Merge branch 'master' into feat/jumpstart-model-id-list

Author: evakravi

CHANGELOG.md

@@ -1,5 +1,24 @@
 # Changelog
 
+## v2.86.0 (2022-04-12)
+
+### Features
+
+ * Adds Spark Processing Notebook to Notebook Tests
+
+## v2.85.0 (2022-04-11)
+
+### Features
+
+ * update lambda code on pipeline create/update/upsert for Lamb…
+ * jumpstart model url
+ * add serverless inference image_uri retrieve support
+
+### Bug Fixes and Other Changes
+
+ * Add back the Fix for Pipeline variables related customer issues
+ * Support file URIs in ProcessingStep's code parameter
+
 ## v2.84.0 (2022-04-07)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.84.1.dev0
+2.86.1.dev0

doc/frameworks/tensorflow/deploying_tensorflow_serving.rst

@@ -272,8 +272,8 @@ More information on how to create ``export_outputs`` can be found in `specifying
 refer to TensorFlow's `Save and Restore <https://www.tensorflow.org/guide/saved_model>`_ documentation for other ways to control the
 inference-time behavior of your SavedModels.
 
-Providing Python scripts for pre/pos-processing
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Providing Python scripts for pre/post-processing
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 You can add your customized Python code to process your input and output data.
 This customized Python code must be named ``inference.py`` and specified through the ``entry_point`` parameter:

src/sagemaker/model.py

@@ -478,7 +478,7 @@ def _script_mode_env_vars(self):
         dir_name = None
         if self.uploaded_code:
             script_name = self.uploaded_code.script_name
-            if self.enable_network_isolation():
+            if self.repacked_model_data or self.enable_network_isolation():
                 dir_name = "/opt/ml/model/code"
             else:
                 dir_name = self.uploaded_code.s3_prefix

tests/scripts/run-notebook-test.sh

@@ -119,6 +119,7 @@ echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
 --region us-west-2 \
 --lifecycle-config-name $LIFECYCLE_CONFIG_NAME \
 --notebook-instance-role-arn $SAGEMAKER_ROLE_ARN \
+./amazon-sagemaker-examples/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.ipynb \
 ./amazon-sagemaker-examples/advanced_functionality/kmeans_bring_your_own_model/kmeans_bring_your_own_model.ipynb \
 ./amazon-sagemaker-examples/advanced_functionality/tensorflow_iris_byom/tensorflow_BYOM_iris.ipynb \
 ./amazon-sagemaker-examples/sagemaker-python-sdk/1P_kmeans_highlevel/kmeans_mnist.ipynb \

tests/unit/sagemaker/model/test_model.py

@@ -665,3 +665,25 @@ def test_all_framework_models_add_jumpstart_base_name(
 
         sagemaker_session.create_model.reset_mock()
         sagemaker_session.endpoint_from_production_variants.reset_mock()
+
+
+@patch("sagemaker.utils.repack_model")
+def test_script_mode_model_uses_proper_sagemaker_submit_dir(repack_model, sagemaker_session):
+
+    source_dir = "s3://blah/blah/blah"
+    t = Model(
+        entry_point=ENTRY_POINT_INFERENCE,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        source_dir=source_dir,
+        image_uri=IMAGE_URI,
+        model_data=MODEL_DATA,
+    )
+    t.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
+
+    assert (
+        sagemaker_session.create_model.call_args_list[0][0][2]["Environment"][
+            "SAGEMAKER_SUBMIT_DIRECTORY"
+        ]
+        == "/opt/ml/model/code"
+    )