Merge branch 'master' into feat/public-pipeline-execution

Author: Rishabh0255

.gitignore

@@ -40,3 +40,4 @@ env/
 sagemaker_train/src/**/container_drivers/sm_train.sh
 sagemaker_train/src/**/container_drivers/sourcecode.json
 sagemaker_train/src/**/container_drivers/distributed.json
+.kiro

CHANGELOG.md

@@ -1,4 +1,31 @@
 # Changelog
+## v3.7.1 (2026-03-31)
+
+### Features
+- **Telemetry**: Added telemetry emitter to `ScriptProcessor` and `FrameworkProcessor`, enabling SDK usage tracking for processing jobs via the telemetry attribution module (new `PROCESSING` feature enum added to telemetry constants)
+
+### Fixes
+- **ModelBuilder**: Fixed `accept_eula` handling in ModelBuilder's LoRA deployment path — previously hardcoded to `True`, now respects the user-provided value and raises a `ValueError` if not explicitly set to `True`
+- **Evaluate**: Fixed Lambda handler name derivation in the Evaluator — hardcoded the handler to `lambda_function.lambda_handler` instead of deriving it from the source filename, which caused invocation failures when the source file had a non-default name
+
+## v3.7.0 (2026-03-25)
+
+### Fixes
+- **ModelBuilder**: Sync Nova hosting configs with AGISageMakerInference (#5664)
+- **Evaluate**: Remove GPT OSS model evaluation restriction (#5658)
+
+### Features
+- **AWS Batch**: Add support for Quota Management job submission and job priority update (#5659)
+- **AWS Batch**: Extend list_jobs_by_share for quota_share_name (#5669)
+- **Evaluate**: Support IAM role for BaseEvaluator (#5671)
+- **Telemetry**: Add telemetry attribution module for SDK usage provenance (#5661)
+- **MLflow**: Metrics visualization, enhanced wait UI, and eval job links (#5662)
+
+### Chores
+- Updated SDK to use latest LMIv22 image for v3.x (#5640)
+- Migration guide update (#5655)
+- AWS Batch integ test resources are now uniquely named by test run (#5666)
+
 ## v3.6.0 (2026-03-19)
 
 ### Fixes

VERSION

@@ -1 +1 @@
-3.6.0
+3.7.1

pyproject.toml

@@ -33,10 +33,10 @@ classifiers = [
   "Programming Language :: Python :: 3.12",
 ]
 dependencies = [
-  "sagemaker-core>=2.6.0,<3.0.0",
-  "sagemaker-train>=1.6.0,<2.0.0",
-  "sagemaker-serve>=1.6.0,<2.0.0",
-  "sagemaker-mlops>=1.6.0,<2.0.0",
+  "sagemaker-core>=2.7.1,<3.0.0",
+  "sagemaker-train>=1.7.1,<2.0.0",
+  "sagemaker-serve>=1.7.1,<2.0.0",
+  "sagemaker-mlops>=1.7.1,<2.0.0",
 ]
 
 [project.optional-dependencies]

sagemaker-core/CHANGELOG.md

@@ -1,4 +1,23 @@
 # Changelog
+## v2.7.1 (2026-03-31)
+
+### Features
+
+- **Telemetry**: Added telemetry emitter to `ScriptProcessor` and `FrameworkProcessor`, enabling SDK usage tracking for processing jobs via the telemetry attribution module (new `PROCESSING` feature enum added to telemetry constants)
+
+### Bug Fixes
+
+- **ModelBuilder**: Fixed `accept_eula` handling in ModelBuilder's LoRA deployment path — previously hardcoded to `True`, now respects the user-provided value and raises a `ValueError` if not explicitly set to `True`
+- **Evaluate**: Fixed Lambda handler name derivation in the Evaluator — hardcoded the handler to `lambda_function.lambda_handler` instead of deriving it from the source filename, which caused invocation failures when the source file had a non-default name
+
+## v2.7.0 (2026-03-25)
+
+### Bug fixes and Other Changes
+
+- **Enhancement**: Add telemetry attribution module for SDK usage provenance (#5661)
+- **Enhancement**: Updated SDK to use latest LMIv22 image for v3.x (#5640)
+- **Enhancement**: Resources codegen update for eval job links (#5662)
+
 ## v2.6.0 (2026-03-19)
 
