Copybara import of the project:

--
0966deb by Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>:

feat: add Model Garden deploy API

PiperOrigin-RevId: 729261558

Source-Link: googleapis/googleapis@7e791c0

Source-Link: googleapis/googleapis-gen@3dbfe2e
Copy-Tag: eyJwIjoiLmdpdGh1Yi8uT3dsQm90LnlhbWwiLCJoIjoiM2RiZmUyZTZiYjQ3NmQ2MTk3OWUzOWFiOThlZDkxZThjZDdmYjk2YSJ9

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643a485 by Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>:

🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

COPYBARA_INTEGRATE_REVIEW=#4977 from googleapis:owl-bot-copy 97fdb22
PiperOrigin-RevId: 730634172

Author: gcf-owl-bot[bot]

google/cloud/aiplatform_v1beta1/__init__.py

@@ -762,9 +762,12 @@
 )
 from .types.model_evaluation import ModelEvaluation
 from .types.model_evaluation_slice import ModelEvaluationSlice
+from .types.model_garden_service import DeployOperationMetadata
 from .types.model_garden_service import DeployPublisherModelOperationMetadata
 from .types.model_garden_service import DeployPublisherModelRequest
 from .types.model_garden_service import DeployPublisherModelResponse
+from .types.model_garden_service import DeployRequest
+from .types.model_garden_service import DeployResponse
 from .types.model_garden_service import GetPublisherModelRequest
 from .types.model_garden_service import ListPublisherModelsRequest
 from .types.model_garden_service import ListPublisherModelsResponse
@@ -1446,9 +1449,12 @@
     "DeployModelOperationMetadata",
     "DeployModelRequest",
     "DeployModelResponse",
+    "DeployOperationMetadata",
     "DeployPublisherModelOperationMetadata",
     "DeployPublisherModelRequest",
     "DeployPublisherModelResponse",
+    "DeployRequest",
+    "DeployResponse",
     "DeployedIndex",
     "DeployedIndexAuthConfig",
     "DeployedIndexRef",

google/cloud/aiplatform_v1beta1/gapic_metadata.json

@@ -3375,6 +3375,11 @@
         "grpc": {
           "libraryClient": "ModelGardenServiceClient",
           "rpcs": {
+            "Deploy": {
+              "methods": [
+                "deploy"
+              ]
+            },
             "DeployPublisherModel": {
               "methods": [
                 "deploy_publisher_model"
@@ -3395,6 +3400,11 @@
         "grpc-async": {
           "libraryClient": "ModelGardenServiceAsyncClient",
           "rpcs": {
+            "Deploy": {
+              "methods": [
+                "deploy"
+              ]
+            },
             "DeployPublisherModel": {
               "methods": [
                 "deploy_publisher_model"
@@ -3415,6 +3425,11 @@
         "rest": {
           "libraryClient": "ModelGardenServiceClient",
           "rpcs": {
+            "Deploy": {
+              "methods": [
+                "deploy"
+              ]
+            },
             "DeployPublisherModel": {
               "methods": [
                 "deploy_publisher_model"

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

@@ -264,40 +264,40 @@ 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
     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_garden_service/async_client.py

@@ -542,6 +542,107 @@ async def sample_list_publisher_models():
         # Done; return the response.
         return response
 
