v2.0.0

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