fix: kms key does not propapate in register model step

src/sagemaker/estimator.py

@@ -1007,6 +1007,7 @@ def register(
         if compile_model_family is not None:
             model = self._compiled_models[compile_model_family]
         else:
+            kwargs["model_kms_key"] = self.output_kms_key
             model = self.create_model(image_uri=image_uri, **kwargs)
         model.name = model_name
         return model.register(

src/sagemaker/workflow/_utils.py

@@ -60,6 +60,7 @@ def __init__(
         source_dir: str = None,
         dependencies: List = None,
         depends_on: List[str] = None,
+        **kwargs,
     ):
         """Constructs a TrainingStep, given an `EstimatorBase` instance.
 
@@ -98,6 +99,7 @@ def __init__(
                 "inference_script": self._entry_point_basename,
                 "model_archive": self._model_archive,
             },
+            **kwargs,
         )
         repacker.disable_profiler = True
         inputs = TrainingInput(self._model_prefix)

src/sagemaker/workflow/step_collections.py

@@ -105,9 +105,11 @@ def __init__(
         repack_model = False
         if "entry_point" in kwargs:
             repack_model = True
-            entry_point = kwargs["entry_point"]
+            entry_point = kwargs.pop("entry_point", None)
             source_dir = kwargs.get("source_dir")
             dependencies = kwargs.get("dependencies")
+            kwargs = dict(**kwargs, output_kms_key=kwargs.pop("model_kms_key", None))
+
             repack_model_step = _RepackModelStep(
                 name=f"{name}RepackModel",
                 depends_on=depends_on,
@@ -116,6 +118,7 @@ def __init__(
                 entry_point=entry_point,
                 source_dir=source_dir,
                 dependencies=dependencies,
+                **kwargs,
             )
             steps.append(repack_model_step)
             model_data = repack_model_step.properties.ModelArtifacts.S3ModelArtifacts
@@ -124,6 +127,7 @@ def __init__(
         kwargs.pop("entry_point", None)
         kwargs.pop("source_dir", None)
         kwargs.pop("dependencies", None)
+        kwargs.pop("output_kms_key", None)
 
         register_model_step = _RegisterModelStep(
             name=name,

tests/integ/test_workflow.py

@@ -65,6 +65,7 @@
 from sagemaker.workflow.pipeline import Pipeline
 from sagemaker.feature_store.feature_group import FeatureGroup, FeatureDefinition, FeatureTypeEnum
 from tests.integ import DATA_DIR
+from tests.integ.kms_utils import get_or_create_kms_key
 
 
 def ordered(obj):
@@ -849,6 +850,7 @@ def test_model_registration_with_model_repack(
     pipeline_name,
     region_name,
 ):
+    kms_key = get_or_create_kms_key(sagemaker_session, role)
     base_dir = os.path.join(DATA_DIR, "pytorch_mnist")
     entry_point = os.path.join(base_dir, "mnist.py")
     input_path = sagemaker_session.upload_data(
@@ -869,6 +871,7 @@ def test_model_registration_with_model_repack(
         instance_count=instance_count,
         instance_type=instance_type,
         sagemaker_session=sagemaker_session,
+        output_kms_key=kms_key,
     )
     step_train = TrainingStep(
         name="pytorch-train",
@@ -880,12 +883,13 @@ def test_model_registration_with_model_repack(
         name="pytorch-register-model",
         estimator=pytorch_estimator,
         model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
-        content_types=["*"],
-        response_types=["*"],
-        inference_instances=["*"],
-        transform_instances=["*"],
+        content_types=["text/csv"],
+        response_types=["text/csv"],
+        inference_instances=["ml.t2.medium", "ml.m5.large"],
+        transform_instances=["ml.m5.large"],
         description="test-description",
         entry_point=entry_point,
+        model_kms_key=kms_key,
     )
 
     model = Model(