 ### Bug fixes and Other Changes

sagemaker-core/VERSION

@@ -1 +1 @@
-2.6.0
+2.7.1

sagemaker-core/src/sagemaker/core/__init__.py

@@ -15,5 +15,8 @@
 # Partner App
 from sagemaker.core.partner_app.auth_provider import PartnerAppAuthProvider  # noqa: F401
 
+# Attribution
+from sagemaker.core.telemetry.attribution import Attribution, set_attribution  # noqa: F401
+
 # Note: HyperparameterTuner and WarmStartTypes are in sagemaker.train.tuner
 # They are not re-exported from core to avoid circular dependencies

sagemaker-core/src/sagemaker/core/processing.py

@@ -77,6 +77,8 @@
 from sagemaker.core.workflow.execution_variables import ExecutionVariables
 from sagemaker.core.workflow.functions import Join
 from sagemaker.core.workflow.pipeline_context import runnable_by_pipeline
+from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter
+from sagemaker.core.telemetry.constants import Feature
 
 from sagemaker.core._studio import _append_project_tags
 from sagemaker.core.config.config_utils import _append_sagemaker_config_tags
@@ -771,6 +773,7 @@ def __init__(
             network_config=network_config,
         )
 
+    @_telemetry_emitter(feature=Feature.PROCESSING, func_name="ScriptProcessor.run")
     @runnable_by_pipeline
     def run(
         self,
@@ -1171,6 +1174,7 @@ def _package_code(
             os.unlink(tmp.name)
             return s3_uri
 
+    @_telemetry_emitter(feature=Feature.PROCESSING, func_name="FrameworkProcessor.run")
     @runnable_by_pipeline
     def run(
         self,

sagemaker-core/src/sagemaker/core/resources.py

@@ -35788,7 +35788,7 @@ def stop(self) -> None:
             ResourceNotFound: Resource being access is not found.
         """
 
-        client = SageMakerClient().client
+        client = SageMakerClient().sagemaker_client
 
         operation_input_args = {
             "TrainingJobName": self.training_job_name,
@@ -35833,15 +35833,17 @@ def wait(
         progress.add_task("Waiting for TrainingJob...")
         status = Status("Current status:")
 
-        instance_count = (
-            sum(
-                instance_group.instance_count
-                for instance_group in self.resource_config.instance_groups
-            )
-            if self.resource_config.instance_groups
-            and not isinstance(self.resource_config.instance_groups, Unassigned)
-            else self.resource_config.instance_count
-        )
+        instance_count = 1  # Default
+        if not isinstance(self.resource_config, Unassigned):
+            if (hasattr(self.resource_config, 'instance_groups') and 
+                self.resource_config.instance_groups and
+                not isinstance(self.resource_config.instance_groups, Unassigned)):
+                instance_count = sum(
+                    instance_group.instance_count
+                    for instance_group in self.resource_config.instance_groups
+                )
+            elif hasattr(self.resource_config, 'instance_count'):
+                instance_count = self.resource_config.instance_count
 
         if logs:
             multi_stream_logger = MultiLogStreamHandler(

sagemaker-core/src/sagemaker/core/telemetry/attribution.py

@@ -0,0 +1,41 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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.
+"""Attribution module for tracking the provenance of SDK usage."""
+from __future__ import absolute_import
+import os
+from enum import Enum
+
+_CREATED_BY_ENV_VAR = "SAGEMAKER_PYSDK_CREATED_BY"
+
+
+class Attribution(Enum):
+    """Enumeration of known SDK attribution sources."""
+
+    SAGEMAKER_AGENT_PLUGIN = "awslabs/agent-plugins/sagemaker-ai"
+
+
+def set_attribution(attribution: Attribution):
+    """Sets the SDK usage attribution to the specified source.
+
+    Call this at the top of scripts generated by an agent or integration
+    to enable accurate telemetry attribution.
+
+    Args:
+        attribution (Attribution): The attribution source to set.
+
+    Raises:
+        TypeError: If attribution is not an Attribution enum member.
+    """
+    if not isinstance(attribution, Attribution):
+        raise TypeError(f"attribution must be an Attribution enum member, got {type(attribution)}")
+    os.environ[_CREATED_BY_ENV_VAR] = attribution.value