Returns information about an AutoML job created by calling CreateAutoMLJob.
AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.
Parameters:
-
auto_ml_job_name
(Optional[AutoMLJobName], default:
None
)
–
Requests information about an AutoML job using its unique name.
Raises:
Source code in docs_src/capo_sagemaker/_services/sage_maker.py
| def describe_auto_ml_job(
self,
*,
config_overrides: Optional[SageMakerClientConfig] = None,
auto_ml_job_name: Optional[
"capo_sagemaker.types.auto_ml_job_name.AutoMLJobName"
] = None,
) -> "capo_sagemaker.types.describe_auto_ml_job_response.DescribeAutoMLJobResponse":
"""<p>Returns information about an AutoML job created by calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html">CreateAutoMLJob</a>.</p> <note> <p>AutoML jobs created by calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html">CreateAutoMLJobV2</a> cannot be described by <code>DescribeAutoMLJob</code>.</p> </note>
Args:
auto_ml_job_name: <p>Requests information about an AutoML job 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.
"""
def _handler(
req: "OperationRequest[capo_sagemaker.types.describe_auto_ml_job_request.DescribeAutoMLJobRequest]",
) -> OperationResponse[
"capo_sagemaker.types.describe_auto_ml_job_response.DescribeAutoMLJobResponse"
]:
import capo_sagemaker._operations.sage_maker.describe_auto_ml_job
output, http_response = (
capo_sagemaker._operations.sage_maker.describe_auto_ml_job.describe_auto_ml_job(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("DescribeAutoMLJob")
)
input_: capo_sagemaker.types.describe_auto_ml_job_request.DescribeAutoMLJobRequest = {}
if auto_ml_job_name is not None:
input_["auto_ml_job_name"] = auto_ml_job_name
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|