Merge branch 'master' into processing-job-codeartifact-support

Author: akuma12

CHANGELOG.md

@@ -1,5 +1,29 @@
 # Changelog
 
+## v2.216.0 (2024-04-17)
+
+### Features
+
+ * optimum 0.0.21
+ * Add TF 2.14 Graviton Inference support
+ * JumpStart alternative config parsing
+ * TGI 1.4.5
+
+### Bug Fixes and Other Changes
+
+ * chore(deps): bump black from 22.3.0 to 24.3.0 in /requirements/extras
+ * Add back serialization for automatic speech recognition
+ * bump apache-airflow version to 2.8.4
+ * remove trailing slash when uploading to S3 with dataset_builder.to_csv_file
+ * Update Collaborator Check workflow to check for users which are part of collaborator team
+ * forward network_isolation parameter to Estimators when False
+ * Flaky slow test
+ * Revert "Test SM PySDK Variations"
+
+### Documentation Changes
+
+ * Add supported task types to schema builder omission
+
 ## v2.215.0 (2024-04-12)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.215.1.dev0
+2.216.1.dev0

requirements/extras/test_requirements.txt

@@ -9,10 +9,10 @@ coverage>=5.2, <6.2
 mock==4.0.3
 contextlib2==21.6.0
 awslogs==0.14.0
-black==22.3.0
+black==24.3.0
 stopit==1.1.2
 # Update tox.ini to have correct version of airflow constraints file
-apache-airflow==2.8.3
+apache-airflow==2.8.4
 apache-airflow-providers-amazon==7.2.1
 attrs>=23.1.0,<24
 fabric==2.6.0

src/sagemaker/amazon/record_pb2.py

@@ -930,9 +930,9 @@ def _load_config(cls, inputs, auto_ml, expand_role=True, validate_uri=True):
 
         auto_ml_model_deploy_config = {}
         if auto_ml.auto_generate_endpoint_name is not None:
-            auto_ml_model_deploy_config[
-                "AutoGenerateEndpointName"
-            ] = auto_ml.auto_generate_endpoint_name
+            auto_ml_model_deploy_config["AutoGenerateEndpointName"] = (
+                auto_ml.auto_generate_endpoint_name
+            )
         if not auto_ml.auto_generate_endpoint_name and auto_ml.endpoint_name is not None:
             auto_ml_model_deploy_config["EndpointName"] = auto_ml.endpoint_name
 
@@ -1034,9 +1034,9 @@ def _prepare_auto_ml_stop_condition(
         if max_candidates is not None:
             stopping_condition["MaxCandidates"] = max_candidates
         if max_runtime_per_training_job_in_seconds is not None:
-            stopping_condition[
-                "MaxRuntimePerTrainingJobInSeconds"
-            ] = max_runtime_per_training_job_in_seconds
+            stopping_condition["MaxRuntimePerTrainingJobInSeconds"] = (
+                max_runtime_per_training_job_in_seconds
+            )
         if total_job_runtime_in_seconds is not None:
             stopping_condition["MaxAutoMLJobRuntimeInSeconds"] = total_job_runtime_in_seconds
 

src/sagemaker/automl/automl.py

@@ -1446,9 +1446,9 @@ def _load_config(cls, inputs, auto_ml, expand_role=True):
 
         auto_ml_model_deploy_config = {}
         if auto_ml.auto_generate_endpoint_name is not None:
-            auto_ml_model_deploy_config[
-                "AutoGenerateEndpointName"
-            ] = auto_ml.auto_generate_endpoint_name
+            auto_ml_model_deploy_config["AutoGenerateEndpointName"] = (
+                auto_ml.auto_generate_endpoint_name
+            )
         if not auto_ml.auto_generate_endpoint_name and auto_ml.endpoint_name is not None:
             auto_ml_model_deploy_config["EndpointName"] = auto_ml.endpoint_name
 

src/sagemaker/automl/automlv2.py

@@ -397,6 +397,8 @@ def serialize(self, data):
                 raise ValueError(f"Could not open/read file: {data}. {e}")
         if isinstance(data, bytes):
             return data
+        if isinstance(data, dict) and "data" in data:
+            return self.serialize(data["data"])
 
         raise ValueError(f"Object of type {type(data)} is not Data serializable.")
 

src/sagemaker/base_serializers.py

@@ -377,9 +377,11 @@ def _convert_group_resource_response(
                     {
                         "Name": collection_name,
                         "Arn": collection_arn,
-                        "Type": resource_group["Identifier"]["ResourceType"]
-                        if is_model_group
-                        else "Collection",
+                        "Type": (
+                            resource_group["Identifier"]["ResourceType"]
+                            if is_model_group
+                            else "Collection"
+                        ),
                     }
                 )
         return collection_details

src/sagemaker/collection.py

@@ -162,19 +162,19 @@ def _to_request_dict(self):
         profiler_config_request["DisableProfiler"] = self.disable_profiler
 
         if self.system_monitor_interval_millis is not None:
-            profiler_config_request[
-                "ProfilingIntervalInMilliseconds"
-            ] = self.system_monitor_interval_millis
+            profiler_config_request["ProfilingIntervalInMilliseconds"] = (
+                self.system_monitor_interval_millis
+            )
 
         if self.framework_profile_params is not None:
-            profiler_config_request[
-                "ProfilingParameters"
-            ] = self.framework_profile_params.profiling_parameters
+            profiler_config_request["ProfilingParameters"] = (
+                self.framework_profile_params.profiling_parameters
+            )
 
         if self.profile_params is not None:
-            profiler_config_request[
-                "ProfilingParameters"
-            ] = self.profile_params.profiling_parameters
+            profiler_config_request["ProfilingParameters"] = (
+                self.profile_params.profiling_parameters
+            )
 
         return profiler_config_request
 

src/sagemaker/debugger/profiler_config.py

@@ -213,9 +213,7 @@ def _create_estimator(
     vpc_config: Optional[
         Dict[
             str,
-            List[
-                str,
-            ],
+            List[str],
         ]
     ] = None,
     volume_kms_key=None,
@@ -820,9 +818,9 @@ def _get_container_env(self):
             logger.warning("Ignoring invalid container log level: %s", self.container_log_level)
             return self.env
 
-        self.env[
-            "SERVING_OPTS"
-        ] = f'"-Dai.djl.logging.level={_LOG_LEVEL_MAP[self.container_log_level]}"'
+        self.env["SERVING_OPTS"] = (
+            f'"-Dai.djl.logging.level={_LOG_LEVEL_MAP[self.container_log_level]}"'
+        )
         return self.env