list_training_sessions ¶

def list_training_sessions(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    next_token: Optional["capo_sagemaker.types.next_token.NextToken"] = None,
    max_results: Optional["capo_sagemaker.types.max_results.MaxResults"] = None,
    creation_time_after: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
    creation_time_before: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
    name_contains: Optional[
        "capo_sagemaker.types.name_contains.NameContains"
    ] = None,
    status_equals: Optional[
        "capo_sagemaker.types.training_session_status.TrainingSessionStatus"
    ] = None,
) -> "capo_sagemaker.types.list_training_sessions_response.ListTrainingSessionsResponse":
    """<p>Lists training sessions in your account. You can filter results by creation time, name, and status. The operation paginates results. To retrieve additional results, use the <code>NextToken</code> parameter.</p> <p>The following operations are related to <code>ListTrainingSessions</code>:</p> <ul> <li> <p> <code>CreateTrainingSession</code> </p> </li> <li> <p> <code>DescribeTrainingSession</code> </p> </li> </ul>

    Args:
        next_token: <p>If the response was truncated, use the token returned in the previous response to retrieve the next page of results.</p>
        max_results: <p>The maximum number of training sessions to return in the response.</p>
        creation_time_after: <p>A filter that returns only training sessions created after the specified time.</p>
        creation_time_before: <p>A filter that returns only training sessions created before the specified time.</p>
        name_contains: <p>A string in the training session name to filter results. Only sessions whose name contains the specified string are returned.</p>
        status_equals: <p>A filter that returns only training sessions with the specified status.</p>

    Raises:
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.list_training_sessions_request.ListTrainingSessionsRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.list_training_sessions_response.ListTrainingSessionsResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.list_training_sessions

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListTrainingSessions")
    )
    input_: capo_sagemaker.types.list_training_sessions_request.ListTrainingSessionsRequest = {}
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if creation_time_after is not None:
        input_["creation_time_after"] = creation_time_after
    if creation_time_before is not None:
        input_["creation_time_before"] = creation_time_before
    if name_contains is not None:
        input_["name_contains"] = name_contains
    if status_equals is not None:
        input_["status_equals"] = status_equals

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