Sagemaker

Features

* add DLC account numbers for af-south-1 and eu-south-1

Bug Fixes and Other Changes

* use pathlib.PurePosixPath for S3 URLs and Unix paths
* fix regions for updated RL images

Documentation Changes

* update CHANGELOG to reflect v2.0.0 changes

Testing and Release Infrastructure

* remove v2-incompatible notebooks from notebook build

Breaking Changes

* rename s3_input to TrainingInput
* Move _NumpyDeserializer to sagemaker.deserializers.NumpyDeserializer
* rename numpy_to_record_serializer to RecordSerializer
* Move _CsvDeserializer to sagemaker.deserializers and rename to CSVDeserializer
* Move _JsonSerializer to sagemaker.serializers.JSONSerializer
* Move _NPYSerializer to sagemaker.serializers and rename to NumpySerializer
* Move _JsonDeserializer to sagemaker.deserializers.JSONDeserializer
* Move _CsvSerializer to sagemaker.serializers.CSVSerializer
* preserve script path when S3 source_dir is provided
* use image_uris.retrieve() for XGBoost URIs
* deprecate sagemaker.amazon.amazon_estimator.get_image_uri()
* deprecate fw_registry module and use image_uris.retrieve() for SparkML
* deprecate Python SDK CLI
* Remove the content_types module
* deprecate unused parameters
* deprecate fw_utils.create_image_uri()
* use images_uris.retrieve() for Debugger
* deprecate fw_utils.parse_s3_url in favor of s3.parse_s3_url
* deprecate unused functions from utils and fw_utils
* Remove content_type and accept parameters from Predictor
* Add parameters to deploy and remove parameters from create_model
* Add LibSVM serializer for XGBoost predictor
* move ShuffleConfig from sagemaker.session to sagemaker.inputs
* deprecate get_ecr_image_uri_prefix
* rename estimator.train_image() to estimator.training_image_uri()
* deprecate is_version_equal_or_higher and is_version_equal_or_lower
* default wait=True for HyperparameterTuner.fit() and Transformer.transform()
* remove unused bin/sagemaker-submit file

Features

* start new module for retrieving prebuilt SageMaker image URIs
* handle separate training/inference images and EI in image_uris.retrieve
* add support for Amazon algorithms in image_uris.retrieve()
* Add pandas deserializer
* Remove LegacySerializer and LegacyDeserializer
* Add sparse matrix serializer
* Add v2 SerDe compatability
* Add JSON Lines serializer
* add framework upgrade tool
* add 1p algorithm image_uris migration tool
* Update migration tool to support breaking changes to create_model
* support PyTorch 1.6 training

Bug Fixes and Other Changes

* handle named variables in v2 migration tool
* add modifier for s3_input class
* add XGBoost support to image_uris.retrieve()
* add MXNet configuration to image_uris.retrieve()
* add remaining Amazon algorithms for image_uris.retrieve()
* add PyTorch configuration for image_uris.retrieve()
* make image_scope optional for some images in image_uris.retrieve()
* separate logs() from attach()
* use image_uris.retrieve instead of fw_utils.create_image_uri for DLC frameworks
* use images_uris.retrieve() for scikit-learn classes
* use image_uris.retrieve() for RL images
* Rename BaseDeserializer.deserialize data parameter
* Add allow_pickle parameter to NumpyDeserializer
* Fix scipy.sparse imports
* Improve code style of SerDe compatibility
* use image_uris.retrieve for Neo and Inferentia images
* use generated RL version fixtures and update Ray version
* use image_uris.retrieve() for ModelMonitor default image
* use _framework_name for 'protected' attribute
* Fix JSONLinesDeserializer
* upgrade TFS version and fix py_versions KeyError
* Fix PandasDeserializer tests to more accurately mock response
* don't require instance_type for image_uris.retrieve() if only one option
* ignore code cells with shell commands in v2 migration tool
* Support multiple Accept types

Documentation Changes

* fix pip install command
* document name changes for TFS classes
* document v2.0.0 changes
* update KFP full pipeline

Testing and Release Infrastructure

* generate Chainer latest version fixtures from config
* use generated TensorFlow version fixtures
* use generated MXNet version fixtures

Breaking Changes

* Move StreamDeserializer to sagemaker.deserializers
* Move StringDeserializer to sagemaker.deserializers
* rename record_deserializer to RecordDeserializer
* remove "train_" where redundant in parameter/variable names
* Add BytesDeserializer
* rename image to image_uri
* rename image_name to image_uri
* create new inference resources during model.deploy() and model.transformer()
* rename session parameter to sagemaker_session in S3 utility classes
* rename distributions to distribution in TF/MXNet estimators
* deprecate update_endpoint arg in deploy()
* create new inference resources during estimator.deploy() or estimator.transformer()
* deprecate delete_endpoint() for estimators and HyperparameterTuner
* refactor Predictor attribute endpoint to endpoint_name
* make instance_type optional for Airflow model configs
* refactor name of RealTimePredictor to Predictor
* remove check for Python 2 string in sagemaker.predictor._is_sequence_like()
* deprecate sagemaker.utils.to_str()
* drop Python 2 support

Features

* add BaseSerializer and BaseDeserializer
* add Predictor.update_endpoint()

Bug Fixes and Other Changes

* handle "train_*" renames in v2 migration tool
* handle image_uri rename for Session methods in v2 migration tool
* Update BytesDeserializer accept header
* handle image_uri rename for estimators and models in v2 migration tool
* handle image_uri rename in Airflow model config functions in v2 migration tool
* update migration tool for S3 utility functions
* set _current_job_name and base_tuning_job_name in HyperparameterTuner.attach()
* infer base name from job name in estimator.attach()
* ensure generated names are < 63 characters when deploying compiled models
* add TF migration documentation to error message

Documentation Changes

* update documentation with v2.0.0.rc1 changes
* remove 'train_*' prefix from estimator parameters
* update documentation for image_name/image --> image_uri

Testing and Release Infrastructure

* refactor matching logic in v2 migration tool
* add cli modifier for RealTimePredictor and derived classes
* change coverage settings to reduce intermittent errors
* clean up pickle.load logic in integ tests
* use fixture for Python version in framework integ tests
* remove assumption of Python 2 unit test runs

Breaking Changes

* remove estimator parameters for TF legacy mode
* remove legacy `TensorFlowModel` and `TensorFlowPredictor` classes
* force image URI to be passed for legacy TF images
* rename `sagemaker.tensorflow.serving` to `sagemaker.tensorflow.model`
* require `framework_version` and `py_version` for framework estimator and model classes
* change `Model` parameter order to make `model_data` optional

Bug Fixes and Other Changes

* add v2 migration tool

Documentation Changes

* update TF documentation to reflect breaking changes and how to upgrade
* start v2 usage and migration documentation

Testing and Release Infrastructure

* remove scipy from dependencies
* remove TF from optional dependencies

Features

* Neo: Add Granular Target Description support for compilation

Documentation Changes

* Add xgboost doc on bring your own model
* fix typos on processing docs