Bentoml

New Features:
* Automatically discover all pip dependencies via `env(auto_pip_dependencies=True)`
* CLI command auto-completion support

Beta Features:
Contact us via [Slack](https://join.slack.com/t/bentoml/shared_invite/enQtNjcyMTY3MjE4NTgzLTU3ZDc1MWM5MzQxMWQxMzJiNTc1MTJmMzYzMTYwMjQ0OGEwNDFmZDkzYWQxNzgxYWNhNjAxZjk4MzI4OGY1Yjg) for early access and documentation related to these features.
* Adaptive micro-batching in BentoML API server, including performance tracing and benchmark
* Standalone YataiService gRPC server for model management and deployment

Improvements & Bug fixes
* Improved end-to-end tests, covering entire BentoML workflow
* Fixed issues with using YataiService with PostgreSQL databases as storage
* `bentoml delete` command now supports deleting multiple BentoService at once, see `bentoml delete --help`

Improvements:
* [ISSUE-505] Make "application/json" the default Content-Type in DataframeHandler 507
* CLI improvement - Add bento service as column for deployment list 514
* SageMaker deployment - error reading Azure user role info 510 by HenryDashwood
* BentoML cli improvments 520, 519
* Add handler configs to BentoServiceMetadata proto and bentoml.yml file 517
* Add support for list by labels 521

Bug fixes:
* [ISSUE-512] Fix appending saved path to sys.path when loading BentoService 518
* Lambda deployment - ensure requirement dir in PYTHONPATH 508
* SageMaker deployment delete - fix error when endpoint already deleted 522

* Bugfix: `bentoml serve-gunicorn` command was broken in 0.6.0, which also breaks the API Server docker container. This is a minor release including a fix this issue https://github.com/bentoml/BentoML/issues/499

The biggest change in release 0.6.0 is revamped BentoML CLI, introducing new model/deployment management commands and new syntax for CLI inferencing.

1. New commands for managing your model repository:

> bentoml list

BENTO_SERVICE CREATED_AT APIS ARTIFACTS
IrisClassifier:20200123004254_CB6865 2020-01-23 08:43 predict::DataframeHandler model::SklearnModelArtifact
IrisClassifier:20200122010013_E0292E 2020-01-22 09:00 predict::DataframeHandler clf::PickleArtifact

> bentoml get IrisClassifier

> bentoml get IrisClassifier:20200123004254_CB6865

> bentoml get IrisClassifier:latest



2. Add support for using saved BentoServices by `name:version` tag instead of {saved_path}, here are some example commands:


> bentoml serve {saved_path}

> bentoml serve IrisClassifier:latest

> bentoml serve IrisClassifier:20200123004254_CB6865

* Fixed an issue with API server docker image build, where updating conda to newly released version causes the build to fail
* Documentation updates
* Removed the option to configure API endpoint output format by setting the HTTP header

* SageMaker model serving deployment improvements:
* Added num_of_gunicorn_workers_per_instance deployment option
* Gunicorn worker count can be set automatically based on host CPU now
* Improved testing for SageMaker model serving deployment