* Added Tensorflow SavedModel format support * Added support for s3 based model repository * New syntax for BentoServicepack, making it easier to work with multiple models * Fixed REST API server docker image build issue with new release of gunicorn
0.4.8
Not secure
* Fixed an issue with loading Fastai model in FastaiModelArtifact, when the basic_learn submodule is not already imported * Fixed an issue with creating AWS SageMaker deployment, previously it will fail with KeyError in certain condition
0.4.7
Not secure
* Fixed an issue where SQLAlchemy alembic files are not found in PyPI distribution * Fixed an issue with SQLAlchemy alembic overwriting BentoML default logging configuration
0.4.5
Not secure
* Improved BentoML module import time by around 3-4x * List deployments command now shows "age" column denoting how long the deployment has been created * Fixed a bug where serverless deployment failed to install required plugins
Docs: * Updated documentation site https://bentoml.readthedocs.io/
0.4.4
Not secure
New Features: * Support for both Keras and tensorflow.keras module in KerasModelArtifact * New serialization option for KerasModelArtifact that stores model in json and weights files (by ghunkins ) * `bentoml list deployments` provides clean table outputs now * Support for AWS S3 based BentoML repository (Beta)
Bug fixes: * Fixed error with old click version 335 * Fixed REST API Server issue on Windows platform 333
0.4.3
Not secure
* Enhancement to Serverless deployment and SageMaker deployment * Updated default version string format for user-defined BentoService * Added the `versioneer` interface on BentoService for users to define a customized versioning format * Added '--force' option to `bentoml deployment delete` command * Updated clipper base image to 0.4.1
For BentoML developers: * BentoML now packages local BentoML dev branch when bundling a BentoService for deployment