Changelogs » Sagemaker




Release of sagemaker-sparkml-serving-container, supporting Spark major version 2.3.



Initial release of sagemaker-sparkml-serving-container, supporting Spark major version 2.2.


Bug fixes and other changes

* repack model function works without source directory



* Support for TFS preprocessing


Bug fixes and other changes

* run tests if buildspec.yml has been modified
* skip local file check for TF requirements file when source_dir is an S3 URI

Documentation changes

* fix docs in regards to transform_fn for mxnet


Bug fixes and other changes

* pin pytest version to 4.4.1 to avoid pluggy version conflict


Bug fixes and other changes

* update TrainingInputMode with s3_input InputMode



* add RL Ray 0.6.5 support

Bug fixes and other changes

* prevent false positive PR test results
* adjust Ray test script for Ray 0.6.5


Bug fixes and other changes

* add py2 deprecation message for the deep learning framework images



* add document embedding support to Object2Vec algorithm


Bug fixes and other changes

* skip p2/p3 tests in eu-central-1


Bug fixes and other changes

* add automatic model tuning integ test for TF script mode


Bug fixes and other changes

* use unique names for test training jobs


Bug fixes and other changes

* add KMS key option for Endpoint Configs
* skip p2 test in regions without p2s, freeze urllib3, and specify allow_pickle=True for numpy
* use correct TF version in empty framework_version warning
* remove logging level overrides

Documentation changes

* add environment setup instructions to
* add clarification around framework version constants
* remove duplicate content from workflow readme
* remove duplicate content from RL readme


Bug fixes and other changes

* fix propagation of tags to SageMaker endpoint


Documentation changes

* remove duplicate content from Chainer readme


Documentation changes

* remove duplicate content from PyTorch readme and fix internal links


Bug fixes and other changes

* make Local Mode export artifacts even after failure


Bug fixes and other changes

* skip horovod p3 test in region with no p3
* use unique training job names in TensorFlow script mode integ tests


Bug fixes and other changes

* add integ test for tagging
* use unique names for test training jobs
* Wrap horovod code inside main function
* add csv deserializer
* restore notebook test


Bug fixes and other changes

* local data source relative path includes the first directory
* upgrade pylint and fix tagging with SageMaker models

Documentation changes

* add info about unique job names


Bug fixes and other changes

* make start time, end time and period configurable in

Documentation changes

* fix typo of argument spelling in linear learner docstrings


Documentation changes

* spelling error correction


Documentation changes

* move RL readme content into sphinx project


Bug fixes

* hyperparameter query failure on script mode estimator attached to complete job

Other changes

* add EI support for TFS framework

Documentation changes

* add third-party libraries sections to using_chainer and using_pytorch topics


Bug fixes

* fix ECR URI validation
* remove unrestrictive principal * from KMS policy tests.

Documentation changes

* edit description of local mode in overview.rst
* add table of contents to using_chainer topic
* fix formatting for HyperparameterTuner.attach()


Other changes

* add pytest marks for integ tests using local mode
* add account number and unit tests for govcloud

Documentation changes

* move chainer readme content into sphinx and fix broken link in using_mxnet


Documentation changes

* add mandatory sagemaker_role argument to Local mode example.



* enable new release process
* Update inference pipelines documentation
* Migrate content from workflow and pytorch readmes into sphinx project
* Propagate Tags from estimator to model, endpoint, and endpoint config


* bug-fix: pass kms id as parameter for uploading code with Server side encryption
* feature: ``PipelineModel``: Create a Transformer from a PipelineModel
* bug-fix: ``AlgorithmEstimator``: Make SupportedHyperParameters optional
* feature: ``Hyperparameter``: Support scaling hyperparameters
* doc-fix: Remove duplicate content from main README.rst, /tensorflow/README.rst, and /sklearn/README.rst and add links to readthedocs content


* doc-fix: Remove incorrect parameter for EI TFS Python README
* feature: ``Predictor``: delete SageMaker model
* feature: ``PipelineModel``: delete SageMaker model
* bug-fix: Estimator.attach works with training jobs without hyperparameters
* doc-fix: remove duplicate content from mxnet/README.rst
* doc-fix: move overview content in main README into sphynx project
* bug-fix: pass accelerator_type in ``deploy`` for REST API TFS ``Model``
* doc-fix: move content from tf/README.rst into sphynx project
* doc-fix: move content from sklearn/README.rst into sphynx project
* doc-fix: Improve new developer experience in README
* feature: Add support for Coach 0.11.1 for Tensorflow


