Pytorch Geometric Readme Md At Master Pyg Team Pytorch Geometric Githu

There was an error while loading. Please reload this page. 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, torch.compile support, DataPipe support, a large number of common benchmark datasets (based on simple interfaces... This document covers how to install PyTorch Geometric (PyG) and its dependencies for various environments.

PyG is a library built upon PyTorch that provides easy-to-use implementations of Graph Neural Networks (GNNs) for deep learning on graphs. For information about the core concepts and functionality of PyG, see Key Concepts. PyG is available for Python 3.9 to Python 3.13 and supports various environments: Sources: docs/source/install/installation.rst4 docs/source/install/quick-start.html77-99 Since PyG 2.3, you can install and use PyG with minimal dependencies. The basic installation only requires PyTorch:

This provides core functionality but doesn't include specialized extensions for performance optimization. Documentation | PyG 1.0 Paper | PyG 2.0 Paper | Colab Notebooks | External Resources | OGB Examples 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, torch.compile support, DataPipe support, a large number of common benchmark datasets (based on simple interfaces... Whether you are a machine learning researcher or first-time user of machine learning toolkits, here are some reasons to try out PyG for machine learning on graph-structured data.

In this quick tour, we highlight the ease of creating and training a GNN model with only a few lines of code. Build graph learning pipelines with ease PyG 2.5.0: Distributed training, graph tensor representation, RecSys support, native compilation .cls-1{fill:none;}.cls-2{fill:#42a5f5;}.cls-3{fill:#1e88e5;}.cls-4{fill:#1565c0;}.cls-5{fill:#6d44c6;}.cls-6{fill:#f57c00;}.cls-7{fill:#512da8;}.cls-8{fill:#ff9800;}.cls-9{fill:#ffc107;}.cls-10{fill:#815fe0;} PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data. PyG is both friendly to machine learning researchers and first-time users of machine learning toolkits.

We shortly introduce the fundamental concepts of PyG through self-contained examples. For an introduction to Graph Machine Learning, we refer the interested reader to the Stanford CS224W: Machine Learning with Graphs lectures. For an interactive introduction to PyG, we recommend our carefully curated Google Colab notebooks. At its core, PyG provides the following main features: A graph is used to model pairwise relations (edges) between objects (nodes). A single graph in PyG is described by an instance of torch_geometric.data.Data, which holds the following attributes by default:

data.x: Node feature matrix with shape [num_nodes, num_node_features] There was an error while loading. Please reload this page.

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