describe_auto_ml_job_v2 `async` ¶

describe_auto_ml_job_v2(
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    auto_ml_job_name: Optional[AutoMLJobName] = None,
) -> DescribeAutoMLJobV2Response

Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.

Parameters:

auto_ml_job_name (Optional[AutoMLJobName], default: None ) –

Requests information about an AutoML job V2 using its unique name.

Raises:

ResourceNotFound –

Resource being access is not found.
UnknownServiceError –

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

Source code in docs_src/capo_sagemaker/_services/async_sage_maker.py

async def describe_auto_ml_job_v2(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    auto_ml_job_name: Optional[
        "capo_sagemaker.types.auto_ml_job_name.AutoMLJobName"
    ] = None,
) -> "capo_sagemaker.types.describe_auto_ml_job_v2_response.DescribeAutoMLJobV2Response":
    """<p>Returns information about an AutoML job created by calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html">CreateAutoMLJobV2</a> or <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html">CreateAutoMLJob</a>.</p>

    Args:
        auto_ml_job_name: <p>Requests information about an AutoML job V2 using its unique name.</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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker.types.describe_auto_ml_job_v2_request.DescribeAutoMLJobV2Request]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker.types.describe_auto_ml_job_v2_response.DescribeAutoMLJobV2Response"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_auto_ml_job_v2

        (
            output,
            http_response,
        ) = await capo_sagemaker._operations.sage_maker.describe_auto_ml_job_v2.async_describe_auto_ml_job_v2(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeAutoMLJobV2")
    )
    input_: capo_sagemaker.types.describe_auto_ml_job_v2_request.DescribeAutoMLJobV2Request = {}
    if auto_ml_job_name is not None:
        input_["auto_ml_job_name"] = auto_ml_job_name

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

describe_auto_ml_job_v2 async ¶

describe_auto_ml_job_v2 `async` ¶