* doc-fix: fix README for PyPI


* doc-fix: update information about saving models in the MXNet README
* doc-fix: change ReadTheDocs links from latest to stable
* doc-fix: add ``transform_fn`` information and fix ``input_fn`` signature in the MXNet README
* feature: add support for ``Predictor`` to delete endpoint configuration by default when calling ``delete_endpoint()``
* feature: add support for ``Model`` to delete SageMaker model
* feature: add support for ``Transformer`` to delete SageMaker model
* bug-fix: fix default account for SKLearnModel


* enhancement: Include SageMaker Notebook Instance version number in boto3 user agent, if available.
* feature: Support for updating existing endpoint


* enhancement: Add ``tuner`` to imports in ``sagemaker/``


* bug-fix: Handle StopIteration in CloudWatch Logs retrieval
* feature: Update EI TensorFlow latest version to 1.12
* feature: Support for Horovod


* feature: HyperparameterTuner: support VPC config


* enhancement: Workflow: Specify tasks from which training/tuning operator to transform/deploy in related operators
* feature: Supporting inter-container traffic encryption flag


* bug-fix: Workflow: Revert appending Airflow retry id to default job name
* feature: support for Tensorflow 1.12
* feature: support for Tensorflow Serving 1.12
* bug-fix: Revert appending Airflow retry id to default job name
* bug-fix: Session: don't allow get_execution_role() to return an ARN that's not a role but has "role" in the name
* bug-fix: Remove ``__all__`` from ```` files
* doc-fix: Add TFRecord split type to docs
* doc-fix: Mention ``SM_HPS`` environment variable in MXNet README
* doc-fix: Specify that Local Mode supports only framework and BYO cases
* doc-fix: Add missing classes to API docs
* doc-fix: Add information on necessary AWS permissions
* bug-fix: Remove PyYAML to let docker-compose install the right version
* feature: Update TensorFlow latest version to 1.12
* enhancement: Add Model.transformer()
* bug-fix: HyperparameterTuner: make ``include_cls_metadata`` default to ``False`` for everything except Frameworks


* bug-fix: Local Mode: Allow support for SSH in local mode
* bug-fix: Workflow: Append retry id to default Airflow job name to avoid name collisions in retry
* bug-fix: Local Mode: No longer requires s3 permissions to run local entry point file
* feature: Estimators: add support for PyTorch 1.0.0
* bug-fix: Local Mode: Move dependency on sagemaker_s3_output from rl.estimator to model
* doc-fix: Fix quotes in and


* enhancement: Check for S3 paths being passed as entry point
* feature: Add support for AugmentedManifestFile and ShuffleConfig
* bug-fix: Add version bound for requests module to avoid conflicts with docker-compose and docker-py
* bug-fix: Remove unnecessary dependency tensorflow
* doc-fix: Change ``distribution`` to ``distributions``
* bug-fix: Increase docker-compose http timeout and health check timeout to 120.
* feature: Local Mode: Add support for intermediate output to a local directory.
* bug-fix: Update PyYAML version to avoid conflicts with docker-compose
* doc-fix: Correct the numbered list in the table of contents
* doc-fix: Add Airflow API documentation
* feature: HyperparameterTuner: add Early Stopping support


* Documentation: add documentation for Reinforcement Learning Estimator.
* Documentation: update TensorFlow README for Script Mode


* feature: update boto3 to version 1.9.55


* feature: Add 0.10.1 coach version
* feature: Add support for SageMaker Neo
* feature: Estimators: Add RLEstimator to provide support for Reinforcement Learning
* feature: Add support for Amazon Elastic Inference
* feature: Add support for Algorithm Estimators and ModelPackages: includes support for AWS Marketplace
* feature: Add SKLearn Estimator to provide support for SciKit Learn
* feature: Add Amazon SageMaker Semantic Segmentation algorithm to the registry
* feature: Add support for SageMaker Inference Pipelines
* feature: Add support for SparkML serving container


* bug-fix: Fix FileNotFoundError for entry_point without source_dir
* doc-fix: Add missing feature 1.5.0 in change log
* doc-fix: Add README for airflow


* enhancement: Local Mode: add explicit pull for serving
* feature: Estimators: dependencies attribute allows export of additional libraries into the container
* feature: Add APIs to export Airflow transform and deploy config
* bug-fix: Allow code_location argument to be S3 URI in training_config API
* enhancement: Local Mode: add explicit pull for serving


