Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information.
Parameters:
-
ai_benchmark_job_name
(Optional[AIEntityName], default:
None
)
–
The name of the AI benchmark job to describe.
Raises:
Source code in docs_src/capo_sagemaker/_services/sage_maker.py
| def describe_ai_benchmark_job(
self,
*,
config_overrides: Optional[SageMakerClientConfig] = None,
ai_benchmark_job_name: Optional[
"capo_sagemaker.types.ai_entity_name.AIEntityName"
] = None,
) -> "capo_sagemaker.types.describe_ai_benchmark_job_response.DescribeAIBenchmarkJobResponse":
"""<p>Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information.</p>
Args:
ai_benchmark_job_name: <p>The name of the AI benchmark job to describe.</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_ai_benchmark_job_request.DescribeAIBenchmarkJobRequest]",
) -> OperationResponse[
"capo_sagemaker.types.describe_ai_benchmark_job_response.DescribeAIBenchmarkJobResponse"
]:
import capo_sagemaker._operations.sage_maker.describe_ai_benchmark_job
output, http_response = (
capo_sagemaker._operations.sage_maker.describe_ai_benchmark_job.describe_ai_benchmark_job(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("DescribeAIBenchmarkJob")
)
input_: capo_sagemaker.types.describe_ai_benchmark_job_request.DescribeAIBenchmarkJobRequest = {}
if ai_benchmark_job_name is not None:
input_["ai_benchmark_job_name"] = ai_benchmark_job_name
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|