def get_sampling_operation_result(
self,
training_session_name: "capo_sagemakertrainingsessionruntime.types.training_session_name.TrainingSessionName",
operation_id: "capo_sagemakertrainingsessionruntime.types.operation_id.OperationId",
*,
config_overrides: Optional[SageMakerTrainingSessionRuntimeClientConfig] = None,
) -> "capo_sagemakertrainingsessionruntime.types.get_sampling_operation_result_output.GetSamplingOperationResultOutput":
"""<p>Retrieves the current status and result of a previously submitted sampling operation. Poll this operation until the status reaches a terminal state (Completed or Failed).</p>
Args:
training_session_name: <p>The name of the training session that owns the sample operation.</p>
operation_id: <p>The unique identifier of the sample operation to retrieve, returned by <code>ExecuteSamplingOperation</code>.</p>
Raises:
capo_sagemakertrainingsessionruntime.errors.internal_server_exception.InternalServerException: <p>An internal service error occurred. Retry the request using exponential backoff.</p>
capo_sagemakertrainingsessionruntime.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified training session or operation does not exist, or the operation does not belong to the specified training session.</p>
capo_sagemakertrainingsessionruntime.errors.throttling_exception.ThrottlingException: <p>The request was throttled. Retry the request after a brief wait using exponential backoff.</p>
capo_sagemakertrainingsessionruntime.errors.validation_exception.ValidationException: <p>The request is not valid. Check the request syntax and parameters. Examples include invalid sequence numbers, malformed client tokens, missing required fields, or invalid operation configuration.</p>
capo_sagemakertrainingsessionruntime.errors.UnknownServiceError: The service returned an error code this client does not model.
Examples:
Get the result of a sampling operation
Retrieves the status and generated sequences for a sampling operation submitted with ExecuteSamplingOperation.
>>> client.get_sampling_operation_result(training_session_name='my-training-session', operation_id='a1b2c3d4-5678-90ab-cdef-1234567890ab')
"""
def _handler(
req: "OperationRequest[capo_sagemakertrainingsessionruntime.types.get_sampling_operation_result_input.GetSamplingOperationResultInput]",
) -> OperationResponse[
"capo_sagemakertrainingsessionruntime.types.get_sampling_operation_result_output.GetSamplingOperationResultOutput"
]:
import capo_sagemakertrainingsessionruntime._operations.lego_router_service.get_sampling_operation_result
output, http_response = (
capo_sagemakertrainingsessionruntime._operations.lego_router_service.get_sampling_operation_result.get_sampling_operation_result(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
def _redact_output(
output: "capo_sagemakertrainingsessionruntime.types.get_sampling_operation_result_output.GetSamplingOperationResultOutput",
) -> dict[str, Any]:
import capo_sagemakertrainingsessionruntime.types.get_sampling_operation_result_output
return capo_sagemakertrainingsessionruntime.types.get_sampling_operation_result_output.redact_sensitive(
output
)
interceptors_, options_ = self.operation_options(
config_overrides,
OperationLogging(
"GetSamplingOperationResult", redact_output=_redact_output
),
)
input_: capo_sagemakertrainingsessionruntime.types.get_sampling_operation_result_input.GetSamplingOperationResultInput = {
"training_session_name": training_session_name,
"operation_id": operation_id,
}
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output