chore: update Vertex Model artifact resolution to use versioned model…

…s and reduce allow expeirment run name length (#1468)

google/cloud/aiplatform/metadata/artifact.py

@@ -535,14 +535,18 @@ def create_vertex_resource_artifact(cls, resource: Union[models.Model]) -> Artif
         """
         cls.validate_resource_supports_metadata(resource)
         resource.wait()
+
         metadata_type = cls._resource_to_artifact_type[type(resource)]
         uri = rest_utils.make_gcp_resource_rest_url(resource=resource)
 
         return Artifact.create(
             schema_title=metadata_type,
             display_name=getattr(resource.gca_resource, "display_name", None),
             uri=uri,
-            metadata={"resourceName": resource.resource_name},
+            # Note that support for non-versioned resources requires
+            # change to reference `resource_name` please update if
+            # supporting resource other than Model
+            metadata={"resourceName": resource.versioned_resource_name},
             project=resource.project,
             location=resource.location,
             credentials=resource.credentials,

google/cloud/aiplatform/metadata/constants.py

@@ -65,3 +65,6 @@
     schema_version="0.0.1",
     metadata={_VERTEX_EXPERIMENT_TRACKING_LABEL: True},
 )
+
+_TB_RUN_ARTIFACT_POST_FIX_ID = "-tb-run"
+_EXPERIMENT_RUN_MAX_LENGTH = 128 - len(_TB_RUN_ARTIFACT_POST_FIX_ID)

google/cloud/aiplatform/metadata/experiment_run_resource.py

@@ -607,9 +607,9 @@ def _validate_run_id(run_id: str):
             ValueError if run id is too long.
         """
 
-        if len(run_id) > 128:
+        if len(run_id) > constants._EXPERIMENT_RUN_MAX_LENGTH:
             raise ValueError(
-                f"Length of Experiment ID and Run ID cannot be greater than 128. "
+                f"Length of Experiment ID and Run ID cannot be greater than {constants._EXPERIMENT_RUN_MAX_LENGTH}. "
                 f"{run_id} is of length {len(run_id)}"
             )
 
@@ -822,7 +822,7 @@ def _tensorboard_run_id(run_id: str) -> str:
         Returns:
             Resource id for the associated tensorboard run artifact.
         """
-        return f"{run_id}-tb-run"
+        return f"{run_id}{constants._TB_RUN_ARTIFACT_POST_FIX_ID}"
 
     @_v1_not_supported
     def assign_backing_tensorboard(

google/cloud/aiplatform/utils/rest_utils.py

@@ -25,7 +25,10 @@ def make_gcp_resource_rest_url(resource: base.VertexAiResourceNoun) -> str:
     Returns:
         The formatted url of resource.
     """
-    resource_name = resource.resource_name
+    try:
+        resource_name = resource.versioned_resource_name
+    except AttributeError:
+        resource_name = resource.resource_name
     version = resource.api_client._default_version
     api_uri = resource.api_client.api_endpoint
 

tests/unit/aiplatform/test_metadata.py

@@ -993,7 +993,6 @@ def test_init_experiment_wrong_schema(self):
             )
 
     @pytest.mark.usefixtures("get_metadata_store_mock")
-    @pytest.mark.usefixtures()
     def test_start_run(
         self,
         get_experiment_mock,
@@ -1025,6 +1024,25 @@ def test_start_run(
             context=_EXPERIMENT_MOCK.name, child_contexts=[_EXPERIMENT_RUN_MOCK.name]
         )
 
+    @pytest.mark.usefixtures("get_metadata_store_mock", "get_experiment_mock")
+    def test_start_run_fails_when_run_name_too_long(self):
+
+        aiplatform.init(
+            project=_TEST_PROJECT,
+            location=_TEST_LOCATION,
+            experiment=_TEST_EXPERIMENT,
+        )
+
+        run_name_too_long = "".join(
+            "a"
+            for _ in range(
+                constants._EXPERIMENT_RUN_MAX_LENGTH + 2 - len(_TEST_EXPERIMENT)
+            )
+        )
+
+        with pytest.raises(ValueError):
+            aiplatform.start_run(run_name_too_long)
+
     @pytest.mark.usefixtures(
         "get_metadata_store_mock",
         "get_experiment_mock",

tests/unit/aiplatform/test_metadata_resources.py

@@ -28,6 +28,7 @@
 from google.cloud.aiplatform.metadata import artifact
 from google.cloud.aiplatform.metadata import context
 from google.cloud.aiplatform.metadata import execution
+from google.cloud.aiplatform.metadata import utils as metadata_utils
 from google.cloud.aiplatform_v1 import (
     MetadataServiceClient,
     AddExecutionEventsResponse,
@@ -39,6 +40,8 @@
     AddContextArtifactsAndExecutionsResponse,
 )
 
+import test_models
+
 # project
 _TEST_PROJECT = "test-project"
 _TEST_LOCATION = "us-central1"
@@ -543,6 +546,34 @@ def test_add_executions_only(self, add_context_artifacts_and_executions_mock):
         )
 
