Provides the properties of the specified optimization job.
Parameters:
-
optimization_job_name
(Optional[EntityName], default:
None
)
–
The name that you assigned to the optimization job.
Raises:
Source code in docs_src/capo_sagemaker/_services/async_sage_maker.py
| async def describe_optimization_job(
self,
*,
config_overrides: Optional[AsyncSageMakerClientConfig] = None,
optimization_job_name: Optional[
"capo_sagemaker.types.entity_name.EntityName"
] = None,
) -> "capo_sagemaker.types.describe_optimization_job_response.DescribeOptimizationJobResponse":
"""<p>Provides the properties of the specified optimization job.</p>
Args:
optimization_job_name: <p>The name that you assigned to the optimization job.</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_optimization_job_request.DescribeOptimizationJobRequest]",
) -> AsyncOperationResponse[
"capo_sagemaker.types.describe_optimization_job_response.DescribeOptimizationJobResponse"
]:
import capo_sagemaker._operations.sage_maker.describe_optimization_job
(
output,
http_response,
) = await capo_sagemaker._operations.sage_maker.describe_optimization_job.async_describe_optimization_job(
req.options, req.input
)
return AsyncOperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("DescribeOptimizationJob")
)
input_: capo_sagemaker.types.describe_optimization_job_request.DescribeOptimizationJobRequest = {}
if optimization_job_name is not None:
input_["optimization_job_name"] = optimization_job_name
response = await aexecute_pipeline(
AsyncOperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
await response.response.aclose()
return response.output
|