Struct aws_sdk_rekognition::client::fluent_builders::CreateDataset

pub struct CreateDataset { /* private fields */ }

Fluent builder constructing a request to CreateDataset.

Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset by using an Amazon Sagemaker format manifest file or by copying an existing Amazon Rekognition Custom Labels dataset.

To create a training dataset for a project, specify train for the value of DatasetType. To create the test dataset for a project, specify test for the value of DatasetType.

The response from CreateDataset is the Amazon Resource Name (ARN) for the dataset. Creating a dataset takes a while to complete. Use DescribeDataset to check the current status. The dataset created successfully if the value of Status is CREATE_COMPLETE.

To check if any non-terminal errors occurred, call ListDatasetEntries and check for the presence of errors lists in the JSON Lines.

Dataset creation fails if a terminal error occurs (Status = CREATE_FAILED). Currently, you can't access the terminal error information.

For more information, see Creating dataset in the Amazon Rekognition Custom Labels Developer Guide.

This operation requires permissions to perform the rekognition:CreateDataset action. If you want to copy an existing dataset, you also require permission to perform the rekognition:ListDatasetEntries action.

Implementations

impl CreateDataset

pub async fn send(
    self
) -> Result<CreateDatasetOutput, SdkError<CreateDatasetError>>

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 dataset_source(self, input: DatasetSource) -> Self

The source files for the dataset. You can specify the ARN of an existing dataset or specify the Amazon S3 bucket location of an Amazon Sagemaker format manifest file. If you don't specify datasetSource, an empty dataset is created. To add labeled images to the dataset, You can use the console or call UpdateDatasetEntries.

pub fn set_dataset_source(self, input: Option<DatasetSource>) -> Self

The source files for the dataset. You can specify the ARN of an existing dataset or specify the Amazon S3 bucket location of an Amazon Sagemaker format manifest file. If you don't specify datasetSource, an empty dataset is created. To add labeled images to the dataset, You can use the console or call UpdateDatasetEntries.

pub fn dataset_type(self, input: DatasetType) -> Self

The type of the dataset. Specify train to create a training dataset. Specify test to create a test dataset.

pub fn set_dataset_type(self, input: Option<DatasetType>) -> Self

The type of the dataset. Specify train to create a training dataset. Specify test to create a test dataset.

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

The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset.

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

The ARN of the Amazon Rekognition Custom Labels project to which you want to asssign the dataset.

Trait Implementations

impl Clone for CreateDataset

fn clone(&self) -> CreateDataset

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateDataset

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

Formats the value using the given formatter.