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sagemaker

3.23.019M downloads/moaws/sagemaker-python-sdk ↗New releases in your daily digest, changelog included.

Open source library for training and deploying models on Amazon SageMaker.

Last release 2 days ago
24 Sep 2026
Ships on a steady schedule
a new release about every 2 weeks
Nearly every release is documented
notes for 59 of the last 60 stable releases
3 versions withdrawn
withdrawn after publishing
9 years old
692 releases · first in 2017

Release timeline

692 releases since 2017

20172019202120232025

Releases

1.9.34 Sept 2018
bug-fix: Local Mode: Create output/data directory expected by SageMaker Container.
bug-fix: Local Mode: Create output/data directory expected by SageMaker Container.

bug-fix: Estimator accepts the vpc configs made capable by 1.9.1
Open source →
1.9.223 Aug 2018
feature: add support for TensorFlow 1.9
feature: add support for TensorFlow 1.9
Open source →
1.9.122 Aug 2018Deprecated

One column per quarter.

bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

bug-fix: Estimators: Fix serialization of single records
bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.
enhancement: Enable VPC config in training job creation

Open source →

1.9.06 Aug 2018

feature: Estimators: add support for MXNet 1.2.1

feature: Estimators: add support for MXNet 1.2.1

Open source →

1.8.031 Jul 2018

bug-fix: removing PCA from tuner

bug-fix: removing PCA from tuner
feature: Estimators: add support for Amazon k-nearest neighbors(KNN) algorithm

Open source →

1.7.231 Jul 2018

bug-fix: Prediction output for the TF_JSON_SERIALIZER

bug-fix: Prediction output for the TF_JSON_SERIALIZER
enhancement: Add better training job status report

Open source →

1.7.124 Jul 2018

bug-fix: get_execution_role no longer fails if user can't call get_role

bug-fix: get_execution_role no longer fails if user can't call get_role
bug-fix: Session: use existing model instead of failing during create_model()
enhancement: Estimator: allow for different role from the Estimator's when creating a Model or Transformer

Open source →

1.7.017 Jul 2018

feature: Transformer: add support for batch transform jobs

feature: Transformer: add support for batch transform jobs
feature: Documentation: add instructions for using Pipe Mode with TensorFlow

Open source →

1.6.111 Jul 2018

feature: Added multiclass classification support for linear learner algorithm.

feature: Added multiclass classification support for linear learner algorithm.

Open source →

1.6.011 Jul 2018

feature: Add Chainer 4.1.0 support

feature: Add Chainer 4.1.0 support

Open source →

1.5.410 Jul 2018

feature: Added Docker Registry for all 1p algorithms in amazon_estimator.py

feature: Added Docker Registry for all 1p algorithms in amazon_estimator.py
feature: Added get_image_uri method for 1p algorithms in amazon_estimator.py
Support SageMaker algorithms in FRA and SYD regions

Open source →

1.5.36 Jul 2018

bug-fix: Can create TrainingJobAnalytics object without specifying metric_names.

bug-fix: Can create TrainingJobAnalytics object without specifying metric_names.
bug-fix: Session: include role path in get_execution_role() result
bug-fix: Local Mode: fix RuntimeError handling

Open source →

1.5.227 Jun 2018

Support SageMaker algorithms in ICN region

Support SageMaker algorithms in ICN region

Open source →

1.5.126 Jun 2018

enhancement: Let Framework models reuse code uploaded by Framework estimators

enhancement: Let Framework models reuse code uploaded by Framework estimators
enhancement: Unify generation of model uploaded code location
feature: Change minimum required scipy from 1.0.0 to 0.19.0
feature: Allow all Framework Estimators to use a custom docker image.
feature: Option to add Tags on SageMaker Endpoints

Open source →

1.5.020 Jun 2018

feature: Add Support for PyTorch Framework

feature: Add Support for PyTorch Framework
feature: Estimators: add support for TensorFlow 1.7.0
feature: Estimators: add support for TensorFlow 1.8.0
feature: Allow Local Serving of Models in S3
enhancement: Allow option for HyperparameterTuner to not include estimator metadata in job
bug-fix: Estimators: Join tensorboard thread after fitting

Open source →

1.4.215 Jun 2018

bug-fix: Estimators: Fix attach for LDA

bug-fix: Estimators: Fix attach for LDA
bug-fix: Estimators: allow code_location to have no key prefix
bug-fix: Local Mode: Fix s3 training data download when there is a trailing slash

Open source →

1.4.15 Jun 2018

bug-fix: Local Mode: Fix for non Framework containers

bug-fix: Local Mode: Fix for non Framework containers

Open source →

1.4.05 Jun 2018

bug-fix: Remove __all__ and add noqa in __init__

bug-fix: Remove all and add noqa in init
bug-fix: Estimators: Change max_iterations hyperparameter key for KMeans
bug-fix: Estimators: Remove unused argument job_details for EstimatorBase.attach()
bug-fix: Local Mode: Show logs in Jupyter notebooks
feature: HyperparameterTuner: Add support for hyperparameter tuning jobs
feature: Analytics: Add functions for metrics in Training and Hyperparameter Tuning jobs
feature: Estimators: add support for tagging training jobs

Open source →

1.3.024 May 2018

* feature: Add chainer

feature: Add chainer

Open source →

1.2.522 May 2018

bug-fix: Change module names to string type in __all__

bug-fix: Change module names to string type in all
feature: Save training output files in local mode
bug-fix: tensorflow-serving-api: SageMaker does not conflict with tensorflow-serving-api module version
feature: Local Mode: add support for local training data using file://
feature: Updated TensorFlow Serving api protobuf files
bug-fix: No longer poll for logs from stopped training jobs

Open source →

1.2.426 Apr 2018

feature: Estimators: add support for Amazon Random Cut Forest algorithm

feature: Estimators: add support for Amazon Random Cut Forest algorithm

Open source →

1.2.319 Apr 2018

bug-fix: Fix local mode not using the right s3 bucket

bug-fix: Fix local mode not using the right s3 bucket

Open source →

1.2.23 Apr 2018

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

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

Open source →

1.2.13 Apr 2018

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

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

Open source →

1.2.02 Apr 2018

feature: Add Support for Local Mode

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

Open source →

1.1.328 Mar 2018

bug-fix: TensorFlow: Display updated data correctly for TensorBoard launched from run_tensorboard_locally=True

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: Estimators: fix inaccurate hyper-parameters in kmeans, pca and linear learner
feature: Estimators: Add new hyperparameters for linear learner.

Open source →

1.1.215 Mar 2018

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

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

Open source →

1.1.16 Mar 2018

feature: Estimators: add requirements.txt support for TensorFlow

feature: Estimators: add requirements.txt support for TensorFlow

Open source →

1.1.027 Feb 2018

feature: Estimators: add support for TensorFlow-1.5.0

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

Open source →

1.0.225 Jan 2018

feature: Estimators: add support for Amazon FactorizationMachines algorithm

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

Open source →

1.0.123 Dec 2017

api-change: Model: Remove support for 'supplemental_containers' when creating Model

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

Open source →

1.0.030 Nov 2017

* Initial commit

Initial commit

Open source →

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