A toolkit to compress and accelerate deep network models
DA2Lite
DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models.
Install
git clone https://github.com/da2so/DA2Lite.git
You will need a machine with a GPU and CUDA installed.
Then, you prepare runtime environment:
pip install -r requirements.txt
Use
Run
main.py(DA2Lite) runs with two main configurations like as follows:
CUDA_VISIBLE_DEVICES=0 python main.py --train_config_file=./configs/train/cifar10/cifar10/vgg16.yaml --compress_config_file=./configs/compress/tucker.yaml
The first one is train_config_file, which indicates training configurations and the other is compress_config_file, which represents compress configurations.
The details of available configurations are described in Here.
After you run DA2Lite to compress a DNN model, logging and compressed model are saved in ./log directory.
The following shows the format of saving:
- YYYY-MM-DD.HH.MM.SS : format of saved directory for an instance.