NNI v2.5 Release

Model Compression

• New major version of pruning framework (doc)
  • Iterative pruning is more automated, users can use less code to implement iterative pruning.
  • Support exporting intermediate models in the iterative pruning process.
  • The implementation of the pruning algorithm is closer to the paper.
  • Users can easily customize their own iterative pruning by using PruningScheduler.
  • Optimize the basic pruners underlying generate mask logic, easier to extend new functions.
  • Optimized the memory usage of the pruners.
• MobileNetV2 end-to-end example (notebook)
• Improved QAT quantizer (doc)
  • Support dtype and scheme customization
  • Support dp multi-gpu training
  • Support load_calibration_config
• Model speed-up now supports directly loading the mask (doc)
• Support speed-up depth-wise convolution
• Support bn-folding for LSQ quantizer
• Support QAT and LSQ resume from PTQ
• Added doc for observer quantizer (doc)

Neural Architecture Search

• NAS benchmark (doc)
  • Support benchmark table lookup in experiments
  • New data preparation approach
• Improved quick start doc
• Experimental CGO execution engine (doc)

Hyper-Parameter Optimization

• New training platform: Alibaba DSW+DLC (doc)
• Support passing ConfigSpace definition directly to BOHB (doc) (thanks to @khituras)
• Reformatted experiment config doc
• Added example config files for Windows (thanks to @politecat314)
• FrameworkController now supports reuse mode

Fixed Bugs

• Experiment cannot start due to platform timestamp format (issue #4077 #4083)
• Cannot use 1e-5 in search space (issue #4080)
• Dependency version conflict caused by ConfigSpace (issue #3909) (thanks to @jexxers)
• Hardware-aware SPOS example does not work (issue #4198)
• Web UI show wrong remaining time when duration exceeds limit (issue #4015)
• cudnn.deterministic is always set in AMC pruner (#4117) thanks to @mstczuo

And...

New emoticons!

Install from pypi