[ml] Re-enable samples testing (Azure#31907)

* Re-enable samples testing

* Re-enable samples testing for sub-sections

* Fix authentication/MLClient sample

* Update workspace and compute name

* Fix compute sample

* Add helper files

* Update example for compute operations begin_delete

* Update ml_samples_misc

* Make paths absolute

* Extract hand_resource_exists_error

* Update spark configuration samples

* Update workspace name

sdk/ml/automl.tests.yml

@@ -8,3 +8,5 @@ extends:
       Public:
         SubscriptionConfiguration: $(python-ml-automl-sub-scope)
         Location: eastus
+        MatrixReplace:
+          - TestSamples=.*/true

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

@@ -253,7 +253,7 @@ def begin_delete(self, name: str, *, action: str = "Delete") -> LROPoller[None]:
 
         .. admonition:: Example:
 
-            .. literalinclude:: ../../../../samples/ml_samples_misc.py
+            .. literalinclude:: ../../../../samples/ml_samples_compute.py
                 :start-after: [START compute_operations_delete]
                 :end-before: [END compute_operations_delete]
                 :language: python

sdk/ml/azure-ai-ml/samples/ml_samples_authentication.py

@@ -28,65 +28,70 @@ class MLClientSamples(object):
     def ml_auth_azure_default_credential(self):
         subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]
         resource_group = os.environ["RESOURCE_GROUP_NAME"]
+        workspace_name = "test-ws1"
 
         # [START create_ml_client_default_credential]
-        from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
-
         from azure.ai.ml import MLClient
-
-        ml_client = MLClient(subscription_id, resource_group, credential=DefaultAzureCredential())
-        # [END create_ml_client_default_credential]
-
-        # [START create_ml_client_sovereign_cloud]
         from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
 
-        from azure.ai.ml import MLClient
-
-        kwargs = {"cloud": "AzureChinaCloud"}
         ml_client = MLClient(
-            subscription_id,
-            resource_group,
-            credential=DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_CHINA),
-            **kwargs
+            subscription_id=subscription_id,
+            resource_group_name=resource_group,
+            workspace_name=workspace_name,
+            credential=DefaultAzureCredential(),
         )
-        # [END create_ml_client_sovereign_cloud]
+        # [END create_ml_client_default_credential]
 
         # [START create_ml_client_from_config_default]
         from azure.ai.ml import MLClient
 
-        client = MLClient.from_config(credential=DefaultAzureCredential(), path="src")
+        client = MLClient.from_config(credential=DefaultAzureCredential(), path="./sdk/ml/azure-ai-ml/samples/src")
         # [END create_ml_client_from_config_default]
 
         # [START create_ml_client_from_config_custom_filename]
         from azure.ai.ml import MLClient
 
         client = MLClient.from_config(
-            credential=DefaultAzureCredential(), file_name="team_workspace_configuration.json"
+            credential=DefaultAzureCredential(),
+            file_name="./sdk/ml/azure-ai-ml/samples/team_workspace_configuration.json",
         )
         # [END create_ml_client_from_config_custom_filename]
 
         # [START ml_client_create_or_update]
-        from azure.ai.ml.entities import AmlTokenConfiguration, command
+        from azure.ai.ml import Input, command
+        from azure.ai.ml.constants import AssetTypes
+        from azure.ai.ml.entities import ManagedOnlineEndpoint, UserIdentityConfiguration
 
-        command_job = command(
-            description="description",
-            environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
-            inputs=inputs,
-            code="./tests/test_configs/training/",
-            command="echo ${{inputs.uri}} ${{inputs.data_asset}} ${{inputs.local_data}}",
-            display_name="builder_command_job",
-            compute="testCompute",
-            experiment_name="mfe-test1-dataset",
-            identity=AmlTokenConfiguration(),
+        client = MLClient(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group,
+            workspace_name=workspace_name,
+            credential=DefaultAzureCredential(),
+        )
+        job = command(
+            code="./sdk/ml/azure-ai-ml/samples/src",
+            command="echo hello world",
+            environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:1",
+            compute="cpu-cluster",
+            identity=UserIdentityConfiguration(),
         )
-        created_job = client.create_or_update(command_job)
+
+        client.create_or_update(job)
         # [END ml_client_create_or_update]
 
         # [START ml_client_begin_create_or_update]
+        from random import randint
+
         from azure.ai.ml.entities import ManagedOnlineEndpoint
 
+        client = MLClient(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group,
+            workspace_name=workspace_name,
+            credential=DefaultAzureCredential(),
+        )
         endpoint = ManagedOnlineEndpoint(
-            name="online_endpoint_name",
+            name=f"online-endpoint-name-{randint(1, 1000)}",
             description="this is a sample online endpoint",
             auth_mode="key",
         )
@@ -99,7 +104,7 @@ def ml_auth_azure_default_credential(self):
         from azure.ai.ml.entities import UserIdentityConfiguration
 
         job = command(
-            code="./src",
+            code="./sdk/ml/azure-ai-ml/samples/src",
             command="python read_data.py --input_data ${{inputs.input_data}}",
             inputs={"input_data": Input(type=AssetTypes.MLTABLE, path="./sample_data")},
             environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:1",
@@ -126,9 +131,18 @@ def ml_auth_azure_default_credential(self):
         )
         # [END aml_token_configuration]
 
