Installation

Note

SpaMosaic supports running on both GPU and CPU environments. You can choose either depending on your hardware, although a GPU will generally provide faster training and inference.

Clone the GitHub repository and navigate into the project directory:

git clone https://github.com/JinmiaoChenLab/SpaMosaic.git
cd SpaMosaic

Create a new conda environment and activate it:

conda env create -f environment.yml
conda activate spamosaic-env

Install core dependencies

CPU-only example

# PyTorch 2.0.0 (CPU build)
pip install torch==2.0.0+cpu --index-url https://download.pytorch.org/whl/cpu

# PyTorch Geometric (CPU wheels; must match Torch 2.0.0)
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv torch_geometric \
    -f https://data.pyg.org/whl/torch-2.0.0+cpu.html

pip install harmony-pytorch --no-deps

GPU examples

# Example: PyTorch 2.0.0 with CUDA 11.7
pip install torch==2.0.0+cu117 --index-url https://download.pytorch.org/whl/cu117

# PyTorch Geometric (match Torch 2.0.0 and CUDA version)
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv torch_geometric \
    -f https://data.pyg.org/whl/torch-2.0.0+cu117.html

pip install harmony-pytorch --no-deps

Note

• The GPU commands above are examples using CUDA 11.7.

• You should install the PyTorch build that matches your local CUDA toolkit or driver.

• See the official PyTorch installation guide for other versions: https://pytorch.org/get-started/locally/

• Matching PyTorch Geometric wheels can be found here: https://data.pyg.org/whl/

Install SpaMosaic

pip install spamosaic