fix deployment creation when registry model is used (Azure#40577)

* fix deployment using model from registry

* update recordings

* update changelog

* fix code formatting

* fix deployment using model from registry

Author: pdhotems

sdk/ml/azure-ai-ml/CHANGELOG.md

@@ -4,6 +4,7 @@
 ### Bugs Fixed
  - Fix for compute Instance, disableLocalAuth property should be depend on ssh public access enabled.
  - Removing Git-related properties from job properties if a PAT token is detected in the repository URL.
+ - Fix deployment creation for registry models
 
  ### Other Changes
   - Hub and Project are officially GA'd and no longer experimental.

sdk/ml/azure-ai-ml/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ml/azure-ai-ml",
-  "Tag": "python/ml/azure-ai-ml_9b9dd0f330"
+  "Tag": "python/ml/azure-ai-ml_53a7a4a178"
 }

sdk/ml/azure-ai-ml/azure/ai/ml/operations/_operation_orchestrator.py

@@ -332,7 +332,8 @@ def _get_environment_arm_id(self, environment: Environment, register_asset: bool
 
     def _get_model_arm_id(self, model: Model, register_asset: bool = True) -> Union[str, Model]:
         try:
-            self._validate_datastore_name(model.path)
+            if not is_registry_id_for_resource(model.id):
+                self._validate_datastore_name(model.path)
 
             if register_asset:
                 if model.id:

sdk/ml/azure-ai-ml/tests/conftest.py

@@ -473,6 +473,18 @@ def data_asset_registry_client(e2e_ws_scope: OperationScope, auth: ClientSecretC
     )
 
 
+@pytest.fixture
+def sdkv2_registry_client(e2e_ws_scope: OperationScope, auth: ClientSecretCredential) -> MLClient:
+    """return a machine learning client using default e2e testing workspace"""
+    return MLClient(
+        credential=auth,
+        subscription_id=e2e_ws_scope.subscription_id,
+        resource_group_name=e2e_ws_scope.resource_group_name,
+        logging_enable=getenv(E2E_TEST_LOGGING_ENABLED),
+        registry_name="sdkv2-testFeed",
+    )
+
+
 @pytest.fixture
 def only_registry_client(e2e_ws_scope: OperationScope, auth: ClientSecretCredential) -> MLClient:
     """return a machine learning client using default e2e testing workspace"""

sdk/ml/azure-ai-ml/tests/online_services/e2etests/test_online_deployment.py

@@ -2,8 +2,8 @@
 
 import pytest
 from devtools_testutils import AzureRecordedTestCase
-
-from azure.ai.ml import MLClient, load_online_deployment, load_online_endpoint
+from pathlib import Path
+from azure.ai.ml import MLClient, load_online_deployment, load_online_endpoint, load_model
 from azure.ai.ml.entities import ManagedOnlineDeployment, ManagedOnlineEndpoint, Model, CodeConfiguration, Environment
 
 
@@ -50,6 +50,54 @@ def test_online_deployment_create(
         finally:
             client.online_endpoints.begin_delete(name=endpoint.name)
 
+    def test_online_deployment_create_when_registry_assets(
+        self,
+        sdkv2_registry_client: MLClient,
+        client: MLClient,
+        randstr: Callable[[], str],
+        rand_online_name: Callable[[], str],
+        rand_online_deployment_name: Callable[[], str],
+    ) -> None:
+        # create a model in registry
+        model_name = randstr("test-registry-model")
+        model = Model(name=model_name, path="./tests/test_configs/deployments/model-1/model")
+        model = sdkv2_registry_client.models.create_or_update(model)
+        assert model.name == model_name
+
+        # create a endpoint
+        endpoint_yaml = "tests/test_configs/deployments/online/simple_online_endpoint_mir.yaml"
+        endpoint = load_online_endpoint(endpoint_yaml)
+        endpoint_name = rand_online_name("endpoint_name")
+        endpoint.name = endpoint_name
+        endpoint = client.online_endpoints.begin_create_or_update(endpoint).result()
+        assert endpoint.name == endpoint_name
+
+        # create a deployment
+        deployment_yaml = "tests/test_configs/deployments/online/online_deployment_1.yaml"
+        deployment = load_online_deployment(deployment_yaml)
+        deployment.endpoint_name = endpoint.name
+        deployment.name = rand_online_deployment_name("deployment_name")
+        deployment.model = model
+
+        try:
+            client.online_deployments.begin_create_or_update(deployment).result()
+            dep = client.online_deployments.get(name=deployment.name, endpoint_name=endpoint.name)
+            assert dep.name == deployment.name
+
+            deps = client.online_deployments.list(endpoint_name=endpoint.name)
+            assert len(list(deps)) > 0
+
+            endpoint.traffic = {deployment.name: 100}
+            client.online_endpoints.begin_create_or_update(endpoint).result()
+            endpoint_updated = client.online_endpoints.get(endpoint.name)
+            assert endpoint_updated.traffic[deployment.name] == 100
+            client.online_endpoints.invoke(
+                endpoint_name=endpoint.name,
+                request_file="tests/test_configs/deployments/model-1/sample-request.json",
+            )
+        finally:
+            client.online_endpoints.begin_delete(name=endpoint.name)
+
     def test_online_deployment_update(
         self, client: MLClient, rand_online_name: Callable[[], str], rand_online_deployment_name: Callable[[], str]
     ) -> None: