This is a fork of pyg-lib that enables non-disjoint temporal sampling.

• Installation

We provide pre-built Python wheels for all major OS/PyTorch/CUDA combinations from Python 3.9 till 3.13, see here. Note that currently, Windows wheels are not supported (we are working on fixing this as soon as possible).

To install the wheels, simply run

pip install pyg-lib -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html

where

• ${TORCH} should be replaced by either 1.13.0, 2.0.0, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0, 2.6.0, 2.7.0, or 2.8.0
• ${CUDA} should be replaced by either cpu, cu102, cu117, cu118, cu121, cu124, cu126, cu128, or cu129

The following combinations are supported:

Nightly wheels are provided for Linux from Python 3.9 till 3.13:

pip install pyg-lib -f https://data.pyg.org/whl/nightly/torch-${TORCH}+${CUDA}.html

pip install ninja wheel
pip install --no-build-isolation git+https://github.com/pyg-team/pyg-lib.git