This is the Linux app named PyTorch Geometric whose latest release can be downloaded as PyG2.4.0_Modelcompilation,on-diskdatasets,hierarchicalsamplingsourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
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It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations.
- PyTorch Geometric makes implementing Graph Neural Networks a breeze
- Data handling of graphs
- Common benchmark datasets
- Data transforms
- Learning methods on graphs
This is an application that can also be fetched from https://sourceforge.net/projects/pytorch-geometric.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.