aws_sdk_sagemaker/operation/create_auto_ml_job_v2/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_auto_ml_job_v2::_create_auto_ml_job_v2_output::CreateAutoMlJobV2OutputBuilder;

pub use crate::operation::create_auto_ml_job_v2::_create_auto_ml_job_v2_input::CreateAutoMlJobV2InputBuilder;

impl crate::operation::create_auto_ml_job_v2::builders::CreateAutoMlJobV2InputBuilder {
    /// 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_v2::CreateAutoMlJobV2Output,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_auto_ml_job_v2::CreateAutoMLJobV2Error,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_auto_ml_job_v2();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateAutoMLJobV2`.
///
/// <p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.</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>
/// <p>AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.</p><note>
/// <p><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> are new versions of <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html">CreateAutoMLJob</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html">DescribeAutoMLJob</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>For the list of available problem types supported by <code>CreateAutoMLJobV2</code>, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLProblemTypeConfig.html">AutoMLProblemTypeConfig</a>.</p>
/// <p>You can find the best-performing model after you run an AutoML job V2 by calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a>.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateAutoMLJobV2FluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_auto_ml_job_v2::builders::CreateAutoMlJobV2InputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_auto_ml_job_v2::CreateAutoMlJobV2Output,
        crate::operation::create_auto_ml_job_v2::CreateAutoMLJobV2Error,
    > for CreateAutoMLJobV2FluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_auto_ml_job_v2::CreateAutoMlJobV2Output,
            crate::operation::create_auto_ml_job_v2::CreateAutoMLJobV2Error,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateAutoMLJobV2FluentBuilder {
    /// Creates a new `CreateAutoMLJobV2FluentBuilder`.
    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 CreateAutoMLJobV2 as a reference.
    pub fn as_input(&self) -> &crate::operation::create_auto_ml_job_v2::builders::CreateAutoMlJobV2InputBuilder {
        &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_v2::CreateAutoMlJobV2Output,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_auto_ml_job_v2::CreateAutoMLJobV2Error,
            ::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_v2::CreateAutoMLJobV2::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_auto_ml_job_v2::CreateAutoMLJobV2::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_v2::CreateAutoMlJobV2Output,
        crate::operation::create_auto_ml_job_v2::CreateAutoMLJobV2Error,
        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 `AutoMLJobInputDataConfig`.
    ///
    /// To override the contents of this collection use [`set_auto_ml_job_input_data_config`](Self::set_auto_ml_job_input_data_config).
    ///
    /// <p>An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html#sagemaker-CreateAutoMLJob-request-InputDataConfig">InputDataConfig</a> attribute in the <code>CreateAutoMLJob</code> input parameters. The supported formats depend on the problem type:</p>
    /// <ul>
    /// <li>
    /// <p>For tabular problem types: <code>S3Prefix</code>, <code>ManifestFile</code>.</p></li>
    /// <li>
    /// <p>For image classification: <code>S3Prefix</code>, <code>ManifestFile</code>, <code>AugmentedManifestFile</code>.</p></li>
    /// <li>
    /// <p>For text classification: <code>S3Prefix</code>.</p></li>
    /// <li>
    /// <p>For time-series forecasting: <code>S3Prefix</code>.</p></li>
    /// <li>
    /// <p>For text generation (LLMs fine-tuning): <code>S3Prefix</code>.</p></li>
    /// </ul>
    pub fn auto_ml_job_input_data_config(mut self, input: crate::types::AutoMlJobChannel) -> Self {
        self.inner = self.inner.auto_ml_job_input_data_config(input);
        self
    }
    /// <p>An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html#sagemaker-CreateAutoMLJob-request-InputDataConfig">InputDataConfig</a> attribute in the <code>CreateAutoMLJob</code> input parameters. The supported formats depend on the problem type:</p>
    /// <ul>
    /// <li>
