This example uses the Spektral GNN library to predict the heat capacity of various molecules in the QM9 dataset.
In particular this example shows Edge Conditioned Convolutional Networks but any dataset or network from the Spektral library should work the same.
Install the Poplar SDK following the the instructions in the Getting Started guide for your IPU system. Make sure to source the enable.sh script for poplar.
- Update pip:
python3 -m pip install -U pip - Install the Graphcore TensorFlow wheel:
pip3 install tensorflow-2* - Install the Spektral GNN library requirements:
pip3 install -r requirements.txt - Run the example:
python3 qm9_ipu.py
The above script will download the QM9 dataset automatically which is just about 40MB in size.
Dataset references:
- L. C. Blum, J.-L. Reymond, 970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13, J. Am. Chem. Soc., 131:8732, 2009
- M. Rupp, A. Tkatchenko, K.-R. Müller, O. A. von Lilienfeld: Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Physical Review Letters, 108(5):058301, 2012
This example is licensed under the MIT license - see the LICENSE file at the top-level of this repository.
This directory includes derived work from the following:
Spektral, https://github.com/danielegrattarola/spektral
MIT License
Copyright (c) 2019 Daniele Grattarola
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