CHANGELOG.md

Changelog

v1.23.0 (2019-05-27)

Features

• support MXNet 1.4 with MMS

Documentation changes

• update using_sklearn.rst parameter name

v1.22.0 (2019-05-23)

Features

• add encryption option to "record_set"

Bug fixes and other changes

• honor source_dir from S3

v1.21.2 (2019-05-22)

Bug fixes and other changes

• set _current_job_name in attach()
• emit training jobs tags to estimator

v1.21.1 (2019-05-21)

Bug fixes and other changes

• repack model function works without source directory

v1.21.0 (2019-05-20)

Features

• Support for TFS preprocessing

v1.20.3 (2019-05-15)

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

v1.20.2 (2019-05-13)

Bug fixes and other changes

• pin pytest version to 4.4.1 to avoid pluggy version conflict

v1.20.1 (2019-05-09)

Bug fixes and other changes

• update TrainingInputMode with s3_input InputMode

v1.20.0 (2019-05-08)

Features

• 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

v1.19.1 (2019-05-06)

Bug fixes and other changes

• add py2 deprecation message for the deep learning framework images

v1.19.0 (2019-04-30)

Features

• add document embedding support to Object2Vec algorithm

v1.18.19 (2019-04-30)

Bug fixes and other changes

• skip p2/p3 tests in eu-central-1

v1.18.18 (2019-04-29)

Bug fixes and other changes

• add automatic model tuning integ test for TF script mode

v1.18.17 (2019-04-25)

Bug fixes and other changes

• use unique names for test training jobs

v1.18.16 (2019-04-24)

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 CONTRIBUTING.md
• add clarification around framework version constants
• remove duplicate content from workflow readme
• remove duplicate content from RL readme

v1.18.15 (2019-04-18)

Bug fixes and other changes

• fix propagation of tags to SageMaker endpoint

v1.18.14.post1 (2019-04-17)

Documentation changes

• remove duplicate content from Chainer readme

v1.18.14.post0 (2019-04-15)

Documentation changes

• remove duplicate content from PyTorch readme and fix internal links

v1.18.14 (2019-04-11)

Bug fixes and other changes

• make Local Mode export artifacts even after failure

v1.18.13 (2019-04-10)

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

v1.18.12 (2019-04-08)

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

v1.18.11 (2019-04-04)

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

v1.18.10 (2019-04-03)

Bug fixes and other changes

• make start time, end time and period configurable in sagemaker.analytics.TrainingJobAnalytics

Documentation changes

• fix typo of argument spelling in linear learner docstrings

v1.18.9.post1 (2019-04-02)

Documentation changes

• spelling error correction

v1.18.9.post0 (2019-04-01)

Documentation changes

• move RL readme content into sphinx project

v1.18.9 (2019-03-28)

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

v1.18.8 (2019-03-26)

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()

v1.18.7 (2019-03-21)

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

v1.18.6.post0 (2019-03-20)

Documentation changes

• add mandatory sagemaker_role argument to Local mode example.

v1.18.6 (2019-03-20)

Changes

• 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

1.18.5

• 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

1.18.4

• 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

1.18.3.post1

• doc-fix: fix README for PyPI

1.18.3

• 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

1.18.2

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

1.18.1

• enhancement: Add tuner to imports in sagemaker/__init__.py

1.18.0

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

1.17.2

• feature: HyperparameterTuner: support VPC config

1.17.1

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

1.17.0

• 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 __init__.py 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

1.16.3

• 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 estimator.py and model.py

1.16.2

• 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

1.16.1.post1

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

1.16.1

• feature: update boto3 to version 1.9.55

1.16.0

• 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

1.15.2

• 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

1.15.1

• 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

1.15.0

• 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

1.14.2

• 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

1.14.1

• feature: Estimators: add support for Amazon Object2Vec algorithm

1.14.0

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

1.13.0

• 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

1.12.0

• feature: add support for TensorFlow 1.11.0

1.11.3

• 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

1.11.2

• 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

1.11.1

• enhancement: Local Mode: add training environment variables for AWS region and job name
• doc-fix: Instruction on how to use preview version of PyTorch - 1.0.0.dev.
• doc-fix: add role to MXNet estimator example in readme
• bug-fix: default TensorFlow json serializer accepts dict of numpy arrays

1.11.0

• 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

1.10.1

• 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

1.10.0

• feature: add support for TensorFlow 1.10.0

1.9.3.1

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

1.9.3

• 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

1.9.2

• feature: add support for TensorFlow 1.9

1.9.1

• 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

1.9.0

• feature: Estimators: add support for MXNet 1.2.1

1.8.0

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

1.7.2

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

1.7.1

• 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

1.7.0

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

1.6.1

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

1.6.0

• feature: Add Chainer 4.1.0 support

1.5.4

• 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

1.5.3

• 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

1.5.2

• Support SageMaker algorithms in ICN region

1.5.1

• 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

1.5.0

• 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

1.4.2

• 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

1.4.1

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

1.4.0

• 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

1.3.0

• feature: Add chainer

1.2.5

• 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

1.2.4

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

1.2.3

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

1.2.2

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

1.2.1

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

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

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

1.1.2

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

1.1.1

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

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

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

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

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

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

1.0.0

• Initial commit