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.