Amazon SageMaker Python SDK¶
Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker.
With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.
Here you’ll find an overview and API documentation for SageMaker Python SDK. The project homepage is in Github: https://github.com/aws/sagemaker-python-sdk, where you can find the SDK source and installation instructions for the library.
- Using the SageMaker Python SDK
- Train a Model with the SageMaker Python SDK
- Using Models Trained Outside of Amazon SageMaker
- SageMaker Automatic Model Tuning
- SageMaker Batch Transform
- Local Mode
- Secure Training and Inference with VPC
- Secure Training with Network Isolation (Internet-Free) Mode
- Inference Pipelines
- SageMaker Workflow
The SageMaker Python SDK consists of a few primary classes:
A managed environment for MXNet training and hosting on Amazon SageMaker
A managed environment for TensorFlow training and hosting on Amazon SageMaker
A managed enrionment for Scikit-learn training and hosting on Amazon SageMaker
A managed environment for PyTorch training and hosting on Amazon SageMaker
A managed environment for Chainer training and hosting on Amazon SageMaker
A managed environment for Reinforcement Learning training and hosting on Amazon SageMaker
A managed environment for SparkML hosting on Amazon SageMaker
SageMaker First-Party Algorithms¶
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.
Amazon SageMaker Operators for Kubernetes¶
Amazon SageMaker Operators for use with Kubernetes.
- Amazon SageMaker Operators for Kubernetes
- Using Amazon Sagemaker Jobs
SageMaker APIs to export configurations for creating and managing Airflow workflows.