 
+get_model_with_version_mock = test_models.get_model_with_version
+_VERTEX_MODEL_ARTIFACT_URI = f"https://{_TEST_LOCATION}-aiplatform.googleapis.com/v1/{test_models._TEST_MODEL_OBJ_WITH_VERSION.name}"
+
+
+@pytest.fixture
+def list_vertex_model_artifact_mock():
+    with patch.object(MetadataServiceClient, "list_artifacts") as list_artifacts_mock:
+        list_artifacts_mock.return_value = [
+            GapicArtifact(
+                name=_TEST_ARTIFACT_NAME,
+                uri=_VERTEX_MODEL_ARTIFACT_URI,
+                display_name=_TEST_DISPLAY_NAME,
+                schema_title=_TEST_SCHEMA_TITLE,
+                schema_version=_TEST_SCHEMA_VERSION,
+                description=_TEST_DESCRIPTION,
+                metadata=_TEST_METADATA,
+            )
+        ]
+        yield list_artifacts_mock
+
+
+@pytest.fixture
+def list_artifact_empty_mock():
+    with patch.object(MetadataServiceClient, "list_artifacts") as list_artifacts_mock:
+        list_artifacts_mock.return_value = []
+        yield list_artifacts_mock
+
+
 class TestExecution:
     def setup_method(self):
         reload(initializer)
@@ -680,6 +711,86 @@ def test_add_artifact(self, add_execution_events_mock):
             events=[Event(artifact=_TEST_ARTIFACT_NAME, type_=Event.Type.OUTPUT)],
         )
 
+    @pytest.mark.usefixtures("get_execution_mock", "get_model_with_version_mock")
+    def test_add_vertex_model(
+        self, add_execution_events_mock, list_vertex_model_artifact_mock
+    ):
+        aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
+
+        my_execution = execution.Execution.get_or_create(
+            resource_id=_TEST_EXECUTION_ID,
+            schema_title=_TEST_SCHEMA_TITLE,
+            display_name=_TEST_DISPLAY_NAME,
+            schema_version=_TEST_SCHEMA_VERSION,
+            description=_TEST_DESCRIPTION,
+            metadata=_TEST_METADATA,
+            metadata_store_id=_TEST_METADATA_STORE,
+        )
+
+        my_model = aiplatform.Model(test_models._TEST_MODEL_NAME)
+        my_execution.assign_output_artifacts(artifacts=[my_model])
+
+        list_vertex_model_artifact_mock.assert_called_once_with(
+            request=dict(
+                parent="projects/test-project/locations/us-central1/metadataStores/default",
+                filter=metadata_utils._make_filter_string(
+                    schema_title="google.VertexModel", uri=_VERTEX_MODEL_ARTIFACT_URI
+                ),
+            )
+        )
+
+        add_execution_events_mock.assert_called_once_with(
+            execution=_TEST_EXECUTION_NAME,
+            events=[Event(artifact=_TEST_ARTIFACT_NAME, type_=Event.Type.OUTPUT)],
+        )
+
+    @pytest.mark.usefixtures("get_execution_mock", "get_model_with_version_mock")
+    def test_add_vertex_model_not_resolved(
+        self, add_execution_events_mock, list_artifact_empty_mock, create_artifact_mock
+    ):
+        aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
+
+        my_execution = execution.Execution.get_or_create(
+            resource_id=_TEST_EXECUTION_ID,
+            schema_title=_TEST_SCHEMA_TITLE,
+            display_name=_TEST_DISPLAY_NAME,
+            schema_version=_TEST_SCHEMA_VERSION,
+            description=_TEST_DESCRIPTION,
+            metadata=_TEST_METADATA,
+            metadata_store_id=_TEST_METADATA_STORE,
+        )
+
+        my_model = aiplatform.Model(test_models._TEST_MODEL_NAME)
+        my_execution.assign_output_artifacts(artifacts=[my_model])
+
+        list_artifact_empty_mock.assert_called_once_with(
+            request=dict(
+                parent="projects/test-project/locations/us-central1/metadataStores/default",
+                filter=metadata_utils._make_filter_string(
+                    schema_title="google.VertexModel", uri=_VERTEX_MODEL_ARTIFACT_URI
+                ),
+            )
+        )
+
+        expected_artifact = GapicArtifact(
+            schema_title="google.VertexModel",
+            display_name=test_models._TEST_MODEL_OBJ_WITH_VERSION.display_name,
+            uri=_VERTEX_MODEL_ARTIFACT_URI,
+            metadata={"resourceName": test_models._TEST_MODEL_OBJ_WITH_VERSION.name},
+            state=GapicArtifact.State.LIVE,
+        )
+
+        create_artifact_mock.assert_called_once_with(
+            parent="projects/test-project/locations/us-central1/metadataStores/default",
+            artifact=expected_artifact,
+            artifact_id=None,
+        )
+
+        add_execution_events_mock.assert_called_once_with(
+            execution=_TEST_EXECUTION_NAME,
+            events=[Event(artifact=_TEST_ARTIFACT_NAME, type_=Event.Type.OUTPUT)],
+        )
+
     @pytest.mark.usefixtures("get_execution_mock")
     def test_query_input_and_output_artifacts(
         self, query_execution_inputs_and_outputs_mock