Release 1.2 - 12/2/2019

Major Features

Feature Engineering
- New feature engineering interface
- Feature selection algorithms: Gradient feature selector & GBDT selector
- Examples for feature engineering
Neural Architecture Search (NAS) on NNI
- New NAS interface
- NAS algorithms: ENAS, DARTS, P-DARTS (in PyTorch)
- NAS in classic mode (each trial runs independently)
Model compression
- New model pruning algorithms: lottery ticket pruning approach, L1Filter pruner, Slim pruner, FPGM pruner
- New model quantization algorithms: QAT quantizer, DoReFa quantizer
- Support the API for exporting compressed model.
Training Service
- Support OpenPAI token authentication
Examples:
- An example to automatically tune rocksdb configuration with NNI.
- A new MNIST trial example supports tensorflow 2.0.
Engineering Improvements
- For remote training service, trial jobs require no GPU are now scheduled with round-robin policy instead of random.
- Pylint rules added to check pull requests, new pull requests need to comply with these pylint rules.
Web Portal & User Experience
- Support user to add customized trial.
- User can zoom out/in in detail graphs, except Hyper-parameter.
Documentation
- Improved NNI API documentation with more API docstring.