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