- Fix local _SageMakerContainer detached mode (aws#1374)
- Add docs for debugger job support in operator
- add us-gov-west-1 to neo supported regions
- Check that session is a LocalSession when using local mode
- add tflite to Neo-supported frameworks
- ignore tags with 'aws:' prefix when creating an EndpointConfig based on an existing one
- allow custom image when calling deploy or create_model with various frameworks
- fix description of default model_dir for TF
- add more details about PyTorch eia
- make repack_model only removes py file when new entry_point provided
- handle empty inputs/outputs in ProcessingJob.from_processing_name()
- use DLC images for GovCloud
- generate test job name at test start instead of module start
- skip pytorch ei test in unsupported regions
- correct MultiString/MULTI_STRING docstring
- pytorch 1.3.1 eia support
- Update Kubernetes Operator default tag
- improve docstring for tuner.best_estimator()
- correct Estimator code_location default S3 path
- change default compile model max run to 15 mins
- fix PR builds to run on changes to their own buildspecs
- programmatically determine partition based on region
- upgrade framework versions
- use sagemaker_session when initializing Constraints and Statistics
- add sagemaker_session parameter to DataCaptureConfig
- make AutoML.deploy use self.sagemaker_session by default
- unset region during integ tests
- use sagemaker_session fixture in all Airflow tests
- remove remaining TF legacy mode integ tests
- enable Neo integ tests
- remove TF framework mode notebooks from PR build
- don't create docker network for all integ tests
- don't use os.path.join for S3 path when repacking TFS model
- dynamically determine AWS domain based on region
- allow download_folder to download file even if bucket is more restricted
- configure pylint to recognize boto3 and botocore as third-party imports
- add multiple notebooks to notebook PR build
- enable network isolation for amazon estimators
- clarify channel environment variables in PyTorch documentation
- fix HyperparameterTuner.attach for Marketplace algorithms
- move requests library from required packages to test dependencies
- create Session or LocalSession if not specified in Model
- remove hardcoded list of target devices in compile()
- Fix typo with SM_MODEL_DIR, missing quotes
- add documentation guidelines to CONTRIBUTING.md
- Removed section numbering
- remove NEO_ALLOWED_TARGET_INSTANCE_FAMILY
- remove labels from issue templates
- account for EI and version-based ECR repo naming in serving_image_uri()
- correct broken AutoML API documentation link
- fix MXNet version lists
- disable Debugger defaults in unsupported regions
- modify session and kms_utils to check for S3 bucket before creation
- update docker-compose and PyYAML dependencies
- enable smdebug for Horovod (MPI) training setup
- create lib dir for dependencies safely (only if it doesn't exist yet).
- create the correct session for MultiDataModel
- update links to the local mode notebooks examples.
- Remove outdated badges from README
- update links to TF notebook examples to link to script mode examples.
- clean up headings, verb tenses, names, etc. in MXNet overview
- Update SageMaker operator Helm chart installation guide
- choose faster notebook for notebook PR build
- properly fail PR build if has-matching-changes fails
- properly fail PR build if has-matching-changes fails
- do not use script for TFS when entry_point is not provided
- remove usage of pkg_resources
- update py2 warning message since python 2 is deprecated
- cleanup experiments, trials, and trial components in integ tests
- add additional information to Transformer class transform function doc string
- Append serving to model framework name for PyTorch, MXNet, and TensorFlow
- Use serving_image_uri for Airflow
- revise Processing docstrings for formatting and class links
- Add processing readthedocs
- Remove version number from default version comment
- remove remaining instances of python-dateutil pin
- upgrade boto3 and remove python-dateutil pin
- Add issue templates and configure issue template chooser
- Update error type in delete_endpoint docstring
- add version requirement for using "requirements.txt" when serving an MXNet model
- update container dependency versions for MXNet and PyTorch
- Update supported versions of PyTorch
- ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
- add Debugger API docs
- add tests to quick canary
- honor 'wait' flag when updating endpoint
- add default framework version warning message in Model classes
- Adding role arn explanation for sagemaker role
- allow predictor to be returned from AutoML.deploy()
- add PR checklist item about unique_name_from_base()
- use unique_name_from_base for multi-algo tuning test
- update copyright year in license header
- add version requirement for using "requirement.txt" when serving a PyTorch model
- add SageMaker Debugger overview
- clarify requirements.txt usage for Chainer, MXNet, and Scikit-learn
- change "associate" to "create" for OpenID connector
- fix typo and improve clarity on installing packages via "requirements.txt"
- fix PyTorchModel deployment crash on Windows
- make PyTorch empty framework_version warning include the latest PyTorch version
- allow disabling debugger_hook_config
- relax urllib3 and requests restrictions.
