BentoML-0.4.0 Beta

• 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-0.3.4

• 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

BentoML-0.3.1

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

BentoML-0.3.0

• 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:
  

BentoML-0.2.2 Beta

• 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.:

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:

BentoML-0.2.1 Beta

• Improved inline python docs and new documentation site launched https://bentoml.readthedocs.io
• Support running examples on google colab
• OpenAPI docs endpoint beta
• Configurable prediction and feedback logging in API server
• Serverless deployment improved
• Bug fixes

BentoML-0.2.0 Beta

New in BentoML version 0.2.0:

• Support for H2O models
• Support for deploying BentoArchive to Amazon SageMaker endpoint
• Support for querying deployment status and delete deployment created with BentoML
• Fixed kubernetes ingress configuration for gunicorn server #136

BentoML-0.1.2 Beta

• Breaking Change: updated the format of config file bentml.yml' in the generated archive to include more information. Newer version BentoML won't be able to load archive generated before 0.1.2
• Added support for deploying to serverless platforms, including AWS lambda and google cloud platform functions
• Added REST API index page documentation on available endpoints

BentoML-0.1.1 Beta

• Added Xgboost support
• Added DataframeHandler options includes 'typ', 'input_columns', 'orient'
• BentoML cli command now supporst json string as input, e.g. --input="{"col": {"0": "bc"}}"
• BentoML cli command now supports input file from s3, e.g. --input=s3://my-bucket/test.csv
• Added gunicorn support, serve-gunicorn is now default in generated Docker image
• Added @ver decorator for specifying versions with semantic versioning

BentoML-0.0.9 Beta

0.0.9-beta

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