// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_inference_component::_create_inference_component_input::CreateInferenceComponentInputBuilder;
pub use crate::operation::create_inference_component::_create_inference_component_output::CreateInferenceComponentOutputBuilder;
impl crate::operation::create_inference_component::builders::CreateInferenceComponentInputBuilder {
/// 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_inference_component::CreateInferenceComponentOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_inference_component::CreateInferenceComponentError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let mut fluent_builder = client.create_inference_component();
fluent_builder.inner = self;
fluent_builder.send().await
}
}
/// Fluent builder constructing a request to `CreateInferenceComponent`.
///
/// <p>Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateInferenceComponentFluentBuilder {
handle: ::std::sync::Arc<crate::client::Handle>,
inner: crate::operation::create_inference_component::builders::CreateInferenceComponentInputBuilder,
config_override: ::std::option::Option<crate::config::Builder>,
}
impl
crate::client::customize::internal::CustomizableSend<
crate::operation::create_inference_component::CreateInferenceComponentOutput,
crate::operation::create_inference_component::CreateInferenceComponentError,
> for CreateInferenceComponentFluentBuilder
{
fn send(
self,
config_override: crate::config::Builder,
) -> crate::client::customize::internal::BoxFuture<
crate::client::customize::internal::SendResult<
crate::operation::create_inference_component::CreateInferenceComponentOutput,
crate::operation::create_inference_component::CreateInferenceComponentError,
>,
> {
::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
}
}
impl CreateInferenceComponentFluentBuilder {
/// Creates a new `CreateInferenceComponentFluentBuilder`.
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 CreateInferenceComponent as a reference.
pub fn as_input(&self) -> &crate::operation::create_inference_component::builders::CreateInferenceComponentInputBuilder {
&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_inference_component::CreateInferenceComponentOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_inference_component::CreateInferenceComponentError,
::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_inference_component::CreateInferenceComponent::operation_runtime_plugins(
self.handle.runtime_plugins.clone(),
&self.handle.conf,
self.config_override,
);
crate::operation::create_inference_component::CreateInferenceComponent::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_inference_component::CreateInferenceComponentOutput,
crate::operation::create_inference_component::CreateInferenceComponentError,
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 unique name to assign to the inference component.</p>
pub fn inference_component_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.inference_component_name(input.into());
self
}
/// <p>A unique name to assign to the inference component.</p>
pub fn set_inference_component_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_inference_component_name(input);
self
}
/// <p>A unique name to assign to the inference component.</p>
pub fn get_inference_component_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_inference_component_name()
}
/// <p>The name of an existing endpoint where you host the inference component.</p>
pub fn endpoint_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.endpoint_name(input.into());
self
}
/// <p>The name of an existing endpoint where you host the inference component.</p>
pub fn set_endpoint_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_endpoint_name(input);
self
}
/// <p>The name of an existing endpoint where you host the inference component.</p>
pub fn get_endpoint_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_endpoint_name()
}
/// <p>The name of an existing production variant where you host the inference component.</p>
pub fn variant_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.variant_name(input.into());
self
}
/// <p>The name of an existing production variant where you host the inference component.</p>
pub fn set_variant_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_variant_name(input);
self
}
/// <p>The name of an existing production variant where you host the inference component.</p>
pub fn get_variant_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_variant_name()
}
/// <p>Details about the resources to deploy with this inference component, including the model, container, and compute resources.</p>
pub fn specification(mut self, input: crate::types::InferenceComponentSpecification) -> Self {
self.inner = self.inner.specification(input);
self
}
/// <p>Details about the resources to deploy with this inference component, including the model, container, and compute resources.</p>
pub fn set_specification(mut self, input: ::std::option::Option<crate::types::InferenceComponentSpecification>) -> Self {
self.inner = self.inner.set_specification(input);
self
}
/// <p>Details about the resources to deploy with this inference component, including the model, container, and compute resources.</p>
pub fn get_specification(&self) -> &::std::option::Option<crate::types::InferenceComponentSpecification> {
self.inner.get_specification()
}
/// <p>Runtime settings for a model that is deployed with an inference component.</p>
pub fn runtime_config(mut self, input: crate::types::InferenceComponentRuntimeConfig) -> Self {
self.inner = self.inner.runtime_config(input);
self
}
/// <p>Runtime settings for a model that is deployed with an inference component.</p>
pub fn set_runtime_config(mut self, input: ::std::option::Option<crate::types::InferenceComponentRuntimeConfig>) -> Self {
self.inner = self.inner.set_runtime_config(input);
self
}
/// <p>Runtime settings for a model that is deployed with an inference component.</p>
pub fn get_runtime_config(&self) -> &::std::option::Option<crate::types::InferenceComponentRuntimeConfig> {
self.inner.get_runtime_config()
}
///
/// Appends an item to `Tags`.
///
/// To override the contents of this collection use [`set_tags`](Self::set_tags).
///
/// <p>A list of key-value pairs associated with the model. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services resources</a> in the <i>Amazon Web Services General Reference</i>.</p>
pub fn tags(mut self, input: crate::types::Tag) -> Self {
self.inner = self.inner.tags(input);
self
}
/// <p>A list of key-value pairs associated with the model. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services resources</a> in the <i>Amazon Web Services General Reference</i>.</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>A list of key-value pairs associated with the model. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services resources</a> in the <i>Amazon Web Services General Reference</i>.</p>
pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
self.inner.get_tags()
}
}