Release 0.2.0 - 9/29/2018

Major Features

Support for OpenPAI (aka pai) Training Service
- Support training services on pai mode. NNI trials will be scheduled to run on OpenPAI cluster
- NNI trial's output (including logs and model file) will be copied to OpenPAI HDFS for further debugging and checking
Support SMAC tuner
- SMAC is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO to handle categorical parameters. The SMAC supported by NNI is a wrapper on SMAC3
Support NNI installation on conda and python virtual environment
Others
- Update ga squad example and related documentation
- WebUI UX small enhancement and bug fix

Initial release of Neural Network Intelligence (NNI).

Installation and Deployment
- Support pip install and source codes install
- Support training services on local mode(including Multi-GPU mode) as well as multi-machines mode
Tuners, Accessors and Trial
- Support AutoML algorithms including: hyperopt_tpe, hyperopt_annealing, hyperopt_random, and evolution_tuner
- Support assessor(early stop) algorithms including: medianstop algorithm
- Provide Python API for user defined tuners and accessors
- Provide Python API for user to wrap trial code as NNI deployable codes
Experiments
- Provide a command line toolkit 'nnictl' for experiments management
- Provide a web UI for viewing experiments details and managing experiments
Continuous Integration
- Support CI by providing out-of-box integration with travis-ci on ubuntu
Others
- Support simple GPU job scheduling