PyTorch Geometric (PyG)

PyG is the standard library for Graph Neural Networks built on PyTorch. It provides data structures for graphs, 60+ GNN layer implementations, scalable mini-batch training, and support for heterogeneous graphs.

Installation

Tested against torch-geometric 2.7.x (Oct 2025). Requires Python 3.10+ and PyTorch 2.6+.

# 1. Install PyTorch first (match your CUDA/CPU setup — see https://pytorch.org/get-started/locally/)
uv pip install torch

# 2. Core PyG (no extension wheels required for basic usage)
uv pip install torch_geometric

Optional accelerated ops (pyg-lib, torch-scatter, torch-sparse, torch-cluster) are not required for basic PyG usage (since PyG 2.3). Install version-matched wheels from the PyG wheel index after checking your PyTorch and CUDA versions:

python -c "import torch; print(torch.__version__, torch.version.cuda)"
# Then install wheels for your torch+CUDA combo, e.g.:
uv pip install pyg-lib torch-scatter torch-sparse torch-cluster \
  -f https://data.pyg.org/whl/torch-2.8.0+cu128.html

Check your version:

import torch_geometric
print(torch_geometric.__…