Installation

1. Install PyTorch

We recommend users to install thgsp in an conda virtual environment. Install PyTorch1.5 or later following the official instructions. Type the following in the terminal to check if PyTorch is installed successfully.

python -c "import torch; print(torch.__version__);print(torch.version.cuda)"
>>> 2.5.1  # pytorch version is 2.5.1
>>> 12.1  # my cuda version is 12.1

2. Install PyTorch Extensions

Matthias Fey provides excellent PyTorch extensions for graph-related computations. You can install pytorch_scatter, pytorch_sparse and pytorch_cluster following the official guide. Briefly speaking, given PyTorch2.5.1 built with cuda12.1, the following commands finish the PyTorch extension installation on Linux.

# Linux Bash
export CUDA=cu121
export TORCH=2.5.1
pip install torch-scatter torch-sparse   torch-cluster -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html

For Windows CMD, try:

# Windows CMD
set CUDA=cu121
set TORCH=2.5.1
pip install torch-scatter  torch-sparse  torch-cluster  -f https://data.pyg.org/whl/torch-%TORCH%+%CUDA%.html

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NOTE ${CUDA} (or %CUDA% on Windows) and ${TORCH} (or %TORCH% on Windows) should be replaced by a specific CUDA version (cpu, cu121) and PyTorch version (1.8.1, 2.5.1), respectively. For example, for PyTorch 2.5.1 and CUDA 12.1, type:

pip install torch-scatter torch-sparse   torch-cluster  -f https://data.pyg.org/whl/torch-2.5.1+cu121.html

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3. Install thgsp ⭐

Installation via Prebuilt Pip Wheels

We provide prebuilt pip wheels for your convenience. Please check them on my website. You can install thgsp wheels on Linux via the block below.

# Linux Bash
export CUDA=cu121
export TORCH=2.5.1
pip install thgsp -f https://wheel.torchgsp.xyz/whl/torch-${TORCH}+${CUDA}

For Windows CMD, use:

# Windows CMD
set CUDA=cu121
set TORCH=2.5.1
pip install thgsp  -f https://wheel.torchgsp.xyz/whl/torch-%TORCH%+%CUDA%

Installation from Source

You can install it from source. Clone the thgsp repository from github.

git clone git@github.com:bwdeng20/thgsp.git # from github

Build thgsp from source, and this may take many minutes to build C++ extensions.

cd thgsp
pip install .

4. Install cupy for linear algebra on GPU (Optional)

If you do NOT have an Nvidia GPU, please skip this section.

Follow the official instruction to install CuPy, cuda-based NumPy and SciPy, via either pip or conda. Given CUDA 12.1, the following command works.

pip install cupy-cuda12x