feat: Added async prediction and explanation support to the `Endpoint…

…` class

* Added the `Endpoint.predict_async` method
* Added the `Endpoint.explain_async` method
* Made it possible to use async clients in classes derived from `VertexAiResourceNounWithFutureManager` that use `@optional_sync`.

PiperOrigin-RevId: 565472250

google/cloud/aiplatform/compat/__init__.py

@@ -39,6 +39,9 @@
     services.model_garden_service_client = services.model_garden_service_client_v1beta1
     services.pipeline_service_client = services.pipeline_service_client_v1beta1
     services.prediction_service_client = services.prediction_service_client_v1beta1
+    services.prediction_service_async_client = (
+        services.prediction_service_async_client_v1beta1
+    )
     services.schedule_service_client = services.schedule_service_client_v1beta1
     services.specialist_pool_service_client = (
         services.specialist_pool_service_client_v1beta1
@@ -144,6 +147,9 @@
     services.model_service_client = services.model_service_client_v1
     services.pipeline_service_client = services.pipeline_service_client_v1
     services.prediction_service_client = services.prediction_service_client_v1
+    services.prediction_service_async_client = (
+        services.prediction_service_async_client_v1
+    )
     services.schedule_service_client = services.schedule_service_client_v1
     services.specialist_pool_service_client = services.specialist_pool_service_client_v1
     services.tensorboard_service_client = services.tensorboard_service_client_v1

google/cloud/aiplatform/compat/services/__init__.py

@@ -60,6 +60,9 @@
 from google.cloud.aiplatform_v1beta1.services.prediction_service import (
     client as prediction_service_client_v1beta1,
 )
+from google.cloud.aiplatform_v1beta1.services.prediction_service import (
+    async_client as prediction_service_async_client_v1beta1,
+)
 from google.cloud.aiplatform_v1beta1.services.schedule_service import (
     client as schedule_service_client_v1beta1,
 )
@@ -109,6 +112,9 @@
 from google.cloud.aiplatform_v1.services.prediction_service import (
     client as prediction_service_client_v1,
 )
+from google.cloud.aiplatform_v1.services.prediction_service import (
+    async_client as prediction_service_async_client_v1,
+)
 from google.cloud.aiplatform_v1.services.schedule_service import (
     client as schedule_service_client_v1,
 )
@@ -136,6 +142,7 @@
     model_service_client_v1,
     pipeline_service_client_v1,
     prediction_service_client_v1,
+    prediction_service_async_client_v1,
     schedule_service_client_v1,
     specialist_pool_service_client_v1,
     tensorboard_service_client_v1,
@@ -155,6 +162,7 @@
     persistent_resource_service_client_v1beta1,
     pipeline_service_client_v1beta1,
     prediction_service_client_v1beta1,
+    prediction_service_async_client_v1beta1,
     schedule_service_client_v1beta1,
     specialist_pool_service_client_v1beta1,
     metadata_service_client_v1beta1,

google/cloud/aiplatform/models.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import asyncio
 import json
 import pathlib
 import re
@@ -226,7 +227,10 @@ def __init__(
         # Lazy load the Endpoint gca_resource until needed
         self._gca_resource = gca_endpoint_compat.Endpoint(name=endpoint_name)
 
-        self._prediction_client = self._instantiate_prediction_client(
+        (
+            self._prediction_client,
+            self._prediction_async_client,
+        ) = self._instantiate_prediction_clients(
             location=self.location,
             credentials=credentials,
         )
@@ -572,7 +576,10 @@ def _construct_sdk_resource_from_gapic(
             credentials=credentials,
         )
 
-        endpoint._prediction_client = cls._instantiate_prediction_client(
+        (
+            endpoint._prediction_client,
+            endpoint._prediction_async_client,
+        ) = cls._instantiate_prediction_clients(
             location=endpoint.location,
             credentials=credentials,
         )
@@ -1384,10 +1391,12 @@ def _undeploy(
         self._sync_gca_resource()
 
     @staticmethod
-    def _instantiate_prediction_client(
+    def _instantiate_prediction_clients(
         location: Optional[str] = None,
         credentials: Optional[auth_credentials.Credentials] = None,
-    ) -> utils.PredictionClientWithOverride:
+    ) -> Tuple[
+        utils.PredictionClientWithOverride, utils.PredictionAsyncClientWithOverride
+    ]:
         """Helper method to instantiates prediction client with optional
         overrides for this endpoint.
 
