start_project_version ¶

def start_project_version(
    self,
    project_version_arn: "capo_rekognition.types.project_version_arn.ProjectVersionArn",
    min_inference_units: "capo_rekognition.types.inference_units.InferenceUnits",
    *,
    config_overrides: Optional[RekognitionClientConfig] = None,
    max_inference_units: Optional[
        "capo_rekognition.types.inference_units.InferenceUnits"
    ] = None,
) -> "capo_rekognition.types.start_project_version_response.StartProjectVersionResponse":
    """<note> <p>This operation applies only to Amazon Rekognition Custom Labels.</p> </note> <p>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 <a>DescribeProjectVersions</a>. </p> <p>Once the model is running, you can detect custom labels in new images by calling <a>DetectCustomLabels</a>.</p> <note> <p>You are charged for the amount of time that the model is running. To stop a running model, call <a>StopProjectVersion</a>.</p> </note> <p>This operation requires permissions to perform the <code>rekognition:StartProjectVersion</code> action.</p>

    Args:
        project_version_arn: <p>The Amazon Resource Name(ARN) of the model version that you want to start.</p>
        min_inference_units: <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. </p> <p>Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
        max_inference_units: <p>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.</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.limit_exceeded_exception.LimitExceededException: <p>An Amazon Rekognition service limit was exceeded. For example, if you start too many jobs concurrently, subsequent calls to start operations (ex: <code>StartLabelDetection</code>) will raise a <code>LimitExceededException</code> exception (HTTP status code: 400) until the number of concurrently running jobs is below the Amazon Rekognition service limit. </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 start an Amazon Rekognition Custom Labels model
        Starts a version of an Amazon Rekognition Custom Labels model.

        >>> client.start_project_version(project_version_arn='arn:aws:rekognition:us-east-1:111122223333:project/my-project/version/1/1690556751958', min_inference_units=1, max_inference_units=1)
    """

    def _handler(
        req: "OperationRequest[capo_rekognition.types.start_project_version_request.StartProjectVersionRequest]",
    ) -> OperationResponse[
        "capo_rekognition.types.start_project_version_response.StartProjectVersionResponse"
    ]:
        import capo_rekognition._operations.rekognition_service.start_project_version

        output, http_response = (
            capo_rekognition._operations.rekognition_service.start_project_version.start_project_version(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartProjectVersion")
    )
    input_: capo_rekognition.types.start_project_version_request.StartProjectVersionRequest = {
        "project_version_arn": project_version_arn,
        "min_inference_units": min_inference_units,
    }
    if max_inference_units is not None:
        input_["max_inference_units"] = max_inference_units

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output