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// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::fmt::Debug)]
pub struct UpdateInferenceExperimentInput {
/// <p>The name of the inference experiment to be updated.</p>
pub name: ::std::option::Option<::std::string::String>,
/// <p>The duration for which the inference experiment will run. If the status of the inference experiment is <code>Created</code>, then you can update both the start and end dates. If the status of the inference experiment is <code>Running</code>, then you can update only the end date.</p>
pub schedule: ::std::option::Option<crate::types::InferenceExperimentSchedule>,
/// <p>The description of the inference experiment.</p>
pub description: ::std::option::Option<::std::string::String>,
/// <p>An array of <code>ModelVariantConfig</code> objects. There is one for each variant, whose infrastructure configuration you want to update.</p>
pub model_variants: ::std::option::Option<::std::vec::Vec<crate::types::ModelVariantConfig>>,
/// <p>The Amazon S3 location and configuration for storing inference request and response data.</p>
pub data_storage_config: ::std::option::Option<crate::types::InferenceExperimentDataStorageConfig>,
/// <p>The configuration of <code>ShadowMode</code> inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.</p>
pub shadow_mode_config: ::std::option::Option<crate::types::ShadowModeConfig>,
}
impl UpdateInferenceExperimentInput {
/// <p>The name of the inference experiment to be updated.</p>
pub fn name(&self) -> ::std::option::Option<&str> {
self.name.as_deref()
}
/// <p>The duration for which the inference experiment will run. If the status of the inference experiment is <code>Created</code>, then you can update both the start and end dates. If the status of the inference experiment is <code>Running</code>, then you can update only the end date.</p>
pub fn schedule(&self) -> ::std::option::Option<&crate::types::InferenceExperimentSchedule> {
self.schedule.as_ref()
}
/// <p>The description of the inference experiment.</p>
pub fn description(&self) -> ::std::option::Option<&str> {
self.description.as_deref()
}
/// <p>An array of <code>ModelVariantConfig</code> objects. There is one for each variant, whose infrastructure configuration you want to update.</p>
///
/// If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use `.model_variants.is_none()`.
pub fn model_variants(&self) -> &[crate::types::ModelVariantConfig] {
self.model_variants.as_deref().unwrap_or_default()
}
/// <p>The Amazon S3 location and configuration for storing inference request and response data.</p>
pub fn data_storage_config(&self) -> ::std::option::Option<&crate::types::InferenceExperimentDataStorageConfig> {
self.data_storage_config.as_ref()
}
/// <p>The configuration of <code>ShadowMode</code> inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.</p>
pub fn shadow_mode_config(&self) -> ::std::option::Option<&crate::types::ShadowModeConfig> {
self.shadow_mode_config.as_ref()
}
}
impl UpdateInferenceExperimentInput {
/// Creates a new builder-style object to manufacture [`UpdateInferenceExperimentInput`](crate::operation::update_inference_experiment::UpdateInferenceExperimentInput).
pub fn builder() -> crate::operation::update_inference_experiment::builders::UpdateInferenceExperimentInputBuilder {
crate::operation::update_inference_experiment::builders::UpdateInferenceExperimentInputBuilder::default()
}
}
/// A builder for [`UpdateInferenceExperimentInput`](crate::operation::update_inference_experiment::UpdateInferenceExperimentInput).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default, ::std::fmt::Debug)]
#[non_exhaustive]
pub struct UpdateInferenceExperimentInputBuilder {
pub(crate) name: ::std::option::Option<::std::string::String>,
pub(crate) schedule: ::std::option::Option<crate::types::InferenceExperimentSchedule>,
pub(crate) description: ::std::option::Option<::std::string::String>,
pub(crate) model_variants: ::std::option::Option<::std::vec::Vec<crate::types::ModelVariantConfig>>,
pub(crate) data_storage_config: ::std::option::Option<crate::types::InferenceExperimentDataStorageConfig>,
pub(crate) shadow_mode_config: ::std::option::Option<crate::types::ShadowModeConfig>,
}
impl UpdateInferenceExperimentInputBuilder {
/// <p>The name of the inference experiment to be updated.</p>
/// This field is required.
pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.name = ::std::option::Option::Some(input.into());
self
}
/// <p>The name of the inference experiment to be updated.</p>
pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.name = input;
self
}
/// <p>The name of the inference experiment to be updated.</p>
pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
&self.name
}
/// <p>The duration for which the inference experiment will run. If the status of the inference experiment is <code>Created</code>, then you can update both the start and end dates. If the status of the inference experiment is <code>Running</code>, then you can update only the end date.</p>
pub fn schedule(mut self, input: crate::types::InferenceExperimentSchedule) -> Self {
self.schedule = ::std::option::Option::Some(input);
self
}
/// <p>The duration for which the inference experiment will run. If the status of the inference experiment is <code>Created</code>, then you can update both the start and end dates. If the status of the inference experiment is <code>Running</code>, then you can update only the end date.</p>
pub fn set_schedule(mut self, input: ::std::option::Option<crate::types::InferenceExperimentSchedule>) -> Self {
self.schedule = input;
self
}
/// <p>The duration for which the inference experiment will run. If the status of the inference experiment is <code>Created</code>, then you can update both the start and end dates. If the status of the inference experiment is <code>Running</code>, then you can update only the end date.</p>
pub fn get_schedule(&self) -> &::std::option::Option<crate::types::InferenceExperimentSchedule> {
&self.schedule
}
/// <p>The description of the inference experiment.</p>
pub fn description(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.description = ::std::option::Option::Some(input.into());
self
}
/// <p>The description of the inference experiment.</p>
pub fn set_description(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.description = input;
self
}
/// <p>The description of the inference experiment.</p>
pub fn get_description(&self) -> &::std::option::Option<::std::string::String> {
&self.description
}
/// Appends an item to `model_variants`.
///
/// To override the contents of this collection use [`set_model_variants`](Self::set_model_variants).
///
/// <p>An array of <code>ModelVariantConfig</code> objects. There is one for each variant, whose infrastructure configuration you want to update.</p>
pub fn model_variants(mut self, input: crate::types::ModelVariantConfig) -> Self {
let mut v = self.model_variants.unwrap_or_default();
v.push(input);
self.model_variants = ::std::option::Option::Some(v);
self
}
/// <p>An array of <code>ModelVariantConfig</code> objects. There is one for each variant, whose infrastructure configuration you want to update.</p>
pub fn set_model_variants(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::ModelVariantConfig>>) -> Self {
self.model_variants = input;
self
}
/// <p>An array of <code>ModelVariantConfig</code> objects. There is one for each variant, whose infrastructure configuration you want to update.</p>
pub fn get_model_variants(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::ModelVariantConfig>> {
&self.model_variants
}
/// <p>The Amazon S3 location and configuration for storing inference request and response data.</p>
pub fn data_storage_config(mut self, input: crate::types::InferenceExperimentDataStorageConfig) -> Self {
self.data_storage_config = ::std::option::Option::Some(input);
self
}
/// <p>The Amazon S3 location and configuration for storing inference request and response data.</p>
pub fn set_data_storage_config(mut self, input: ::std::option::Option<crate::types::InferenceExperimentDataStorageConfig>) -> Self {
self.data_storage_config = input;
self
}
/// <p>The Amazon S3 location and configuration for storing inference request and response data.</p>
pub fn get_data_storage_config(&self) -> &::std::option::Option<crate::types::InferenceExperimentDataStorageConfig> {
&self.data_storage_config
}
/// <p>The configuration of <code>ShadowMode</code> inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.</p>
pub fn shadow_mode_config(mut self, input: crate::types::ShadowModeConfig) -> Self {
self.shadow_mode_config = ::std::option::Option::Some(input);
self
}
/// <p>The configuration of <code>ShadowMode</code> inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.</p>
pub fn set_shadow_mode_config(mut self, input: ::std::option::Option<crate::types::ShadowModeConfig>) -> Self {
self.shadow_mode_config = input;
self
}
/// <p>The configuration of <code>ShadowMode</code> inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.</p>
pub fn get_shadow_mode_config(&self) -> &::std::option::Option<crate::types::ShadowModeConfig> {
&self.shadow_mode_config
}
/// Consumes the builder and constructs a [`UpdateInferenceExperimentInput`](crate::operation::update_inference_experiment::UpdateInferenceExperimentInput).
pub fn build(
self,
) -> ::std::result::Result<
crate::operation::update_inference_experiment::UpdateInferenceExperimentInput,
::aws_smithy_types::error::operation::BuildError,
> {
::std::result::Result::Ok(crate::operation::update_inference_experiment::UpdateInferenceExperimentInput {
name: self.name,
schedule: self.schedule,
description: self.description,
model_variants: self.model_variants,
data_storage_config: self.data_storage_config,
shadow_mode_config: self.shadow_mode_config,
})
}
}