feat: add ImportModelEvaluation in aiplatform v1 model_service.proto

google/cloud/aiplatform_v1/__init__.py

@@ -348,6 +348,7 @@
 from .types.model_service import GetModelEvaluationRequest
 from .types.model_service import GetModelEvaluationSliceRequest
 from .types.model_service import GetModelRequest
+from .types.model_service import ImportModelEvaluationRequest
 from .types.model_service import ListModelEvaluationSlicesRequest
 from .types.model_service import ListModelEvaluationSlicesResponse
 from .types.model_service import ListModelEvaluationsRequest
@@ -720,6 +721,7 @@
  "ImportFeatureValuesOperationMetadata",
  "ImportFeatureValuesRequest",
  "ImportFeatureValuesResponse",
+ "ImportModelEvaluationRequest",
  "Index",
  "IndexEndpoint",
  "IndexEndpointServiceClient",

google/cloud/aiplatform_v1/gapic_metadata.json

@@ -1291,6 +1291,11 @@
  "get_model_evaluation_slice"
  ]
  },
+ "ImportModelEvaluation": {
+ "methods": [
+ "import_model_evaluation"
+ ]
+ },
  "ListModelEvaluationSlices": {
  "methods": [
  "list_model_evaluation_slices"
@@ -1346,6 +1351,11 @@
  "get_model_evaluation_slice"
  ]
  },
+ "ImportModelEvaluation": {
+ "methods": [
+ "import_model_evaluation"
+ ]
+ },
  "ListModelEvaluationSlices": {
  "methods": [
  "list_model_evaluation_slices"

google/cloud/aiplatform_v1/services/model_service/async_client.py

@@ -40,6 +40,7 @@
 from google.cloud.aiplatform_v1.types import model
 from google.cloud.aiplatform_v1.types import model as gca_model
 from google.cloud.aiplatform_v1.types import model_evaluation
+from google.cloud.aiplatform_v1.types import model_evaluation as gca_model_evaluation
 from google.cloud.aiplatform_v1.types import model_evaluation_slice
 from google.cloud.aiplatform_v1.types import model_service
 from google.cloud.aiplatform_v1.types import operation as gca_operation
@@ -886,6 +887,108 @@ def sample_export_model():
  # Done; return the response.
  return response
 
+ async def import_model_evaluation(
+ self,
+ request: Union[model_service.ImportModelEvaluationRequest, dict] = None,
+ *,
+ parent: str = None,
+ model_evaluation: gca_model_evaluation.ModelEvaluation = None,
+ retry: OptionalRetry = gapic_v1.method.DEFAULT,
+ timeout: float = None,
+ metadata: Sequence[Tuple[str, str]] = (),
+ ) -> gca_model_evaluation.ModelEvaluation:
+ r"""Imports an externally generated ModelEvaluation.
+
+ .. code-block:: python
+
+ from google.cloud import aiplatform_v1
+
+ def sample_import_model_evaluation():
+ # Create a client
+ client = aiplatform_v1.ModelServiceClient()
+
+ # Initialize request argument(s)
+ request = aiplatform_v1.ImportModelEvaluationRequest(
+ parent="parent_value",
+ )
+
+ # Make the request
+ response = client.import_model_evaluation(request=request)
+
+ # Handle the response
+ print(response)
+
+ Args:
+ request (Union[google.cloud.aiplatform_v1.types.ImportModelEvaluationRequest, dict]):
+ The request object. Request message for
+ [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1.ModelService.ImportModelEvaluation]
+ parent (:class:`str`):
+ Required. The name of the parent model resource. Format:
+ ``projects/{project}/locations/{location}/models/{model}``
+
+ This corresponds to the ``parent`` field
+ on the ``request`` instance; if ``request`` is provided, this
+ should not be set.
+ model_evaluation (:class:`google.cloud.aiplatform_v1.types.ModelEvaluation`):
+ Required. Model evaluation resource
+ to be imported.
+
+ This corresponds to the ``model_evaluation`` field
+ on the ``request`` instance; if ``request`` is provided, this
+ should not be set.
+ retry (google.api_core.retry.Retry): Designation of what errors, if any,
+ should be retried.
+ timeout (float): The timeout for this request.
+ metadata (Sequence[Tuple[str, str]]): Strings which should be
+ sent along with the request as metadata.
+
+ Returns:
+ google.cloud.aiplatform_v1.types.ModelEvaluation:
+ A collection of metrics calculated by
+ comparing Model's predictions on all of
+ the test data against annotations from
+ the test data.
+
+ """
+ # Create or coerce a protobuf request object.
+ # Quick check: If we got a request object, we should *not* have
+ # gotten any keyword arguments that map to the request.
+ has_flattened_params = any([parent, model_evaluation])
+ if request is not None and has_flattened_params:
+ raise ValueError(
+ "If the `request` argument is set, then none of "
+ "the individual field arguments should be set."
+ )
+
+ request = model_service.ImportModelEvaluationRequest(request)
+
+ # If we have keyword arguments corresponding to fields on the
+ # request, apply these.
+ if parent is not None:
+ request.parent = parent
+ if model_evaluation is not None:
+ request.model_evaluation = model_evaluation
+
+ # Wrap the RPC method; this adds retry and timeout information,
+ # and friendly error handling.
+ rpc = gapic_v1.method_async.wrap_method(
+ self._client._transport.import_model_evaluation,
+ default_timeout=None,
+ client_info=DEFAULT_CLIENT_INFO,
+ )
+
+ # Certain fields should be provided within the metadata header;
+ # add these here.
+ metadata = tuple(metadata) + (
+ gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
+ )
+
+ # Send the request.
+ response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
+
+ # Done; return the response.
+ return response
+
  async def get_model_evaluation(
  self,
  request: Union[model_service.GetModelEvaluationRequest, dict] = None,

