aws_sdk_cleanroomsml/operation/create_trained_model/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_trained_model::_create_trained_model_output::CreateTrainedModelOutputBuilder;

pub use crate::operation::create_trained_model::_create_trained_model_input::CreateTrainedModelInputBuilder;

impl crate::operation::create_trained_model::builders::CreateTrainedModelInputBuilder {
    /// 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_trained_model::CreateTrainedModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_trained_model::CreateTrainedModelError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_trained_model();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateTrainedModel`.
///
/// <p>Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateTrainedModelFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_trained_model::builders::CreateTrainedModelInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_trained_model::CreateTrainedModelOutput,
        crate::operation::create_trained_model::CreateTrainedModelError,
    > for CreateTrainedModelFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_trained_model::CreateTrainedModelOutput,
            crate::operation::create_trained_model::CreateTrainedModelError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateTrainedModelFluentBuilder {
    /// Creates a new `CreateTrainedModelFluentBuilder`.
    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 CreateTrainedModel as a reference.
    pub fn as_input(&self) -> &crate::operation::create_trained_model::builders::CreateTrainedModelInputBuilder {
        &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_trained_model::CreateTrainedModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_trained_model::CreateTrainedModelError,
            ::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_trained_model::CreateTrainedModel::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_trained_model::CreateTrainedModel::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_trained_model::CreateTrainedModelOutput,
        crate::operation::create_trained_model::CreateTrainedModelError,
        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 membership ID of the member that is creating the trained model.</p>
    pub fn membership_identifier(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.membership_identifier(input.into());
        self
    }
    /// <p>The membership ID of the member that is creating the trained model.</p>
    pub fn set_membership_identifier(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_membership_identifier(input);
        self
    }
    /// <p>The membership ID of the member that is creating the trained model.</p>
    pub fn get_membership_identifier(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_membership_identifier()
    }
    /// <p>The name of the trained model.</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 of the trained model.</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 of the trained model.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_name()
    }
    /// <p>The associated configured model algorithm used to train this model.</p>
    pub fn configured_model_algorithm_association_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.configured_model_algorithm_association_arn(input.into());
        self
    }
    /// <p>The associated configured model algorithm used to train this model.</p>
    pub fn set_configured_model_algorithm_association_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_configured_model_algorithm_association_arn(input);
        self
    }
    /// <p>The associated configured model algorithm used to train this model.</p>
    pub fn get_configured_model_algorithm_association_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_configured_model_algorithm_association_arn()
    }
    ///
    /// Adds a key-value pair to `hyperparameters`.
    ///
    /// To override the contents of this collection use [`set_hyperparameters`](Self::set_hyperparameters).
    ///
    /// <p>Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.</p>
    pub fn hyperparameters(
        mut self,
        k: impl ::std::convert::Into<::std::string::String>,
        v: impl ::std::convert::Into<::std::string::String>,
    ) -> Self {
        self.inner = self.inner.hyperparameters(k.into(), v.into());
        self
    }
    /// <p>Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.</p>
    pub fn set_hyperparameters(
        mut self,
        input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    ) -> Self {
        self.inner = self.inner.set_hyperparameters(input);
        self
    }
    /// <p>Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.</p>
    pub fn get_hyperparameters(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_hyperparameters()
    }
    ///
    /// Adds a key-value pair to `environment`.
    ///
    /// To override the contents of this collection use [`set_environment`](Self::set_environment).
    ///
    /// <p>The environment variables to set in the Docker container.</p>
    pub fn environment(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.environment(k.into(), v.into());
        self
    }
    /// <p>The environment variables to set in the Docker container.</p>
    pub fn set_environment(
        mut self,
        input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    ) -> Self {
        self.inner = self.inner.set_environment(input);
        self
    }
    /// <p>The environment variables to set in the Docker container.</p>
    pub fn get_environment(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_environment()
    }
    /// <p>Information about the EC2 resources that are used to train this model.</p>
    pub fn resource_config(mut self, input: crate::types::ResourceConfig) -> Self {
        self.inner = self.inner.resource_config(input);
        self
    }
    /// <p>Information about the EC2 resources that are used to train this model.</p>
    pub fn set_resource_config(mut self, input: ::std::option::Option<crate::types::ResourceConfig>) -> Self {
        self.inner = self.inner.set_resource_config(input);
        self
    }
    /// <p>Information about the EC2 resources that are used to train this model.</p>
    pub fn get_resource_config(&self) -> &::std::option::Option<crate::types::ResourceConfig> {
        self.inner.get_resource_config()
    }
    /// <p>The criteria that is used to stop model training.</p>
    pub fn stopping_condition(mut self, input: crate::types::StoppingCondition) -> Self {
        self.inner = self.inner.stopping_condition(input);
        self
    }
    /// <p>The criteria that is used to stop model training.</p>
    pub fn set_stopping_condition(mut self, input: ::std::option::Option<crate::types::StoppingCondition>) -> Self {
        self.inner = self.inner.set_stopping_condition(input);
        self
    }
    /// <p>The criteria that is used to stop model training.</p>
    pub fn get_stopping_condition(&self) -> &::std::option::Option<crate::types::StoppingCondition> {
        self.inner.get_stopping_condition()
    }
    ///
    /// Appends an item to `incrementalTrainingDataChannels`.
