Merge branch 'master' into hf-inf-spec-pr

Author: bryannahm1

CHANGELOG.md

@@ -1,5 +1,19 @@
 # Changelog
 
+## v2.224.3 (2024-07-03)
+
+### Bug Fixes and Other Changes
+
+ * Upgrade local dependencies
+ * Improve docstrings for estimator tags
+
+## v2.224.2 (2024-06-27)
+
+### Bug Fixes and Other Changes
+
+ * Update DJLModel class for latest container releases
+ * list_models() for python3.8
+
 ## v2.224.1 (2024-06-21)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.224.2.dev0
+2.224.4.dev0

requirements/extras/local_requirements.txt

@@ -1,3 +1,3 @@
 urllib3>=1.26.8,<3.0.0
-docker>=5.0.2,<7.0.0
+docker>=5.0.2,<8.0.0
 PyYAML>=5.4.1,<7

src/sagemaker/estimator.py

@@ -274,7 +274,10 @@ def __init__(
                 AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
             tags (Optional[Tags]):
-                Tags for labeling a training job. For more, see
+                Tags for labeling a training job. These won't be propagated to Models,
+                Endpoints during :meth:`~sagemaker.estimator.EstimatorBase.deploy`. The
+                :meth:`~sagemaker.estimator.EstimatorBase.deploy` takes in a seperate
+                tags parameter. For more on tags, see
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
             subnets (list[str] or list[PipelineVariable]): List of subnet ids. If not
                 specified training job will be created without VPC config.

src/sagemaker/serve/builder/model_builder.py

@@ -881,6 +881,9 @@ def _build_for_model_server(self):  # pylint: disable=R0911, R1710
         if self.model_metadata:
             mlflow_path = self.model_metadata.get(MLFLOW_MODEL_PATH)
 
+        if not self.model and not mlflow_path and not self.inference_spec:
+            raise ValueError("Missing required parameter `model` or 'ml_flow' path or inf_spec")
+
         if self.model_server == ModelServer.TORCHSERVE:
             return self._build_for_torchserve()
 

tests/unit/sagemaker/serve/builder/test_model_builder.py

@@ -147,7 +147,7 @@ def test_model_server_override_djl_without_model_or_mlflow(self, mock_serve_sett
         )
         self.assertRaisesRegex(
             Exception,
-            "Missing required parameter `model` or 'ml_flow' path",
+            "Missing required parameter `model` or 'ml_flow' path or inf_spec",
             builder.build,
             Mode.SAGEMAKER_ENDPOINT,
             mock_role_arn,
@@ -168,12 +168,26 @@ def test_model_server_override_torchserve_with_model(
 
         mock_build_for_ts.assert_called_once()
 
+    @patch("sagemaker.serve.builder.model_builder._ServeSettings")
+    @patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_torchserve")
+    def test_model_server_override_torchserve_with_inf_spec(
+        self, mock_build_for_ts, mock_serve_settings
+    ):
+        mock_setting_object = mock_serve_settings.return_value
+        mock_setting_object.role_arn = mock_role_arn
+        mock_setting_object.s3_model_data_url = mock_s3_model_data_url
+
+        builder = ModelBuilder(model_server=ModelServer.TORCHSERVE, inference_spec="some value")
+        builder.build(sagemaker_session=mock_session)
+
+        mock_build_for_ts.assert_called_once()
+
     @patch("sagemaker.serve.builder.model_builder._ServeSettings")
     def test_model_server_override_torchserve_without_model_or_mlflow(self, mock_serve_settings):
         builder = ModelBuilder(model_server=ModelServer.TORCHSERVE)
         self.assertRaisesRegex(
             Exception,
-            "Missing required parameter `model` or 'ml_flow' path",
+            "Missing required parameter `model` or 'ml_flow' path or inf_spec",
             builder.build,
             Mode.SAGEMAKER_ENDPOINT,
             mock_role_arn,