aws_sdk_sagemaker/operation/create_auto_ml_job/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_auto_ml_job::_create_auto_ml_job_output::CreateAutoMlJobOutputBuilder;

pub use crate::operation::create_auto_ml_job::_create_auto_ml_job_input::CreateAutoMlJobInputBuilder;

impl crate::operation::create_auto_ml_job::builders::CreateAutoMlJobInputBuilder {
    /// 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_auto_ml_job::CreateAutoMlJobOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_auto_ml_job::CreateAutoMLJobError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_auto_ml_job();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateAutoMLJob`.
///
/// <p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.</p>
/// <p>An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.</p>
/// <p>For more information about AutoML jobs, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html">https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html</a> in the SageMaker AI developer guide.</p><note>
/// <p>We recommend using the new versions <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html">CreateAutoMLJobV2</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a>, which offer backward compatibility.</p>
/// <p><code>CreateAutoMLJobV2</code> can manage tabular problem types identical to those of its previous version <code>CreateAutoMLJob</code>, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).</p>
/// <p>Find guidelines about how to migrate a <code>CreateAutoMLJob</code> to <code>CreateAutoMLJobV2</code> in <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html#autopilot-create-experiment-api-migrate-v1-v2">Migrate a CreateAutoMLJob to CreateAutoMLJobV2</a>.</p>
/// </note>
/// <p>You can find the best-performing model after you run an AutoML job by calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a> (recommended) or <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html">DescribeAutoMLJob</a>.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateAutoMLJobFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_auto_ml_job::builders::CreateAutoMlJobInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_auto_ml_job::CreateAutoMlJobOutput,
        crate::operation::create_auto_ml_job::CreateAutoMLJobError,
    > for CreateAutoMLJobFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_auto_ml_job::CreateAutoMlJobOutput,
            crate::operation::create_auto_ml_job::CreateAutoMLJobError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateAutoMLJobFluentBuilder {
    /// Creates a new `CreateAutoMLJobFluentBuilder`.
    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 CreateAutoMLJob as a reference.
    pub fn as_input(&self) -> &crate::operation::create_auto_ml_job::builders::CreateAutoMlJobInputBuilder {
        &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_auto_ml_job::CreateAutoMlJobOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_auto_ml_job::CreateAutoMLJobError,
            ::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_auto_ml_job::CreateAutoMLJob::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_auto_ml_job::CreateAutoMLJob::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_auto_ml_job::CreateAutoMlJobOutput,
        crate::operation::create_auto_ml_job::CreateAutoMLJobError,
        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>Identifies an Autopilot job. The name must be unique to your account and is case insensitive.</p>
    pub fn auto_ml_job_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.auto_ml_job_name(input.into());
        self
    }
    /// <p>Identifies an Autopilot job. The name must be unique to your account and is case insensitive.</p>
    pub fn set_auto_ml_job_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_auto_ml_job_name(input);
        self
    }
    /// <p>Identifies an Autopilot job. The name must be unique to your account and is case insensitive.</p>
    pub fn get_auto_ml_job_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_auto_ml_job_name()
    }
    ///
    /// Appends an item to `InputDataConfig`.
    ///
    /// To override the contents of this collection use [`set_input_data_config`](Self::set_input_data_config).
