Package: ignnition
IGNNITION: Fast prototyping of Graph Neural Networks for Communication NetworksIGNNITIONis the ideal framework for users with no experience in neural network programming (e.g., TensorFlow, PyTorch). With this framework, users can design and run their own Graph Neural Networks (GNN) in a matter of a few hours.Website:https://ignnition.orgDocumentation:https://ignnition.org/doc/IGNNITIONis especially for you if:You are a scientist or engineer that wants to build custom GNNs adapted to your problem (e.g., computer networks, biology, physics, chemistry, recommender systems…)Learn more atIGNNITION at a Glance.How it works?Create your own GNN model in three simple steps:Define a GNN architecture with an intuitive YAML interfaceAdapt your datasetExecute the training with just 3 lines of codeIGNNITION produces an optimized implementation of your GNN without writing a single line of TensorFlow.Quick StartInstallationRecommended: Conda environmentAcondaenvironment definition is available inenvironment.yml. Using it is recommended, as it
ensures that an appropriate Python version and the necessary packages are installed. To use it, first installminicondaand then run the following:conda env create -f environment.yml --name ignnition
conda activate ignnitionThis will create the conda environment withignnitionname and will activate it.Recommended: Python 3.7Please, ensure you use Python 3.7. Otherwise, we do not guarantee the correct installation of dependencies.You can installIGNNITIONwith the following command usingPyPI.pip install ignnitionAlternatively, you can install it from the source code, using the following commands. These commands first download the source code, then prepare the environment, and finally install the library:wget 'https://github.com/BNN-UPC/ignnition'
pip install -r requirements.txt
python setup.py installPlease, find more details in ourinstallation guide.TutorialTo get started withIGNNITION, we have prepared a step-by-step tutorial that explains in detail how to design a basic GNN from scratch.
Clickhere to start this tutorial.After this tutorial, you should be prepared to:Start designingyour own GNN model from scratch.Reuse any model from ourexamples libraryand adapt it to your needs.Please, follow thedocumentationto know all the details of this framework.Main ContributorsD. Pujol-Perich, J. Suárez-Varela, Miquel Ferriol, A. Cabellos-Aparicio, P. Barlet-Ros.Barcelona Neural Networking center, Universitat Politècnica de CatalunyaThis software is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871528.CitingPlease, use the following reference to cite IGNNITION.Plain text:David Pujol-Perich, José Suárez-Varela, Miquel Ferriol, Shihan Xiao, Bo Wu, Albert Cabellos-Aparicio, and Pere Barlet-Ros. 2021. IGNNITION: Bridging the Gap between Graph Neural Networks and Networking Systems. IEEE Network, 35, 6 (2021), 171–177.BibTeX:@article{pujol2021ignnition,
  title={IGNNITION: Bridging the Gap between Graph Neural Networks and Networking Systems},
  author={Pujol-Perich, David and Su{\'a}rez-Varela, Jos{\'e} and Ferriol, Miquel and Xiao, Shihan and Wu, Bo and Cabellos-Aparicio, Albert and Barlet-Ros, Pere},
  journal={IEEE Network},
  volume={35},
  number={6},
  pages={171--177},
  year={2021},
  publisher={IEEE},
  doi={10.1109/MNET.001.2100266}
}LicenseSeeLICENSEfor full of the license text.Copyright Copyright 2020 Universitat Politècnica de Catalunya

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