feat: add model_version_id to BatchPredictionJob in aiplatform v1 batch_prediction_job.proto (#1453)

* feat: add BatchImportModelEvaluationSlices API in aiplatform v1beta1 model_service.proto

PiperOrigin-RevId: 456559576

Source-Link: googleapis/googleapis@f5cd509

Source-Link: googleapis/googleapis-gen@b3549d7
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* feat: add model_version_id to BatchPredictionJob in aiplatform v1 batch_prediction_job.proto

feat: add model_version_id to DeployedModel in aiplatform v1 endpoint.proto

feat: add model_version_id to PredictResponse in aiplatform v1 prediction_service.proto
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Source-Link: googleapis/googleapis@19bd392

Source-Link: googleapis/googleapis-gen@d4dfc80
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google/cloud/aiplatform_v1/services/migration_service/client.py

@@ -196,23 +196,18 @@ def parse_annotated_dataset_path(path: str) -> Dict[str, str]:
     @staticmethod
     def dataset_path(
         project: str,
-        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
+        return "projects/{project}/datasets/{dataset}".format(
             project=project,
-            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(
-            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
-            path,
-        )
+        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
         return m.groupdict() if m else {}
 
     @staticmethod
@@ -240,18 +235,23 @@ def parse_dataset_path(path: str) -> Dict[str, str]:
     @staticmethod
     def dataset_path(
         project: str,
+        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/datasets/{dataset}".format(
+        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
             project=project,
+            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
+        m = re.match(
+            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
+            path,
+        )
         return m.groupdict() if m else {}
 
     @staticmethod

google/cloud/aiplatform_v1/types/batch_prediction_job.py

@@ -66,6 +66,9 @@ class BatchPredictionJob(proto.Message):
             The model resource name may contain version id or version
             alias to specify the version, if no version is specified,
             the default version will be used.
+        model_version_id (str):
+            Output only. The version ID of the Model that
+            produces the predictions via this job.
         unmanaged_container_model (google.cloud.aiplatform_v1.types.UnmanagedContainerModel):
             Contains model information necessary to perform batch
             prediction without requiring uploading to model registry.
@@ -405,6 +408,10 @@ class OutputInfo(proto.Message):
         proto.STRING,
         number=3,
     )
+    model_version_id = proto.Field(
+        proto.STRING,
+        number=30,
+    )
     unmanaged_container_model = proto.Field(
         proto.MESSAGE,
         number=28,

google/cloud/aiplatform_v1/types/endpoint.py

@@ -229,6 +229,9 @@ class DeployedModel(proto.Message):
             version alias to specify the version, if no
             version is specified, the default version will
             be deployed.
+        model_version_id (str):
+            Output only. The version ID of the model that
+            is deployed.
         display_name (str):
             The display name of the DeployedModel. If not provided upon
             creation, the Model's display_name is used.
@@ -311,6 +314,10 @@ class DeployedModel(proto.Message):
         proto.STRING,
         number=2,
     )
+    model_version_id = proto.Field(
+        proto.STRING,
+        number=18,
+    )
     display_name = proto.Field(
         proto.STRING,
         number=3,

google/cloud/aiplatform_v1/types/prediction_service.py

@@ -96,6 +96,10 @@ class PredictResponse(proto.Message):
             Output only. The resource name of the Model
             which is deployed as the DeployedModel that this
             prediction hits.
+        model_version_id (str):
+            Output only. The version ID of the Model
+            which is deployed as the DeployedModel that this
+            prediction hits.
         model_display_name (str):
             Output only. The [display
             name][google.cloud.aiplatform.v1.Model.display_name] of the
@@ -116,6 +120,10 @@ class PredictResponse(proto.Message):
         proto.STRING,
         number=3,
     )
+    model_version_id = proto.Field(
+        proto.STRING,
+        number=5,
+    )
     model_display_name = proto.Field(
         proto.STRING,
         number=4,

google/cloud/aiplatform_v1beta1/__init__.py

@@ -348,6 +348,8 @@
 from .types.model_monitoring import ModelMonitoringObjectiveConfig
 from .types.model_monitoring import SamplingStrategy
 from .types.model_monitoring import ThresholdConfig
+from .types.model_service import BatchImportModelEvaluationSlicesRequest
+from .types.model_service import BatchImportModelEvaluationSlicesResponse
 from .types.model_service import DeleteModelRequest
 from .types.model_service import DeleteModelVersionRequest
 from .types.model_service import ExportModelOperationMetadata
@@ -541,6 +543,8 @@
     "BatchCreateTensorboardTimeSeriesRequest",
     "BatchCreateTensorboardTimeSeriesResponse",
     "BatchDedicatedResources",
+    "BatchImportModelEvaluationSlicesRequest",
+    "BatchImportModelEvaluationSlicesResponse",
     "BatchMigrateResourcesOperationMetadata",
     "BatchMigrateResourcesRequest",
     "BatchMigrateResourcesResponse",

