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list_experiment_run_events

list_experiment_run_events(
    application_identifier: Identifier,
    experiment_definition_identifier: Identifier,
    run: PositiveInteger,
    *,
    config_overrides: Optional[AppConfigClientConfig] = None,
    max_results: Optional[MaxResults] = None,
    next_token: Optional[NextToken] = None,
) -> ExperimentRunEvents

Lists the events for a specified experiment run. Events provide a timeline of actions and state changes that occurred during the run.

Parameters:

  • application_identifier (Identifier) –

    The application ID or name.

  • experiment_definition_identifier (Identifier) –

    The experiment definition ID or name.

  • run (PositiveInteger) –

    The run number.

  • max_results (Optional[MaxResults], default: None ) –

    The maximum number of items to return.

  • next_token (Optional[NextToken], default: None ) –

    A token to start the list from a previously truncated response.

Raises:

Examples:

To list experiment run events The following ListExperimentRunEvents example lists the events for an experiment run.

>>> client.list_experiment_run_events(application_identifier='339ohji', experiment_definition_identifier='bsxyd7k', run=1)
Source code in docs_src/capo_appconfig/_services/app_config.py
def list_experiment_run_events(
    self,
    application_identifier: "capo_appconfig.types.identifier.Identifier",
    experiment_definition_identifier: "capo_appconfig.types.identifier.Identifier",
    run: "capo_appconfig.types.positive_integer.PositiveInteger",
    *,
    config_overrides: Optional[AppConfigClientConfig] = None,
    max_results: Optional["capo_appconfig.types.max_results.MaxResults"] = None,
    next_token: Optional["capo_appconfig.types.next_token.NextToken"] = None,
) -> "capo_appconfig.types.experiment_run_events.ExperimentRunEvents":
    """<p>Lists the events for a specified experiment run. Events provide a timeline of actions and state changes that occurred during the run.</p>

    Args:
        application_identifier: <p>The application ID or name.</p>
        experiment_definition_identifier: <p>The experiment definition ID or name.</p>
        run: <p>The run number.</p>
        max_results: <p>The maximum number of items to return.</p>
        next_token: <p>A token to start the list from a previously truncated response.</p>

    Raises:
        capo_appconfig.errors.bad_request_exception.BadRequestException: <p>The input fails to satisfy the constraints specified by an Amazon Web Services service.</p>
        capo_appconfig.errors.internal_server_exception.InternalServerException: <p>There was an internal failure in the AppConfig service.</p>
        capo_appconfig.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource could not be found.</p>
        capo_appconfig.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To list experiment run events
        The following ListExperimentRunEvents example lists the events for an experiment run.

        >>> client.list_experiment_run_events(application_identifier='339ohji', experiment_definition_identifier='bsxyd7k', run=1)
    """

    def _handler(
        req: "OperationRequest[capo_appconfig.types.list_experiment_run_events_request.ListExperimentRunEventsRequest]",
    ) -> OperationResponse[
        "capo_appconfig.types.experiment_run_events.ExperimentRunEvents"
    ]:
        import capo_appconfig._operations.amazon_app_config.list_experiment_run_events

        output, http_response = (
            capo_appconfig._operations.amazon_app_config.list_experiment_run_events.list_experiment_run_events(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListExperimentRunEvents")
    )
    input_: capo_appconfig.types.list_experiment_run_events_request.ListExperimentRunEventsRequest = {
        "application_identifier": application_identifier,
        "experiment_definition_identifier": experiment_definition_identifier,
        "run": run,
    }
    if max_results is not None:
        input_["max_results"] = max_results
    if next_token is not None:
        input_["next_token"] = next_token

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