STSGCN
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AAAI 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
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
ctx
inconfig/PEMS03/individual_GLU_mask_emb.json
to 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 |