    ///
    /// To override the contents of this collection use [`set_incremental_training_data_channels`](Self::set_incremental_training_data_channels).
    ///
    /// <p>Specifies the incremental training data channels for the trained model.</p>
    /// <p>Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.</p>
    /// <p>Limit: Maximum of 20 channels total (including both <code>incrementalTrainingDataChannels</code> and <code>dataChannels</code>).</p>
    pub fn incremental_training_data_channels(mut self, input: crate::types::IncrementalTrainingDataChannel) -> Self {
        self.inner = self.inner.incremental_training_data_channels(input);
        self
    }
    /// <p>Specifies the incremental training data channels for the trained model.</p>
    /// <p>Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.</p>
    /// <p>Limit: Maximum of 20 channels total (including both <code>incrementalTrainingDataChannels</code> and <code>dataChannels</code>).</p>
    pub fn set_incremental_training_data_channels(
        mut self,
        input: ::std::option::Option<::std::vec::Vec<crate::types::IncrementalTrainingDataChannel>>,
    ) -> Self {
        self.inner = self.inner.set_incremental_training_data_channels(input);
        self
    }
    /// <p>Specifies the incremental training data channels for the trained model.</p>
    /// <p>Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.</p>
    /// <p>Limit: Maximum of 20 channels total (including both <code>incrementalTrainingDataChannels</code> and <code>dataChannels</code>).</p>
    pub fn get_incremental_training_data_channels(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::IncrementalTrainingDataChannel>> {
        self.inner.get_incremental_training_data_channels()
    }
    ///
    /// Appends an item to `dataChannels`.
    ///
    /// To override the contents of this collection use [`set_data_channels`](Self::set_data_channels).
    ///
    /// <p>Defines the data channels that are used as input for the trained model request.</p>
    /// <p>Limit: Maximum of 20 channels total (including both <code>dataChannels</code> and <code>incrementalTrainingDataChannels</code>).</p>
    pub fn data_channels(mut self, input: crate::types::ModelTrainingDataChannel) -> Self {
        self.inner = self.inner.data_channels(input);
        self
    }
    /// <p>Defines the data channels that are used as input for the trained model request.</p>
    /// <p>Limit: Maximum of 20 channels total (including both <code>dataChannels</code> and <code>incrementalTrainingDataChannels</code>).</p>
    pub fn set_data_channels(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::ModelTrainingDataChannel>>) -> Self {
        self.inner = self.inner.set_data_channels(input);
        self
    }
    /// <p>Defines the data channels that are used as input for the trained model request.</p>
    /// <p>Limit: Maximum of 20 channels total (including both <code>dataChannels</code> and <code>incrementalTrainingDataChannels</code>).</p>
    pub fn get_data_channels(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::ModelTrainingDataChannel>> {
        self.inner.get_data_channels()
    }
    /// <p>The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>File</code> - The training data is downloaded to the training instance and made available as files.</p></li>
    /// <li>
    /// <p><code>FastFile</code> - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.</p></li>
    /// <li>
    /// <p><code>Pipe</code> - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.</p></li>
    /// </ul>
    pub fn training_input_mode(mut self, input: crate::types::TrainingInputMode) -> Self {
        self.inner = self.inner.training_input_mode(input);
        self
    }
    /// <p>The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>File</code> - The training data is downloaded to the training instance and made available as files.</p></li>
    /// <li>
    /// <p><code>FastFile</code> - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.</p></li>
    /// <li>
    /// <p><code>Pipe</code> - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.</p></li>
    /// </ul>
    pub fn set_training_input_mode(mut self, input: ::std::option::Option<crate::types::TrainingInputMode>) -> Self {
        self.inner = self.inner.set_training_input_mode(input);
        self
    }
    /// <p>The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>File</code> - The training data is downloaded to the training instance and made available as files.</p></li>
    /// <li>
    /// <p><code>FastFile</code> - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.</p></li>
    /// <li>
    /// <p><code>Pipe</code> - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.</p></li>
    /// </ul>
    pub fn get_training_input_mode(&self) -> &::std::option::Option<crate::types::TrainingInputMode> {
        self.inner.get_training_input_mode()
    }
    /// <p>The description of the trained model.</p>
    pub fn description(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.description(input.into());
        self
    }
    /// <p>The description of the trained model.</p>
    pub fn set_description(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_description(input);
        self
    }
    /// <p>The description of the trained model.</p>
    pub fn get_description(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_description()
    }
    /// <p>The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the trained ML model and the associated data.</p>
    pub fn kms_key_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.kms_key_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the trained ML model and the associated data.</p>
    pub fn set_kms_key_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_kms_key_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the trained ML model and the associated data.</p>
    pub fn get_kms_key_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_kms_key_arn()
    }
    ///
    /// Adds a key-value pair to `tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn tags(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.tags(k.into(), v.into());
        self
    }
    /// <p>The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
    /// <p>The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn get_tags(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_tags()
    }
}