google/cloud/aiplatform_v1/services/model_service/client.py

@@ -43,6 +43,7 @@
 from google.cloud.aiplatform_v1.types import model
 from google.cloud.aiplatform_v1.types import model as gca_model
 from google.cloud.aiplatform_v1.types import model_evaluation
+from google.cloud.aiplatform_v1.types import model_evaluation as gca_model_evaluation
 from google.cloud.aiplatform_v1.types import model_evaluation_slice
 from google.cloud.aiplatform_v1.types import model_service
 from google.cloud.aiplatform_v1.types import operation as gca_operation
@@ -1143,6 +1144,108 @@ def sample_export_model():
  # Done; return the response.
  return response
 
+ def import_model_evaluation(
+ self,
+ request: Union[model_service.ImportModelEvaluationRequest, dict] = None,
+ *,
+ parent: str = None,
+ model_evaluation: gca_model_evaluation.ModelEvaluation = None,
+ retry: OptionalRetry = gapic_v1.method.DEFAULT,
+ timeout: float = None,
+ metadata: Sequence[Tuple[str, str]] = (),
+ ) -> gca_model_evaluation.ModelEvaluation:
+ r"""Imports an externally generated ModelEvaluation.
+
+ .. code-block:: python
+
+ from google.cloud import aiplatform_v1
+
+ def sample_import_model_evaluation():
+ # Create a client
+ client = aiplatform_v1.ModelServiceClient()
+
+ # Initialize request argument(s)
+ request = aiplatform_v1.ImportModelEvaluationRequest(
+ parent="parent_value",
+ )
+
+ # Make the request
+ response = client.import_model_evaluation(request=request)
+
+ # Handle the response
+ print(response)
+
+ Args:
+ request (Union[google.cloud.aiplatform_v1.types.ImportModelEvaluationRequest, dict]):
+ The request object. Request message for
+ [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1.ModelService.ImportModelEvaluation]
+ parent (str):
+ Required. The name of the parent model resource. Format:
+ ``projects/{project}/locations/{location}/models/{model}``
+
+ This corresponds to the ``parent`` field
+ on the ``request`` instance; if ``request`` is provided, this
+ should not be set.
+ model_evaluation (google.cloud.aiplatform_v1.types.ModelEvaluation):
+ Required. Model evaluation resource
+ to be imported.
+
+ This corresponds to the ``model_evaluation`` field
+ on the ``request`` instance; if ``request`` is provided, this
+ should not be set.
+ retry (google.api_core.retry.Retry): Designation of what errors, if any,
+ should be retried.
+ timeout (float): The timeout for this request.
+ metadata (Sequence[Tuple[str, str]]): Strings which should be
+ sent along with the request as metadata.
+
+ Returns:
+ google.cloud.aiplatform_v1.types.ModelEvaluation:
+ A collection of metrics calculated by
+ comparing Model's predictions on all of
+ the test data against annotations from
+ the test data.
+
+ """
+ # Create or coerce a protobuf request object.
+ # Quick check: If we got a request object, we should *not* have
+ # gotten any keyword arguments that map to the request.
+ has_flattened_params = any([parent, model_evaluation])
+ if request is not None and has_flattened_params:
+ raise ValueError(
+ "If the `request` argument is set, then none of "
+ "the individual field arguments should be set."
+ )
+
+ # Minor optimization to avoid making a copy if the user passes
+ # in a model_service.ImportModelEvaluationRequest.
+ # There's no risk of modifying the input as we've already verified
+ # there are no flattened fields.
+ if not isinstance(request, model_service.ImportModelEvaluationRequest):
+ request = model_service.ImportModelEvaluationRequest(request)
+ # If we have keyword arguments corresponding to fields on the
+ # request, apply these.
+ if parent is not None:
+ request.parent = parent
+ if model_evaluation is not None:
+ request.model_evaluation = model_evaluation
+
+ # Wrap the RPC method; this adds retry and timeout information,
+ # and friendly error handling.
+ rpc = self._transport._wrapped_methods[self._transport.import_model_evaluation]
+
+ # Certain fields should be provided within the metadata header;
+ # add these here.
+ metadata = tuple(metadata) + (
+ gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
+ )
+
+ # Send the request.
+ response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
+
+ # Done; return the response.
+ return response
+
  def get_model_evaluation(
  self,
  request: Union[model_service.GetModelEvaluationRequest, dict] = None,

