update_inference_component_runtime_config ¶

def update_inference_component_runtime_config(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    inference_component_name: Optional[
        "capo_sagemaker.types.inference_component_name.InferenceComponentName"
    ] = None,
    desired_runtime_config: Optional[
        "capo_sagemaker.types.inference_component_runtime_config.InferenceComponentRuntimeConfig"
    ] = None,
) -> "capo_sagemaker.types.update_inference_component_runtime_config_output.UpdateInferenceComponentRuntimeConfigOutput":
    """<p>Runtime settings for a model that is deployed with an inference component.</p>

    Args:
        inference_component_name: <p>The name of the inference component to update.</p>
        desired_runtime_config: <p>Runtime settings for a model that is deployed with an inference component.</p>

    Raises:
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.update_inference_component_runtime_config_input.UpdateInferenceComponentRuntimeConfigInput]",
    ) -> OperationResponse[
        "capo_sagemaker.types.update_inference_component_runtime_config_output.UpdateInferenceComponentRuntimeConfigOutput"
    ]:
        import capo_sagemaker._operations.sage_maker.update_inference_component_runtime_config

        output, http_response = (
            capo_sagemaker._operations.sage_maker.update_inference_component_runtime_config.update_inference_component_runtime_config(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateInferenceComponentRuntimeConfig")
    )
    input_: capo_sagemaker.types.update_inference_component_runtime_config_input.UpdateInferenceComponentRuntimeConfigInput = {}
    if inference_component_name is not None:
        input_["inference_component_name"] = inference_component_name
    if desired_runtime_config is not None:
        input_["desired_runtime_config"] = desired_runtime_config

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