Pyg Documentation Pytorch Geometric Documentation

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... 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. PyTorch Geometric is a powerful library built on top of PyTorch that simplifies the implementation of graph neural networks (GNNs). With the ever - increasing importance of graph - based data in various fields such as social network analysis, drug discovery, and computer vision, PyTorch Geometric provides an efficient and flexible framework. However, to fully harness its capabilities, understanding its documentation is crucial.

This blog will guide you through the fundamental concepts, usage methods, common practices, and best practices of the PyTorch Geometric documentation. In PyTorch Geometric, a graph is represented by a Data object. A Data object typically contains node features (x), edge indices (edge_index), and optionally, edge features (edge_attr), node labels (y), and other graph - related attributes. Message passing is a key concept in GNNs. PyTorch Geometric provides a MessagePassing base class that simplifies the implementation of message - passing schemes. It abstracts away the details of message propagation and aggregation.

PyTorch Geometric provides data loaders similar to PyTorch’s DataLoader. The DataLoader in PyTorch Geometric can handle batched graphs, which are represented as a single Batch object. First, you need to install PyTorch Geometric. The installation process depends on your PyTorch version and CUDA support. You can follow the official installation guide on the PyTorch Geometric website. Geometric deep learning (GDL) is an emerging field focused on applying machine learning (ML) techniques to non-Euclidean domains such as graphs, point clouds, and manifolds.

The PyTorch Geometric (PyG) library extends PyTorch to include GDL functionality, for example classes necessary to handle data with irregular structure. PyG is introduced at a high level in Fast Graph Representation Learning with PyTorch Geometric and in detail in the PyG docs. A complete reveiw of GDL is available in the following recently-published (and freely-available) textbook: Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. The authors specify several key GDL architectures including convolutional neural networks (CNNs) operating on grids, Deep Sets architectures operating on sets, and graph neural networks (GNNs) operating on graphs, collections of nodes connected by... PyG is focused in particular on graph-structured data, which naturally encompases set-structured data. In fact, many state-of-the-art GNN architectures are implemented in PyG (see the docs)!

A review of the landscape of GNN architectures is available in Graph Neural Networks: A Review of Methods and Applications. Graphs are data structures designed to encode data structured as a set of objects and relations. Objects are embedded as graph nodes \(u\in\mathcal{V}\), where \(\mathcal{V}\) is the node set. Relations are represented by edges \((i,j)\in\mathcal{E}\) between nodes, where \(\mathcal{E}\) is the edge set. Denote the sizes of the node and edge sets as \(|\mathcal{V}|=n_\mathrm{nodes}\) and \(|\mathcal{E}|=n_\mathrm{edges}\) respectively. The choice of edge connectivity determines the local structure of a graph, which has important downstream effects on graph-based learning algorithms.

Graph construction is the process of embedding input data onto a graph structure. Graph-based learning algorithms are correspondingly imbued with a relational inductive bias based on the choice of graph representation; a graph's edge connectivity defines its local structure. The simplest graph construction routine is to construct no edges, yielding a permutation invariant set of objects. On the other hand, fully-connected graphs connect every node-node pair with an edge, yielding \(n_\mathrm{edges}=n_\mathrm{nodes}(n_\mathrm{nodes}-1)/2\) edges. This representation may be feasible for small inputs like particle clouds corresponding to a jet, but is intractible for large-scale applications such as high-pileup tracking datasets. Notably, dynamic graph construction techniques operate on input point clouds, constructing edges on them dynamically during inference.

