This is the Linux app named PyG 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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PyG
DESCRIPTION
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. 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 easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour).
Features
- Easy-to-use and unified API
- Comprehensive and well-maintained GNN models
- Great flexibility
- Large-scale real-world GNN models
- GraphGym integration
- Train your own GNN model
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/pyg.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