@@ -1399,14 +1408,34 @@ def _instantiate_prediction_client(
 
         Returns:
             prediction_client (prediction_service_client.PredictionServiceClient):
-                Initialized prediction client with optional overrides.
+            prediction_async_client (PredictionServiceAsyncClient):
+                Initialized prediction clients with optional overrides.
         """
-        return initializer.global_config.create_client(
+
+        # Creating an event loop if needed.
+        # PredictionServiceAsyncClient constructor calls `asyncio.get_event_loop`,
+        # which fails when there is no event loop (which does not exist by default
+        # in non-main threads in thread pool used when `sync=False`).
+        try:
+            asyncio.get_event_loop()
+        except RuntimeError:
+            asyncio.set_event_loop(asyncio.new_event_loop())
+
+        async_client = initializer.global_config.create_client(
+            client_class=utils.PredictionAsyncClientWithOverride,
+            credentials=credentials,
+            location_override=location,
+            prediction_client=True,
+        )
+        # We could use `client = async_client._client`, but then client would be
+        # a concrete `PredictionServiceClient`, not `PredictionClientWithOverride`.
+        client = initializer.global_config.create_client(
             client_class=utils.PredictionClientWithOverride,
             credentials=credentials,
             location_override=location,
             prediction_client=True,
         )
+        return (client, async_client)
 
     def update(
         self,
@@ -1581,6 +1610,65 @@ def predict(
                 model_resource_name=prediction_response.model,
             )
 
+    async def predict_async(
+        self,
+        instances: List,
+        *,
+        parameters: Optional[Dict] = None,
+        timeout: Optional[float] = None,
+    ) -> Prediction:
+        """Make an asynchronous prediction against this Endpoint.
+        Example usage:
+            ```
+            response = await my_endpoint.predict_async(instances=[...])
+            my_predictions = response.predictions
+            ```
+
+        Args:
+            instances (List):
+                Required. The instances that are the input to the
+                prediction call. A DeployedModel may have an upper limit
+                on the number of instances it supports per request, and
+                when it is exceeded the prediction call errors in case
+                of AutoML Models, or, in case of customer created
+                Models, the behaviour is as documented by that Model.
+                The schema of any single instance may be specified via
+                Endpoint's DeployedModels'
+                [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model]
+                [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
+                ``instance_schema_uri``.
+            parameters (Dict):
+                Optional. The parameters that govern the prediction. The schema of
+                the parameters may be specified via Endpoint's
+                DeployedModels' [Model's
+                ][google.cloud.aiplatform.v1beta1.DeployedModel.model]
+                [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
+                ``parameters_schema_uri``.
+            timeout (float): Optional. The timeout for this request in seconds.
+
+        Returns:
+            prediction (aiplatform.Prediction):
+                Prediction with returned predictions and Model ID.
+        """
+        self.wait()
+
+        prediction_response = await self._prediction_async_client.predict(
+            endpoint=self._gca_resource.name,
+            instances=instances,
+            parameters=parameters,
+            timeout=timeout,
+        )
+
+        return Prediction(
+            predictions=[
+                json_format.MessageToDict(item)
+                for item in prediction_response.predictions.pb
+            ],
+            deployed_model_id=prediction_response.deployed_model_id,
+            model_version_id=prediction_response.model_version_id,
+            model_resource_name=prediction_response.model,
+        )
+
     def raw_predict(
         self, body: bytes, headers: Dict[str, str]
     ) -> requests.models.Response:
@@ -1676,6 +1764,70 @@ def explain(
             explanations=explain_response.explanations,
         )
 
