describe_training_session ¶

def describe_training_session(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    training_session_name: Optional[
        "capo_sagemaker.types.training_session_name.TrainingSessionName"
    ] = None,
) -> "capo_sagemaker.types.describe_training_session_response.DescribeTrainingSessionResponse":
    """<p>Retrieves detailed information about a training session, including its current status, model configuration, Low-Rank Adaptation (LoRA) parameters, and timestamps.</p> <p>The following operations are related to <code>DescribeTrainingSession</code>:</p> <ul> <li> <p> <code>CreateTrainingSession</code> </p> </li> <li> <p> <code>ListTrainingSessions</code> </p> </li> <li> <p> <code>StopTrainingSession</code> </p> </li> <li> <p> <code>DeleteTrainingSession</code> </p> </li> </ul>

    Args:
        training_session_name: <p>The name of the training session to describe.</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.describe_training_session_request.DescribeTrainingSessionRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.describe_training_session_response.DescribeTrainingSessionResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_training_session

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeTrainingSession")
    )
    input_: capo_sagemaker.types.describe_training_session_request.DescribeTrainingSessionRequest = {}
    if training_session_name is not None:
        input_["training_session_name"] = training_session_name

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