sagemaker-python-sdk and sagemaker-training-toolkit
The Python SDK provides the high-level interface for orchestrating SageMaker training jobs, while the training toolkit is the low-level containerized runtime that executes those jobs, making them complements that work together in a producer-consumer relationship.
About sagemaker-python-sdk
aws/sagemaker-python-sdk
A library for training and deploying machine learning models on Amazon SageMaker
Provides unified APIs for training with PyTorch, MXNet, and Amazon's optimized algorithms, plus support for custom Docker containers. Version 3.x introduces a modular architecture with separate packages for training (`sagemaker-train`) and inference (`sagemaker-serve`), alongside simplified object-oriented interfaces like `ModelTrainer` and `ModelBuilder` that reduce framework-specific boilerplate. Integrates with SageMaker's distributed training, hyperparameter tuning, JumpStart pre-built models, and local development modes for testing before cloud deployment.
About sagemaker-training-toolkit
aws/sagemaker-training-toolkit
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
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