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// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_mlflow_tracking_server::_create_mlflow_tracking_server_input::CreateMlflowTrackingServerInputBuilder;
pub use crate::operation::create_mlflow_tracking_server::_create_mlflow_tracking_server_output::CreateMlflowTrackingServerOutputBuilder;
impl crate::operation::create_mlflow_tracking_server::builders::CreateMlflowTrackingServerInputBuilder {
/// 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_mlflow_tracking_server::CreateMlflowTrackingServerOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let mut fluent_builder = client.create_mlflow_tracking_server();
fluent_builder.inner = self;
fluent_builder.send().await
}
}
/// Fluent builder constructing a request to `CreateMlflowTrackingServer`.
///
/// <p>Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server.html">Create an MLflow Tracking Server</a>.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateMlflowTrackingServerFluentBuilder {
handle: ::std::sync::Arc<crate::client::Handle>,
inner: crate::operation::create_mlflow_tracking_server::builders::CreateMlflowTrackingServerInputBuilder,
config_override: ::std::option::Option<crate::config::Builder>,
}
impl
crate::client::customize::internal::CustomizableSend<
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerOutput,
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerError,
> for CreateMlflowTrackingServerFluentBuilder
{
fn send(
self,
config_override: crate::config::Builder,
) -> crate::client::customize::internal::BoxFuture<
crate::client::customize::internal::SendResult<
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerOutput,
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerError,
>,
> {
::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
}
}
impl CreateMlflowTrackingServerFluentBuilder {
/// Creates a new `CreateMlflowTrackingServerFluentBuilder`.
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 CreateMlflowTrackingServer as a reference.
pub fn as_input(&self) -> &crate::operation::create_mlflow_tracking_server::builders::CreateMlflowTrackingServerInputBuilder {
&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_mlflow_tracking_server::CreateMlflowTrackingServerOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerError,
::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_mlflow_tracking_server::CreateMlflowTrackingServer::operation_runtime_plugins(
self.handle.runtime_plugins.clone(),
&self.handle.conf,
self.config_override,
);
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServer::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_mlflow_tracking_server::CreateMlflowTrackingServerOutput,
crate::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerError,
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 string identifying the tracking server name. This string is part of the tracking server ARN.</p>
pub fn tracking_server_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.tracking_server_name(input.into());
self
}
/// <p>A unique string identifying the tracking server name. This string is part of the tracking server ARN.</p>
pub fn set_tracking_server_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_tracking_server_name(input);
self
}
/// <p>A unique string identifying the tracking server name. This string is part of the tracking server ARN.</p>
pub fn get_tracking_server_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_tracking_server_name()
}
/// <p>The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.</p>
pub fn artifact_store_uri(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.artifact_store_uri(input.into());
self
}
/// <p>The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.</p>
pub fn set_artifact_store_uri(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_artifact_store_uri(input);
self
}
/// <p>The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.</p>
pub fn get_artifact_store_uri(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_artifact_store_uri()
}
/// <p>The size of the tracking server you want to create. You can choose between <code>"Small"</code>, <code>"Medium"</code>, and <code>"Large"</code>. The default MLflow Tracking Server configuration size is <code>"Small"</code>. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.</p>
/// <p>We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.</p>
pub fn tracking_server_size(mut self, input: crate::types::TrackingServerSize) -> Self {
self.inner = self.inner.tracking_server_size(input);
self
}
/// <p>The size of the tracking server you want to create. You can choose between <code>"Small"</code>, <code>"Medium"</code>, and <code>"Large"</code>. The default MLflow Tracking Server configuration size is <code>"Small"</code>. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.</p>
/// <p>We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.</p>
pub fn set_tracking_server_size(mut self, input: ::std::option::Option<crate::types::TrackingServerSize>) -> Self {
self.inner = self.inner.set_tracking_server_size(input);
self
}
/// <p>The size of the tracking server you want to create. You can choose between <code>"Small"</code>, <code>"Medium"</code>, and <code>"Large"</code>. The default MLflow Tracking Server configuration size is <code>"Small"</code>. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.</p>
/// <p>We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.</p>
pub fn get_tracking_server_size(&self) -> &::std::option::Option<crate::types::TrackingServerSize> {
self.inner.get_tracking_server_size()
}
/// <p>The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html#mlflow-create-tracking-server-how-it-works">How it works</a>.</p>
