Merge remote-tracking branch 'origin' into feat/jumpstart-private-model-artifacts

Author: evakravi

CHANGELOG.md

@@ -1,5 +1,28 @@
 # Changelog
 
+## v2.193.0 (2023-10-18)
+
+### Features
+
+ * jumpstart model artifact instance type variants
+ * jumpstart instance specific hyperparameters
+ * Feature Processor event based triggers (#1132)
+ * Support job checkpoint in remote function
+ * jumpstart model package arn instance type variants
+
+### Bug Fixes and Other Changes
+
+ * Fix hyperlinks in feature_processor.scheduler parameter descriptions
+ * add image_uris_unit_test pytest mark
+ * bump apache-airflow to `v2.7.2`
+ * clone distribution in validate_distribution
+ * fix flaky Inference Recommender integration tests
+
+### Documentation Changes
+
+ * Update PipelineModel.register documentation
+ * specify that input_shape in no longer required for torch 2.0 mod…
+
 ## v2.192.1 (2023-10-13)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.192.2.dev0
+2.193.1.dev0

doc/amazon_sagemaker_featurestore.rst

@@ -230,9 +230,11 @@ The following code from the fraud detection example shows a minimal
        enable_online_store=True
    )
 
-Creating a feature group takes time as the data is loaded. You will need
-to wait until it is created before you can use it. You can check status
-using the following method.
+Creating a feature group takes time as the data is loaded. You will
+need to wait until it is created before you can use it. You can
+check status using the following method. Note that it can take
+approximately 10-15 minutes to provision an online ``FeatureGroup``
+with the ``InMemory`` ``StorageType``.
 
 .. code:: python
 
@@ -480,7 +482,9 @@ Feature Store `DatasetBuilder API Reference
 .. rubric:: Delete a feature group
    :name: bCe9CA61b78
 
-You can delete a feature group with the ``delete`` function.
+You can delete a feature group with the ``delete`` function. Note that it
+can take approximately 10-15 minutes to delete an online ``FeatureGroup``
+with the ``InMemory`` ``StorageType``.
 
 .. code:: python
 

requirements/extras/test_requirements.txt

@@ -12,7 +12,7 @@ awslogs==0.14.0
 black==22.3.0
 stopit==1.1.2
 # Update tox.ini to have correct version of airflow constraints file
-apache-airflow==2.7.1
+apache-airflow==2.7.2
 apache-airflow-providers-amazon==7.2.1
 attrs>=23.1.0,<24
 fabric==2.6.0

src/sagemaker/djl_inference/model.py

@@ -781,7 +781,7 @@ def serving_image_uri(self, region_name):
             str: The appropriate image URI based on the given parameters.
         """
         if not self.djl_version:
-            self.djl_version = "0.23.0"
+            self.djl_version = "0.24.0"
 
         return image_uris.retrieve(
             self._framework(),

src/sagemaker/feature_store/feature_processor/__init__.py

@@ -30,8 +30,16 @@
     to_pipeline,
     schedule,
     describe,
+    put_trigger,
+    delete_trigger,
+    enable_trigger,
+    disable_trigger,
     delete_schedule,
     list_pipelines,
     execute,
     TransformationCode,
+    FeatureProcessorPipelineEvents,
+)
+from sagemaker.feature_store.feature_processor._enums import (  # noqa: F401
+    FeatureProcessorPipelineExecutionStatus,
 )

src/sagemaker/feature_store/feature_processor/_constants.py

@@ -17,6 +17,7 @@
 
 DEFAULT_INSTANCE_TYPE = "ml.m5.xlarge"
 DEFAULT_SCHEDULE_STATE = "ENABLED"
+DEFAULT_TRIGGER_STATE = "ENABLED"
 UNDERSCORE = "_"
 RESOURCE_NOT_FOUND_EXCEPTION = "ResourceNotFoundException"
 RESOURCE_NOT_FOUND = "ResourceNotFound"
@@ -36,6 +37,8 @@
 FEATURE_PROCESSOR_TAG_KEY = "sm-fs-fe:created-from"
 FEATURE_PROCESSOR_TAG_VALUE = "fp-to-pipeline"
 FEATURE_GROUP_ARN_REGEX_PATTERN = r"arn:(.*?):sagemaker:(.*?):(.*?):feature-group/(.*?)$"
+PIPELINE_ARN_REGEX_PATTERN = r"arn:(.*?):sagemaker:(.*?):(.*?):pipeline/(.*?)$"
+EVENTBRIDGE_RULE_ARN_REGEX_PATTERN = r"arn:(.*?):events:(.*?):(.*?):rule/(.*?)$"
 SAGEMAKER_WHL_FILE_S3_PATH = "s3://ada-private-beta/sagemaker-2.151.1.dev0-py2.py3-none-any.whl"
 S3_DATA_DISTRIBUTION_TYPE = "FullyReplicated"
 PIPELINE_CONTEXT_NAME_TAG_KEY = "sm-fs-fe:feature-engineering-pipeline-context-name"
@@ -45,3 +48,7 @@
     PIPELINE_CONTEXT_NAME_TAG_KEY,
     PIPELINE_VERSION_CONTEXT_NAME_TAG_KEY,
 ]
+BASE_EVENT_PATTERN = {
+    "source": ["aws.sagemaker"],
+    "detail": {"currentPipelineExecutionStatus": [], "pipelineArn": []},
+}

src/sagemaker/feature_store/feature_processor/_enums.py

@@ -21,3 +21,13 @@ class FeatureProcessorMode(Enum):
 
     PYSPARK = "pyspark"  # Execute a pyspark job.
     PYTHON = "python"  # Execute a regular python script.
+
+
+class FeatureProcessorPipelineExecutionStatus(Enum):
+    """Enum of feature_processor pipeline execution status."""
+
+    EXECUTING = "Executing"
+    STOPPING = "Stopping"
+    STOPPED = "Stopped"
+    FAILED = "Failed"
+    SUCCEEDED = "Succeeded"