Sagemaker

Features

* allow setting the default bucket in Session

Bug fixes and other changes

* allow processing users to run code in s3

Features

* support Multi-Model endpoints

Bug fixes and other changes

* update PR template with items about tests, regional endpoints, and API docs

Bug fixes and other changes

* 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)

Documentation changes

* mention that the entry_point needs to be named inference.py for tfs

Bug fixes and other changes

* 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

Features

* 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

Bug fixes and other changes

* 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

Bug fixes and other changes

* Documentation for Amazon Sagemaker Operators