Merge remote-tracking branch 'origin/master' into test-tf-2-16-inf

Author: shantanutrip

CHANGELOG.md

@@ -1,5 +1,36 @@
 # Changelog
 
+## v2.229.0 (2024-08-15)
+
+### Features
+
+ * Support for ModelBuilder In_Process Mode (1/2)
+ * Pulling in dependencies (in_process mode) using conda environment
+ * Add optional CodeArtifact login to FrameworkProcessing job script
+ * implemented security-monitoring to send metrics to CW #1510
+
+### Bug Fixes and Other Changes
+
+ * alt configs model deployment and training issues
+ * fix keras extension in integ test
+ * update image_uri_configs  08-13-2024 07:17:54 PST
+ * trn1 instance family does not support volume size
+ * Update model.py
+ * removed log statement
+ * update image_uri_configs  08-09-2024 07:18:00 PST
+ * Added torchrun compatibility for distributet training across multiple GPUs in a single node (single instance)
+ * BiasConfig type hint
+ * add model monitor image accounts for ap-southeast-5 and eu-central-2
+ * aligned UTC times with PST
+ * ensure hpt jobs inherit tags from config
+ * add JumpStart PDT and OSU regions
+ * chore(deps): bump certifi in /src/sagemaker/serve/utils
+ * Updates for DJL 0.29.0 release
+ * chore(deps): bump apache-airflow from 2.9.2 to 2.9.3 in /requirements/extras
+ * chore(deps): bump torch from 2.0.1 to 2.2.0 in /tests/data/serve_resources/mlflow/pytorch
+ * avoided printing stack trace and escaped input
+ * removing kwargs as this is breaking predictor_cls param for mode…
+
 ## v2.228.0 (2024-08-06)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.228.1.dev0
+2.229.1.dev0

src/sagemaker/jumpstart/artifacts/metric_definitions.py

@@ -96,16 +96,17 @@ def _retrieve_default_training_metric_definitions(
         else []
     )
 
-    instance_specific_metric_name: str
-    for instance_specific_metric_definition in instance_specific_metric_definitions:
-        instance_specific_metric_name = instance_specific_metric_definition["Name"]
-        default_metric_definitions = list(
-            filter(
-                lambda metric_definition: metric_definition["Name"]
-                != instance_specific_metric_name,
-                default_metric_definitions,
+    if instance_specific_metric_definitions:
+        instance_specific_metric_name: str
+        for instance_specific_metric_definition in instance_specific_metric_definitions:
+            instance_specific_metric_name = instance_specific_metric_definition["Name"]
+            default_metric_definitions = list(
+                filter(
+                    lambda metric_definition: metric_definition["Name"]
+                    != instance_specific_metric_name,
+                    default_metric_definitions,
+                )
             )
-        )
-        default_metric_definitions.append(instance_specific_metric_definition)
+            default_metric_definitions.append(instance_specific_metric_definition)
 
     return default_metric_definitions

src/sagemaker/jumpstart/factory/model.py

@@ -259,6 +259,7 @@ def _add_instance_type_to_kwargs(
         sagemaker_session=kwargs.sagemaker_session,
         model_type=kwargs.model_type,
         config_name=kwargs.config_name,
+        hub_arn=kwargs.hub_arn,
     )
 
     if specs.inference_configs and kwargs.config_name not in specs.inference_configs.configs:
@@ -780,6 +781,7 @@ def _add_config_name_to_deploy_kwargs(
             sagemaker_session=temp_session,
             model_type=kwargs.model_type,
             config_name=kwargs.config_name,
+            hub_arn=kwargs.hub_arn,
         )
         default_config_name = _select_inference_config_from_training_config(
             specs=specs, training_config_name=training_config_name

src/sagemaker/jumpstart/hub/parser_utils.py

@@ -10,16 +10,20 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
+# pylint: skip-file
 """This module contains utilities related to SageMaker JumpStart Hub."""
 from __future__ import absolute_import
 
 import re
-from typing import Any, Dict
+from typing import Any, Dict, List, Optional
 
 
 def camel_to_snake(camel_case_string: str) -> str:
     """Converts camelCaseString or UpperCamelCaseString to snake_case_string."""
     snake_case_string = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", camel_case_string)
+    if "-" in snake_case_string:
+        # remove any hyphen from the string for accurate conversion.
+        snake_case_string = snake_case_string.replace("-", "")
     return re.sub("([a-z0-9])([A-Z])", r"\1_\2", snake_case_string).lower()
 
