Distributed PyTorch implementation of multi-headed graph convolutional neural networks
To install required packages with only basic capability (torch,
torch_geometric, and related packages)
and to serialize+store the processed data for later sessions (pickle5):
pip install -r requirements.txt
pip install -r requirements-torch.txt
pip install -r requirements-pyg.txt
If you plan to modify the code, include packages for formatting (black) and
testing (pytest) the code:
pip install -r requirements-dev.txt
Detailed dependency installation instructions are available on the Wiki
After checking out HydgraGNN, we recommend to install HydraGNN in a developer mode so that you can use the files in your current location and update them if needed:
python -m pip install -e .
Or, simply type the following in the HydraGNN directory:
export PYTHONPATH=$PWD:$PYTHONPATH
Alternatively, if you have no plane to update, you can install HydraGNN in your python tree as a static package:
python setup.py install
There are two main options for running the code; both require a JSON input file for configurable options.
- Training a model, including continuing from a previously trained model using configuration options:
import hydragnn
hydragnn.run_training("examples/configuration.json")
- Making predictions from a previously trained model:
import hydragnn
hydragnn.run_prediction("examples/configuration.json", model)
Built in examples are provided for testing purposes only. One source of data to create HydraGNN surrogate predictions is DFT output on the OLCF Constellation: https://doi.ccs.ornl.gov/
Detailed instructions are available on the Wiki
HydraGNN uses a JSON configuration file (examples in examples/):
There are many options for HydraGNN; the dataset and model type are particularly important:
["Verbosity"]["level"]:0,1,2,3,4["Dataset"]["name"]:CuAu_32atoms,FePt_32atoms,FeSi_1024atoms["NeuralNetwork"]["Architecture"]["model_type"]:PNA,MFC,GIN,GAT,CGCNN,SchNet,DimeNet,EGNN
"HydraGNN: Distributed PyTorch implementation of multi-headed graph convolutional neural networks", Copyright ID#: 81929619 https://doi.org/10.11578/dc.20211019.2
We encourage you to contribute to HydraGNN! Please check the guidelines on how to do so.