Bentoml

* Introduced SklearnModelArtifact, adding more scikit-learn specific optimizations over previous general PickleArtifact
* Fixed a number of issues with AWS Lambda Serverless deployment
* Improved error message and CLI outputs of AWS SageMaker deployment

* Fixed an issue with initializing BentoML logging and repository file direcotry

* Redesigned deployment component available now, take a look at the deploy command:`bentoml deployment --help`

* Multiple image support in ImageHandler

* Yatai Service Beta Release - a new component in BentoML providing a model registry and deployment manager for your BentoService. It's a stateful service that can run in your local machine for a personal project, or hosted on a server and shared by a machine learning team.

bentoml-release-v0.3.4
* Add `pip_dependencies` option to `bentoml.env` decorator, and making it the recommended approach for adding PyPI dependencies
* Fixed an issue related OpenAPI doc spec with ImageHandler

BentoML Developer Notes
* DEV: added versioneer.py for version management, now using git tags to manage releases
* DEV: Yatai service protobufs and generated interfaces are in the REPO now

This is a minor release with mostly bug fixes:

* Added `bentoml config` cli command for configuring local BentoML preferences and configs
* Fixed an issue when serving Keras model with API server in docker
* Fixed an issue when dependency missing in docker environment when using ImageHandler

* Fast.ai support, find example notebooks here: https://github.com/bentoml/gallery/tree/master/fast-ai

* PyTorch support - fixed a number of issues related to PyTorch model serialization and updated example notebook here: https://github.com/bentoml/BentoML/blob/master/examples/pytorch-fashion-mnist/pytorch-fashion-mnist.ipynb

* Keras Support - fixed a number of issues related to serving Keras model as API server

* Clipper deployment support - easily deploy BentoML service to Clipper cluster, read more about it here: https://github.com/bentoml/BentoML/blob/master/examples/deploy-with-clipper/deploy-iris-classifier-to-clipper.ipynb

* ImageHandler improvements - API server's web UI now support posting images to API server for testing API endpoint:

* Fast.ai support is in beta now, check out the example notebook here: https://colab.research.google.com/github/bentoml/gallery/blob/master/fast-ai/pet-classification/notebook.ipynb

* Improved OpenAPI docs endpoint:

* DataframeHandler allows specifying input types now - users can also generate API Client library that respects the expected input format for each BentoML API service user defined, e.g.:

python
class MyClassifier(BentoService):

api(DataframeHandler, input_types=['int8', 'int8', 'float', 'str', 'bool'])
def predict(self, df):
...

or specifying both column name & type:
api(DataframeHandler, input_types={'id': 'string', 'age': 'int' })
def predict(self, df):
...


* API server index page now provides web UI for testing API endpoints and shows instructions for how to generate Client API library: