stop_inference_experiment ¶

def stop_inference_experiment(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    name: Optional[
        "capo_sagemaker.types.inference_experiment_name.InferenceExperimentName"
    ] = None,
    model_variant_actions: Optional[
        "capo_sagemaker.types.model_variant_action_map.ModelVariantActionMap"
    ] = None,
    desired_model_variants: Optional[
        "capo_sagemaker.types.model_variant_config_list.ModelVariantConfigList"
    ] = None,
    desired_state: Optional[
        "capo_sagemaker.types.inference_experiment_stop_desired_state.InferenceExperimentStopDesiredState"
    ] = None,
    reason: Optional[
        "capo_sagemaker.types.inference_experiment_status_reason.InferenceExperimentStatusReason"
    ] = None,
) -> "capo_sagemaker.types.stop_inference_experiment_response.StopInferenceExperimentResponse":
    """<p>Stops an inference experiment.</p>

    Args:
        name: <p>The name of the inference experiment to stop.</p>
        model_variant_actions: <p> Array of key-value pairs, with names of variants mapped to actions. The possible actions are the following: </p> <ul> <li> <p> <code>Promote</code> - Promote the shadow variant to a production variant</p> </li> <li> <p> <code>Remove</code> - Delete the variant</p> </li> <li> <p> <code>Retain</code> - Keep the variant as it is</p> </li> </ul>
        desired_model_variants: <p> An array of <code>ModelVariantConfig</code> objects. There is one for each variant that you want to deploy after the inference experiment stops. Each <code>ModelVariantConfig</code> describes the infrastructure configuration for deploying the corresponding variant. </p>
        desired_state: <p> The desired state of the experiment after stopping. The possible states are the following: </p> <ul> <li> <p> <code>Completed</code>: The experiment completed successfully</p> </li> <li> <p> <code>Cancelled</code>: The experiment was canceled</p> </li> </ul>
        reason: <p>The reason for stopping the experiment.</p>

    Raises:
        capo_sagemaker.errors.conflict_exception.ConflictException: <p>There was a conflict when you attempted to modify a SageMaker entity such as an <code>Experiment</code> or <code>Artifact</code>.</p>
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.stop_inference_experiment_request.StopInferenceExperimentRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.stop_inference_experiment_response.StopInferenceExperimentResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.stop_inference_experiment

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StopInferenceExperiment")
    )
    input_: capo_sagemaker.types.stop_inference_experiment_request.StopInferenceExperimentRequest = {}
    if name is not None:
        input_["name"] = name
    if model_variant_actions is not None:
        input_["model_variant_actions"] = model_variant_actions
    if desired_model_variants is not None:
        input_["desired_model_variants"] = desired_model_variants
    if desired_state is not None:
        input_["desired_state"] = desired_state
    if reason is not None:
        input_["reason"] = reason

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