    /// <p>For tabular problem types: <code>S3Prefix</code>, <code>ManifestFile</code>.</p></li>
    /// <li>
    /// <p>For image classification: <code>S3Prefix</code>, <code>ManifestFile</code>, <code>AugmentedManifestFile</code>.</p></li>
    /// <li>
    /// <p>For text classification: <code>S3Prefix</code>.</p></li>
    /// <li>
    /// <p>For time-series forecasting: <code>S3Prefix</code>.</p></li>
    /// <li>
    /// <p>For text generation (LLMs fine-tuning): <code>S3Prefix</code>.</p></li>
    /// </ul>
    pub fn set_auto_ml_job_input_data_config(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::AutoMlJobChannel>>) -> Self {
        self.inner = self.inner.set_auto_ml_job_input_data_config(input);
        self
    }
    /// <p>An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html#sagemaker-CreateAutoMLJob-request-InputDataConfig">InputDataConfig</a> attribute in the <code>CreateAutoMLJob</code> input parameters. The supported formats depend on the problem type:</p>
    /// <ul>
    /// <li>
    /// <p>For tabular problem types: <code>S3Prefix</code>, <code>ManifestFile</code>.</p></li>
    /// <li>
    /// <p>For image classification: <code>S3Prefix</code>, <code>ManifestFile</code>, <code>AugmentedManifestFile</code>.</p></li>
    /// <li>
    /// <p>For text classification: <code>S3Prefix</code>.</p></li>
    /// <li>
    /// <p>For time-series forecasting: <code>S3Prefix</code>.</p></li>
    /// <li>
    /// <p>For text generation (LLMs fine-tuning): <code>S3Prefix</code>.</p></li>
    /// </ul>
    pub fn get_auto_ml_job_input_data_config(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::AutoMlJobChannel>> {
        self.inner.get_auto_ml_job_input_data_config()
    }
    /// <p>Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.</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.</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.</p>
    pub fn get_output_data_config(&self) -> &::std::option::Option<crate::types::AutoMlOutputDataConfig> {
        self.inner.get_output_data_config()
    }
    /// <p>Defines the configuration settings of one of the supported problem types.</p>
    pub fn auto_ml_problem_type_config(mut self, input: crate::types::AutoMlProblemTypeConfig) -> Self {
        self.inner = self.inner.auto_ml_problem_type_config(input);
        self
    }
    /// <p>Defines the configuration settings of one of the supported problem types.</p>
    pub fn set_auto_ml_problem_type_config(mut self, input: ::std::option::Option<crate::types::AutoMlProblemTypeConfig>) -> Self {
        self.inner = self.inner.set_auto_ml_problem_type_config(input);
        self
    }
    /// <p>Defines the configuration settings of one of the supported problem types.</p>
    pub fn get_auto_ml_problem_type_config(&self) -> &::std::option::Option<crate::types::AutoMlProblemTypeConfig> {
        self.inner.get_auto_ml_problem_type_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()
    }
    ///
    /// 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, such as 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, such as 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, such as 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>The security configuration for traffic encryption or Amazon VPC settings.</p>
    pub fn security_config(mut self, input: crate::types::AutoMlSecurityConfig) -> Self {
        self.inner = self.inner.security_config(input);
        self
    }
    /// <p>The security configuration for traffic encryption or Amazon VPC settings.</p>
    pub fn set_security_config(mut self, input: ::std::option::Option<crate::types::AutoMlSecurityConfig>) -> Self {
        self.inner = self.inner.set_security_config(input);
        self
    }
    /// <p>The security configuration for traffic encryption or Amazon VPC settings.</p>
    pub fn get_security_config(&self) -> &::std::option::Option<crate::types::AutoMlSecurityConfig> {
        self.inner.get_security_config()
    }
    /// <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. For the list of default values per problem type, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a>.</p><note>
    /// <ul>
    /// <li>
    /// <p>For tabular problem types: You must either provide both the <code>AutoMLJobObjective</code> and indicate the type of supervised learning problem in <code>AutoMLProblemTypeConfig</code> (<code>TabularJobConfig.ProblemType</code>), or none at all.</p></li>