    ///
    /// <p>An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to <code>InputDataConfig</code> supported by <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.</p>
    pub fn input_data_config(mut self, input: crate::types::AutoMlChannel) -> Self {
        self.inner = self.inner.input_data_config(input);
        self
    }
    /// <p>An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to <code>InputDataConfig</code> supported by <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.</p>
    pub fn set_input_data_config(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::AutoMlChannel>>) -> Self {
        self.inner = self.inner.set_input_data_config(input);
        self
    }
    /// <p>An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to <code>InputDataConfig</code> supported by <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.</p>
    pub fn get_input_data_config(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::AutoMlChannel>> {
        self.inner.get_input_data_config()
    }
    /// <p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.</p>
    pub fn output_data_config(mut self, input: crate::types::AutoMlOutputDataConfig) -> Self {
        self.inner = self.inner.output_data_config(input);
        self
    }
    /// <p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.</p>
    pub fn set_output_data_config(mut self, input: ::std::option::Option<crate::types::AutoMlOutputDataConfig>) -> Self {
        self.inner = self.inner.set_output_data_config(input);
        self
    }
    /// <p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.</p>
    pub fn get_output_data_config(&self) -> &::std::option::Option<crate::types::AutoMlOutputDataConfig> {
        self.inner.get_output_data_config()
    }
    /// <p>Defines the type of supervised learning problem available for the candidates. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types"> SageMaker Autopilot problem types</a>.</p>
    pub fn problem_type(mut self, input: crate::types::ProblemType) -> Self {
        self.inner = self.inner.problem_type(input);
        self
    }
    /// <p>Defines the type of supervised learning problem available for the candidates. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types"> SageMaker Autopilot problem types</a>.</p>
    pub fn set_problem_type(mut self, input: ::std::option::Option<crate::types::ProblemType>) -> Self {
        self.inner = self.inner.set_problem_type(input);
        self
    }
    /// <p>Defines the type of supervised learning problem available for the candidates. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types"> SageMaker Autopilot problem types</a>.</p>
    pub fn get_problem_type(&self) -> &::std::option::Option<crate::types::ProblemType> {
        self.inner.get_problem_type()
    }
    /// <p>Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a> for the default values.</p>
    pub fn auto_ml_job_objective(mut self, input: crate::types::AutoMlJobObjective) -> Self {
        self.inner = self.inner.auto_ml_job_objective(input);
        self
    }
    /// <p>Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a> for the default values.</p>
    pub fn set_auto_ml_job_objective(mut self, input: ::std::option::Option<crate::types::AutoMlJobObjective>) -> Self {
        self.inner = self.inner.set_auto_ml_job_objective(input);
        self
    }
    /// <p>Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a> for the default values.</p>
    pub fn get_auto_ml_job_objective(&self) -> &::std::option::Option<crate::types::AutoMlJobObjective> {
        self.inner.get_auto_ml_job_objective()
    }
    /// <p>A collection of settings used to configure an AutoML job.</p>
    pub fn auto_ml_job_config(mut self, input: crate::types::AutoMlJobConfig) -> Self {
        self.inner = self.inner.auto_ml_job_config(input);
        self
    }
    /// <p>A collection of settings used to configure an AutoML job.</p>
    pub fn set_auto_ml_job_config(mut self, input: ::std::option::Option<crate::types::AutoMlJobConfig>) -> Self {
        self.inner = self.inner.set_auto_ml_job_config(input);
        self
    }
    /// <p>A collection of settings used to configure an AutoML job.</p>
    pub fn get_auto_ml_job_config(&self) -> &::std::option::Option<crate::types::AutoMlJobConfig> {
        self.inner.get_auto_ml_job_config()
    }
    /// <p>The ARN of the role that is used to access the data.</p>
    pub fn role_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.role_arn(input.into());
        self
    }
    /// <p>The ARN of the role that is used to access the data.</p>
    pub fn set_role_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_role_arn(input);
        self
    }
    /// <p>The ARN of the role that is used to access the data.</p>
    pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_role_arn()
    }
    /// <p>Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.</p>
    pub fn generate_candidate_definitions_only(mut self, input: bool) -> Self {
        self.inner = self.inner.generate_candidate_definitions_only(input);
        self
    }
    /// <p>Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.</p>
    pub fn set_generate_candidate_definitions_only(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_generate_candidate_definitions_only(input);
        self
    }
    /// <p>Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.</p>
    pub fn get_generate_candidate_definitions_only(&self) -> &::std::option::Option<bool> {
        self.inner.get_generate_candidate_definitions_only()
    }
    ///
    /// Appends an item to `Tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>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 <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web ServicesResources</a>. Tag keys must be unique per resource.</p>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p>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 <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web ServicesResources</a>. Tag keys must be unique per resource.</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>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 <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web ServicesResources</a>. Tag keys must be unique per resource.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
    /// <p>Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.</p>
    pub fn model_deploy_config(mut self, input: crate::types::ModelDeployConfig) -> Self {
        self.inner = self.inner.model_deploy_config(input);
        self
    }
    /// <p>Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.</p>
    pub fn set_model_deploy_config(mut self, input: ::std::option::Option<crate::types::ModelDeployConfig>) -> Self {
        self.inner = self.inner.set_model_deploy_config(input);
        self
    }
    /// <p>Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.</p>
    pub fn get_model_deploy_config(&self) -> &::std::option::Option<crate::types::ModelDeployConfig> {
        self.inner.get_model_deploy_config()
    }
}