* feature: Estimator: add script mode and Python 3 support for TensorFlow
* bug-fix: Changes to use correct S3 bucket and time range for dataframes in TrainingJobAnalytics.
* bug-fix: Local Mode: correctly handle the case where the model output folder doesn't exist yet
* feature: Add APIs to export Airflow training, tuning and model config
* doc-fix: Fix typos in tensorflow serving documentation
* doc-fix: Add estimator base classes to API docs
* feature: HyperparameterTuner: add support for Automatic Model Tuning's Warm Start Jobs
* feature: HyperparameterTuner: Make input channels optional
* feature: Add support for Chainer 5.0
* feature: Estimator: add support for MetricDefinitions
* feature: Estimators: add support for Amazon IP Insights algorithm


* bug-fix: support ``CustomAttributes`` argument in local mode ``invoke_endpoint`` requests
* enhancement: add ``content_type`` parameter to ``sagemaker.tensorflow.serving.Predictor``
* doc-fix: add TensorFlow Serving Container docs
* doc-fix: fix rendering error in README.rst
* enhancement: Local Mode: support optional input channels
* build: added pylint
* build: upgrade docker-compose to 1.23
* enhancement: Frameworks: update warning for not setting framework_version as we aren't planning a breaking change anymore
* feature: Estimator: add script mode and Python 3 support for TensorFlow
* enhancement: Session: remove hardcoded 'training' from job status error message
* bug-fix: Updated Cloudwatch namespace for metrics in TrainingJobsAnalytics
* bug-fix: Changes to use correct s3 bucket and time range for dataframes in TrainingJobAnalytics.
* enhancement: Remove MetricDefinition lookup via tuning job in TrainingJobAnalytics


* feature: Estimators: add support for Amazon Object2Vec algorithm


* feature: add support for sagemaker-tensorflow-serving container
* feature: Estimator: make input channels optional


* feature: Estimator: add input mode to training channels
* feature: Estimator: add model_uri and model_channel_name parameters
* enhancement: Local Mode: support output_path. Can be either file:// or s3://
* enhancement: Added image uris for SageMaker built-in algorithms for SIN/LHR/BOM/SFO/YUL
* feature: Estimators: add support for MXNet 1.3.0, which introduces a new training script format
* feature: Documentation: add explanation for the new training script format used with MXNet
* feature: Estimators: add ``distributions`` for customizing distributed training with the new training script format


* feature: add support for TensorFlow 1.11.0


* feature: Local Mode: Add support for Batch Inference
* feature: Add timestamp to secondary status in training job output
* bug-fix: Local Mode: Set correct default values for additional_volumes and additional_env_vars
* enhancement: Local Mode: support nvidia-docker2 natively
* warning: Frameworks: add warning for upcoming breaking change that makes framework_version required


* enhancement: Enable setting VPC config when creating/deploying models
* enhancement: Local Mode: accept short lived credentials with a warning message
* bug-fix: Local Mode: pass in job name as parameter for training environment variable


* enhancement: Local Mode: add training environment variables for AWS region and job name
* doc-fix: Instruction on how to use preview version of PyTorch -
* doc-fix: add role to MXNet estimator example in readme
* bug-fix: default TensorFlow json serializer accepts dict of numpy arrays


* bug-fix: setting health check timeout limit on local mode to 30s
* bug-fix: make Hyperparameters in local mode optional.
* enhancement: add support for volume KMS key to Transformer
* feature: add support for GovCloud


* feature: add train_volume_kms_key parameter to Estimator classes
* doc-fix: add deprecation warning for current MXNet training script format
* doc-fix: add docs on deploying TensorFlow model directly from existing model
* doc-fix: fix code example for using Gzip compression for TensorFlow training data


* feature: add support for TensorFlow 1.10.0

* doc-fix: fix rst warnings in README.rst


* 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


* feature: add support for TensorFlow 1.9


* 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


* feature: Estimators: add support for MXNet 1.2.1


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


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


* 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


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


* feature: Added multiclass classification support for linear learner algorithm.


* feature: Add Chainer 4.1.0 support


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


* 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


* Support SageMaker algorithms in ICN region


* 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


* 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


* 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


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


* 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


* feature: Add chainer


* 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


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


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


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


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


* 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


* 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.


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


* feature: Estimators: add ``requirements.txt`` support for TensorFlow


* 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


* 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


* 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


* 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


* 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


* Initial commit