https://github.com/ORNL/HydraGNN

Distributed PyTorch implementation of multi-headed graph convolutional neural networks
https://github.com/ORNL/HydraGNN

machine-learning

Last synced: 2 months ago
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Distributed PyTorch implementation of multi-headed graph convolutional neural networks

• Host: GitHub
• URL: https://github.com/ORNL/HydraGNN
• Owner: ORNL
• License: bsd-3-clause
• Created: 2021-05-28T03:32:03.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-04-18T19:42:30.000Z (2 months ago)
• Last Synced: 2024-04-18T21:53:30.979Z (2 months ago)
• Topics: machine-learning
• Language: Python
• Homepage:
• Size: 7.66 MB
• Stars: 51
• Watchers: 8
• Forks: 18
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Citation: CITATION.cff

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• awesome-stars - ORNL/HydraGNN - Distributed PyTorch implementation of multi-headed graph convolutional neural networks (Python)

README

        
# HydraGNN

Distributed PyTorch implementation of multi-headed graph convolutional neural networks



## Dependencies

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](https://github.com/ORNL/HydraGNN/wiki/Install)

## Installation

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

```

## Running the code

There are two main options for running the code; both require a JSON input file

for configurable options.

1. Training a model, including continuing from a previously trained model using

configuration options:

```

import hydragnn

hydragnn.run_training("examples/configuration.json")

```

2. Making predictions from a previously trained model:

```

import hydragnn

hydragnn.run_prediction("examples/configuration.json", model)

```

### Datasets

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](https://github.com/ORNL/HydraGNN/wiki/Datasets)

### Configurable settings

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`

### Citations

"HydraGNN: Distributed PyTorch implementation of multi-headed graph convolutional neural networks", Copyright ID#: 81929619

https://doi.org/10.11578/dc.20211019.2

## Contributing

We encourage you to contribute to HydraGNN! Please check the

[guidelines](CONTRIBUTING.md) on how to do so.