Merge branch 'aws:master' into master

Author: rohangujarathi

CHANGELOG.md

@@ -1,5 +1,25 @@
 # Changelog
 
+## v2.50.0 (2021-07-28)
+
+### Features
+
+ * add KIX region to image_uris
+
+### Bug Fixes and Other Changes
+
+ * Rename `PredictorBase.delete_endpoint` as `PredictorBase.delete_predictor`
+ * incorrect default argument for callback output parameter
+
+### Documentation Changes
+
+ * Remove years from copyright boilerplate
+ * Fix documentation formatting for PySpark and SparkJar processors
+
+### Testing and Release Infrastructure
+
+ * enable py38 tox env
+
 ## v2.49.2 (2021-07-21)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.49.3.dev0
+2.50.1.dev0

src/sagemaker/clarify.py

@@ -305,10 +305,11 @@ def __init__(
         """Initializes config for SHAP.
 
         Args:
-            baseline (str or list): A list of rows (at least one) or S3 object URI to be used as
-                the baseline dataset in the Kernel SHAP algorithm. The format should be the same
-                as the dataset format. Each row should contain only the feature columns/values
-                and omit the label column/values.
+            baseline (None or str or list): None or S3 object Uri or A list of rows (at least one)
+                to be used asthe baseline dataset in the Kernel SHAP algorithm. The format should
+                be the same as the dataset format. Each row should contain only the feature
+                columns/values and omit the label column/values. If None a baseline will be
+                calculated automatically by using K-means or K-prototypes in the input dataset.
             num_samples (int): Number of samples to be used in the Kernel SHAP algorithm.
                 This number determines the size of the generated synthetic dataset to compute the
                 SHAP values.

src/sagemaker/estimator.py

@@ -2319,9 +2319,13 @@ def _model_source_dir(self):
             str: Either a local or an S3 path pointing to the ``source_dir`` to be
                 used for code by the model to be deployed
         """
-        return (
-            self.source_dir if self.sagemaker_session.local_mode else self.uploaded_code.s3_prefix
-        )
+        if self.sagemaker_session.local_mode:
+            return self.source_dir
+
+        if self.uploaded_code is not None:
+            return self.uploaded_code.s3_prefix
+
+        return None
 
     def _model_entry_point(self):
         """Get the appropriate value to pass as ``entry_point`` to a model constructor.
@@ -2333,7 +2337,10 @@ def _model_entry_point(self):
         if self.sagemaker_session.local_mode or (self._model_source_dir() is None):
             return self.entry_point
 
-        return self.uploaded_code.script_name
+        if self.uploaded_code is not None:
+            return self.uploaded_code.script_name
+
+        return None
 
     def hyperparameters(self):
         """Return the hyperparameters as a dictionary to use for training.

src/sagemaker/predictor.py

@@ -62,7 +62,7 @@ def predict(self, *args, **kwargs) -> Any:
         """Perform inference on the provided data and return a prediction."""
 
     @abc.abstractmethod
-    def delete_endpoint(self, *args, **kwargs) -> None:
+    def delete_predictor(self, *args, **kwargs) -> None:
         """Destroy resources associated with this predictor."""
 
     @property
@@ -330,6 +330,8 @@ def delete_endpoint(self, delete_endpoint_config=True):
 
         self.sagemaker_session.delete_endpoint(self.endpoint_name)
 
+    delete_predictor = delete_endpoint
+
     def delete_model(self):
         """Deletes the Amazon SageMaker models backing this predictor."""
         request_failed = False

src/sagemaker/serverless/predictor.py

@@ -61,7 +61,7 @@ def predict(self, data: dict) -> dict:
             response["ResponseMetadata"]["HTTPHeaders"]["content-type"],
         )
 
-    def delete_endpoint(self) -> None:
+    def delete_predictor(self) -> None:
         """Destroy the Lambda function specified in the constructor."""
         self._client.delete_function(FunctionName=self._function_name)
 

src/sagemaker/workflow/_utils.py

@@ -312,6 +312,9 @@ def arguments(self) -> RequestType:
                     model = self.estimator.create_model(**self.kwargs)
                     self.image_uri = model.image_uri
 
+                if self.model_data is None:
+                    self.model_data = model.model_data
+
                 # reset placeholder
                 self.estimator.output_path = output_path
 

src/sagemaker/workflow/step_collections.py

@@ -146,7 +146,7 @@ def __init__(
                 else:
                     sagemaker_session = model_entity.sagemaker_session
                     role = model_entity.role
-                if hasattr(model_entity, "entry_point"):
+                if hasattr(model_entity, "entry_point") and model_entity.entry_point is not None:
                     repack_model = True
                     entry_point = model_entity.entry_point
                     source_dir = model_entity.source_dir
@@ -169,6 +169,7 @@ def __init__(
                     model_entity.model_data = (
                         repack_model_step.properties.ModelArtifacts.S3ModelArtifacts
                     )
+
                     # remove kwargs consumed by model repacking step
                     kwargs.pop("output_kms_key", None)
 

tests/unit/sagemaker/serverless/test_predictor.py

@@ -48,7 +48,7 @@ def test_predict(mock_client):
 def test_delete_endpoint(mock_client):
     predictor = LambdaPredictor(FUNCTION_NAME, client=mock_client)
 
-    predictor.delete_endpoint()
+    predictor.delete_predictor()
 
     mock_client.delete_function.assert_called_once()
     _, kwargs = mock_client.delete_function.call_args