- Add uri as return statement for upload_string_as_file_body
- refactor logic in fw_utils and fill in docstrings
- increase poll from 5 to 30 for DescribeEndpoint lambda.
- fix test_auto_ml tests for regions without ml.c4.xlarge hosts.
- fix test_processing for regions without m4.xlarge instances.
- reduce test's describe frequency to eliminate throttling error.
- Increase number of retries when describing an endpoint since tf-2.0 has larger images and takes longer to start.
- generalize Model Monitor documentation from SageMaker Studio tutorial
- Add support for TF-2.0.0.
- create ProcessingJob from ARN and from name
- Make tf tests tf-1.15 and tf-2.0 compatible.
- add Model Monitor documentation
- add link to Amazon algorithm estimator parent class to clarify **kwargs
- use name_from_base in auto_ml.py but unique_name_from_base in tests.
- make test's custom bucket include region and account name.
- add Keras to the list of Neo-supported frameworks
- add link to parent classes to clarify **kwargs
- add link to framework-related parent classes to clarify **kwargs
- allow setting the default bucket in Session
- set integration test parallelization to 512
- shorten base job name to avoid collision
- multi model integration test to create ECR repo with unique names to allow independent parallel executions
- Revert "feature: allow setting the default bucket in Session (#1168)"
- add AutoML README
- add missing classes to API docs
- allow setting the default bucket in Session
- allow processing users to run code in s3
- support Multi-Model endpoints
- update PR template with items about tests, regional endpoints, and API docs
- modify schedule cleanup to abide by latest validations
- lower log level when getting execution role from a SageMaker Notebook
- Fix "ValueError: too many values to unpack (expected 2)" is occurred in windows local mode
- allow ModelMonitor and Processor to take IAM role names (in addition to ARNs)
- mention that the entry_point needs to be named inference.py for tfs
- create auto ml job for tests that based on existing job
- fixing py2 support for latest TF version
- fix tags in deploy call for generic estimators
- make multi algo integration test assertion less specific
- add support for TF 1.15.0, PyTorch 1.3.1 and MXNet 1.6rc0.
- add S3Downloader.list(s3_uri) functionality
- introduce SageMaker AutoML
- wrap up Processing feature
- add a few minor features to Model Monitoring
- add enable_sagemaker_metrics flag
- Amazon SageMaker Model Monitoring
- add utils.generate_tensorboard_url function
- Add jobs list to Estimator
- remove unnecessary boto model files
- update boto version to >=1.10.32
- correct Debugger tests
- fix bug in monitor.attach() for empty network_config
- Import smdebug_rulesconfig from PyPI
- bump the version to 1.45.0 (publishes 1.46.0) for re:Invent-2019
- correct AutoML imports and expose current_job_name
- correct Model Monitor eu-west-3 image name.
