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All Projects → Davidham3 → Stsgcn

Davidham3 / Stsgcn

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

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python
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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
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