create_batch_prediction async ¶

async def create_batch_prediction(
    self,
    batch_prediction_id: "capo_machine_learning.types.entity_id.EntityId",
    ml_model_id: "capo_machine_learning.types.entity_id.EntityId",
    batch_prediction_data_source_id: "capo_machine_learning.types.entity_id.EntityId",
    output_uri: "capo_machine_learning.types.s3_url.S3Url",
    *,
    config_overrides: Optional[AsyncMachineLearningClientConfig] = None,
    batch_prediction_name: Optional[
        "capo_machine_learning.types.entity_name.EntityName"
    ] = None,
) -> "capo_machine_learning.types.create_batch_prediction_output.CreateBatchPredictionOutput":
    """<p>Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a <code>DataSource</code>. This operation creates a new <code>BatchPrediction</code>, and uses an <code>MLModel</code> and the data files referenced by the <code>DataSource</code> as information sources. </p> <p> <code>CreateBatchPrediction</code> is an asynchronous operation. In response to <code>CreateBatchPrediction</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>BatchPrediction</code> status to <code>PENDING</code>. After the <code>BatchPrediction</code> completes, Amazon ML sets the status to <code>COMPLETED</code>. </p> <p>You can poll for status updates by using the <a>GetBatchPrediction</a> operation and checking the <code>Status</code> parameter of the result. After the <code>COMPLETED</code> status appears, the results are available in the location specified by the <code>OutputUri</code> parameter.</p>

    Args:
        batch_prediction_id: <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>.</p>
        batch_prediction_name: <p>A user-supplied name or description of the <code>BatchPrediction</code>. <code>BatchPredictionName</code> can only use the UTF-8 character set.</p>
        ml_model_id: <p>The ID of the <code>MLModel</code> that will generate predictions for the group of observations. </p>
        batch_prediction_data_source_id: <p>The ID of the <code>DataSource</code> that points to the group of observations to predict.</p>
        output_uri: <p>The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the <code>s3 key</code> portion of the <code>outputURI</code> field: ':', '//', '/./', '/../'.</p> <p>Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</p>

    Raises:
        capo_machine_learning.errors.idempotent_parameter_mismatch_exception.IdempotentParameterMismatchException: <p>A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.</p>
        capo_machine_learning.errors.internal_server_exception.InternalServerException: <p>An error on the server occurred when trying to process a request.</p>
        capo_machine_learning.errors.invalid_input_exception.InvalidInputException: <p>An error on the client occurred. Typically, the cause is an invalid input value.</p>
        capo_machine_learning.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_machine_learning.types.create_batch_prediction_input.CreateBatchPredictionInput]",
    ) -> AsyncOperationResponse[
        "capo_machine_learning.types.create_batch_prediction_output.CreateBatchPredictionOutput"
    ]:
        import capo_machine_learning._operations.amazon_ml_20141212.create_batch_prediction

        (
            output,
            http_response,
        ) = await capo_machine_learning._operations.amazon_ml_20141212.create_batch_prediction.async_create_batch_prediction(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateBatchPrediction")
    )
    input_: capo_machine_learning.types.create_batch_prediction_input.CreateBatchPredictionInput = {
        "batch_prediction_id": batch_prediction_id,
        "ml_model_id": ml_model_id,
        "batch_prediction_data_source_id": batch_prediction_data_source_id,
        "output_uri": output_uri,
    }
    if batch_prediction_name is not None:
        input_["batch_prediction_name"] = batch_prediction_name

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