list_ml_model_training_jobs `async` ¶

list_ml_model_training_jobs(
    *,
    config_overrides: Optional[AsyncneptunedataClientConfig] = None,
    max_items: Optional[PositiveInteger] = None,
    neptune_iam_role_arn: Optional[str] = None,
) -> ListMLModelTrainingJobsOutput

Lists Neptune ML model-training jobs. See Model training using the modeltraining command.

When invoking this operation in a Neptune cluster that has IAM authentication enabled, the IAM user or role making the request must have a policy attached that allows the neptune-db:neptune-db:ListMLModelTrainingJobs IAM action in that cluster.

Parameters:

max_items (Optional[PositiveInteger], default: None ) –

The maximum number of items to return (from 1 to 1024; the default is 10).
neptune_iam_role_arn (Optional[str], default: None ) –

The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

Raises:

BadRequestException –

Raised when a request is submitted that cannot be processed.
ClientTimeoutException –

Raised when a request timed out in the client.
ConstraintViolationException –

Raised when a value in a request field did not satisfy required constraints.
IllegalArgumentException –

Raised when an argument in a request is not supported.
InvalidArgumentException –

Raised when an argument in a request has an invalid value.
InvalidParameterException –

Raised when a parameter value is not valid.
MissingParameterException –

Raised when a required parameter is missing.
MLResourceNotFoundException –

Raised when a specified machine-learning resource could not be found.
PreconditionsFailedException –

Raised when a precondition for processing a request is not satisfied.
TooManyRequestsException –

Raised when the number of requests being processed exceeds the limit.
UnsupportedOperationException –

Raised when a request attempts to initiate an operation that is not supported.
UnknownServiceError –

The service returned an error code this client does not model.

Source code in docs_src/capo_neptunedata/_services/async_neptunedata.py

async def list_ml_model_training_jobs(
    self,
    *,
    config_overrides: Optional[AsyncneptunedataClientConfig] = None,
    max_items: Optional[
        "capo_neptunedata.types.positive_integer.PositiveInteger"
    ] = None,
    neptune_iam_role_arn: Optional[str] = None,
) -> "capo_neptunedata.types.list_ml_model_training_jobs_output.ListMLModelTrainingJobsOutput":
    """<p>Lists Neptune ML model-training jobs. See <a href="https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning-api-modeltraining.html">Model training using the <code>modeltraining</code> command</a>.</p> <p>When invoking this operation in a Neptune cluster that has IAM authentication enabled, the IAM user or role making the request must have a policy attached that allows the <a href="https://docs.aws.amazon.com/neptune/latest/userguide/iam-dp-actions.html#neptune-db:listmlmodeltrainingjobs">neptune-db:neptune-db:ListMLModelTrainingJobs</a> IAM action in that cluster.</p>

    Args:
        max_items: <p>The maximum number of items to return (from 1 to 1024; the default is 10).</p>
        neptune_iam_role_arn: <p>The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.</p>

    Raises:
        capo_neptunedata.errors.bad_request_exception.BadRequestException: <p>Raised when a request is submitted that cannot be processed.</p>
        capo_neptunedata.errors.client_timeout_exception.ClientTimeoutException: <p>Raised when a request timed out in the client.</p>
        capo_neptunedata.errors.constraint_violation_exception.ConstraintViolationException: <p>Raised when a value in a request field did not satisfy required constraints.</p>
        capo_neptunedata.errors.illegal_argument_exception.IllegalArgumentException: <p>Raised when an argument in a request is not supported.</p>
        capo_neptunedata.errors.invalid_argument_exception.InvalidArgumentException: <p>Raised when an argument in a request has an invalid value.</p>
        capo_neptunedata.errors.invalid_parameter_exception.InvalidParameterException: <p>Raised when a parameter value is not valid.</p>
        capo_neptunedata.errors.missing_parameter_exception.MissingParameterException: <p>Raised when a required parameter is missing.</p>
        capo_neptunedata.errors.ml_resource_not_found_exception.MLResourceNotFoundException: <p>Raised when a specified machine-learning resource could not be found.</p>
        capo_neptunedata.errors.preconditions_failed_exception.PreconditionsFailedException: <p>Raised when a precondition for processing a request is not satisfied.</p>
        capo_neptunedata.errors.too_many_requests_exception.TooManyRequestsException: <p>Raised when the number of requests being processed exceeds the limit.</p>
        capo_neptunedata.errors.unsupported_operation_exception.UnsupportedOperationException: <p>Raised when a request attempts to initiate an operation that is not supported.</p>
        capo_neptunedata.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_neptunedata.types.list_ml_model_training_jobs_input.ListMLModelTrainingJobsInput]",
    ) -> AsyncOperationResponse[
        "capo_neptunedata.types.list_ml_model_training_jobs_output.ListMLModelTrainingJobsOutput"
    ]:
        import capo_neptunedata._operations.amazon_neptune_dataplane.list_ml_model_training_jobs

        (
            output,
            http_response,
        ) = await capo_neptunedata._operations.amazon_neptune_dataplane.list_ml_model_training_jobs.async_list_ml_model_training_jobs(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListMLModelTrainingJobs")
    )
    input_: capo_neptunedata.types.list_ml_model_training_jobs_input.ListMLModelTrainingJobsInput = {}
    if max_items is not None:
        input_["max_items"] = max_items
    if neptune_iam_role_arn is not None:
        input_["neptune_iam_role_arn"] = neptune_iam_role_arn

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output

list_ml_model_training_jobs async ¶

list_ml_model_training_jobs `async` ¶