    /// <li>
    /// <p>For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the <code>AutoMLJobObjective</code> field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html">Metrics for fine-tuning LLMs in Autopilot</a>.</p></li>
    /// </ul>
    /// </note>
    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. For the list of default values per problem type, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a>.</p><note>
    /// <ul>
    /// <li>
    /// <p>For tabular problem types: You must either provide both the <code>AutoMLJobObjective</code> and indicate the type of supervised learning problem in <code>AutoMLProblemTypeConfig</code> (<code>TabularJobConfig.ProblemType</code>), or none at all.</p></li>
    /// <li>
    /// <p>For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the <code>AutoMLJobObjective</code> field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html">Metrics for fine-tuning LLMs in Autopilot</a>.</p></li>
    /// </ul>
    /// </note>
    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. For the list of default values per problem type, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">AutoMLJobObjective</a>.</p><note>
    /// <ul>
    /// <li>
    /// <p>For tabular problem types: You must either provide both the <code>AutoMLJobObjective</code> and indicate the type of supervised learning problem in <code>AutoMLProblemTypeConfig</code> (<code>TabularJobConfig.ProblemType</code>), or none at all.</p></li>
    /// <li>
    /// <p>For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the <code>AutoMLJobObjective</code> field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html">Metrics for fine-tuning LLMs in Autopilot</a>.</p></li>
    /// </ul>
    /// </note>
    pub fn get_auto_ml_job_objective(&self) -> &::std::option::Option<crate::types::AutoMlJobObjective> {
        self.inner.get_auto_ml_job_objective()
    }
    /// <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()
    }
    /// <p>This structure specifies how to split the data into train and validation datasets.</p>
    /// <p>The validation and training datasets must contain the same headers. For jobs created by calling <code>CreateAutoMLJob</code>, the validation dataset must be less than 2 GB in size.</p><note>
    /// <p>This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.</p>
    /// </note>
    pub fn data_split_config(mut self, input: crate::types::AutoMlDataSplitConfig) -> Self {
        self.inner = self.inner.data_split_config(input);
        self
    }
    /// <p>This structure specifies how to split the data into train and validation datasets.</p>
    /// <p>The validation and training datasets must contain the same headers. For jobs created by calling <code>CreateAutoMLJob</code>, the validation dataset must be less than 2 GB in size.</p><note>
    /// <p>This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.</p>
    /// </note>
    pub fn set_data_split_config(mut self, input: ::std::option::Option<crate::types::AutoMlDataSplitConfig>) -> Self {
        self.inner = self.inner.set_data_split_config(input);
        self
    }
    /// <p>This structure specifies how to split the data into train and validation datasets.</p>
    /// <p>The validation and training datasets must contain the same headers. For jobs created by calling <code>CreateAutoMLJob</code>, the validation dataset must be less than 2 GB in size.</p><note>
    /// <p>This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.</p>
    /// </note>
    pub fn get_data_split_config(&self) -> &::std::option::Option<crate::types::AutoMlDataSplitConfig> {
        self.inner.get_data_split_config()
    }
    /// <p>Specifies the compute configuration for the AutoML job V2.</p>
    pub fn auto_ml_compute_config(mut self, input: crate::types::AutoMlComputeConfig) -> Self {
        self.inner = self.inner.auto_ml_compute_config(input);
        self
    }
    /// <p>Specifies the compute configuration for the AutoML job V2.</p>
    pub fn set_auto_ml_compute_config(mut self, input: ::std::option::Option<crate::types::AutoMlComputeConfig>) -> Self {
        self.inner = self.inner.set_auto_ml_compute_config(input);
        self
    }
    /// <p>Specifies the compute configuration for the AutoML job V2.</p>
    pub fn get_auto_ml_compute_config(&self) -> &::std::option::Option<crate::types::AutoMlComputeConfig> {
        self.inner.get_auto_ml_compute_config()
    }
}