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
- SageMaker Model Monitoring
- SageMaker Debugger
- SageMaker Processing
The SageMaker Python SDK consists of a variety 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.
Amazon SageMaker Model Monitoring¶
You can use Amazon SageMaker Model Monitoring to automatically detect concept drift by monitoring your machine learning models.
Amazon SageMaker Debugger¶
You can use Amazon SageMaker Debugger to automatically detect anomalies while training your machine learning models.
- 1 Amazon SageMaker Debugger
- 1.1 Background
- 1.2 Setup
- 1.3 Capture real-time debugging data during model training in Amazon SageMaker
- 1.4 Continuous analyses through rules
- 1.5 Capture real-time TensorBoard data from the debugging hook
- 1.6 Interactive analysis using SageMaker Debugger SDK and visualizations
- 1.7 Default behavior and opting out
- 1.8 Learn More
Amazon SageMaker Processing¶
You can use Amazon SageMaker Processing to perform data processing tasks such as data pre- and post-processing, feature engineering, data validation, and model evaluation