- use DLC prod images
- remove unused env variable for Model Monitoring
- aws model update
- rename get_debugger_artifacts to latest_job_debugger_artifacts
- remove retain flag from update_endpoint
- correct S3Downloader behavior
- consume smdebug_ruleconfig .whl for ITs
- disable DebuggerHook and Rules for TF distributions
- incorporate smdebug_ruleconfigs pkg until availability in PyPI
- remove pre/post scripts per latest validations
- update rules_config .whl
- remove py_version from SKLearnProcessor
- AutoML improvements
- stop overwriting custom rules volume and type
- fix tests due to latest server-side validations
- Minor processing changes
- minor processing changes (instance_count + docs)
- update api to latest
- Eureka master
- Add support for xgboost version 0.90-2
- SageMaker Debugger revision
- Add support for SageMaker Debugger [WIP]
- Fix linear learner crash when num_class is string and predict type is
multiclass_classifier - Additional Processing Jobs integration tests
- Migrate to updated Processing Jobs API
- Processing Jobs revision round 2
- Processing Jobs revision
- remove instance_pools parameter from tuner
- Multi-Algorithm Hyperparameter Tuning Support
- Import Processors in init files
- Remove SparkML Processors and corresponding unit tests
- Processing Jobs Python SDK support
- Documentation for Amazon Sagemaker Operators
- move sagemaker config loading to LocalSession since it is only used for local code support.
- fix docstring wording.
- add pyyaml dependencies to the required list.
- Correct info on code_location parameter
- Remove local mode dependencies from required.
- separating sagemaker dependencies into more use case specific installable components.
- remove docker-compose as a required dependency.
- remove red from possible colors when streaming logs
- clarify that source_dir can be an S3 URI
- clarify how to use parameter servers with distributed MXNet training
- use regional endpoint for STS in builds and tests
- update link to point to ReadTheDocs
- exclude regions for P2 tests
- add support for me-south-1 region
- validation args now use default framework_version for TensorFlow
- Add support for PyTorch 1.2.0
- use default bucket for checkpoint_s3_uri integ test
- use sts regional endpoint when creating default bucket
- use us-west-2 endpoint for sts in buildspec
- take checkpoint_s3_uri and checkpoint_local_path in Framework class
- add kwargs to create_model for 1p to work with kms
- paginating describe log streams
- model local mode
- update tfs documentation for requirements.txt
- support content_type in FileSystemInput
- allowing account overrides in special regions
- update using_mxnet.rst
- Revert "fix issue-987 error by adding instance_type in endpoint_name (#1058)"
- fix issue-987 error by adding instance_type in endpoint_name
- preserve EnableNetworkIsolation setting in attach
- enable kms support for repack_model
- support binary by NoneSplitter.
- stop CI unit test code checks from running in parallel
- re-enable airflow_config tests
- lazy import of tensorflow module
- skip airflow_config tests as they're blocking the release build
- skip lda tests in regions that does not support it.
- add airflow_config tests to canaries
- use correct STS endpoint for us-iso-east-1
- add estimator preparation to airflow configuration
- correct airflow workflow for BYO estimators.
- enable sklearn for network isolation mode
- use new ECR images in us-iso-east-1 for TF and MXNet
- expose kms_key parameter for deploying from training and hyperparameter tuning jobs
- Update sklearn default predict_fn
- add support to TF 1.14 serving with elastic accelerator.
- pass enable_network_isolation when creating TF and SKLearn models
- expose vpc_config_override in transformer() methods
- use Estimator.create_model in Estimator.transformer
- pass enable_network_isolation in Estimator.create_model
- use p2 instead of p3 for the Horovod test
- copy dependencies into new folder when repacking model
- make get_caller_identity_arn get role from DescribeNotebookInstance
- add https to regional STS endpoint
- clean up git support integ tests
- Estimator.fit like logs for transformer
- handler for stopping transform job
- remove hardcoded creds from integ test
- remove hardcoded creds from integ test
- Fix get_image_uri warning log for default xgboost version.
- add enable_network_isolation to generic Estimator class
- use regional endpoint when creating AWS STS client
- update Sagemaker Neo regions
- use cpu_instance_type fixture for stop_transform_job test
- hyperparameter tuning with spot instances and checkpoints
- skip efs and fsx integ tests in all regions
- clarify some Local Mode limitations
- update: disable efs fsx integ tests in non-pdx regions
- fix canary test failure issues
- use us-east-1 for PR test runs
- updated description for "accept" parameter in batch transform
- clean up resources created by file system set up when setup fails
- skip EFS tests until they are confirmed fixed.
