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: Apache MXNet and TensorFlow. You can also train and deploy models with algorithms provided by Amazon, these 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.

Here you’ll find API docs for SageMaker Python SDK. The project home-page is in Github: https://github.com/aws/sagemaker-python-sdk, there you can find the SDK source, installation instructions and a general overview of the library there.

Overview

The SageMaker Python SDK consists of a few primary interfaces:

• Estimators
• HyperparameterTuner
• Predictors
• Session
• Model
• Analytics

MXNet

A managed environment for MXNet training and hosting on Amazon SageMaker

• MXNet
  • MXNet Estimator
  • MXNet Model
  • MXNet Predictor

TensorFlow

A managed environment for TensorFlow training and hosting on Amazon SageMaker

• TensorFlow
  • TensorFlow Estimator
  • TensorFlow Model
  • TensorFlow Predictor

SageMaker First-Party Algorithms

Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.

• K-means
• PCA
• LinearLearner
• Amazon Estimators
• FactorizationMachines
• LDA
• NTM