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.
overview
The SageMaker Python SDK consists of a few primary classes:
estimators tuner model pipeline predictors transformer session analytics
A managed environment for MXNet training and hosting on Amazon SageMaker
using_mxnet
sagemaker.mxnet
A managed environment for TensorFlow training and hosting on Amazon SageMaker
using_tf
sagemaker.tensorflow
A managed enrionment for Scikit-learn training and hosting on Amazon SageMaker
using_sklearn
sagemaker.sklearn
A managed environment for PyTorch training and hosting on Amazon SageMaker
using_pytorch
sagemaker.pytorch
A managed environment for Chainer training and hosting on Amazon SageMaker
using_chainer
sagemaker.chainer
A managed environment for Reinforcement Learning training and hosting on Amazon SageMaker
using_rl
sagemaker.rl
A managed environment for SparkML hosting on Amazon SageMaker
sagemaker.sparkml
Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.
sagemaker.amazon.amazon_estimator factorization_machines ipinsights kmeans knn lda linear_learner ntm object2vec pca randomcutforest
SageMaker APIs to export configurations for creating and managing Airflow workflows.
using_workflow
sagemaker.workflow.airflow