SageMaker Python SDK v1.2.2

1.2.2

• bug-fix: Estimators: fix valid range of hyper-parameter 'loss' in linear learner

SageMaker Python SDK 1.2.1

1.2.1

• bug-fix: Change Local Mode to use a sagemaker-local docker network

v1.2.0: Update default versions to TensorFlow 1.6 and MXNet 1.1 (#118)

1.2.0

• feature: Add Support for Local Mode
• feature: Estimators: add support for TensorFlow 1.6.0
• feature: Estimators: add support for MXNet 1.1.0
• feature: Frameworks: Use more idiomatic ECR repository naming scheme

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