SageMaker Python SDK 1.1.3

• bug-fix: TensorFlow: Display updated data correctly for TensorBoard launched from run_tensorboard_locally=True
• feature: Tests: create configurable sagemaker_session pytest fixture for all integration tests
• bug-fix: AmazonEstimators: fix inaccurate hyper-parameters in kmeans, pca and linear learner
• feature: Add new hyperparameters for linear learner.

SageMaker Python SDK 1.1.2

• bug-fix: AmazonEstimators: do not call create bucket if data location is provided

SageMaker Python SDK 1.1.1

• feature: Estimators: add requirements.txt support for TensorFlow

SageMaker Python SDK 1.1.0

• feature: Estimators: add support for TensorFlow-1.5.0
• feature: Estimators: add support for MXNet-1.0.0
• feature: Tests: use sagemaker_timestamp when creating endpoint names in integration tests
• feature: Session: print out billable seconds after training completes
• bug-fix: Estimators: fix LinearLearner and add unit tests
• bug-fix: Tests: fix timeouts for PCA async integration test
• feature: Predictors: allow predictor.predict() in the JSON serializer to accept dictionaries

SageMaker Python SDK 1.0.4

• feature: Estimators: add support for Amazon Neural Topic Model(NTM) algorithm
• feature: Documentation: fix description of an argument of sagemaker.session.train
• feature: Documentation: add FM and LDA to the documentation
• feature: Estimators: add support for async fit
• bug-fix: Estimators: fix estimator role expansion

SageMaker Python SDK 1.0.3

• feature: Estimators: add support for Amazon LDA algorithm
• feature: Hyperparameters: add data_type to hyperparameters
• feature: Documentation: update TensorFlow examples following API change
• feature: Session: support multi-part uploads
• feature: add new SageMaker CLI

SageMaker Python SDK 1.0.2

• feature: Estimators: add support for Amazon FactorizationMachines algorithm
• feature: Session: correctly handle TooManyBuckets error_code in default_bucket method
• feature: Tests: add training failure tests for TF and MXNet
• feature: Documentation: show how to make predictions against existing endpoint
• feature: Estimators: implement write_spmatrix_to_sparse_tensor to support any scipy.sparse matrix

SageMaker Python SDK 1.0.1

• api-change: Model: remove support for supplemental_containers when creating Model
• feature: Documentation: multiple updates
• feature: Tests: ignore tests data in tox.ini, increase timeout for endpoint creation, capture exceptions during endpoint deletion, tests for input-output functions
• feature: Logging: change to describe job every 30s when showing logs
• feature: Session: use custom user agent at all times
• feature: Setup: add Travis file

SageMaker Python SDK 1.0.0

SageMaker Python SDK is an open source library for training and deploying machine learning 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 Amazon algorithms, 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.