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CHANGELOG

1.16.2.dev

  • 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

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