update apache airflow and update test requirements

CHANGELOG.md

@@ -1,5 +1,16 @@
 # Changelog
 
+## v2.155.0 (2023-05-15)
+
+### Features
+
+ * Add support for SageMaker Serverless inference Provisioned Concurrency feature
+
+### Bug Fixes and Other Changes
+
+ * Revert "fix: make RemoteExecutor context manager non-blocking on pend…
+ * Add BOM to no No P2 Availability region list
+
 ## v2.154.0 (2023-05-11)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.154.1.dev0
+2.155.1.dev0

requirements/extras/test_requirements.txt

@@ -12,9 +12,9 @@ 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==2.5.1
+apache-airflow==2.6.0
 apache-airflow-providers-amazon==7.2.1
-attrs==22.1.0
+attrs>=23.1.0,<24
 fabric==2.6.0
 requests==2.27.1
 sagemaker-experiments==0.1.35
@@ -23,3 +23,7 @@ pyvis==0.2.1
 pandas>=1.3.5,<1.5
 scikit-learn==1.0.2
 cloudpickle==2.2.1
+scipy==1.7.3
+urllib3==1.26.8
+docker>=5.0.2,<7.0.0
+PyYAML==6.0

setup.py

@@ -47,7 +47,7 @@ def read_requirements(filename):
 
 # Declare minimal set for installation
 required_packages = [
-    "attrs>=20.3.0,<23",
+    "attrs>=23.1.0,<24",
     "boto3>=1.26.131,<2.0",
     "cloudpickle==2.2.1",
     "google-pasta",
@@ -60,7 +60,7 @@ def read_requirements(filename):
     "pandas",
     "pathos",
     "schema",
-    "PyYAML==5.4.1",
+    "PyYAML==6.0",
     "jsonschema",
     "platformdirs",
     "tblib==1.7.0",
@@ -75,7 +75,7 @@ def read_requirements(filename):
 # Meta dependency groups
 extras["all"] = [item for group in extras.values() for item in group]
 # Tests specific dependencies (do not need to be included in 'all')
-extras["test"] = (extras["all"] + read_requirements("requirements/extras/test_requirements.txt"),)
+extras["test"] = (read_requirements("requirements/extras/test_requirements.txt"),)
 
 setup(
     name="sagemaker",

src/sagemaker/djl_inference/model.py

@@ -854,11 +854,13 @@ def generate_serving_properties(self, serving_properties=None) -> Dict[str, str]
         if self.low_cpu_mem_usage:
             serving_properties["option.low_cpu_mem_usage"] = self.low_cpu_mem_usage
         # This is a workaround due to a bug in our built in handler for huggingface
-        # TODO: This needs to be fixed when new dlc is published
+        # TODO: Remove this logic whenever 0.20.0 image is out of service
         if (
             serving_properties["option.entryPoint"] == "djl_python.huggingface"
             and self.dtype
             and self.dtype != "auto"
+            and self.djl_version
+            and int(self.djl_version.split(".")[1]) < 21
         ):
             serving_properties["option.dtype"] = "auto"
             serving_properties.pop("option.load_in_8bit", None)

src/sagemaker/experiments/run.py

@@ -633,7 +633,10 @@ def _extract_run_name_from_tc_name(trial_component_name: str, experiment_name: s
         Returns:
             str: The name of the Run object supplied by a user.
         """
-        return trial_component_name.replace("{}{}".format(experiment_name, DELIMITER), "", 1)
+        # TODO: we should revert the lower casting once backend fix reaches prod
+        return trial_component_name.replace(
+            "{}{}".format(experiment_name.lower(), DELIMITER), "", 1
+        )
 
