Description

.. image:: https://github.com/aws/sagemaker-python-sdk/raw/master/branding/icon/sagemaker-banner.png :height: 100px :alt: SageMaker ==================== SageMaker Python SDK ==================== .. image:: https://img.shields.io/pypi/v/sagemaker.svg :target: https://pypi.python.org/pypi/sagemaker :alt: Latest Version .. image:: https://img.shields.io/pypi/pyversions/sagemaker.svg :target: https://pypi.python.org/pypi/sagemaker :alt: Supported Python Versions .. image:: https://img.shields.io/badge/code_style-black-000000.svg :target: https://github.com/python/black :alt: Code style: black .. image:: https://readthedocs.org/projects/sagemaker/badge/?version=stable :target: https://sagemaker.readthedocs.io/en/stable/ :alt: Documentation Status .. image:: https://github.com/aws/sagemaker-python-sdk/actions/workflows/codebuild-ci-health.yml/badge.svg :target: https://github.com/aws/sagemaker-python-sdk/actions/workflows/codebuild-ci-health.yml :alt: CI Health SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks **Apache MXNet** and **PyTorch**. You can also train and deploy models with **Amazon algorithms**, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have **your own algorithms** built into SageMaker compatible Docker containers, you can train and host models using these as well. 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 see our `V3 examples folder <https://github.com/aws/sagemaker-python-sdk/tree/master/v3-examples>`__ for example notebooks and usage patterns. Migrating to V3 ---------------- **Upgrading to 3.x** To upgrade to the latest version of SageMaker Python SDK 3.x: :: pip install --upgrade sagemaker If you prefer to downgrade to the 2.x version: :: pip install sagemaker==2.* See `SageMaker V2 Examples <#sagemaker-v2-examples>`__ for V2 documentation and examples. **Key Benefits of 3.x** * **Modular Architecture**: Separate PyPI packages for core, training, and serving capabilities * `sagemaker-core <https://pypi.org/project/sagemaker-core/>`__ * `sagemaker-train <https://pypi.org/project/sagemaker-train/>`__ * `sagemaker-serve <https://pypi.org/project/sagemaker-serve/>`__ * `sagemaker-mlops <https://pypi.org/project/sagemaker-mlops/>`__ * **Unified Training & Inference**: Single classes (ModelTrainer, ModelBuilder) replace multiple framework-specific classes * **Object-Oriented API**: Structured interface with auto-generated configs aligned with AWS APIs * **Simplified Workflows**: Reduced boilerplate and more intuitive interfaces **Training Experience** V3 introduces the unified ModelTrainer class to reduce complexity of initial setup and deployment for model training. This replaces the V2 Estimator class and framework-specific classes (PyTorchEstimator, SKLearnEstimator, etc.). This example shows how to train a model using a custom training container with training data from S3. *SageMaker Python SDK 2.x:* .. code:: python from sagemaker.estimator import Estimator estimator = Estimator( image_uri="my-training-image", role="arn:aws:iam::123456789012:role/SageMakerRole", instance_count=1, instance_type="ml.m5.xlarge", output_path="s3://my-bucket/output" ) estimator.fit({"training": "s3://my-bucket/train"}) *SageMaker Python SDK 3.x:* .. code:: python from sagemaker.train import ModelTrainer from sagemaker.train.configs import InputData trainer = ModelTrainer( training_image="my-training-image", role="arn:aws:iam::123456789012:role/SageMakerRole" ) train_data = InputData( channel_name="training", data_source="s3://my-bucket/train" ) trainer.train(input_data_config=[train_data]) **See more examples:** `SageMaker V3 Examples <#sagemaker-v3-examples>`__ **Inference Experience** V3 introduces the unified ModelBuilder class for model deployment and inference. This replaces the V2 Model class and framework-specific classes (PyTorchModel, TensorFlowModel, SKLearnModel, XGBoostModel, etc.). This example shows how to deploy a trained model for real-time inference. *SageMaker Python SDK 2.x:* .. code:: python fro