google/cloud/aiplatform_v1beta1/gapic_metadata.json

@@ -1266,6 +1266,11 @@
         "grpc": {
           "libraryClient": "ModelServiceClient",
           "rpcs": {
+            "BatchImportModelEvaluationSlices": {
+              "methods": [
+                "batch_import_model_evaluation_slices"
+              ]
+            },
             "DeleteModel": {
               "methods": [
                 "delete_model"
@@ -1346,6 +1351,11 @@
         "grpc-async": {
           "libraryClient": "ModelServiceAsyncClient",
           "rpcs": {
+            "BatchImportModelEvaluationSlices": {
+              "methods": [
+                "batch_import_model_evaluation_slices"
+              ]
+            },
             "DeleteModel": {
               "methods": [
                 "delete_model"

google/cloud/aiplatform_v1beta1/services/migration_service/client.py

@@ -196,40 +196,40 @@ def parse_annotated_dataset_path(path: str) -> Dict[str, str]:
     @staticmethod
     def dataset_path(
         project: str,
+        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/datasets/{dataset}".format(
+        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
             project=project,
+            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
+        m = re.match(
+            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
+            path,
+        )
         return m.groupdict() if m else {}
 
     @staticmethod
     def dataset_path(
         project: str,
-        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
+        return "projects/{project}/datasets/{dataset}".format(
             project=project,
-            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(
-            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
-            path,
-        )
+        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
         return m.groupdict() if m else {}
 
     @staticmethod

google/cloud/aiplatform_v1beta1/services/model_service/async_client.py

@@ -1539,6 +1539,117 @@ async def sample_import_model_evaluation():
         # Done; return the response.
         return response
 
+    async def batch_import_model_evaluation_slices(
+        self,
+        request: Union[
+            model_service.BatchImportModelEvaluationSlicesRequest, dict
+        ] = None,
+        *,
+        parent: str = None,
+        model_evaluation_slices: Sequence[
+            model_evaluation_slice.ModelEvaluationSlice
+        ] = None,
+        retry: OptionalRetry = gapic_v1.method.DEFAULT,
+        timeout: float = None,
+        metadata: Sequence[Tuple[str, str]] = (),
+    ) -> model_service.BatchImportModelEvaluationSlicesResponse:
+        r"""Imports a list of externally generated
+        ModelEvaluationSlice.
+
+        .. code-block:: python
+
+            from google.cloud import aiplatform_v1beta1
+
+            async def sample_batch_import_model_evaluation_slices():
+                # Create a client
+                client = aiplatform_v1beta1.ModelServiceAsyncClient()
+
+                # Initialize request argument(s)
+                request = aiplatform_v1beta1.BatchImportModelEvaluationSlicesRequest(
+                    parent="parent_value",
+                )
+
+                # Make the request
+                response = await client.batch_import_model_evaluation_slices(request=request)
+
+                # Handle the response
+                print(response)
+
+        Args:
+            request (Union[google.cloud.aiplatform_v1beta1.types.BatchImportModelEvaluationSlicesRequest, dict]):
+                The request object. Request message for
+                [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.BatchImportModelEvaluationSlices]
+            parent (:class:`str`):
+                Required. The name of the parent ModelEvaluation
+                resource. Format:
+                ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}``
+
+                This corresponds to the ``parent`` field
+                on the ``request`` instance; if ``request`` is provided, this
+                should not be set.
+            model_evaluation_slices (:class:`Sequence[google.cloud.aiplatform_v1beta1.types.ModelEvaluationSlice]`):
+                Required. Model evaluation slice
+                resource to be imported.
+
+                This corresponds to the ``model_evaluation_slices`` 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_v1beta1.types.BatchImportModelEvaluationSlicesResponse:
+                Response message for
+                [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.BatchImportModelEvaluationSlices]
+
+        """
+        # 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_slices])
+        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.BatchImportModelEvaluationSlicesRequest(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_slices:
+            request.model_evaluation_slices.extend(model_evaluation_slices)
+
+        # Wrap the RPC method; this adds retry and timeout information,
+        # and friendly error handling.
+        rpc = gapic_v1.method_async.wrap_method(
+            self._client._transport.batch_import_model_evaluation_slices,
+            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,