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)"
>>> 1.10.1  # pytorch version is 1.10.1
>>> 11.3    # my cuda version is 11.3

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. Given PyTorch1.10.1 built with cuda11.3, the following commands suffice on Linux.

# Linux Bash
export CUDA=cu113
export TORCH=1.10.1
pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
pip install torch-cluster -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html

For Windows CMD, try:

# Windows CMD
set CUDA=cu113
set TORCH=1.10.1
pip install torch-scatter -f https://data.pyg.org/whl/torch-%TORCH%+%CUDA%.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-%TORCH%+%CUDA%.html
pip install 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, cu111, cu113) and PyTorch version (1.8.1, 1.9.0, 1.9.1, 1.10.0, 1.10.1), respectively. For example, for PyTorch 1.10.1 and CUDA 11.3, type:

pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.1+cu113.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-1.10.1+cu113.html
pip install torch-cluster -f https://data.pyg.org/whl/torch-1.10.1+cu113.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=cu113
export TORCH=1.10.1
pip install thgsp -f https://wheel.torchgsp.xyz/whl/torch-${TORCH}+${CUDA}

For Windows CMD, use:

# Windows CMD
set CUDA=cu113
set TORCH=1.10.1
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, a GPU-based NumPy and SciPy, via either pip or conda. Given CUDA v11.3, the following command works.

pip install cupy-cuda113