes the builder and constructs a [`CreateSolutionVersionInput`](crate::operation::create_solution_version::CreateSolutionVersionInput).
pub fn build(
self,
) -> ::std::result::Result<crate::operation::create_solution_version::CreateSolutionVersionInput, ::aws_smithy_types::error::operation::BuildError>
{
::std::result::Result::Ok(crate::operation::create_solution_version::CreateSolutionVersionInput {
name: self.name,
solution_arn: self.solution_arn,
training_mode: self.training_mode,
tags: self.tags,
})
}
}