 
@@ -29,20 +33,29 @@ def snake_to_upper_camel(snake_case_string: str) -> str:
     return upper_camel_case_string
 
 
-def walk_and_apply_json(json_obj: Dict[Any, Any], apply) -> Dict[Any, Any]:
-    """Recursively walks a json object and applies a given function to the keys."""
+def walk_and_apply_json(
+    json_obj: Dict[Any, Any], apply, stop_keys: Optional[List[str]] = ["metrics"]
+) -> Dict[Any, Any]:
+    """Recursively walks a json object and applies a given function to the keys.
+
+    stop_keys (Optional[list[str]]): List of field keys that should stop the application function.
+        Any children of these keys will not have the application function applied to them.
+    """
 
     def _walk_and_apply_json(json_obj, new):
         if isinstance(json_obj, dict) and isinstance(new, dict):
             for key, value in json_obj.items():
                 new_key = apply(key)
-                if isinstance(value, dict):
-                    new[new_key] = {}
-                    _walk_and_apply_json(value, new=new[new_key])
-                elif isinstance(value, list):
-                    new[new_key] = []
-                    for item in value:
-                        _walk_and_apply_json(item, new=new[new_key])
+                if (stop_keys and new_key not in stop_keys) or stop_keys is None:
+                    if isinstance(value, dict):
+                        new[new_key] = {}
+                        _walk_and_apply_json(value, new=new[new_key])
+                    elif isinstance(value, list):
+                        new[new_key] = []
+                        for item in value:
+                            _walk_and_apply_json(item, new=new[new_key])
+                    else:
+                        new[new_key] = value
                 else:
                     new[new_key] = value
         elif isinstance(json_obj, dict) and isinstance(new, list):
@@ -51,6 +64,8 @@ def _walk_and_apply_json(json_obj, new):
             new.update(json_obj)
         elif isinstance(json_obj, list) and isinstance(new, list):
             new.append(json_obj)
+        elif isinstance(json_obj, str) and isinstance(new, list):
+            new.append(json_obj)
         return new
 
     return _walk_and_apply_json(json_obj, new={})

src/sagemaker/jumpstart/types.py

@@ -1174,7 +1174,7 @@ def __init__(self, spec: Optional[Dict[str, Any]], is_hub_content=False):
             spec (Dict[str, Any]): Dictionary representation of training config ranking.
         """
         if is_hub_content:
-            spec = {camel_to_snake(key): val for key, val in spec.items()}
+            spec = walk_and_apply_json(spec, camel_to_snake)
         self.from_json(spec)
 
     def from_json(self, json_obj: Dict[str, Any]) -> None:
@@ -1400,7 +1400,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 
         if self.training_supported:
             if self._is_hub_content:
-                self.training_ecr_uri: Optional[str] = json_obj["training_ecr_uri"]
+                self.training_ecr_uri: Optional[str] = json_obj.get("training_ecr_uri")
                 self._non_serializable_slots.append("training_ecr_specs")
             else:
                 self.training_ecr_specs: Optional[JumpStartECRSpecs] = (

src/sagemaker/jumpstart/utils.py

@@ -33,6 +33,7 @@
 
 from sagemaker.jumpstart import constants, enums
 from sagemaker.jumpstart import accessors
+from sagemaker.jumpstart.hub.parser_utils import camel_to_snake, snake_to_upper_camel
 from sagemaker.s3 import parse_s3_url
 from sagemaker.jumpstart.exceptions import (
     DeprecatedJumpStartModelError,
@@ -1103,6 +1104,17 @@ def get_jumpstart_configs(
             metadata_configs.config_rankings.get("overall").rankings if metadata_configs else []
         )
 
+    if hub_arn:
+        return (
+            {
+                config_name: metadata_configs.configs[
+                    camel_to_snake(snake_to_upper_camel(config_name))
+                ]
+                for config_name in config_names
+            }
+            if metadata_configs
+            else {}
+        )
     return (
         {config_name: metadata_configs.configs[config_name] for config_name in config_names}
         if metadata_configs

