Released under MIT license, built on PyTorch , PyTorch Geometric(PyG) is a python framework for deep learning on irregular structures like graphs, point clouds and manifolds, a.k.a Geometric Deep Learning  and contains much relational learning and 3D data processing methods. Graph Neural Network (GNN) is one of the widely used representations learning methods but the implementation of it is quite challenging as the throughput of GPU needs to be achieved on highly sparse and irregular data of varying sizes. PyG overcomes this bottleneck by providing dedicated CUDA kernels for sparse data and mini-batch handlers for varying sizes. Methods implemented in PyG framework are supported by both CPU and GPU.

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PyTorch Geometric was submitted as a workshop paper at ICLR 2019, as FAST GRAPH REPRESENTATION LEARNING WITH PYTORCH GEOMETRIC. The framework was developed by Matthias Fey,_ eJan Eric Lenssn_ from TU Dortmund University.

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