     @staticmethod
     def _append_run_tc_label_to_tags(tags: Optional[List[Dict[str, str]]] = None) -> list:
@@ -869,6 +872,8 @@ def list_runs(
     Returns:
         list: A list of ``Run`` objects.
     """
+
+    # all trial components retrieved by default
     tc_summaries = _TrialComponent.list(
         experiment_name=experiment_name,
         created_before=created_before,

src/sagemaker/inference_recommender/inference_recommender_mixin.py

@@ -145,10 +145,10 @@ def right_size(
         )
 
         if endpoint_configurations or traffic_pattern or stopping_conditions or resource_limit:
-            LOGGER.info("Advance Job parameters were specified. Running Advanced job...")
+            LOGGER.info("Advanced Job parameters were specified. Running Advanced job...")
             job_type = "Advanced"
         else:
-            LOGGER.info("Advance Job parameters were not specified. Running Default job...")
+            LOGGER.info("Advanced Job parameters were not specified. Running Default job...")
             job_type = "Default"
 
         self._init_sagemaker_session_if_does_not_exist()

src/sagemaker/remote_function/client.py

@@ -301,6 +301,7 @@ def wrapper(*args, **kwargs):
                             s3_uri=s3_path_join(
                                 job_settings.s3_root_uri, job.job_name, EXCEPTION_FOLDER
                             ),
+                            hmac_key=job.hmac_key,
                         )
                     except ServiceError as serr:
                         chained_e = serr.__cause__
@@ -337,6 +338,7 @@ def wrapper(*args, **kwargs):
                 return serialization.deserialize_obj_from_s3(
                     sagemaker_session=job_settings.sagemaker_session,
                     s3_uri=s3_path_join(job_settings.s3_root_uri, job.job_name, RESULTS_FOLDER),
+                    hmac_key=job.hmac_key,
                 )
 
             if job.describe()["TrainingJobStatus"] == "Stopped":
@@ -745,7 +747,7 @@ def map(self, func, *iterables):
         futures = map(self.submit, itertools.repeat(func), *iterables)
         return [future.result() for future in futures]
 
-    def shutdown(self, wait=True):
+    def shutdown(self):
         """Prevent more function executions to be submitted to this executor."""
         with self._state_condition:
             self._shutdown = True
@@ -756,15 +758,15 @@ def shutdown(self, wait=True):
             self._state_condition.notify_all()
 
         if self._workers is not None:
-            self._workers.shutdown(wait)
+            self._workers.shutdown(wait=True)
 
     def __enter__(self):
         """Create an executor instance and return it"""
         return self
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         """Make sure the executor instance is shutdown."""
-        self.shutdown(wait=False)
+        self.shutdown()
         return False
 
     @staticmethod
@@ -861,6 +863,7 @@ def from_describe_response(describe_training_job_response, sagemaker_session):
                 job_return = serialization.deserialize_obj_from_s3(
                     sagemaker_session=sagemaker_session,
                     s3_uri=s3_path_join(job.s3_uri, RESULTS_FOLDER),
+                    hmac_key=job.hmac_key,
                 )
             except DeserializationError as e:
                 client_exception = e
@@ -872,6 +875,7 @@ def from_describe_response(describe_training_job_response, sagemaker_session):
                 job_exception = serialization.deserialize_exception_from_s3(
                     sagemaker_session=sagemaker_session,
                     s3_uri=s3_path_join(job.s3_uri, EXCEPTION_FOLDER),
+                    hmac_key=job.hmac_key,
                 )
             except ServiceError as serr:
                 chained_e = serr.__cause__
@@ -961,6 +965,7 @@ def result(self, timeout: float = None) -> Any:
                     self._return = serialization.deserialize_obj_from_s3(
                         sagemaker_session=self._job.sagemaker_session,
                         s3_uri=s3_path_join(self._job.s3_uri, RESULTS_FOLDER),
+                        hmac_key=self._job.hmac_key,
                     )
                     self._state = _FINISHED
                     return self._return
@@ -969,6 +974,7 @@ def result(self, timeout: float = None) -> Any:
                         self._exception = serialization.deserialize_exception_from_s3(
                             sagemaker_session=self._job.sagemaker_session,
                             s3_uri=s3_path_join(self._job.s3_uri, EXCEPTION_FOLDER),
+                            hmac_key=self._job.hmac_key,
                         )
                     except ServiceError as serr:
                         chained_e = serr.__cause__