Merge branch 'master' into fix/triton-inference-spec-hmac-exposure

Author: pravali96

.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 +1,22 @@
 # Changelog
+## 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
+3.7.0

migration.md

@@ -25,7 +25,7 @@
 
 ## Migration Tool (MCP Server)
 
-An AI-powered migration tool is available as an MCP server that can analyze your V2 code, transform it to V3, validate the results, and answer migration questions interactively through your IDE.
+This AI-powered migration tool is available as an MCP server to analyze your V2 code, transform it to V3, validate the results, and answer migration questions interactively through your IDE.
 
 ### Installation
 
@@ -61,32 +61,22 @@ Add the following to your MCP configuration file:
 {
   "mcpServers": {
     "sagemaker-sdk-helper": {
-      "command": "sagemaker-sdk-helper",
-      "args": ["--log-level", "INFO"]
+      "command": "/path/to/installation",
+      "args": ["--log-level", "INFO"],
+      "autoApprove": [
+        "ask_question",
+        "transform_code",
+        "validate_code",
+        "analyze_code"
+      ]
+    }
     }
-  }
 }
+
 ```
 
 > **Note**: If you installed in a virtual environment, use the full path to the executable (find it with `which sagemaker-sdk-helper`).
 
-**With SDK source artifacts (recommended, 20-30% better accuracy):**
-
-```json
-{
-  "mcpServers": {
-    "sagemaker-sdk-helper": {
-      "command": "/path/to/.venv/bin/sagemaker-sdk-helper",
-      "args": [
-        "--log-level", "INFO",
-        "--v2-artifacts", "/path/to/sdk_v2/sagemaker-python-sdk",
-        "--v3-artifacts", "/path/to/sdk_v3/sagemaker-python-sdk"
-      ]
-    }
-  }
-}
-```
-
 After updating the config, restart your IDE or reconnect MCP servers (in Kiro: Command Palette → "MCP: Reconnect Servers").
 
 ### Kiro CLI
@@ -97,10 +87,16 @@ For Kiro CLI, add the same configuration to `~/.kiro/settings/mcp.json`:
 {
   "mcpServers": {
     "sagemaker-sdk-helper": {
-      "command": "sagemaker-sdk-helper",
-      "args": ["--log-level", "INFO"]
+      "command": "/path/to/installation",
+      "args": ["--log-level", "INFO"],
+      "autoApprove": [
+        "ask_question",
+        "transform_code",
+        "validate_code",
+        "analyze_code"
+      ]
+    }
     }
-  }
 }
 ```
 

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>=2.7.0,<3.0.0",
+  "sagemaker-train>=1.7.0,<2.0.0",
+  "sagemaker-serve>=1.7.0,<2.0.0",
+  "sagemaker-mlops>=1.7.0,<2.0.0",
 ]
 
 [project.optional-dependencies]

sagemaker-core/CHANGELOG.md

@@ -1,4 +1,12 @@
 # Changelog
+## 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
+2.7.0

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/image_uri_config/djl-lmi.json

@@ -46,7 +46,7 @@
                 "mx-central-1": "637423239942"
             },
             "repository": "djl-inference",
-            "tag_prefix": "0.36.0-lmi21.0.0-cu129"
+            "tag_prefix": "0.36.0-lmi22.0.0-cu129"
         },
         "0.35.0": {
             "registries": {

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,