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.
- install docker
- install nvidia-docker
- build image using
cd docker && docker build -t stsgcn/mxnet_1.41_cu100 . - download the data STSGCN_data.tar.gz with code:
p72z - uncompress data file using
tar -zxvf data.tar.gz - modify the term
ctxinconfig/PEMS03/individual_GLU_mask_emb.jsonto match your GPU devices - 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 |