Returns a list of trained model inference jobs in a specified collaboration.
Parameters:
-
next_token
(Optional[NextToken], default:
None
)
–
The token value retrieved from a previous call to access the next page of results.
-
max_results
(Optional[MaxResults], default:
None
)
–
The maximum size of the results that is returned per call.
-
collaboration_identifier
(UUID)
–
The collaboration ID of the collaboration that contains the trained model inference jobs that you are interested in.
-
trained_model_arn
(Optional[TrainedModelArn], default:
None
)
–
The Amazon Resource Name (ARN) of the trained model that was used to create the trained model inference jobs that you are interested in.
-
trained_model_version_identifier
(Optional[UUID], default:
None
)
–
The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned.
Raises:
Source code in docs_src/capo_cleanroomsml/_services/clean_rooms_ml.py
| def list_collaboration_trained_model_inference_jobs(
self,
collaboration_identifier: "capo_cleanroomsml.types.uuid.UUID",
*,
config_overrides: Optional[CleanRoomsMLClientConfig] = None,
next_token: Optional["capo_cleanroomsml.types.next_token.NextToken"] = None,
max_results: Optional["capo_cleanroomsml.types.max_results.MaxResults"] = None,
trained_model_arn: Optional[
"capo_cleanroomsml.types.trained_model_arn.TrainedModelArn"
] = None,
trained_model_version_identifier: Optional[
"capo_cleanroomsml.types.uuid.UUID"
] = None,
) -> "capo_cleanroomsml.types.list_collaboration_trained_model_inference_jobs_response.ListCollaborationTrainedModelInferenceJobsResponse":
"""<p>Returns a list of trained model inference jobs in a specified collaboration.</p>
Args:
next_token: <p>The token value retrieved from a previous call to access the next page of results.</p>
max_results: <p>The maximum size of the results that is returned per call.</p>
collaboration_identifier: <p>The collaboration ID of the collaboration that contains the trained model inference jobs that you are interested in.</p>
trained_model_arn: <p>The Amazon Resource Name (ARN) of the trained model that was used to create the trained model inference jobs that you are interested in.</p>
trained_model_version_identifier: <p>The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned.</p>
Raises:
capo_cleanroomsml.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
capo_cleanroomsml.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
capo_cleanroomsml.errors.validation_exception.ValidationException: <p>The request parameters for this request are incorrect.</p>
capo_cleanroomsml.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_cleanroomsml.types.list_collaboration_trained_model_inference_jobs_request.ListCollaborationTrainedModelInferenceJobsRequest]",
) -> OperationResponse[
"capo_cleanroomsml.types.list_collaboration_trained_model_inference_jobs_response.ListCollaborationTrainedModelInferenceJobsResponse"
]:
import capo_cleanroomsml._operations.aws_stark_control_service.list_collaboration_trained_model_inference_jobs
output, http_response = (
capo_cleanroomsml._operations.aws_stark_control_service.list_collaboration_trained_model_inference_jobs.list_collaboration_trained_model_inference_jobs(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides,
OperationLogging("ListCollaborationTrainedModelInferenceJobs"),
)
input_: capo_cleanroomsml.types.list_collaboration_trained_model_inference_jobs_request.ListCollaborationTrainedModelInferenceJobsRequest = {
"collaboration_identifier": collaboration_identifier
}
if next_token is not None:
input_["next_token"] = next_token
if max_results is not None:
input_["max_results"] = max_results
if trained_model_arn is not None:
input_["trained_model_arn"] = trained_model_arn
if trained_model_version_identifier is not None:
input_["trained_model_version_identifier"] = (
trained_model_version_identifier
)
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|