Struct aws_sdk_rekognition::operation::start_project_version::builders::StartProjectVersionFluentBuilder

pub struct StartProjectVersionFluentBuilder { /* private fields */ }

Fluent builder constructing a request to StartProjectVersion.

This operation applies only to Amazon Rekognition Custom Labels.

Starts the running of the version of a model. Starting a model takes a while to complete. To check the current state of the model, use DescribeProjectVersions.

Once the model is running, you can detect custom labels in new images by calling DetectCustomLabels.

You are charged for the amount of time that the model is running. To stop a running model, call StopProjectVersion.

This operation requires permissions to perform the rekognition:StartProjectVersion action.

Implementations

impl StartProjectVersionFluentBuilder

pub fn as_input(&self) -> &StartProjectVersionInputBuilder

Access the StartProjectVersion as a reference.

pub async fn send( self ) -> Result<StartProjectVersionOutput, SdkError<StartProjectVersionError, HttpResponse>>

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, which can be set when configuring the client.

pub fn customize( self ) -> CustomizableOperation<StartProjectVersionOutput, StartProjectVersionError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

pub fn project_version_arn(self, input: impl Into<String>) -> Self

The Amazon Resource Name(ARN) of the model version that you want to start.

pub fn set_project_version_arn(self, input: Option<String>) -> Self

The Amazon Resource Name(ARN) of the model version that you want to start.

pub fn get_project_version_arn(&self) -> &Option<String>

The Amazon Resource Name(ARN) of the model version that you want to start.

pub fn min_inference_units(self, input: i32) -> Self

The minimum number of inference units to use. A single inference unit represents 1 hour of processing.

Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

pub fn set_min_inference_units(self, input: Option<i32>) -> Self

The minimum number of inference units to use. A single inference unit represents 1 hour of processing.

Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

pub fn get_min_inference_units(&self) -> &Option<i32>

The minimum number of inference units to use. A single inference unit represents 1 hour of processing.

Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

pub fn max_inference_units(self, input: i32) -> Self

The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Rekognition Custom Labels doesn't auto-scale the model.

pub fn set_max_inference_units(self, input: Option<i32>) -> Self

The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Rekognition Custom Labels doesn't auto-scale the model.

pub fn get_max_inference_units(&self) -> &Option<i32>

The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Rekognition Custom Labels doesn't auto-scale the model.

Trait Implementations

impl Clone for StartProjectVersionFluentBuilder

fn clone(&self) -> StartProjectVersionFluentBuilder

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for StartProjectVersionFluentBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.