tests/integ/test_tf.py

@@ -85,6 +85,11 @@ def test_mnist_with_checkpoint_config(
     tensorflow_training_latest_version,
     tensorflow_training_latest_py_version,
 ):
+    if Version(tensorflow_training_latest_version) >= Version("2.16"):
+        pytest.skip(
+            "This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
+            "https://github.com/tensorflow/io/issues/2039"
+        )
     checkpoint_s3_uri = "s3://{}/checkpoints/tf-{}".format(
         sagemaker_session.default_bucket(), sagemaker_timestamp()
     )
@@ -235,6 +240,11 @@ def test_mnist_distributed_cpu(
     tensorflow_training_latest_version,
     tensorflow_training_latest_py_version,
 ):
+    if Version(tensorflow_training_latest_version) >= Version("2.16"):
+        pytest.skip(
+            "This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
+            "https://github.com/tensorflow/io/issues/2039"
+        )
     _create_and_fit_estimator(
         sagemaker_session,
         tensorflow_training_latest_version,

tests/integ/test_tuner.py

@@ -19,6 +19,7 @@
 import numpy as np
 import pytest
 from botocore.exceptions import ClientError
+from packaging.version import Version
 
 import tests.integ
 from sagemaker import KMeans, LDA, RandomCutForest, image_uris
@@ -691,6 +692,11 @@ def test_tuning_tf(
     tensorflow_training_latest_version,
     tensorflow_training_latest_py_version,
 ):
+    if Version(tensorflow_training_latest_version) >= Version("2.16"):
+        pytest.skip(
+            "This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
+            "https://github.com/tensorflow/io/issues/2039"
+        )
     resource_path = os.path.join(DATA_DIR, "tensorflow_mnist")
     script_path = "mnist.py"
 
@@ -735,6 +741,11 @@ def test_tuning_tf_vpc_multi(
     tensorflow_training_latest_py_version,
 ):
     """Test Tensorflow multi-instance using the same VpcConfig for training and inference"""
+    if Version(tensorflow_training_latest_version) >= Version("2.16"):
+        pytest.skip(
+            "This test is failing in TensorFlow 2.16 beacuse of an upstream bug: "
+            "https://github.com/tensorflow/io/issues/2039"
+        )
     instance_type = cpu_instance_type
     instance_count = 2
 

tests/unit/sagemaker/jumpstart/hub/test_utils.py

@@ -15,7 +15,7 @@
 from unittest.mock import patch, Mock
 from sagemaker.jumpstart.types import HubArnExtractedInfo
 from sagemaker.jumpstart.constants import JUMPSTART_DEFAULT_REGION_NAME
-from sagemaker.jumpstart.hub import utils
+from sagemaker.jumpstart.hub import parser_utils, utils
 
 
 def test_get_info_from_hub_resource_arn():
@@ -254,3 +254,52 @@ def test_get_hub_model_version_wildcard_char(mock_session):
     )
 
     assert result == "2.0.0"
+
+
+def test_walk_and_apply_json():
+    test_json = {
+        "CamelCaseKey": "value",
+        "CamelCaseObjectKey": {
+            "CamelCaseObjectChildOne": "value1",
+            "CamelCaseObjectChildTwo": "value2",
+        },
+        "IgnoreMyChildren": {"ShouldNotBeTouchedOne": "const1", "ShouldNotBeTouchedTwo": "const2"},
+        "ShouldNotIgnoreMyChildren": {"NopeNope": "no"},
+    }
+
+    result = parser_utils.walk_and_apply_json(
+        test_json, parser_utils.camel_to_snake, ["ignore_my_children"]
+    )
+    assert result == {
+        "camel_case_key": "value",
+        "camel_case_object_key": {
+            "camel_case_object_child_one": "value1",
+            "camel_case_object_child_two": "value2",
+        },
+        "ignore_my_children": {
+            "ShouldNotBeTouchedOne": "const1",
+            "ShouldNotBeTouchedTwo": "const2",
+        },
+        "should_not_ignore_my_children": {"nope_nope": "no"},
+    }
+
+
+def test_walk_and_apply_json_no_stop():
+    test_json = {
+        "CamelCaseKey": "value",
+        "CamelCaseObjectKey": {
+            "CamelCaseObjectChildOne": "value1",
+            "CamelCaseObjectChildTwo": "value2",
+        },
+        "CamelCaseObjectListKey": {"instance.ml.type.xlarge": [{"ShouldChangeMe": "string"}]},
+    }
+
+    result = parser_utils.walk_and_apply_json(test_json, parser_utils.camel_to_snake)
+    assert result == {
+        "camel_case_key": "value",
+        "camel_case_object_key": {
+            "camel_case_object_child_one": "value1",
+            "camel_case_object_child_two": "value2",
+        },
+        "camel_case_object_list_key": {"instance.ml.type.xlarge": [{"should_change_me": "string"}]},
+    }