+    async def deploy(
+        self,
+        request: Optional[Union[model_garden_service.DeployRequest, dict]] = None,
+        *,
+        retry: OptionalRetry = gapic_v1.method.DEFAULT,
+        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
+        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
+    ) -> operation_async.AsyncOperation:
+        r"""Deploys a model to a new endpoint.
+
+        .. code-block:: python
+
+            # This snippet has been automatically generated and should be regarded as a
+            # code template only.
+            # It will require modifications to work:
+            # - It may require correct/in-range values for request initialization.
+            # - It may require specifying regional endpoints when creating the service
+            #   client as shown in:
+            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
+            from google.cloud import aiplatform_v1beta1
+
+            async def sample_deploy():
+                # Create a client
+                client = aiplatform_v1beta1.ModelGardenServiceAsyncClient()
+
+                # Initialize request argument(s)
+                request = aiplatform_v1beta1.DeployRequest(
+                    publisher_model_name="publisher_model_name_value",
+                    destination="destination_value",
+                )
+
+                # Make the request
+                operation = client.deploy(request=request)
+
+                print("Waiting for operation to complete...")
+
+                response = (await operation).result()
+
+                # Handle the response
+                print(response)
+
+        Args:
+            request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeployRequest, dict]]):
+                The request object. Request message for
+                [ModelGardenService.Deploy][google.cloud.aiplatform.v1beta1.ModelGardenService.Deploy].
+            retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
+                should be retried.
+            timeout (float): The timeout for this request.
+            metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be
+                sent along with the request as metadata. Normally, each value must be of type `str`,
+                but for metadata keys ending with the suffix `-bin`, the corresponding values must
+                be of type `bytes`.
+
+        Returns:
+            google.api_core.operation_async.AsyncOperation:
+                An object representing a long-running operation.
+
+                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.DeployResponse` Response message for
+                   [ModelGardenService.Deploy][google.cloud.aiplatform.v1beta1.ModelGardenService.Deploy].
+
+        """
+        # Create or coerce a protobuf request object.
+        # - Use the request object if provided (there's no risk of modifying the input as
+        #   there are no flattened fields), or create one.
+        if not isinstance(request, model_garden_service.DeployRequest):
+            request = model_garden_service.DeployRequest(request)
+
+        # Wrap the RPC method; this adds retry and timeout information,
+        # and friendly error handling.
+        rpc = self._client._transport._wrapped_methods[self._client._transport.deploy]
+
+        # Certain fields should be provided within the metadata header;
+        # add these here.
+        metadata = tuple(metadata) + (
+            gapic_v1.routing_header.to_grpc_metadata(
+                (("destination", request.destination),)
+            ),
+        )
+
+        # Validate the universe domain.
+        self._client._validate_universe_domain()
+
+        # Send the request.
+        response = await rpc(
+            request,
+            retry=retry,
+            timeout=timeout,
+            metadata=metadata,
+        )
+
+        # Wrap the response in an operation future.
+        response = operation_async.from_gapic(
+            response,
+            self._client._transport.operations_client,
+            model_garden_service.DeployResponse,
+            metadata_type=model_garden_service.DeployOperationMetadata,
+        )
+
+        # Done; return the response.
+        return response
+
     async def deploy_publisher_model(
         self,
         request: Optional[

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

@@ -1049,6 +1049,107 @@ def sample_list_publisher_models():
         # Done; return the response.
         return response
 
