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¶

A high level interface for SageMaker training

Predictors¶

Make real-time predictions against SageMaker endpoints with Python objects

Session¶

Model¶

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¶

The Amazon SageMaker K-means algorithm.

PCA¶

The Amazon SageMaker PCA algorithm.

LinearLearner¶

The Amazon SageMaker LinearLearner algorithm.

Amazon Estimators¶

Base class for Amazon Estimator implementations

FactorizationMachines¶

The Amazon SageMaker Factorization Machines algorithm.

LDA¶

The Amazon SageMaker LDA algorithm.

NTM¶

The Amazon SageMaker NTM algorithm.