torch-geometric

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.__version__)

Conda: the pyg conda channel is no longer maintained for PyTorch >2.5 — use uv pip install and the wheel index above instead.

PyG 2.7 notes

PyG 2.7 dropped Python 3.9 and PyTorch ≤2.5. See the 2.7.0 release notes for PyTorch 2.6–2.8 compatibility tables. torch_geometric.distributed is deprecated — use standard torch.distributed DDP (see references/scaling.md).

Core Concepts

Graph Data: Data and HeteroData

A graph lives in a Data object. The key attributes:

from torch_geometric.data import Data

data = Data(
    x=node_features,          # [num_nodes, num_node_features]
    edge_index=edge_index,     # [2, num_edges] — COO format, dtype=torch.long
    edge_attr=edge_features,   # [num_edges, num_edge_features]
    y=labels,                  # node-level [num_nodes, *] or graph-level [1, *]
    pos=positions,             # [num_nodes, num_dimensions] (for point clouds/spatial)
)