Installation guide

Install from PyPI

Note

Installing scSLAT within a new conda environment is recommended.

First, create a clean environment and activate it. Then we install scSLAT. We give two examples on GPU and CPU respectively. We recommend you install on a machine equipped CUDA-enabled GPU, scSLAT will be 5x-10x faster than running on CPU.

CUDA-enabled

Warning

Install in machine with old NVIDIA driver may raise error, please update NVIDIA driver to the latest version.

PyG Team provides pre-built wheels for specific CUDA version (here). If your CUDA version is in the list, please install the corresponding version torch and pyg dependencies. At last install scSLAT. We provide an example for CUDA 11.7:

conda create -n scSLAT python=3.8 -y && conda activate scSLAT
pip install scSLAT
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html

CPU-only

conda create -n scSLAT python=3.8 -y && conda activate scSLAT

pip install scSLAT
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cpu.html

Install from Github

You can also install latest version of scSLAT from Github, clone the repo and install:

conda create -n scSLAT python=3.8 -y && conda activate scSLAT

git clone git@github.com:gao-lab/SLAT.git
cd SLAT
pip install -e ".[dev, docs]"

Docker

Dockerfile of scSLAT is available at env/Dockerfile. You can also pull the docker image from here by :

docker pull huhansan666666/slat:0.2.1

Install from Conda (Ongoing)

We plan to provide a conda package of scSLAT in the future.