aws_sdk_sagemaker/operation/create_compilation_job/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_compilation_job::_create_compilation_job_output::CreateCompilationJobOutputBuilder;

pub use crate::operation::create_compilation_job::_create_compilation_job_input::CreateCompilationJobInputBuilder;

impl crate::operation::create_compilation_job::builders::CreateCompilationJobInputBuilder {
    /// 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_compilation_job::CreateCompilationJobOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_compilation_job::CreateCompilationJobError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_compilation_job();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateCompilationJob`.
///
/// <p>Starts a model compilation job. After the model has been compiled, Amazon SageMaker AI saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.</p>
/// <p>If you choose to host your model using Amazon SageMaker AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.</p>
/// <p>In the request body, you provide the following:</p>
/// <ul>
/// <li>
/// <p>A name for the compilation job</p></li>
/// <li>
/// <p>Information about the input model artifacts</p></li>
/// <li>
/// <p>The output location for the compiled model and the device (target) that the model runs on</p></li>
/// <li>
/// <p>The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker AI assumes to perform the model compilation job.</p></li>
/// </ul>
/// <p>You can also provide a <code>Tag</code> to track the model compilation job's resource use and costs. The response body contains the <code>CompilationJobArn</code> for the compiled job.</p>
/// <p>To stop a model compilation job, use <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_StopCompilationJob.html">StopCompilationJob</a>. To get information about a particular model compilation job, use <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeCompilationJob.html">DescribeCompilationJob</a>. To get information about multiple model compilation jobs, use <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListCompilationJobs.html">ListCompilationJobs</a>.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateCompilationJobFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_compilation_job::builders::CreateCompilationJobInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_compilation_job::CreateCompilationJobOutput,
        crate::operation::create_compilation_job::CreateCompilationJobError,
    > for CreateCompilationJobFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_compilation_job::CreateCompilationJobOutput,
            crate::operation::create_compilation_job::CreateCompilationJobError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateCompilationJobFluentBuilder {
    /// Creates a new `CreateCompilationJobFluentBuilder`.
    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 CreateCompilationJob as a reference.
    pub fn as_input(&self) -> &crate::operation::create_compilation_job::builders::CreateCompilationJobInputBuilder {
        &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_compilation_job::CreateCompilationJobOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_compilation_job::CreateCompilationJobError,
            ::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_compilation_job::CreateCompilationJob::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_compilation_job::CreateCompilationJob::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_compilation_job::CreateCompilationJobOutput,
        crate::operation::create_compilation_job::CreateCompilationJobError,
        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>A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.</p>
    pub fn compilation_job_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.compilation_job_name(input.into());
        self
    }
    /// <p>A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.</p>
    pub fn set_compilation_job_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_compilation_job_name(input);
        self
    }
    /// <p>A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.</p>
    pub fn get_compilation_job_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_compilation_job_name()
    }
    /// <p>The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.</p>
    /// <p>During model compilation, Amazon SageMaker AI needs your permission to:</p>
    /// <ul>
    /// <li>
    /// <p>Read input data from an S3 bucket</p></li>
    /// <li>
    /// <p>Write model artifacts to an S3 bucket</p></li>
    /// <li>
    /// <p>Write logs to Amazon CloudWatch Logs</p></li>
    /// <li>
    /// <p>Publish metrics to Amazon CloudWatch</p></li>
    /// </ul>
    /// <p>You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the <code>iam:PassRole</code> permission. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">Amazon SageMaker AI Roles.</a></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 Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.</p>
    /// <p>During model compilation, Amazon SageMaker AI needs your permission to:</p>
    /// <ul>
    /// <li>
    /// <p>Read input data from an S3 bucket</p></li>
    /// <li>
    /// <p>Write model artifacts to an S3 bucket</p></li>
    /// <li>
    /// <p>Write logs to Amazon CloudWatch Logs</p></li>
    /// <li>
    /// <p>Publish metrics to Amazon CloudWatch</p></li>
    /// </ul>
