This operation applies only to Amazon Rekognition Custom Labels.
Stops a running model. The operation might take a while to complete. To check the current status, call DescribeProjectVersions. Only applies to Custom Labels projects.
This operation requires permissions to perform the rekognition:StopProjectVersion action.
Parameters:
-
project_version_arn
(ProjectVersionArn)
–
The Amazon Resource Name (ARN) of the model version that you want to stop.
This operation requires permissions to perform the rekognition:StopProjectVersion action.
Raises:
Examples:
To stop an Amazon Rekognition Custom Labels model.
Stops a version of an Amazon Rekognition Custom Labels model.
>>> await client.stop_project_version(project_version_arn='arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958')
Source code in docs_src/capo_rekognition/_services/async_rekognition.py
| async def stop_project_version(
self,
project_version_arn: "capo_rekognition.types.project_version_arn.ProjectVersionArn",
*,
config_overrides: Optional[AsyncRekognitionClientConfig] = None,
) -> "capo_rekognition.types.stop_project_version_response.StopProjectVersionResponse":
"""<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>Stops a running model. The operation might take a while to complete. To check the current status, call <a>DescribeProjectVersions</a>. Only applies to Custom Labels projects.</p> <p>This operation requires permissions to perform the <code>rekognition:StopProjectVersion</code> action.</p>
Args:
project_version_arn: <p>The Amazon Resource Name (ARN) of the model version that you want to stop.</p> <p>This operation requires permissions to perform the <code>rekognition:StopProjectVersion</code> action.</p>
Raises:
capo_rekognition.errors.access_denied_exception.AccessDeniedException: <p>You are not authorized to perform the action.</p>
capo_rekognition.errors.internal_server_error.InternalServerError: <p>Amazon Rekognition experienced a service issue. Try your call again.</p>
capo_rekognition.errors.invalid_parameter_exception.InvalidParameterException: <p>Input parameter violated a constraint. Validate your parameter before calling the API operation again.</p>
capo_rekognition.errors.provisioned_throughput_exceeded_exception.ProvisionedThroughputExceededException: <p>The number of requests exceeded your throughput limit. If you want to increase this limit, contact Amazon Rekognition.</p>
capo_rekognition.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is already being used.</p>
capo_rekognition.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource specified in the request cannot be found.</p>
capo_rekognition.errors.throttling_exception.ThrottlingException: <p>Amazon Rekognition is temporarily unable to process the request. Try your call again.</p>
capo_rekognition.errors.UnknownServiceError: The service returned an error code this client does not model.
Examples:
To stop an Amazon Rekognition Custom Labels model.
Stops a version of an Amazon Rekognition Custom Labels model.
>>> await client.stop_project_version(project_version_arn='arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958')
"""
async def _handler(
req: "AsyncOperationRequest[capo_rekognition.types.stop_project_version_request.StopProjectVersionRequest]",
) -> AsyncOperationResponse[
"capo_rekognition.types.stop_project_version_response.StopProjectVersionResponse"
]:
import capo_rekognition._operations.rekognition_service.stop_project_version
(
output,
http_response,
) = await capo_rekognition._operations.rekognition_service.stop_project_version.async_stop_project_version(
req.options, req.input
)
return AsyncOperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("StopProjectVersion")
)
input_: capo_rekognition.types.stop_project_version_request.StopProjectVersionRequest = {
"project_version_arn": project_version_arn
}
response = await aexecute_pipeline(
AsyncOperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
await response.response.aclose()
return response.output
|