Changelogs » Sagemaker

Sagemaker

2.2

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

1.18.0

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

1.17.2

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* feature: HyperparameterTuner: support VPC config

1.17.1

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* 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

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* 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

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* 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

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* 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

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* feature: update boto3 to version 1.9.55

1.16.0

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* 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

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* 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

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* 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

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* 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

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* 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

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* feature: Estimators: add support for Amazon Object2Vec algorithm

1.14.0

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

1.13.0

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* 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

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* feature: add support for TensorFlow 1.11.0

1.11.3

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* 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

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* 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

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* 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

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* 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

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* 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

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* feature: add support for TensorFlow 1.10.0

1.9.3.1

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

1.9.3

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* 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

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* feature: add support for TensorFlow 1.9

1.9.1

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* 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

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* feature: Estimators: add support for MXNet 1.2.1

1.8.0

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

1.7.2

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

1.7.1

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* 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

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

1.6.1

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

1.6.0

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* feature: Add Chainer 4.1.0 support

1.5.4

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* 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

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* 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

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* Support SageMaker algorithms in ICN region

1.5.1

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* 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

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* 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

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* 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

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

1.4.0

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* 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

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* feature: Add chainer

1.2.5

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* 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

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

1.2.3

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

1.2.2

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

1.2.1

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

1.2.0

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* 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

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* 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

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

1.1.1

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

1.1.0

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* 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

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* 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

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* 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

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* 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

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* 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

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* Initial commit