For example, EdgeConv and GravNet GNN layers dynamically construct edges between nodes projected into a latent space; multiple such layers may be applied in sequence, yielding many intermediate graph representations on an input point... In general, nodes can have positions \(\{p_i\}_{i=1}^{n_\mathrm{nodes}}\), \(p_i\in\mathbb{R}^{n_\mathrm{space\_dim}}\), and features (attributes) \(\{x_i\}_{i=1}^{n_\mathrm{nodes}}\), \(x_i\in\mathbb{R}^{n_\mathrm{node\_dim}}\). In some applications like GNN-based particle tracking, node positions are taken to be the features. In others, e.g. jet identification, positional information may be used to seed dynamic graph consturction while kinematic features are propagated as edge features. Edges, too, can have features \(\{e_{ij}\}_{(i,j)\in\mathcal{E}}\), \(e_{ij}\in\mathbb{R}^{n_\mathrm{edge\_dim}}\), but do not have positions; instead, edges are defined by the nodes they connect, and may therefore be represented by, for example, the distance between the respective...

In PyG, graphs are stored as instances of the data class, whose fields fully specify the graph: The PyG Introduction By Example tutorial covers the basics of graph creation, batching, transformation, and inference using this data class. 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: 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

There was an error while loading. Please reload this page. 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: PyTorch Geometric (PyG) is a powerful extension of PyTorch designed for deep learning on irregular data structures such as graphs and point clouds.

It provides an efficient and flexible framework for implementing and training Graph Neural Networks (GNNs), enabling researchers and practitioners to handle large-scale graph data with ease. PyG offers a wide range of functionalities, including scalable data handling, optimized message passing operations, and pre-implemented GNN architectures, making it ideal for tasks like node classification, link prediction, and graph clustering. Built on top of PyTorch, it leverages automatic differentiation and GPU acceleration, allowing seamless integration with existing machine learning workflows. The official documentation provides a comprehensive guide for getting started, including installation instructions, fundamental concepts, and hands-on examples to help users quickly become proficient in applying GNNs to real-world problems. The Introduction by Example section of the PyTorch Geometric documentation provides a hands-on walkthrough of the key functionalities of the library by constructing and training a simple GNN. It begins by demonstrating how to define a basic graph structure using the torch_geometric.data.Data class, highlighting the importance of node features, edge indices, and labels.

The section then introduces data loading utilities, particularly how datasets are handled within PyG using torch_geometric.datasets. Following this, it walks through the creation of a simple GNN model using PyTorch Geometric’s torch_geometric.nn module, showcasing layers such as GCNConv for message passing. The tutorial proceeds by setting up a training loop, including loss computation and backpropagation, to illustrate how a GNN can be trained efficiently on graph data. Finally, the section provides a brief evaluation of the model’s performance, demonstrating how predictions can be made and analyzed. Overall, this introduction serves as a concise yet comprehensive guide to understanding the fundamental workflow of using PyG for graph-based deep learning. The Notebooks section of the PyTorch Geometric documentation provides interactive, executable Jupyter notebooks that serve as practical tutorials for various graph learning tasks using PyG.

These notebooks cover a range of topics, including an introduction to PyG’s core functionalities, implementing GNNs for node classification, and performing more advanced tasks such as link prediction and graph generation. Each notebook walks users through key concepts with step-by-step explanations and code, making it easier to understand and apply PyG’s features in real-world scenarios. The section also includes examples of working with benchmark graph datasets, utilizing different GNN architectures, and optimizing model performance. Importantly, these notebooks can run on SMU HPC systems. The Colab notebooks can be run on SMU HPC systems by either copying pasting relavant code or by downloading (File; Download; Download .ipynb) and then uploading the notebooks to your HPC Portal Jupyter Lab... The tutorials in PyTorch Geometric offer in-depth guidance on key aspects of designing and deploying GNNs for complex graph-based learning tasks.

These tutorials cover the Design of Graph Neural Networks, providing insights into various GNN architectures and optimization strategies to improve model performance. The Working with Graph Datasets section delves into handling, preprocessing, and efficiently loading large-scale graph data. The Use-Cases & Applications tutorial explores real-world implementations of GNNs in domains such as social networks, molecular analysis, and recommendation systems. Lastly, the Distributed Training tutorial focuses on scaling GNN models across multiple GPUs or machines to handle large datasets efficiently. Together, these tutorials equip users with the knowledge to advance from basic implementations to scalable, high-performance graph learning solutions.

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