fix: Make test_processor_with_role_as_pipeline_parameter more concrete (#3618)

Author: qidewenwhen

src/sagemaker/processing.py

@@ -1361,7 +1361,7 @@ def __init__(
         self,
         estimator_cls: type,
         framework_version: str,
-        role: str,
+        role: Union[str, PipelineVariable],
         instance_count: Union[int, PipelineVariable],
         instance_type: Union[str, PipelineVariable],
         py_version: str = "py3",
@@ -1389,8 +1389,9 @@ def __init__(
                 estimator
             framework_version (str): The version of the framework. Value is ignored when
                 ``image_uri`` is provided.
-            role (str): An AWS IAM role name or ARN. Amazon SageMaker Processing uses
-                this role to access AWS resources, such as data stored in Amazon S3.
+            role (str or PipelineVariable): An AWS IAM role name or ARN. Amazon SageMaker
+                Processing uses this role to access AWS resources, such as data stored
+                in Amazon S3.
             instance_count (int or PipelineVariable): The number of instances to run a
                 processing job with.
             instance_type (str or PipelineVariable): The type of EC2 instance to use for

tests/integ/sagemaker/workflow/test_processing_steps.py

@@ -385,8 +385,10 @@ def test_multi_step_framework_processing_pipeline_same_source_dir(
 
     SOURCE_DIR = "/pipeline/test_source_dir"
 
+    role_param = ParameterString(name="Role", default_value=role)
+
     framework_processor_tf = FrameworkProcessor(
-        role=role,
+        role=role_param,
         instance_type="ml.m5.xlarge",
         instance_count=1,
         estimator_cls=TensorFlow,
@@ -400,7 +402,7 @@ def test_multi_step_framework_processing_pipeline_same_source_dir(
         instance_type="ml.m5.xlarge",
         instance_count=1,
         base_job_name="my-job",
-        role=role,
+        role=role_param,
         estimator_cls=SKLearn,
         sagemaker_session=pipeline_session,
     )
@@ -431,7 +433,10 @@ def test_multi_step_framework_processing_pipeline_same_source_dir(
     )
 
     pipeline = Pipeline(
-        name=pipeline_name, steps=[step_1, step_2], sagemaker_session=pipeline_session
+        name=pipeline_name,
+        steps=[step_1, step_2],
+        sagemaker_session=pipeline_session,
+        parameters=[role_param],
     )
     try:
         pipeline.create(role)

tests/unit/sagemaker/workflow/test_processing_step.py

@@ -1064,3 +1064,54 @@ def test_spark_processor_local_code(spark_processor, processing_input, pipeline_
     step_def = json.loads(pipeline.definition())["Steps"][0]
     step_def2 = json.loads(pipeline.definition())["Steps"][0]
     assert step_def == step_def2
+
+
+_PARAM_ROLE_NAME = "Role"
+
+
+@pytest.mark.parametrize(
+    "processor_args",
+    [
+        (
+            ScriptProcessor(
+                role=ParameterString(name=_PARAM_ROLE_NAME, default_value=ROLE),
+                image_uri=IMAGE_URI,
+                instance_count=1,
+                instance_type="ml.m4.xlarge",
+                command=["python3"],
+            ),
+            {"code": DUMMY_S3_SCRIPT_PATH},
+        ),
+        (
+            Processor(
+                role=ParameterString(name=_PARAM_ROLE_NAME, default_value=ROLE),
+                image_uri=IMAGE_URI,
+                instance_count=1,
+                instance_type="ml.m4.xlarge",
+            ),
+            {},
+        ),
+    ],
+)
+@patch("os.path.exists", return_value=True)
+@patch("os.path.isfile", return_value=True)
+def test_processor_with_role_as_pipeline_parameter(
+    exists_mock, isfile_mock, processor_args, pipeline_session
+):
+    processor, run_inputs = processor_args
+    processor.sagemaker_session = pipeline_session
+    processor.run(**run_inputs)
+
+    step_args = processor.run(**run_inputs)
+    step = ProcessingStep(
+        name="MyProcessingStep",
+        step_args=step_args,
+    )
+    pipeline = Pipeline(
+        name="MyPipeline",
+        steps=[step],
+        sagemaker_session=pipeline_session,
+    )
+
+    step_def = json.loads(pipeline.definition())["Steps"][0]
+    assert step_def["Arguments"]["RoleArn"] == {"Get": f"Parameters.{_PARAM_ROLE_NAME}"}

tests/unit/test_processing.py

@@ -34,7 +34,6 @@
 from sagemaker.sklearn.processing import SKLearnProcessor
 from sagemaker.pytorch.processing import PyTorchProcessor
 from sagemaker.tensorflow.processing import TensorFlowProcessor
-from sagemaker.workflow import ParameterString
 from sagemaker.xgboost.processing import XGBoostProcessor
 from sagemaker.mxnet.processing import MXNetProcessor
 from sagemaker.network import NetworkConfig
@@ -738,62 +737,6 @@ def test_processor_with_required_parameters(sagemaker_session):
     sagemaker_session.process.assert_called_with(**expected_args)
 
 
-def test_processor_with_role_as_pipeline_parameter(sagemaker_session):
-
-    role = ParameterString(name="Role", default_value=ROLE)
-
-    processor = Processor(
-        role=role,
-        image_uri=CUSTOM_IMAGE_URI,
-        instance_count=1,
-        instance_type="ml.m4.xlarge",
-        sagemaker_session=sagemaker_session,
-    )
-
-    processor.run()
-
-    expected_args = _get_expected_args(processor._current_job_name)
-    assert expected_args["role_arn"] == role.default_value
-
-
-@patch("os.path.exists", return_value=True)
-@patch("os.path.isfile", return_value=True)
-def test_script_processor_with_role_as_pipeline_parameter(
-    exists_mock, isfile_mock, sagemaker_session
-):
-    role = ParameterString(name="Role", default_value=ROLE)
-
-    script_processor = ScriptProcessor(
-        role=role,
-        image_uri=CUSTOM_IMAGE_URI,
-        instance_count=1,
-        instance_type="ml.m4.xlarge",
-        sagemaker_session=sagemaker_session,
-        command=["python3"],
-    )
-
-    run_args = script_processor.get_run_args(
-        code="/local/path/to/processing_code.py",
-        inputs=_get_data_inputs_all_parameters(),
-        outputs=_get_data_outputs_all_parameters(),
-        arguments=["--drop-columns", "'SelfEmployed'"],
-    )
-
-    script_processor.run(
-        code=run_args.code,
-        inputs=run_args.inputs,
-        outputs=run_args.outputs,
-        arguments=run_args.arguments,
-        wait=True,
-        logs=False,
-        job_name="my_job_name",
-        experiment_config={"ExperimentName": "AnExperiment"},
-    )
-
-    expected_args = _get_expected_args(script_processor._current_job_name)
-    assert expected_args["role_arn"] == role.default_value
-
-
 def test_processor_with_missing_network_config_parameters(sagemaker_session):
     processor = Processor(
         role=ROLE,