-        # Get a list of workspaces in a resource group
-        for ws in ml_client.workspaces.list():
-            print(ws.name, ":", ws.location, ":", ws.description)
+        # [START create_ml_client_sovereign_cloud]
+        from azure.ai.ml import MLClient
+        from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
+
+        kwargs = {"cloud": "AzureChinaCloud"}
+        ml_client = MLClient(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group,
+            credential=DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_CHINA),
+            **kwargs,
+        )
+        # [END create_ml_client_sovereign_cloud]
 
 
 if __name__ == "__main__":

sdk/ml/azure-ai-ml/samples/ml_samples_command_configurations.py

@@ -20,14 +20,14 @@
 
 class CommandConfigurationOptions(object):
     def ml_command_config(self):
-        from azure.identity import DefaultAzureCredential
-
         from azure.ai.ml import MLClient
+        from azure.identity import DefaultAzureCredential
 
         subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]
         resource_group = os.environ["RESOURCE_GROUP_NAME"]
+        workspace_name = "test-ws1"
         credential = DefaultAzureCredential()
-        ml_client = MLClient(credential, subscription_id, resource_group, workspace_name="test-ws1")
+        ml_client = MLClient(credential, subscription_id, resource_group, workspace_name=workspace_name)
 
         from azure.ai.ml import Input, Output
         from azure.ai.ml.entities import CommandJob, CommandJobLimits

sdk/ml/azure-ai-ml/samples/ml_samples_component_configurations.py

@@ -40,7 +40,8 @@ def ml_component_config(self):
         from azure.ai.ml import load_component
 
         component = load_component(
-            source="../tests/test_configs/components/helloworld_component.yml", params_override=[{"version": "1.0.2"}]
+            source="./sdk/ml/azure-ai-ml/tests/test_configs/components/helloworld_component.yml",
+            params_override=[{"version": "1.0.2"}],
         )
         registered_component = ml_client.components.create_or_update(component)
         # [END configure_load_component]

sdk/ml/azure-ai-ml/samples/ml_samples_compute.py

@@ -16,20 +16,51 @@
 """
 
 import os
+from random import randint
+
+from azure.ai.ml import MLClient
+from azure.core.exceptions import ResourceExistsError, ResourceNotFoundError
+from azure.identity import DefaultAzureCredential
+
+
+def handle_resource_exists_error(func):
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except (ResourceExistsError, ResourceNotFoundError) as e:
+            pass
+
+    return wrapper
+
+
+subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]
+resource_group = os.environ["RESOURCE_GROUP_NAME"]
+workspace_name = "test-ws1"
+credential = DefaultAzureCredential()
+ml_client = MLClient(credential, subscription_id, resource_group, workspace_name=workspace_name)
+
+compute_name_1 = f"compute-{randint(1, 1000)}"
+compute_name_2 = f"compute-{randint(1, 1000)}"
+ci_name = f"ci-{randint(1, 1000)}"
 
 
 class ComputeConfigurationOptions(object):
-    def ml_compute_config(self):
-        from azure.ai.ml import MLClient
-        from azure.identity import DefaultAzureCredential
+    @handle_resource_exists_error
+    def ml_compute_config_setup_0(self):
+        # [START compute_instance]
+        from azure.ai.ml.entities import ComputeInstance
 
-        subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]
-        resource_group = os.environ["RESOURCE_GROUP_NAME"]
-        credential = DefaultAzureCredential()
-        ml_client = MLClient(credential, subscription_id, resource_group, workspace_name="test-ws1")
+        ci = ComputeInstance(
+            name=ci_name,
+            size="Standard_DS2_v2",
+        )
+        ml_client.compute.begin_create_or_update(ci)
+        # [END compute_instance]
 
+    @handle_resource_exists_error
+    def ml_compute_config_setup_1(self):
         # [START compute_operations_get]
-        cpu_cluster = ml_client.compute.get("cpucluster")
+        cpu_cluster = ml_client.compute.get("cpu-cluster")
         # [END compute_operations_get]
 
         # [START load_compute]
@@ -46,14 +77,16 @@ def ml_compute_config(self):
         # [END compute_operations_list]
 
         # [START compute_operations_list_nodes]
-        node_list = ml_client.compute.list_nodes(name="cpucluster")
+        node_list = ml_client.compute.list_nodes(name="cpu-cluster")
         # [END compute_operations_list_nodes]
 
