Installation Pytorch Geometric Documentation

Crandi Man

-Oct 18, 2025, 10:18 AM

installation pytorch geometric documentation

PyG is available for Python 3.10 to Python 3.13. We do not recommend installation as a root user on your system Python. Please setup a virtual environment, e.g., via venv, Anaconda/Miniconda, or create a Docker image. From PyG 2.3 onwards, you can install and use PyG without any external library required except for PyTorch. For this, simply run: If you want to utilize the full set of features from PyG, there exists several additional libraries you may want to install:

pyg-lib: Heterogeneous GNN operators and graph sampling routines PyTorch Geometric (PyG) is a powerful library built on top of PyTorch that provides tools for working with graph data in deep learning. It offers a wide range of functionalities such as graph neural network layers, data handling, and pre - processing techniques. However, installing PyG can be a bit tricky due to its dependencies on specific versions of PyTorch and CUDA. This blog will guide you through the process of installing PyTorch Geometric, explain its usage, and share common and best practices. PyG provides a compatibility matrix that shows which versions of PyG are compatible with different versions of PyTorch and CUDA.

You can find this matrix on the official PyG documentation page. It is essential to refer to this matrix before installation to avoid compatibility issues. First, you need to install PyTorch. You can use the official PyTorch website’s installation wizard to get the appropriate command for your system. For example, if you have CUDA 11.3 and want to install PyTorch with CUDA support, you can use the following command: There are multiple ways to install PyG.

The easiest way is to use the official installation script provided by PyG. You need to specify the versions of PyTorch and CUDA you have installed. For example, if you have PyTorch 1.11.0 and CUDA 11.3: You can verify the installation by running the following Python code: 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. 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.

PyTorch Geometric (PyG) is a popular extension library for PyTorch that makes it easy to build and train Graph Neural Networks (GNNs). It provides efficient tools and data structures to work with graph structured data like social networks, molecules and knowledge graphs. PyG includes ready made GNN layers, dataset loaders and batching utilities all while integrating seamlessly with PyTorch’s familiar workflow. Some Basic Funtions of PyTorch Geometric are listed below: 1. Graph Neural Network (GNN) Layers: PyG comes with a wide range of GNN models and layers such as:

These layers help capture local structure and information flow within graph nodes and edges. 2. Data Representation: Graphs in PyG are represented using the Data object which stores: Are you ready to dive into the world of graph neural networks? PyTorch Geometric is your ticket to this exciting field, but first, you need to know how to install it properly. In this comprehensive guide, we'll walk you through everything you need to know about installing PyTorch Geometric, from the basics to advanced techniques.

Whether you're a beginner or an experienced data scientist, this article has got you covered! Before we jump into the installation process, let's quickly cover what PyTorch Geometric is and why it's so important in the world of machine learning. PyTorch Geometric (PyG) is a powerful library built on top of PyTorch that specializes in deep learning on irregularly structured data, such as graphs and point clouds. It provides a wide range of tools and methods for creating and training graph neural networks (GNNs), which are increasingly important in fields like social network analysis, recommendation systems, and molecular property prediction. To get started with PyTorch Geometric, you'll need to have a few things in place: CUDA (optional, but recommended for GPU acceleration)

Hi there! I‘m excited to show you how to install PyTorch Geometric (PyG), an amazing library that brings the power of deep learning to graph data. Whether you want to implement complex neural network architectures or perform blazing fast graph analytics, PyG is up to the task. By the end of this guide, you‘ll know exactly how to install PyG and start building high performance graph models! Before we install PyG, let me quickly summarize what it is and why it‘s so useful: PyTorch Geometric is a geometric deep learning extension library for PyTorch.

It provides useful data structures and methods specifically designed for deep learning on spatially structured data like graphs, 3D meshes, and point clouds. Here are some key advantages PyG provides: pip install torch-geometric Copy PIP instructions Graph Neural Network Library for PyTorch 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... 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... There was an error while loading.

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Installation Pytorch Geometric Documentation

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