+    def deploy(
+        self,
+        request: Optional[Union[model_garden_service.DeployRequest, dict]] = None,
+        *,
+        retry: OptionalRetry = gapic_v1.method.DEFAULT,
+        timeout: Union[float, object] = gapic_v1.method.DEFAULT,
+        metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
+    ) -> operation.Operation:
+        r"""Deploys a model to a new endpoint.
+
+        .. code-block:: python
+
+            # This snippet has been automatically generated and should be regarded as a
+            # code template only.
+            # It will require modifications to work:
+            # - It may require correct/in-range values for request initialization.
+            # - It may require specifying regional endpoints when creating the service
+            #   client as shown in:
+            #   https://googleapis.dev/python/google-api-core/latest/client_options.html
+            from google.cloud import aiplatform_v1beta1
+
+            def sample_deploy():
+                # Create a client
+                client = aiplatform_v1beta1.ModelGardenServiceClient()
+
+                # Initialize request argument(s)
+                request = aiplatform_v1beta1.DeployRequest(
+                    publisher_model_name="publisher_model_name_value",
+                    destination="destination_value",
+                )
+
+                # Make the request
+                operation = client.deploy(request=request)
+
+                print("Waiting for operation to complete...")
+
+                response = operation.result()
+
+                # Handle the response
+                print(response)
+
+        Args:
+            request (Union[google.cloud.aiplatform_v1beta1.types.DeployRequest, dict]):
+                The request object. Request message for
+                [ModelGardenService.Deploy][google.cloud.aiplatform.v1beta1.ModelGardenService.Deploy].
+            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, Union[str, bytes]]]): Key/value pairs which should be
+                sent along with the request as metadata. Normally, each value must be of type `str`,
+                but for metadata keys ending with the suffix `-bin`, the corresponding values must
+                be of type `bytes`.
+
+        Returns:
+            google.api_core.operation.Operation:
+                An object representing a long-running operation.
+
+                The result type for the operation will be :class:`google.cloud.aiplatform_v1beta1.types.DeployResponse` Response message for
+                   [ModelGardenService.Deploy][google.cloud.aiplatform.v1beta1.ModelGardenService.Deploy].
+
+        """
+        # Create or coerce a protobuf request object.
+        # - Use the request object if provided (there's no risk of modifying the input as
+        #   there are no flattened fields), or create one.
+        if not isinstance(request, model_garden_service.DeployRequest):
+            request = model_garden_service.DeployRequest(request)
+
+        # Wrap the RPC method; this adds retry and timeout information,
+        # and friendly error handling.
+        rpc = self._transport._wrapped_methods[self._transport.deploy]
+
+        # Certain fields should be provided within the metadata header;
+        # add these here.
+        metadata = tuple(metadata) + (
+            gapic_v1.routing_header.to_grpc_metadata(
+                (("destination", request.destination),)
+            ),
+        )
+
+        # Validate the universe domain.
+        self._validate_universe_domain()
+
+        # Send the request.
+        response = rpc(
+            request,
+            retry=retry,
+            timeout=timeout,
+            metadata=metadata,
+        )
+
+        # Wrap the response in an operation future.
+        response = operation.from_gapic(
+            response,
+            self._transport.operations_client,
+            model_garden_service.DeployResponse,
+            metadata_type=model_garden_service.DeployOperationMetadata,
+        )
+
+        # Done; return the response.
+        return response
+
     def deploy_publisher_model(
         self,
         request: Optional[

google/cloud/aiplatform_v1beta1/services/model_garden_service/transports/base.py

@@ -145,6 +145,11 @@ def _prep_wrapped_messages(self, client_info):
                 default_timeout=None,
                 client_info=client_info,
             ),
+            self.deploy: gapic_v1.method.wrap_method(
+                self.deploy,
+                default_timeout=None,
+                client_info=client_info,
+            ),
             self.deploy_publisher_model: gapic_v1.method.wrap_method(
                 self.deploy_publisher_model,
                 default_timeout=None,
@@ -239,6 +244,15 @@ def list_publisher_models(
     ]:
         raise NotImplementedError()
 
+    @property
+    def deploy(
+        self,
+    ) -> Callable[
+        [model_garden_service.DeployRequest],
+        Union[operations_pb2.Operation, Awaitable[operations_pb2.Operation]],
+    ]:
+        raise NotImplementedError()
+
     @property
     def deploy_publisher_model(
         self,

google/cloud/aiplatform_v1beta1/services/model_garden_service/transports/grpc.py

@@ -401,6 +401,32 @@ def list_publisher_models(
             )
         return self._stubs["list_publisher_models"]
 
+    @property
+    def deploy(
+        self,
+    ) -> Callable[[model_garden_service.DeployRequest], operations_pb2.Operation]:
+        r"""Return a callable for the deploy method over gRPC.
+
+        Deploys a model to a new endpoint.
+
+        Returns:
+            Callable[[~.DeployRequest],
+                    ~.Operation]:
+                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 "deploy" not in self._stubs:
+            self._stubs["deploy"] = self._logged_channel.unary_unary(
+                "/google.cloud.aiplatform.v1beta1.ModelGardenService/Deploy",
+                request_serializer=model_garden_service.DeployRequest.serialize,
+                response_deserializer=operations_pb2.Operation.FromString,
+            )
+        return self._stubs["deploy"]
+
     @property
     def deploy_publisher_model(
         self,