- add note to CONTRIBUTING to clarify automated formatting
- add checkpoint section to using_mxnet topic
- change AMI ids in tests to be dynamic based on regions
- skip efs tests in non us-west-2 regions
- refactor tests to use common retry method
- update py2 warning message
- add logic to use asimov image for TF 1.14 py2
- changed EFS directory path instructions in documentation and Docstrings
- support training inputs from EFS and FSx
- Add support for Managed Spot Training and Checkpoint support
- Integration Tests now dynamically checks AZs
- eliminate dependency on mnist dataset website
- refactor using_sklearn and fix minor errors in using_pytorch and using_chainer
- add XGBoost Estimator as new framework
- fix tests for new regions
- add update_endpoint for PipelineModel
- refactor the using Chainer topic
- region build from staging pr
- Refactor Using PyTorch topic for consistency
- fix integration test failures masked by timeout bug
- prevent multiple values error in sklearn.transformer()
- model.transformer() passes tags to create_model()
- rework CONTRIBUTING.md to include a development workflow
- prevent integration test's timeout functions from hiding failures
- correct typo in using_sklearn.rst
- support for TensorFlow 1.14
- ignore FI18 flake8 rule
- allow Airflow enabled estimators to use absolute path entry_point
- update sklearn document to include 3p dependency installation
- refactor and edit using_mxnet topic
- allow serving image to be specified when calling MXNet.deploy
- waiting for training tags to propagate in the test
- removing unnecessary tests cases
- Replaced generic ValueError with custom subclass when reporting unexpected resource status
- correct wording for Cloud9 environment setup instructions
- enable line-too-long Pylint check
- improving Chainer integ tests
- update TensorFlow script mode dependency list
- improve documentation of some functions
- update PyTorch version
- allow serving script to be defined for deploy() and transformer() with frameworks
- format and add missing docstring placeholders
- add MXNet 1.4.1 support
- add instructions for setting up Cloud9 environment.
- update using_tensorflow topic
- Git integration for CodeCommit
- deal with credentials for Git support for GitHub
- modify TODO on disabled Pylint check
- enable consider-using-ternary Pylint check
- enable chained-comparison Pylint check
- enable too-many-public-methods Pylint check
- enable consider-using-in Pylint check
- set num_processes_per_host only if provided by user
- fix attach for 1P algorithm estimators
- enable ungrouped-imports Pylint check
- enable wrong-import-order Pylint check
- enable attribute-defined-outside-init Pylint check
- enable consider-merging-isinstance Pylint check
- enable inconsistent-return-statements Pylint check
- enable simplifiable-if-expression pylint checks
- fix list serialization for 1P algos
- enable no-else-return and no-else-raise pylint checks
- enable unidiomatic-typecheck pylint check
- git support for hosting models
- allow custom model name during deploy
- remove TODO comment on import-error Pylint check
- enable wrong-import-position pylint check
- Revert "change: enable wrong-import-position pylint check (#907)"
- enable signature-differs pylint check
- enable wrong-import-position pylint check
- enable logging-not-lazy pylint check
- reset default output path in Transformer.transform
- Add ap-northeast-1 to Neo algorithms region map
- enable logging-format-interpolation pylint check
- remove superfluous parens per Pylint rule
- add pypi, rtd, black badges to readme
- correct code per len-as-condition Pylint check
- tighten pylint config and expand C and R exceptions
- Update displaytime.sh
- fix notebook tests
- separate unit, local mode, and notebook tests in different buildspecs
- refactor the overview topic in the sphinx project
- support Endpoint_type for TF transform
- fix git test in test_estimator.py
- Add ap-northeast-1 to Neo algorithms region map
- print build execution time
- remove unnecessary failure case tests
- build spec improvements.
