Releases: microsoft/nn-Meter
Releases · microsoft/nn-Meter
nn-Meter v2.0 Release
Major Updates
Building tools is coming! Provide nn-Meter building tools for users to build latency predictor for their own devices (#43, #59, #66)
- Provide a unified interface to connect with TFLite and OpenVINO platforms, and support users to connect their own devices
- Support operator fusion rules detection on target backend, and support users to design new test cases.
- Provide tools to build kernel latency predictor for several built-in kernels or user-customized kernels.
- Support both Tensorflow and PyTorch implementation of fusion rule test cases and kernels.
- Provide examples for using nn-Meter building tools.
Minor Updates & Bug Fixes
tflite benchmark tools
The TFLite Benchmark Tools with version tensorflow==2.1
and tensorflow==2.7
for nn-Meter builder.
nn-Meter v1.1 Release
Major Updates
- Add nn-Meter Bench Dataset (#25)
- Add GNN dataloader for nn-Meter Bench Dataset (#27)
- Support torch v1.9, tensorflow v2.6, nni v2.5 (#36)
- Add notebook examples for nn-Meter usage (#26, #29)
Minor Updates & Bug Fixes
- Support hardware latency prediction for ProxylessNAS in NNI (microsoft/nni#4206)
- Refine shape attributes to sync with NNI (fix issue microsoft/nni#4198, PR #30, #33)
- Refactor of nn-Meter Project (#41)
nn-Meter v1.0 Release
Release 1.0 - 9/1/2021 (initial release)
Initial release of nn-Meter.
Major Features
- Support pip install and source codes install
- Support latency prediction for a CNN model with a predefined predictor (edge device)
- Provide command line interface
nn-meter
after installation, and python binding modulenn_meter
- Provide Docs
Known Issues
- Synchronization with NNI: a stable NNI-based torch converter relies on NNI>=2.5.
- Can not support
torch.jit._overload_method
due to issues from torch.
prediction models and fusion rules
Merge pull request #12 from microsoft/dev/setup-refactor Refine setup process 2