google/cloud/aiplatform_v1/services/model_service/transports/base.py

@@ -29,6 +29,7 @@
 from google.cloud.aiplatform_v1.types import model
 from google.cloud.aiplatform_v1.types import model as gca_model
 from google.cloud.aiplatform_v1.types import model_evaluation
+from google.cloud.aiplatform_v1.types import model_evaluation as gca_model_evaluation
 from google.cloud.aiplatform_v1.types import model_evaluation_slice
 from google.cloud.aiplatform_v1.types import model_service
 from google.longrunning import operations_pb2 # type: ignore
@@ -144,6 +145,11 @@ def _prep_wrapped_messages(self, client_info):
  self.export_model: gapic_v1.method.wrap_method(
  self.export_model, default_timeout=None, client_info=client_info,
  ),
+ self.import_model_evaluation: gapic_v1.method.wrap_method(
+ self.import_model_evaluation,
+ default_timeout=None,
+ client_info=client_info,
+ ),
  self.get_model_evaluation: gapic_v1.method.wrap_method(
  self.get_model_evaluation,
  default_timeout=None,
@@ -236,6 +242,18 @@ def export_model(
  ]:
  raise NotImplementedError()
 
+ @property
+ def import_model_evaluation(
+ self,
+ ) -> Callable[
+ [model_service.ImportModelEvaluationRequest],
+ Union[
+ gca_model_evaluation.ModelEvaluation,
+ Awaitable[gca_model_evaluation.ModelEvaluation],
+ ],
+ ]:
+ raise NotImplementedError()
+
  @property
  def get_model_evaluation(
  self,

google/cloud/aiplatform_v1/services/model_service/transports/grpc.py

@@ -28,6 +28,7 @@
 from google.cloud.aiplatform_v1.types import model
 from google.cloud.aiplatform_v1.types import model as gca_model
 from google.cloud.aiplatform_v1.types import model_evaluation
+from google.cloud.aiplatform_v1.types import model_evaluation as gca_model_evaluation
 from google.cloud.aiplatform_v1.types import model_evaluation_slice
 from google.cloud.aiplatform_v1.types import model_service
 from google.longrunning import operations_pb2 # type: ignore
@@ -413,6 +414,35 @@ def export_model(
  )
  return self._stubs["export_model"]
 
+ @property
+ def import_model_evaluation(
+ self,
+ ) -> Callable[
+ [model_service.ImportModelEvaluationRequest],
+ gca_model_evaluation.ModelEvaluation,
+ ]:
+ r"""Return a callable for the import model evaluation method over gRPC.
+
+ Imports an externally generated ModelEvaluation.
+
+ Returns:
+ Callable[[~.ImportModelEvaluationRequest],
+ ~.ModelEvaluation]:
+ A function that, when called, will call the underlying RPC
+ on the server.
+ """
+ # Generate a "stub function" on-the-fly which will actually make
+ # the request.
+ # gRPC handles serialization and deserialization, so we just need
+ # to pass in the functions for each.
+ if "import_model_evaluation" not in self._stubs:
+ self._stubs["import_model_evaluation"] = self.grpc_channel.unary_unary(
+ "/google.cloud.aiplatform.v1.ModelService/ImportModelEvaluation",
+ request_serializer=model_service.ImportModelEvaluationRequest.serialize,
+ response_deserializer=gca_model_evaluation.ModelEvaluation.deserialize,
+ )
+ return self._stubs["import_model_evaluation"]
+
  @property
  def get_model_evaluation(
  self,