refactor: divide pipeline related tests

sdk/ml/azure-ai-ml/azure/ai/ml/_utils/_arm_id_utils.py

@@ -81,6 +81,49 @@ def get_datastore_arm_id(datastore_name: str = None, operation_scope: OperationS
     )
 
 
+class AMLLabelledArmId(object):
+    """Parser for versioned arm id: e.g. /subscription/.../code/my-
+    code/labels/default.
+
+    :param arm_id: The labelled ARM id.
+    :type arm_id: str
+    :raises ~azure.ai.ml.exceptions.ValidationException: Raised if the ARM id is incorrectly formatted.
+    """
+
+    REGEX_PATTERN = (
+        "^/?subscriptions/([^/]+)/resourceGroups/(["
+        "^/]+)/providers/Microsoft.MachineLearningServices/workspaces/([^/]+)/([^/]+)/([^/]+)/labels/(["
+        "^/]+)"
+    )
+
+    def __init__(self, arm_id=None):
+        self.is_registry_id = None
+        if arm_id:
+            match = re.match(AMLLabelledArmId.REGEX_PATTERN, arm_id)
+            if match:
+                self.subscription_id = match.group(1)
+                self.resource_group_name = match.group(2)
+                self.workspace_name = match.group(3)
+                self.asset_type = match.group(4)
+                self.asset_name = match.group(5)
+                self.asset_label = match.group(6)
+            else:
+                match = re.match(REGISTRY_VERSION_PATTERN, arm_id)
+                if match:
+                    self.asset_name = match.group(3)
+                    self.asset_label = match.group(4)
+                    self.is_registry_id = True
+                else:
+                    msg = "Invalid AzureML ARM versioned Id {}"
+                    raise ValidationException(
+                        message=msg.format(arm_id),
+                        no_personal_data_message=msg.format("[arm_id]"),
+                        error_type=ValidationErrorType.INVALID_VALUE,
+                        error_category=ErrorCategory.USER_ERROR,
+                        target=ErrorTarget.ARM_RESOURCE,
+                    )
+
+
 class AMLNamedArmId:
     """Parser for named arm id (no version): e.g.
     /subscription/.../compute/cpu-cluster.

sdk/ml/azure-ai-ml/azure/ai/ml/constants/_common.py

@@ -29,6 +29,7 @@
 NAMED_RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}/{}/{}"
 LEVEL_ONE_NAMED_RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/{}/{}"
 VERSIONED_RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}/{}/{}/versions/{}"
+LABELLED_RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}/{}/{}/labels/{}"
 DATASTORE_RESOURCE_ID = (
     "/subscriptions/{}/resourceGroups/{}/providers/Microsoft.MachineLearningServices/workspaces/{}/datastores/{}"
 )
@@ -37,6 +38,7 @@
 )
 ASSET_ID_FORMAT = "azureml://locations/{}/workspaces/{}/{}/{}/versions/{}"
 VERSIONED_RESOURCE_NAME = "{}:{}"
+LABELLED_RESOURCE_NAME = "{}@{}"
 PYTHON = "python"
 AML_TOKEN_YAML = "aml_token"
 AAD_TOKEN_YAML = "aad_token"
@@ -132,6 +134,8 @@
     "AzureFile": "https://{}.file.{}",
 }
 
+DEFAULT_LABEL_NAME = "default"
+DEFAULT_COMPONENT_VERSION = "azureml_default"
 ANONYMOUS_COMPONENT_NAME = "azureml_anonymous"
 GIT_PATH_PREFIX = "git+"
 SCHEMA_VALIDATION_ERROR_TEMPLATE = (

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

@@ -30,7 +30,8 @@
 from azure.ai.ml._utils._azureml_polling import AzureMLPolling
 from azure.ai.ml._utils._endpoint_utils import polling_wait
 from azure.ai.ml._utils._logger_utils import OpsLogger
-from azure.ai.ml.constants._common import AzureMLResourceType, LROConfigurations
+from azure.ai.ml.constants._common import AzureMLResourceType, LROConfigurations, DEFAULT_LABEL_NAME, \
+    DEFAULT_COMPONENT_VERSION
 from azure.ai.ml.entities import Component, ValidationResult
 from azure.ai.ml.entities._assets import Code
 from azure.ai.ml.exceptions import ComponentException, ErrorCategory, ErrorTarget, ValidationException
@@ -172,17 +173,16 @@ def get(self, name: str, version: Optional[str] = None, label: Optional[str] = N
                 error_category=ErrorCategory.USER_ERROR,
             )
 