- use deep learning images
- Update buildspec.yml
- allow only one integration test run per time
- remove unnecessary P3 tests from TFS integration tests
- add pytest.mark.local_mode annotation to broken tests
- add TensorFlow 1.13 support
- add git_config and git_clone, validate method
- add pytest.mark.local_mode annotation to broken tests
- network isolation mode in training
- Integrate black into development process
- moving not canary TFS tests to local mode
- update Sagemaker Neo regions and instance families
- fix punctuation in MXNet version list
- clean up MXNet and TF documentation
- prevent race condition in vpc tests
- Update setup.py
- Add DataProcessing Fields for Batch Transform
- add wait argument to estimator deploy
- fix logger creation in Chainer integ test script
- emit estimator transformer tags to model
- Add extra_args to enable encrypted objects upload
- downgrade c5 in integ tests and test all TF Script Mode images
- include FrameworkModel and ModelPackage in API docs
- use unique job name in hyperparameter tuning test
- repack_model support dependencies and code location
- skip p2 tests in ap-south-east
- add better default transform job name handling within Transformer
- TFS support for pre/processing functions
- add region check for Neo service
- support MXNet 1.4 with MMS
- update using_sklearn.rst parameter name
- add encryption option to "record_set"
- honor source_dir from S3
- set _current_job_name in attach()
- emit training jobs tags to estimator
- repack model function works without source directory
- Support for TFS preprocessing
- run tests if buildspec.yml has been modified
- skip local file check for TF requirements file when source_dir is an S3 URI
- fix docs in regards to transform_fn for mxnet
- pin pytest version to 4.4.1 to avoid pluggy version conflict
- update TrainingInputMode with s3_input InputMode
- add RL Ray 0.6.5 support
- prevent false positive PR test results
- adjust Ray test script for Ray 0.6.5
- add py2 deprecation message for the deep learning framework images
- add document embedding support to Object2Vec algorithm
- skip p2/p3 tests in eu-central-1
- add automatic model tuning integ test for TF script mode
- use unique names for test training jobs
- 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
- 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
- fix propagation of tags to SageMaker endpoint
- remove duplicate content from Chainer readme
- remove duplicate content from PyTorch readme and fix internal links
- make Local Mode export artifacts even after failure
- skip horovod p3 test in region with no p3
- use unique training job names in TensorFlow script mode integ tests
- add integ test for tagging
- use unique names for test training jobs
- Wrap horovod code inside main function
- add csv deserializer
- restore notebook test
- local data source relative path includes the first directory
- upgrade pylint and fix tagging with SageMaker models
- add info about unique job names
- make start time, end time and period configurable in sagemaker.analytics.TrainingJobAnalytics
- fix typo of argument spelling in linear learner docstrings
- spelling error correction
- move RL readme content into sphinx project
- hyperparameter query failure on script mode estimator attached to complete job
- add EI support for TFS framework
- add third-party libraries sections to using_chainer and using_pytorch topics
- fix ECR URI validation
- remove unrestrictive principal * from KMS policy tests.
- edit description of local mode in overview.rst
- add table of contents to using_chainer topic
- fix formatting for HyperparameterTuner.attach()
- add pytest marks for integ tests using local mode
- add account number and unit tests for govcloud
- move chainer readme content into sphinx and fix broken link in using_mxnet
- 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
deployfor REST API TFSModel - 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_fninformation and fixinput_fnsignature in the MXNet README - feature: add support for
Predictorto delete endpoint configuration by default when callingdelete_endpoint() - feature: add support for
Modelto delete SageMaker model - feature: add support for
Transformerto 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
tunerto imports insagemaker/__init__.py
- 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__init__.pyfiles - doc-fix: Add TFRecord split type to docs
- doc-fix: Mention
SM_HPSenvironment 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_metadatadefault toFalsefor 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 estimator.py and model.py
- 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
distributiontodistributions - 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
CustomAttributesargument in local modeinvoke_endpointrequests - enhancement: add
content_typeparameter tosagemaker.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
distributionsfor 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 - 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
- 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 amazon_estimator.py
- feature: Added get_image_uri method for 1p algorithms in amazon_estimator.py
- 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
HyperparameterTunerto 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_sessionpytest 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.txtsupport for TensorFlow
- feature: Estimators: add support for TensorFlow-1.5.0
- feature: Estimators: add support for MXNet-1.0.0
- feature: Tests: use
sagemaker_timestampwhen 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