+    @handle_resource_exists_error
+    def ml_compute_config_setup_2(self):
         # [START compute_operations_create_update]
         from azure.ai.ml.entities import AmlCompute
 
         compute_obj = AmlCompute(
-            name="example-compute",
+            name=compute_name_1,
             tags={"key1": "value1", "key2": "value2"},
             min_instances=0,
             max_instances=10,
@@ -62,9 +95,13 @@ def ml_compute_config(self):
         registered_compute = ml_client.compute.begin_create_or_update(compute_obj)
         # [END compute_operations_create_update]
 
+    @handle_resource_exists_error
+    def ml_compute_config_setup_3(self):
         # [START compute_operations_attach]
+        from azure.ai.ml.entities import AmlCompute
+
         compute_obj = AmlCompute(
-            name="example-compute-2",
+            name=compute_name_2,
             tags={"key1": "value1", "key2": "value2"},
             min_instances=0,
             max_instances=10,
@@ -74,52 +111,24 @@ def ml_compute_config(self):
         # [END compute_operations_attach]
 
         # [START compute_operations_update]
-        compute_obj = ml_client.compute.get("cpucluster")
+        compute_obj = ml_client.compute.get("cpu-cluster")
         compute_obj.idle_time_before_scale_down = 200
         updated_compute = ml_client.compute.begin_update(compute_obj)
         # [END compute_operations_update]
 
-        # [START compute_operations_delete]
-        ml_client.compute.begin_delete("example-compute", action="Detach")
-
-        ml_client.compute.begin_delete("example-compute-2")
-        # [END compute_operations_delete]
-
-        compute_obj = ComputeInstance(
-            name="example-compute",
-            tags={"key1": "value1", "key2": "value2"},
-            min_instances=0,
-            max_instances=10,
-            idle_time_before_scale_down=100,
-        )
-        registered_compute = ml_client.compute.begin_create_or_update(compute_obj)
-
-        # [START compute_operations_start]
-        ml_client.compute.begin_start("example-compute")
-        # [END compute_operations_start]
-
-        # [START compute_operations_stop]
-        ml_client.compute.begin_stop("example-compute")
-        # [END compute_operations_stop]
-
-        # [START compute_operations_restart]
-        ml_client.compute.begin_stop("example-compute")
-        ml_client.compute.begin_restart("example-compute")
-        # [END compute_operations_restart]
-
         # [START compute_operations_list_usage]
-        print(ml_client.compute.list_usage())
+        usage_list = ml_client.compute.list_usage()
         # [END compute_operations_list_usage]
 
         # [START compute_operations_list_sizes]
-        print(ml_client.compute.list_sizes())
+        size_list = ml_client.compute.list_sizes()
         # [END compute_operations_list_sizes]
 
         # [START amlcompute]
         from azure.ai.ml.entities import AmlCompute, IdentityConfiguration, ManagedIdentityConfiguration
 
         aml_compute = AmlCompute(
-            name="my-compute",
+            name="my-aml-compute",
             min_instances=0,
             max_instances=10,
             idle_time_before_scale_down=100,
@@ -158,17 +167,13 @@ def ml_compute_config(self):
         on_behalf_of_config = AssignedUserConfiguration(user_tenant_id="12345", user_object_id="abcdef")
         # [END assigned_user_configuration]
 
-        # [START compute_instance]
-        from azure.ai.ml.entities import ComputeInstance
+        # [START compute_operations_stop]
+        ml_client.compute.begin_stop(ci_name)
+        # [END compute_operations_stop]
 
-        ci = ComputeInstance(
-            name="my-ci-resource",
-            location="eastus",
-            size="Standard_DS2_v2",
-            ssh_public_access_enabled=False,
-            ssh_key_value="ssh-rsa ABCDEFGHIJKLMNOPQRSTUVWXYZ administrator@MININT-2023",
-        )
-        # [END compute_instance]
+        # [START compute_operations_start]
+        ml_client.compute.begin_start(ci_name)
+        # [END compute_operations_start]
 
         # [START vm_ssh_settings]
         from azure.ai.ml.entities import VirtualMachineSshSettings
@@ -261,7 +266,20 @@ def ml_compute_config(self):
         materialization_compute = MaterializationComputeResource(instance_type="standard_e4s_v3")
         # [END materialization_compute_resource]
 
+        # [START compute_operations_restart]
+        ml_client.compute.begin_restart(ci_name)
+        # [END compute_operations_restart]
+
+        # [START compute_operations_delete]
+        ml_client.compute.begin_delete(compute_name_1, action="Detach")
+
+        ml_client.compute.begin_delete(compute_name_2)
+        # [END compute_operations_delete]
+
 
 if __name__ == "__main__":
     sample = ComputeConfigurationOptions()
-    sample.ml_compute_config()
+    sample.ml_compute_config_setup_0()
+    sample.ml_compute_config_setup_1()
+    sample.ml_compute_config_setup_2()
+    sample.ml_compute_config_setup_3()