+        if not version and not label:
+            label = DEFAULT_LABEL_NAME
+
+        if label == DEFAULT_LABEL_NAME:
+            label = None
+            version = DEFAULT_COMPONENT_VERSION
+
         if label:
             return _resolve_label_to_asset(self, name, label)
 
-        if not version:
-            msg = "Must provide either version or label."
-            raise ValidationException(
-                message=msg,
-                target=ErrorTarget.COMPONENT,
-                no_personal_data_message=msg,
-                error_category=ErrorCategory.USER_ERROR,
-            )
         result = (
             self._version_operation.get(
                 name=name,

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

@@ -15,6 +15,7 @@
 from azure.ai.ml._utils._arm_id_utils import (
     AMLNamedArmId,
     AMLVersionedArmId,
+    AMLLabelledArmId,
     get_arm_id_with_version,
     is_ARM_id_for_resource,
     is_registry_id_for_resource,
@@ -23,7 +24,6 @@
 )
 from azure.ai.ml._utils._asset_utils import _resolve_label_to_asset
 from azure.ai.ml._utils._storage_utils import AzureMLDatastorePathUri
-from azure.ai.ml._utils.utils import is_private_preview_enabled # pylint: disable=unused-import
 from azure.ai.ml.constants._common import (
     ARM_ID_PREFIX,
     AZUREML_RESOURCE_PROVIDER,
@@ -36,7 +36,10 @@
     NAMED_RESOURCE_ID_FORMAT,
     VERSIONED_RESOURCE_ID_FORMAT,
     VERSIONED_RESOURCE_NAME,
+    LABELLED_RESOURCE_NAME,
     AzureMLResourceType,
+    LABELLED_RESOURCE_ID_FORMAT,
+    DEFAULT_LABEL_NAME,
 )
 from azure.ai.ml.entities import Component
 from azure.ai.ml.entities._assets import Code, Data, Environment, Model
@@ -149,6 +152,19 @@ def get_asset_arm_id(
                         "CLI and SDK. Learn more at aka.ms/curatedenv"
                     )
                     return f"azureml:{asset}"
+
+                name, label = parse_name_label(asset)
+                # TODO: remove this condition after label is fully supported for all versioned resources
+                if label == DEFAULT_LABEL_NAME and azureml_type == AzureMLResourceType.COMPONENT:
+                    return LABELLED_RESOURCE_ID_FORMAT.format(
+                        self._operation_scope.subscription_id,
+                        self._operation_scope.resource_group_name,
+                        AZUREML_RESOURCE_PROVIDER,
+                        self._operation_scope.workspace_name,
+                        azureml_type,
+                        name,
+                        label,
+                    )
                 name, version = self._resolve_name_version_from_name_label(asset, azureml_type)
                 if not version:
                     name, version = parse_prefixed_name_version(asset)
@@ -396,6 +412,12 @@ def resolve_azureml_id(self, arm_id: str = None, **kwargs) -> str:
                     return VERSIONED_RESOURCE_NAME.format(arm_id_obj.asset_name, arm_id_obj.asset_version)
             except ValidationException:
                 pass  # fall back to named arm id
+            try:
+                arm_id_obj = AMLLabelledArmId(arm_id)
+                if self._match(arm_id_obj):
+                    return LABELLED_RESOURCE_NAME.format(arm_id_obj.asset_name, arm_id_obj.asset_label)
+            except ValidationException:
+                pass  # fall back to named arm id
             try:
                 arm_id_obj = AMLNamedArmId(arm_id)
                 if self._match(arm_id_obj):

sdk/ml/azure-ai-ml/tests/component/e2etests/test_component.py

@@ -850,3 +850,28 @@ def test_create_pipeline_component_from_job(self, client: MLClient, randstr: Cal
         component = PipelineComponent(name=name, source_job_id=job.id)
         rest_component = client.components.create_or_update(component)
         assert rest_component.name == name
+
+    def test_component_with_default_label(
+        self,
+        client: MLClient,
+        randstr: Callable[[str], str],
+    ) -> None:
+        yaml_path: str = "./tests/test_configs/components/helloworld_component.yml"
+        component_name = randstr("component_name")
+
+        create_component(client, component_name, path=yaml_path)
+
+        target_component = client.components.get(component_name, label="latest")
+
+        for default_component in [
+            client.components.get(component_name),
+            client.components.get(component_name, label="default"),
+        ]:
+            expected_component_dict = target_component._to_dict()
+            default_component_dict = default_component._to_dict()
+            assert pydash.omit(default_component_dict, "id") == pydash.omit(expected_component_dict, "id")
+
+            assert default_component.id.endswith(f"/components/{component_name}/labels/default")
+
+            node = default_component()
+            assert node._to_rest_object()["componentId"] == default_component.id

sdk/ml/azure-ai-ml/tests/component/unittests/test_component_operations.py

@@ -110,6 +110,15 @@ def test_get(self, mock_component_operation: ComponentOperations) -> None:
         assert "version='1'" in create_call_args_str
         mock_component_entity._from_rest_object.assert_called_once()
 
+    def test_get_default(self, mock_component_operation: ComponentOperations) -> None:
+        with patch("azure.ai.ml.operations._component_operations.Component") as mock_component_entity:
+            mock_component_operation.get("mock_component")
+
+        mock_component_operation._version_operation.get.assert_called_once()
+        create_call_args_str = str(mock_component_operation._version_operation.get.call_args)
+        assert "name='mock_component'" in create_call_args_str
+        mock_component_entity._from_rest_object.assert_called_once()
+
     def test_archive_version(self, mock_component_operation: ComponentOperations):
         name = "random_name"
         component = Mock(ComponentVersionData(properties=Mock(ComponentVersionDetails())))

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

@@ -529,7 +529,7 @@ def enable_pipeline_private_preview_features(mocker: MockFixture):
 
 @pytest.fixture()
 def enable_environment_id_arm_expansion(mocker: MockFixture):
-    mocker.patch("azure.ai.ml.operations._operation_orchestrator.is_private_preview_enabled", return_value=False)
+    mocker.patch("azure.ai.ml._utils.utils.is_private_preview_enabled", return_value=False)
 
 
 @pytest.fixture(autouse=True)

sdk/ml/azure-ai-ml/tests/datastore/e2etests/test_datastore.py

@@ -7,8 +7,6 @@
 from azure.ai.ml.entities._datastore._on_prem import HdfsDatastore
 from azure.ai.ml.entities._credentials import NoneCredentialConfiguration
 from azure.ai.ml.entities._datastore.datastore import Datastore
-from azure.core.paging import ItemPaged
-from azure.mgmt.storage import StorageManagementClient
 
 from devtools_testutils import AzureRecordedTestCase, is_live
 

sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline.py

@@ -2410,3 +2410,25 @@ def pipeline_with_group(group: ParamClass):
         }
         assert actual_job["inputs"] == expected_job_inputs
         assert actual_job["jobs"]["microsoft_samples_command_component_basic_inputs"]["inputs"] == expected_node_inputs
+
+    def test_dsl_pipeline_with_default_component(
+        self,
+        client: MLClient,
+        randstr: Callable[[str], str],
+    ) -> None:
+        yaml_path: str = "./tests/test_configs/components/helloworld_component.yml"
+        component_name = randstr("component_name")
+        component: Component = load_component(source=yaml_path, params_override=[{"name": component_name}])
+        client.components.create_or_update(component)
+
+        default_component_func = client.components.get(component_name)
+
+        @dsl.pipeline()
+        def pipeline_with_default_component():
+            node1 = default_component_func(component_in_path=job_input)
+            node1.compute = "cpu-cluster"
+
+        # component from client.components.get
+        pipeline_job = client.jobs.create_or_update(pipeline_with_default_component())
+        created_pipeline_job: PipelineJob = client.jobs.get(pipeline_job.name)
+        assert created_pipeline_job.jobs["node1"].component == f"{component_name}@default"