STSGCN

AAAI 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

url: paper/AAAI2020-STSGCN.pdf

Usage

Docker is recommended.

1. install docker
2. install nvidia-docker
3. build image using cd docker && docker build -t stsgcn/mxnet_1.41_cu100 .
4. download the data STSGCN_data.tar.gz with code: p72z
5. uncompress data file using tar -zxvf data.tar.gz
6. modify the term ctx in config/PEMS03/individual_GLU_mask_emb.json to match your GPU devices
7. run code using docker run -ti --rm --runtime=nvidia -v $PWD:/mxnet stsgcn/mxnet_1.41_cu100 python3 main.py --config config/PEMS03/individual_GLU_mask_emb.json

If you are using Microsoft OpenPAI, modify the configurations saved in the folder pai_jobs to train STSGCNs on your clusters.

repo structure

name|description -|- config|configurations of STSGCN docker|dockerfile models|core of STSGCN pai_job|Microsoft OpenPAI configurations paper|paper of STSGCN test|pytest files load_params.py|read parameters from local files main.py|code of training STSGCN pytest.ini|pytest configurations requirements.txt|python packages requirements utils.py|tools