Resnet26d for DAWNBench inference task on ImageNet
We run Resnet26d on Alibaba Cloud ecs.ebman1.26xlarge, which consists of 4 npu core and 104 vCPUs.
The model and inference pipeline are improved by Apsara AI Acceleration(AIACC) team in Alibaba Cloud.
The following instructions show how to achieve the performance that we submitted to DAWNBench step by step.
- install miniconda and dependencies
wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh sh Miniconda3-latest-Linux-x86_64.sh conda install python=3.6 conda install glog=0.3.4 conda install opencv pip install pillow opencv-python pip install torch torchvision pip install hgai-centos_rel_1.0.4.sp1.whl
- git clone DAWNBench_Inference code
git clone https://github.com/ali-perseus/DAWNBench_Inference.git
- run the following commands to replicate our results submitted to DAWNBench,
##resize and crop using torchvision ##edit preprocress.py if necessary python3 ./preprocress.py ##edit build.sh if necessary ##build sh ./build.sh ##run test sh ./test.sh
4.Congratulations! the result is as follows:
final inference time : 0.0739278 ms final Prec@5: 0.93156