+    async def explain_async(
+        self,
+        instances: List[Dict],
+        *,
+        parameters: Optional[Dict] = None,
+        deployed_model_id: Optional[str] = None,
+        timeout: Optional[float] = None,
+    ) -> Prediction:
+        """Make a prediction with explanations against this Endpoint.
+
+        Example usage:
+            ```
+            response = await my_endpoint.explain_async(instances=[...])
+            my_explanations = response.explanations
+            ```
+
+        Args:
+            instances (List):
+                Required. The instances that are the input to the
+                prediction call. A DeployedModel may have an upper limit
+                on the number of instances it supports per request, and
+                when it is exceeded the prediction call errors in case
+                of AutoML Models, or, in case of customer created
+                Models, the behaviour is as documented by that Model.
+                The schema of any single instance may be specified via
+                Endpoint's DeployedModels'
+                [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model]
+                [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
+                ``instance_schema_uri``.
+            parameters (Dict):
+                The parameters that govern the prediction. The schema of
+                the parameters may be specified via Endpoint's
+                DeployedModels' [Model's
+                ][google.cloud.aiplatform.v1beta1.DeployedModel.model]
+                [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
+                ``parameters_schema_uri``.
+            deployed_model_id (str):
+                Optional. If specified, this ExplainRequest will be served by the
+                chosen DeployedModel, overriding this Endpoint's traffic split.
+            timeout (float): Optional. The timeout for this request in seconds.
+
+        Returns:
+            prediction (aiplatform.Prediction):
+                Prediction with returned predictions, explanations, and Model ID.
+        """
+        self.wait()
+
+        explain_response = await self._prediction_async_client.explain(
+            endpoint=self.resource_name,
+            instances=instances,
+            parameters=parameters,
+            deployed_model_id=deployed_model_id,
+            timeout=timeout,
+        )
+
+        return Prediction(
+            predictions=[
+                json_format.MessageToDict(item)
+                for item in explain_response.predictions.pb
+            ],
+            deployed_model_id=explain_response.deployed_model_id,
+            explanations=explain_response.explanations,
+        )
+
     @classmethod
     def list(
         cls,

google/cloud/aiplatform/utils/__init__.py

@@ -49,6 +49,7 @@
     model_service_client_v1beta1,
     pipeline_service_client_v1beta1,
     prediction_service_client_v1beta1,
+    prediction_service_async_client_v1beta1,
     schedule_service_client_v1beta1,
     tensorboard_service_client_v1beta1,
     vizier_service_client_v1beta1,
@@ -68,6 +69,7 @@
     model_service_client_v1,
     pipeline_service_client_v1,
     prediction_service_client_v1,
+    prediction_service_async_client_v1,
     schedule_service_client_v1,
     tensorboard_service_client_v1,
     vizier_service_client_v1,
@@ -89,6 +91,7 @@
     index_endpoint_service_client_v1beta1.IndexEndpointServiceClient,
     model_service_client_v1beta1.ModelServiceClient,
     prediction_service_client_v1beta1.PredictionServiceClient,
+    prediction_service_async_client_v1beta1.PredictionServiceAsyncClient,
     pipeline_service_client_v1beta1.PipelineServiceClient,
     job_service_client_v1beta1.JobServiceClient,
     match_service_client_v1beta1.MatchServiceClient,
@@ -104,6 +107,7 @@
     metadata_service_client_v1.MetadataServiceClient,
     model_service_client_v1.ModelServiceClient,
     prediction_service_client_v1.PredictionServiceClient,
+    prediction_service_async_client_v1.PredictionServiceAsyncClient,
     pipeline_service_client_v1.PipelineServiceClient,
     job_service_client_v1.JobServiceClient,
     schedule_service_client_v1.ScheduleServiceClient,
@@ -616,6 +620,18 @@ class PredictionClientWithOverride(ClientWithOverride):
     )
 
 
+class PredictionAsyncClientWithOverride(ClientWithOverride):
+    _is_temporary = False
+    _default_version = compat.DEFAULT_VERSION
+    _version_map = (
+        (compat.V1, prediction_service_async_client_v1.PredictionServiceAsyncClient),
+        (
+            compat.V1BETA1,
+            prediction_service_async_client_v1beta1.PredictionServiceAsyncClient,
+        ),
+    )
+
+
 class MatchClientWithOverride(ClientWithOverride):
     _is_temporary = False
     _default_version = compat.V1BETA1

tests/system/aiplatform/test_model_interactions.py

@@ -16,6 +16,7 @@
 #
 
 import json
+import pytest
 
 from google.cloud import aiplatform
 
@@ -64,3 +65,11 @@ def test_prediction(self):
         )
         assert raw_prediction_response.status_code == 200
         assert len(json.loads(raw_prediction_response.text)) == 1
+
+    @pytest.mark.asyncio
+    async def test_endpoint_predict_async(self):
+        # Test the Endpoint.predict_async method.
+        prediction_response = await self.endpoint.predict_async(
+            instances=[_PREDICTION_INSTANCE]
+        )
+        assert len(prediction_response.predictions) == 1