feat: add explanation metadata get_metadata_protobuf for reuse

README.rst

@@ -304,7 +304,7 @@ You can also create a batch prediction job asynchronously by including the `sync
   batch_prediction_job.state
 
   # block until job is complete
-  batch_prediction_job.wait() 
+  batch_prediction_job.wait()
 
 
 Endpoints
@@ -393,7 +393,7 @@ To create a Vertex Pipeline run:
 Explainable AI: Get Metadata
 ----------------------------
 
-To get metadata from TensorFlow 1 models:
+To get metadata in dictionary format from TensorFlow 1 models:
 
 .. code-block:: Python
 
@@ -404,7 +404,7 @@ To get metadata from TensorFlow 1 models:
         )
   generated_md = builder.get_metadata()
 
-To get metadata from TensorFlow 2 models:
+To get metadata in dictionary format from TensorFlow 2 models:
 
 .. code-block:: Python
 
@@ -413,6 +413,20 @@ To get metadata from TensorFlow 2 models:
   builder = saved_model_metadata_builder.SavedModelMetadataBuilder('gs://python/to/my/model/dir')
   generated_md = builder.get_metadata()
 
+To use Explanation Metadata in endpoint deployment and model upload:
+
+.. code-block:: Python
+
+  explanation_metadata = builder.get_metadata_protobuf()
+
+  # To deploy a model to an endpoint with explanation
+  model.deploy(..., explanation_metadata=explanation_metadata)
+
+  # To deploy a model to a created endpoint with explanation
+  endpoint.deploy(..., explanation_metadata=explanation_metadata)
+
+  # To upload a model with explanation
+  aiplatform.Model.upload(..., explanation_metadata=explanation_metadata)
 
 
 Next Steps

google/cloud/aiplatform/explain/metadata/metadata_builder.py

@@ -28,3 +28,7 @@ class MetadataBuilder(_ABC):
     @abc.abstractmethod
     def get_metadata(self):
         """Returns the current metadata as a dictionary."""
+
+    @abc.abstractmethod
+    def get_metadata_protobuf(self):
+        """Returns the current metadata as ExplanationMetadata protobuf"""

google/cloud/aiplatform/explain/metadata/tf/v1/saved_model_metadata_builder.py

@@ -113,10 +113,17 @@ def get_metadata(self) -> Dict[str, Any]:
         Returns:
             Json format of the explanation metadata.
         """
-        current_md = explanation_metadata.ExplanationMetadata(
+        return json_format.MessageToDict(self.get_metadata_protobuf()._pb)
+
+    def get_metadata_protobuf(self) -> explanation_metadata.ExplanationMetadata:
+        """Returns the current metadata as a Protobuf object.
+
+        Returns:
+            ExplanationMetadata object format of the explanation metadata.
+        """
+        return explanation_metadata.ExplanationMetadata(
             inputs=self._inputs, outputs=self._outputs,
         )
-        return json_format.MessageToDict(current_md._pb)
 
 
 def _create_input_metadata_from_signature(

google/cloud/aiplatform/explain/metadata/tf/v2/saved_model_metadata_builder.py

@@ -127,7 +127,14 @@ def get_metadata(self) -> Dict[str, Any]:
         Returns:
             Json format of the explanation metadata.
         """
-        current_md = explanation_metadata.ExplanationMetadata(
+        return json_format.MessageToDict(self.get_metadata_protobuf()._pb)
+
+    def get_metadata_protobuf(self) -> explanation_metadata.ExplanationMetadata:
+        """Returns the current metadata as a Protobuf object.
+
+        Returns:
+            ExplanationMetadata object format of the explanation metadata.
+        """
+        return explanation_metadata.ExplanationMetadata(
             inputs=self._inputs, outputs=self._outputs,
         )
-        return json_format.MessageToDict(current_md._pb)

tests/unit/aiplatform/test_explain_saved_model_metadata_builder_tf1_test.py

@@ -18,6 +18,9 @@
 import tensorflow.compat.v1 as tf
 
 from google.cloud.aiplatform.explain.metadata.tf.v1 import saved_model_metadata_builder
+from google.cloud.aiplatform.compat.types import (
+    explanation_metadata_v1beta1 as explanation_metadata,
+)
 
 
 class SavedModelMetadataBuilderTF1Test(tf.test.TestCase):
@@ -68,6 +71,18 @@ def test_get_metadata_correct_inputs(self):
 
         assert md_builder.get_metadata() == expected_md
 
+    def test_get_metadata_protobuf_correct_inputs(self):
+        self._set_up()
+        md_builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
+            self.model_path, tags=[tf.saved_model.tag_constants.SERVING]
+        )
+        expected_object = explanation_metadata.ExplanationMetadata(
+            inputs={"x": {"input_tensor_name": "inp:0"}},
+            outputs={"y": {"output_tensor_name": "Relu:0"}},
+        )
+
+        assert md_builder.get_metadata_protobuf() == expected_object
+
     def test_get_metadata_double_output(self):
         self._set_up()
         md_builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
@@ -80,3 +95,16 @@ def test_get_metadata_double_output(self):
         }
 
         assert md_builder.get_metadata() == expected_md
+
+    def test_get_metadata_protobuf_double_output(self):
+        self._set_up()
+        md_builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
+            self.model_path, signature_name="double", outputs_to_explain=["lin"]
+        )
+
+        expected_object = explanation_metadata.ExplanationMetadata(
+            inputs={"x": {"input_tensor_name": "inp:0"}},
+            outputs={"lin": {"output_tensor_name": "Add:0"}},
+        )
+
+        assert md_builder.get_metadata_protobuf() == expected_object

tests/unit/aiplatform/test_explain_saved_model_metadata_builder_tf2_test.py

@@ -20,10 +20,13 @@
 import numpy as np
 
 from google.cloud.aiplatform.explain.metadata.tf.v2 import saved_model_metadata_builder
+from google.cloud.aiplatform.compat.types import (
+    explanation_metadata_v1beta1 as explanation_metadata,
+)
 
 
 class SavedModelMetadataBuilderTF2Test(tf.test.TestCase):
-    def test_get_metadata_sequential(self):
+    def _set_up_sequential(self):
         # Set up for the sequential.
         self.seq_model = tf.keras.models.Sequential()
         self.seq_model.add(tf.keras.layers.Dense(32, activation="relu", input_dim=10))
@@ -32,6 +35,9 @@ def test_get_metadata_sequential(self):
         self.saved_model_path = self.get_temp_dir()
         tf.saved_model.save(self.seq_model, self.saved_model_path)
 
+    def test_get_metadata_sequential(self):
+        self._set_up_sequential()
+
         builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
             self.saved_model_path
         )
@@ -42,6 +48,19 @@ def test_get_metadata_sequential(self):
         }
         assert expected_md == generated_md
 
+    def test_get_metadata_protobuf_sequential(self):
+        self._set_up_sequential()
+
+        builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
+            self.saved_model_path
+        )
+        generated_object = builder.get_metadata_protobuf()
+        expected_object = explanation_metadata.ExplanationMetadata(
+            inputs={"dense_input": {"input_tensor_name": "dense_input"}},
+            outputs={"dense_2": {"output_tensor_name": "dense_2"}},
+        )
+        assert expected_object == generated_object
+
     def test_get_metadata_functional(self):
         inputs1 = tf.keras.Input(shape=(10,), name="model_input1")
         inputs2 = tf.keras.Input(shape=(10,), name="model_input2")