list_trials ¶

def list_trials(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    experiment_name: Optional[
        "capo_sagemaker.types.experiment_entity_name.ExperimentEntityName"
    ] = None,
    trial_component_name: Optional[
        "capo_sagemaker.types.experiment_entity_name.ExperimentEntityName"
    ] = None,
    created_after: Optional["capo_sagemaker.types.timestamp.Timestamp"] = None,
    created_before: Optional["capo_sagemaker.types.timestamp.Timestamp"] = None,
    sort_by: Optional["capo_sagemaker.types.sort_trials_by.SortTrialsBy"] = None,
    sort_order: Optional["capo_sagemaker.types.sort_order.SortOrder"] = None,
    max_results: Optional["capo_sagemaker.types.max_results.MaxResults"] = None,
    next_token: Optional["capo_sagemaker.types.next_token.NextToken"] = None,
) -> "capo_sagemaker.types.list_trials_response.ListTrialsResponse":
    """<p>Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.</p>

    Args:
        experiment_name: <p>A filter that returns only trials that are part of the specified experiment.</p>
        trial_component_name: <p>A filter that returns only trials that are associated with the specified trial component.</p>
        created_after: <p>A filter that returns only trials created after the specified time.</p>
        created_before: <p>A filter that returns only trials created before the specified time.</p>
        sort_by: <p>The property used to sort results. The default value is <code>CreationTime</code>.</p>
        sort_order: <p>The sort order. The default value is <code>Descending</code>.</p>
        max_results: <p>The maximum number of trials to return in the response. The default value is 10.</p>
        next_token: <p>If the previous call to <code>ListTrials</code> didn't return the full set of trials, the call returns a token for getting the next set of trials.</p>

    Raises:
        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.list_trials_request.ListTrialsRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.list_trials_response.ListTrialsResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.list_trials

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListTrials")
    )
    input_: capo_sagemaker.types.list_trials_request.ListTrialsRequest = {}
    if experiment_name is not None:
        input_["experiment_name"] = experiment_name
    if trial_component_name is not None:
        input_["trial_component_name"] = trial_component_name
    if created_after is not None:
        input_["created_after"] = created_after
    if created_before is not None:
        input_["created_before"] = created_before
    if sort_by is not None:
        input_["sort_by"] = sort_by
    if sort_order is not None:
        input_["sort_order"] = sort_order
    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