tensorflow2

Spektral

GNN for molecular heat capacity prediction using the Spektral library, optimised for Graphcore's IPU.

Framework	Domain	Model	Datasets	Tasks	Training	Inference	Reference
TensorFlow 2	GNNs	Spektral	QM9		✅ Min. 1 IPU (POD4) required	❌	Edge Conditioned Convolutional Networks

Instructions summary

1. Install and enable the Poplar SDK (see Poplar SDK setup)

2. Install the system and Python requirements (see Environment setup)

3. Download the MolHIV dataset (See Dataset setup)

Poplar SDK setup

To check if your Poplar SDK has already been enabled, run:

 echo $POPLAR_SDK_ENABLED

If no path is provided, then follow these steps:

1. Navigate to your Poplar SDK root directory

2. Enable the Poplar SDK with:

cd poplar-<OS version>-<SDK version>-<hash>
. enable.sh

More detailed instructions on setting up your Poplar environment are available in the Poplar quick start guide.

Environment setup

To prepare your environment, follow these steps:

1. Create and activate a Python3 virtual environment:

python3 -m venv <venv name>
source <venv path>/bin/activate

2. Navigate to the Poplar SDK root directory

3. Install the TensorFlow 2 and IPU TensorFlow add-ons wheels:

cd <poplar sdk root dir>
pip3 install tensorflow-2.X.X...<OS_arch>...x86_64.whl
pip3 install ipu_tensorflow_addons-2.X.X...any.whl

For the CPU architecture you are running on

4. Build the custom ops:

cd static_ops && make

More detailed instructions on setting up your TensorFlow 2 environment are available in the TensorFlow 2 quick start guide.

Dataset setup

QM9

Download from the QM9 dataset source, or let the scripts download the dataset automatically when needed.

Disk space required: 283MB

.
├── dsgdb9nsd_000001.xyz
    .
    .
    .
└── dsgdb9nsd_133885.xyz

133885 files

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

Usage

Run the example:

python3 qm9_ipu.py

License

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

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.