pub fn mlflow_version(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.mlflow_version(input.into());
self
}
/// <p>The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html#mlflow-create-tracking-server-how-it-works">How it works</a>.</p>
pub fn set_mlflow_version(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_mlflow_version(input);
self
}
/// <p>The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html#mlflow-create-tracking-server-how-it-works">How it works</a>.</p>
pub fn get_mlflow_version(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_mlflow_version()
}
/// <p>The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have <code>AmazonS3FullAccess</code> permissions. For more information on IAM permissions for tracking server creation, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-iam.html">Set up IAM permissions for MLflow</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) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have <code>AmazonS3FullAccess</code> permissions. For more information on IAM permissions for tracking server creation, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-iam.html">Set up IAM permissions for MLflow</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) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have <code>AmazonS3FullAccess</code> permissions. For more information on IAM permissions for tracking server creation, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-iam.html">Set up IAM permissions for MLflow</a>.</p>
pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_role_arn()
}
/// <p>Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to <code>True</code>. To disable automatic model registration, set this value to <code>False</code>. If not specified, <code>AutomaticModelRegistration</code> defaults to <code>False</code>.</p>
pub fn automatic_model_registration(mut self, input: bool) -> Self {
self.inner = self.inner.automatic_model_registration(input);
self
}
/// <p>Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to <code>True</code>. To disable automatic model registration, set this value to <code>False</code>. If not specified, <code>AutomaticModelRegistration</code> defaults to <code>False</code>.</p>
pub fn set_automatic_model_registration(mut self, input: ::std::option::Option<bool>) -> Self {
self.inner = self.inner.set_automatic_model_registration(input);
self
}
/// <p>Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to <code>True</code>. To disable automatic model registration, set this value to <code>False</code>. If not specified, <code>AutomaticModelRegistration</code> defaults to <code>False</code>.</p>
pub fn get_automatic_model_registration(&self) -> &::std::option::Option<bool> {
self.inner.get_automatic_model_registration()
}
/// <p>The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.</p>
pub fn weekly_maintenance_window_start(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.weekly_maintenance_window_start(input.into());
self
}
/// <p>The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.</p>
pub fn set_weekly_maintenance_window_start(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_weekly_maintenance_window_start(input);
self
}
/// <p>The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.</p>
pub fn get_weekly_maintenance_window_start(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_weekly_maintenance_window_start()
}
///
/// Appends an item to `Tags`.
///
/// To override the contents of this collection use [`set_tags`](Self::set_tags).
///
/// <p>Tags consisting of key-value pairs used to manage metadata for the tracking server.</p>
pub fn tags(mut self, input: crate::types::Tag) -> Self {
self.inner = self.inner.tags(input);
self
}
/// <p>Tags consisting of key-value pairs used to manage metadata for the tracking server.</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>Tags consisting of key-value pairs used to manage metadata for the tracking server.</p>
pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
self.inner.get_tags()
}
/// <p>Expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage. Defaults to caller's account ID if not provided.</p>
pub fn s3_bucket_owner_account_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.s3_bucket_owner_account_id(input.into());
self
}
/// <p>Expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage. Defaults to caller's account ID if not provided.</p>
pub fn set_s3_bucket_owner_account_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_s3_bucket_owner_account_id(input);
self
}
/// <p>Expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage. Defaults to caller's account ID if not provided.</p>
pub fn get_s3_bucket_owner_account_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_s3_bucket_owner_account_id()
}
/// <p>Enable Amazon S3 Ownership checks when interacting with Amazon S3 buckets from a SageMaker Managed MLflow Tracking Server. Defaults to <code>True</code> if not provided.</p>
pub fn s3_bucket_owner_verification(mut self, input: bool) -> Self {
self.inner = self.inner.s3_bucket_owner_verification(input);
self
}
/// <p>Enable Amazon S3 Ownership checks when interacting with Amazon S3 buckets from a SageMaker Managed MLflow Tracking Server. Defaults to <code>True</code> if not provided.</p>
pub fn set_s3_bucket_owner_verification(mut self, input: ::std::option::Option<bool>) -> Self {
self.inner = self.inner.set_s3_bucket_owner_verification(input);
self
}
/// <p>Enable Amazon S3 Ownership checks when interacting with Amazon S3 buckets from a SageMaker Managed MLflow Tracking Server. Defaults to <code>True</code> if not provided.</p>
pub fn get_s3_bucket_owner_verification(&self) -> &::std::option::Option<bool> {
self.inner.get_s3_bucket_owner_verification()
}
}