Struct aws_sdk_rekognition::client::fluent_builders::StartProjectVersion

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pub struct StartProjectVersion { /* private fields */ }

Expand description

Fluent builder constructing a request to StartProjectVersion.

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.

For more information, see Running a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels Guide.

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

Implementations

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impl StartProjectVersion

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pub async fn customize(
self
) -> Result<CustomizableOperation<StartProjectVersion, AwsResponseRetryClassifier>, SdkError<StartProjectVersionError>>

Consume this builder, creating a customizable operation that can be modified before being sent. The operation’s inner http::Request can be modified as well.

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

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.

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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.

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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.

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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.

For information about the number of transactions per second (TPS) that an inference unit can support, see Running a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels Guide.

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

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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.

For information about the number of transactions per second (TPS) that an inference unit can support, see Running a trained Amazon Rekognition Custom Labels model in the Amazon Rekognition Custom Labels Guide.

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

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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.

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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.