feature: enable sklearn for network isolation mode

src/sagemaker/model.py

@@ -810,7 +810,10 @@ def _framework_env_vars(self):
         """Placeholder docstring"""
         if self.uploaded_code:
             script_name = self.uploaded_code.script_name
-            dir_name = self.uploaded_code.s3_prefix
+            if self.enable_network_isolation():
+                dir_name = "/opt/ml/model/code"
+            else:
+                dir_name = self.uploaded_code.s3_prefix
         else:
             script_name = self.entry_point
             dir_name = "file://" + self.source_dir

src/sagemaker/sklearn/model.py

@@ -135,10 +135,13 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
             )
 
         deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
-        self._upload_code(deploy_key_prefix)
+        self._upload_code(key_prefix=deploy_key_prefix, repack=self.enable_network_isolation())
         deploy_env = dict(self.env)
         deploy_env.update(self._framework_env_vars())
 
         if self.model_server_workers:
             deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)
-        return sagemaker.container_def(deploy_image, self.model_data, deploy_env)
+        model_data_uri = (
+            self.repacked_model_data if self.enable_network_isolation() else self.model_data
+        )
+        return sagemaker.container_def(deploy_image, model_data_uri, deploy_env)

tests/unit/test_model.py

@@ -154,6 +154,25 @@ def test_prepare_container_def(time, sagemaker_session):
     }
 
 
+@patch("shutil.rmtree", MagicMock())
+@patch("tarfile.open", MagicMock())
+@patch("os.listdir", MagicMock(return_value=["blah.py"]))
+@patch("time.strftime", return_value=TIMESTAMP)
+def test_prepare_container_def_with_network_isolation(time, sagemaker_session):
+    model = DummyFrameworkModel(sagemaker_session, enable_network_isolation=True)
+    assert model.prepare_container_def(INSTANCE_TYPE) == {
+        "Environment": {
+            "SAGEMAKER_PROGRAM": ENTRY_POINT,
+            "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
+            "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
+            "SAGEMAKER_REGION": REGION,
+            "SAGEMAKER_ENABLE_CLOUDWATCH_METRICS": "false",
+        },
+        "Image": MODEL_IMAGE,
+        "ModelDataUrl": MODEL_DATA,
+    }
+
+
 @patch("shutil.rmtree", MagicMock())
 @patch("tarfile.open", MagicMock())
 @patch("os.path.exists", MagicMock(return_value=True))

tests/unit/test_sklearn.py

@@ -23,6 +23,7 @@
 from sagemaker.sklearn import defaults
 from sagemaker.sklearn import SKLearn
 from sagemaker.sklearn import SKLearnPredictor, SKLearnModel
+from sagemaker.fw_utils import UploadedCode
 
 DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data")
 SCRIPT_PATH = os.path.join(DATA_DIR, "dummy_script.py")
@@ -168,6 +169,25 @@ def test_create_model(sagemaker_session):
     assert model_values["Image"] == default_image_uri
 
 
+@patch("sagemaker.model.FrameworkModel._upload_code")
+def test_create_model_with_network_isolation(upload, sagemaker_session):
+    source_dir = "s3://mybucket/source"
+    repacked_model_data = "s3://mybucket/prefix/model.tar.gz"
+
+    sklearn_model = SKLearnModel(
+        model_data=source_dir,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        entry_point=SCRIPT_PATH,
+        enable_network_isolation=True,
+    )
+    sklearn_model.uploaded_code = UploadedCode(s3_prefix=repacked_model_data, script_name="script")
+    sklearn_model.repacked_model_data = repacked_model_data
+    model_values = sklearn_model.prepare_container_def(CPU)
+    assert model_values["Environment"]["SAGEMAKER_SUBMIT_DIRECTORY"] == "/opt/ml/model/code"
+    assert model_values["ModelDataUrl"] == repacked_model_data
+
+
 def test_create_model_from_estimator(sagemaker_session, sklearn_version):
     container_log_level = '"logging.INFO"'
     source_dir = "s3://mybucket/source"