NNI v1.5 Release

New Features and Documentation

Hyper-Parameter Optimizing

• New tuner: Population Based Training (PBT)
• Trials can now report infinity and NaN as result

Neural Architecture Search

• New NAS algorithm: TextNAS
• ENAS and DARTS now support visualization through web UI.

Model Compression

• New Pruner: GradientRankFilterPruner
• Compressors will validate configuration by default
• Refactor: Adding optimizer as an input argument of pruner, for easy support of DataParallel and more efficient iterative pruning. This is a broken change for the usage of iterative pruning algorithms.
• Model compression examples are refactored and improved
• Added documentation for implementing compressing algorithm

Training Service

• Kubeflow now supports pytorchjob crd v1 (thanks external contributor @jiapinai)
• Experimental DLTS support

Overall Documentation Improvement

• Documentation is significantly improved on grammar, spelling, and wording (thanks external contributor Anthony Hart (@AHartNtkn))

Fixed Bugs

• ENAS cannot have more than one LSTM layers (thanks external contributor marsggbo (@marsggbo))
• NNI manager's timers will never unsubscribe (thanks external contributor Guilherme Nagatomo (@guilhermehn))
• NNI manager may exhaust head memory (thanks external contributor Guanghui Ren(任广辉) (@Sundrops))
• Batch tuner does not support customized trials (#2075)
• Experiment cannot be killed if it failed on start (#2080)
• Non-number type metrics break web UI (#2278)
• A bug in lottery ticket pruner
• Other minor glitches