Update pyproject.toml and prepare for v3.0.1 release (#5329)

* Update pyproject.toml

* Correct typo

Author: zhaoqizqwang

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## v3.0.1 (2025-11-19)
+
+* Update project dependencies to include submodules: sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops
+
 ## v3.0.0 (2025-11-19)
 
 ### Major Version Release
@@ -10,7 +14,7 @@
 
 * Version 3.0.0 represents a significant milestone in our product's evolution. This major release introduces a modernized architecture, enhanced performance, and powerful new features while maintaining our commitment to user experience and reliability.
 * Older interfaces such as Estimator, Model, Predictor and all their subclasses will not be supported in V3. 
-* Please review documenation of interfaces for parameters support (especially ModelBuilder)
+* Please review documentation of interfaces for parameters support (especially ModelBuilder)
 
 ## v2.254.1 (2025-10-31)
 

README.rst

@@ -42,17 +42,17 @@ For detailed documentation, including the API reference, see `Read the Docs <htt
 To install SageMaker Python SDK, see `Installing SageMaker Python SDK <#installing-the-sagemaker-python-sdk>`_.
 
 ❗🔥 SageMaker V3 Release
------------------
+-------------------------
 
 Version 3.0.0 represents a significant milestone in our product's evolution. This major release introduces a modernized architecture, enhanced performance, and powerful new features while maintaining our commitment to user experience and reliability.
 
 **Important: Please review these breaking changes before upgrading.**
 
 * Older interfaces such as Estimator, Model, Predictor and all their subclasses will not be supported in V3. 
-* Please review documenation of interfaces for parameters support (especially ModelBuilder) in our V3 examples folder.
+* Please review documentation of interfaces for parameters support (especially ModelBuilder) in our V3 examples folder.
 
 SageMaker V2 Examples
------------------
+---------------------
 
 #. `Using the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html>`__
 #. `Using MXNet <https://sagemaker.readthedocs.io/en/stable/using_mxnet.html>`__

VERSION

@@ -1 +1 @@
-3.0
+3.0.1

pyproject.toml

@@ -34,6 +34,9 @@ classifiers = [
 ]
 dependencies = [
   "sagemaker-core>=2.0.0,<3.0.0",
+  "sagemaker-train<2.0.0",
+  "sagemaker-serve<2.0.0",
+  "sagemaker-mlops<2.0.0",
 ]
 
 [project.optional-dependencies]

sagemaker-mlops/README.md

@@ -109,138 +109,4 @@ pip install -e sagemaker-core
 pip install -e sagemaker-train
 pip install -e sagemaker-serve
 pip install -e sagemaker-mlops
-```
-
-## Usage
-
-### Importing Workflow Primitives (from Core)
-
-```python
-# Import primitives from Core
-from sagemaker.core.workflow import (
-    Parameter,
-    ParameterString,
-    ParameterInteger,
-    Join,
-    ExecutionVariables,
-    Condition,
-    ConditionEquals,
-)
-```
-
-### Importing Workflow Orchestration (from MLOps)
-
-```python
-# Import orchestration from MLOps
-from sagemaker.mlops.workflow import (
-    Pipeline,
-    TrainingStep,
-    ProcessingStep,
-    ModelStep,
-    ConditionStep,
-)
-
-# Import model building from Serve (re-exported from MLOps for convenience)
-from sagemaker.mlops import ModelBuilder
-```
-
-### Creating a Pipeline
-
-```python
-from sagemaker.core.workflow import ParameterString
-from sagemaker.mlops.workflow import Pipeline, TrainingStep
-from sagemaker.train import Estimator
-
-# Define parameters (primitives from Core)
-input_data = ParameterString(name="InputData", default_value="s3://bucket/data")
-
-# Create estimator (from Train)
-estimator = Estimator(
-    image_uri="...",
-    role="...",
-    instance_count=1,
-    instance_type="ml.m5.xlarge",
-)
-
-# Create training step (orchestration from MLOps)
-train_step = TrainingStep(
-    name="TrainModel",
-    estimator=estimator,
-    inputs={"training": input_data},
-)
-
-# Create pipeline (orchestration from MLOps)
-pipeline = Pipeline(
-    name="MyPipeline",
-    parameters=[input_data],
-    steps=[train_step],
-)
-
-# Execute pipeline
-pipeline.upsert(role_arn="...")
-pipeline.start()
-```
-
-### Using ModelBuilder
-
-```python
-from sagemaker.serve import ModelBuilder
-from sagemaker.train import ModelTrainer
-
-# Create model trainer (from Train)
-trainer = ModelTrainer(
-    model_path="s3://bucket/model.tar.gz",
-    role="...",
-)
-
-# Build model (orchestration from MLOps)
-builder = ModelBuilder(model_trainer=trainer)
-model = builder.build()
-```
-
-## Dependency Hierarchy
-
-This package depends on:
-
-- **sagemaker-core** (>=2.0.0): Foundation primitives
-- **sagemaker-train** (>=0.1.0): Training functionality
-- **sagemaker-serve** (>=0.1.0): Serving functionality
-
-Lower-level packages should NOT import from this package to maintain clean architecture.
-
-## Architecture Violations Fixed
-
-Creating this package resolved the following architectural violations:
-
-1. **Workflow violations (7)**: Core was importing from Train/Serve through workflow orchestration files
-2. **Serve→Train violations (2)**: ModelBuilder in Serve was accepting ModelTrainer objects from Train
-
-After this change:
-- Workflow violations: 0 (down from 7)
-- Serve→Train violations: 0 (down from 2)
-- Clean architecture established: Core ← Train/Serve ← MLOps
-
-## Development
-
-### Running Tests
-
-```bash
-pytest sagemaker-mlops/tests/ -v
-```
-
-### Code Style
-
-This package follows the same code style as other SageMaker packages:
-
-```bash
-black sagemaker-mlops/src/
-flake8 sagemaker-mlops/src/
-```
-
-## Contributing
-
-See the main [CONTRIBUTING.md](../CONTRIBUTING.md) for contribution guidelines.
-
-## License
-
-This package is licensed under the Apache License 2.0. See [LICENSE](../LICENSE) for details.
+```

sagemaker-mlops/VERSION

@@ -0,0 +1 @@
+1.0

sagemaker-mlops/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sagemaker-mlops"
-version = "0.1.0"
+dynamic = ["version"]
 description = "SageMaker MLOps package for workflow orchestration and model building"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -47,6 +47,9 @@ dev = [
 package-dir = {"" = "src"}
 include-package-data = true
 
+[tool.setuptools.dynamic]
+version = { file = "VERSION"}
+
 [tool.setuptools.packages.find]
 where = ["src"]
 include = ["sagemaker*"]

sagemaker-serve/README.md

@@ -0,0 +1,13 @@
+# SageMaker Serve
+
+SageMaker Serve package for model serving and deployment.
+
+## Installation
+
+```bash
+pip install sagemaker-serve
+```
+
+## Usage
+
+This package provides functionality for serving and deploying machine learning models on Amazon SageMaker.

sagemaker-serve/VERSION

@@ -0,0 +1 @@
+1.0

sagemaker-serve/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sagemaker-serve"
-version = "0.1.0"
+dynamic = ["version"]
 description = "SageMaker Serve package for model serving and deployment"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -61,6 +61,9 @@ where = ["src"]
 include = ["sagemaker*"]
 namespaces = true
 
+[tool.setuptools.dynamic]
+version = { file = "VERSION"}
+
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 python_files = ["test_*.py"]