Merge branch 'master' into runtime_env_fix

Author: zhaoqizqwang

.github/workflows/submodule-codebuild-ci.yml

@@ -2,11 +2,12 @@ name: Sagemaker PR Checks
 on:
   pull_request_target:
     branches:
-      - "master-v3"
+      - "master*"
     paths:
       - 'sagemaker-train/**'
       - 'sagemaker-serve/**'
       - 'sagemaker-mlops/**'
+      - 'sagemaker-core/**'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.head_ref }}
@@ -55,7 +56,7 @@ jobs:
       - uses: actions/checkout@v3
         with:
           fetch-depth: 0
-          token: ${{ secrets.REPO_ACCESS_KEY }}  # or use appropriate token
+          token: ${{ secrets.GH_PAT }}  # or use appropriate token
           ref: ${{ github.event.pull_request.base.ref }}  # Target branch (master-v3)
       - name: Detect Changes
         id: check-changes
@@ -93,6 +94,13 @@ jobs:
               SUBMODULES=$(echo $SUBMODULES | sed 's/\]$/,"sagemaker-mlops"\]/')
             fi
           fi
+          if echo "$CHANGES" | grep -q "^sagemaker-core/"; then
+            if [ "$SUBMODULES" = '[]' ]; then
+              SUBMODULES='["sagemaker-core"]'
+            else
+              SUBMODULES=$(echo $SUBMODULES | sed 's/\]$/,"sagemaker-core"\]/')
+            fi
+          fi
           echo "Final SUBMODULES: $SUBMODULES"
           echo "submodules=$SUBMODULES" >> $GITHUB_OUTPUT
 
@@ -101,6 +109,7 @@ jobs:
     needs: [detect-changes]
     if: needs.detect-changes.outputs.submodules != '[]'
     strategy:
+      fail-fast: false
       matrix:
         submodule: ${{ fromJson(needs.detect-changes.outputs.submodules) }}
     steps:
@@ -144,6 +153,7 @@ jobs:
     needs: [detect-changes]
     if: needs.detect-changes.outputs.submodules != '[]'
     strategy:
+      fail-fast: false
       matrix:
         submodule: ${{ fromJson(needs.detect-changes.outputs.submodules) }}
     steps:
@@ -158,4 +168,4 @@ jobs:
         uses: aws-actions/aws-codebuild-run-build@v1
         with:
           project-name: ${{ github.event.repository.name }}-ci-${{ matrix.submodule }}-integ-tests
-          source-version-override: 'refs/pull/${{ github.event.pull_request.number }}/head^{${{ github.event.pull_request.head.sha }}}'
+          source-version-override: 'refs/pull/${{ github.event.pull_request.number }}/head^{${{ github.event.pull_request.head.sha }}}'

CHANGELOG.md

@@ -1,5 +1,13 @@
 # Changelog
 
+## v3.1.0 (2025-12-03)
+
+### Features
+
+* Fine-tuning SDK: SFT, RLVR, and RLAIF techniques with standardized parameter design
+* AIRegistry Integration: Added CRUD operations for datasets and evaluators
+* Enhanced Training Experience: Implemented MLFlow metrics tracking and deployment workflows
+
 ## v3.0.1 (2025-11-19)
 
 * Update project dependencies to include submodules: sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops

README.rst

@@ -409,4 +409,50 @@ SageMaker V2 Examples
 #. `SageMaker Autopilot <src/sagemaker/automl/README.rst>`__
 #. `Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>`__
 #. `SageMaker Debugger <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html>`__
-#. `SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>`__
+#. `SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>`__
+
+🚀 Model Fine-Tuning Support Now Available in V3
+-------------------------------------------------
+
+We're excited to announce model fine-tuning capabilities in SageMaker Python SDK V3!
+
+**What's New**
+
+Four new trainer classes for fine-tuning foundation models:
+
+* SFTTrainer - Supervised fine-tuning
+* DPOTrainer - Direct preference optimization  
+* RLAIFTrainer - RL from AI feedback
+* RLVRTrainer - RL from verifiable rewards
+
+**Quick Example**
+
+.. code:: python
+
+    from sagemaker.train import SFTTrainer
+    from sagemaker.train.common import TrainingType
+
+    trainer = SFTTrainer(
+        model="meta-llama/Llama-2-7b-hf",
+        training_type=TrainingType.LORA,
+        model_package_group_name="my-models",
+        training_dataset="s3://bucket/train.jsonl"
+    )
+
+    training_job = trainer.train()
+
+**Key Features**
+
+* ✨ LoRA & full fine-tuning  
+* 📊 MLflow integration with real-time metrics  
+* 🚀 Deploy to SageMaker or Bedrock  
+* 📈 Built-in evaluation (11 benchmarks)  
+* ☁️ Serverless training  
+
+**Get Started**
+
+.. code:: python
+
+    pip install sagemaker>=3.1.0
+
+`📓 Example notebooks <https://github.com/aws/sagemaker-python-sdk/tree/master/v3-examples/model-customization-examples>`__

VERSION

@@ -1 +1 @@
-3.0.1
+3.1.0

pyproject.toml

@@ -33,10 +33,10 @@ classifiers = [
   "Programming Language :: Python :: 3.12",
 ]
 dependencies = [
-  "sagemaker-core>=2.0.0,<3.0.0",
-  "sagemaker-train<2.0.0",
-  "sagemaker-serve<2.0.0",
-  "sagemaker-mlops<2.0.0",
+  "sagemaker-core>=2.1.0,<3.0.0",
+  "sagemaker-train>=1.1.0,<2.0.0",
+  "sagemaker-serve>=1.1.0,<2.0.0",
+  "sagemaker-mlops>=1.1.0,<2.0.0",
 ]
 
 [project.optional-dependencies]
@@ -59,4 +59,4 @@ addopts = ["-vv"]
 testpaths = ["tests"]
 
 [tool.black]
-line-length = 100
+line-length = 100

sagemaker-core/VERSION

@@ -1 +1,2 @@
-2.0.1
+2.1.0
+

sagemaker-core/pyproject.toml

@@ -12,7 +12,7 @@ authors = [
 readme = "README.rst"
 dependencies = [
   # Add your dependencies here (Include lower and upper bounds as applicable)
-    "boto3>=1.35.75,<2.0.0",
+    "boto3>=1.42.2,<2.0.0",
     "pydantic>=2.0.0,<3.0.0",
     "PyYAML>=6.0, <7.0",
     "jsonschema<5.0.0",