    /// <p>You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the <code>iam:PassRole</code> permission. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">Amazon SageMaker AI Roles.</a></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 Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.</p>
    /// <p>During model compilation, Amazon SageMaker AI needs your permission to:</p>
    /// <ul>
    /// <li>
    /// <p>Read input data from an S3 bucket</p></li>
    /// <li>
    /// <p>Write model artifacts to an S3 bucket</p></li>
    /// <li>
    /// <p>Write logs to Amazon CloudWatch Logs</p></li>
    /// <li>
    /// <p>Publish metrics to Amazon CloudWatch</p></li>
    /// </ul>
    /// <p>You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the <code>iam:PassRole</code> permission. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">Amazon SageMaker AI Roles.</a></p>
    pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_role_arn()
    }
    /// <p>The Amazon Resource Name (ARN) of a versioned model package. Provide either a <code>ModelPackageVersionArn</code> or an <code>InputConfig</code> object in the request syntax. The presence of both objects in the <code>CreateCompilationJob</code> request will return an exception.</p>
    pub fn model_package_version_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.model_package_version_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of a versioned model package. Provide either a <code>ModelPackageVersionArn</code> or an <code>InputConfig</code> object in the request syntax. The presence of both objects in the <code>CreateCompilationJob</code> request will return an exception.</p>
    pub fn set_model_package_version_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_model_package_version_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of a versioned model package. Provide either a <code>ModelPackageVersionArn</code> or an <code>InputConfig</code> object in the request syntax. The presence of both objects in the <code>CreateCompilationJob</code> request will return an exception.</p>
    pub fn get_model_package_version_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_model_package_version_arn()
    }
    /// <p>Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.</p>
    pub fn input_config(mut self, input: crate::types::InputConfig) -> Self {
        self.inner = self.inner.input_config(input);
        self
    }
    /// <p>Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.</p>
    pub fn set_input_config(mut self, input: ::std::option::Option<crate::types::InputConfig>) -> Self {
        self.inner = self.inner.set_input_config(input);
        self
    }
    /// <p>Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.</p>
    pub fn get_input_config(&self) -> &::std::option::Option<crate::types::InputConfig> {
        self.inner.get_input_config()
    }
    /// <p>Provides information about the output location for the compiled model and the target device the model runs on.</p>
    pub fn output_config(mut self, input: crate::types::OutputConfig) -> Self {
        self.inner = self.inner.output_config(input);
        self
    }
    /// <p>Provides information about the output location for the compiled model and the target device the model runs on.</p>
    pub fn set_output_config(mut self, input: ::std::option::Option<crate::types::OutputConfig>) -> Self {
        self.inner = self.inner.set_output_config(input);
        self
    }
    /// <p>Provides information about the output location for the compiled model and the target device the model runs on.</p>
    pub fn get_output_config(&self) -> &::std::option::Option<crate::types::OutputConfig> {
        self.inner.get_output_config()
    }
    /// <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html">VpcConfig</a> object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html">Protect Compilation Jobs by Using an Amazon Virtual Private Cloud</a>.</p>
    pub fn vpc_config(mut self, input: crate::types::NeoVpcConfig) -> Self {
        self.inner = self.inner.vpc_config(input);
        self
    }
    /// <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html">VpcConfig</a> object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html">Protect Compilation Jobs by Using an Amazon Virtual Private Cloud</a>.</p>
    pub fn set_vpc_config(mut self, input: ::std::option::Option<crate::types::NeoVpcConfig>) -> Self {
        self.inner = self.inner.set_vpc_config(input);
        self
    }
    /// <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html">VpcConfig</a> object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html">Protect Compilation Jobs by Using an Amazon Virtual Private Cloud</a>.</p>
    pub fn get_vpc_config(&self) -> &::std::option::Option<crate::types::NeoVpcConfig> {
        self.inner.get_vpc_config()
    }
    /// <p>Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.</p>
    pub fn stopping_condition(mut self, input: crate::types::StoppingCondition) -> Self {
        self.inner = self.inner.stopping_condition(input);
        self
    }
    /// <p>Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.</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>Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.</p>
    pub fn get_stopping_condition(&self) -> &::std::option::Option<crate::types::StoppingCondition> {
        self.inner.get_stopping_condition()
    }
    ///
    /// 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 Services Resources</a>.</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 Services Resources</a>.</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 Services Resources</a>.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
}