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Installation

Conda

The easiest way to install rapids-singlecell is to use one of the yaml file provided in the conda folder. These yaml files install everything needed to run the example notebooks and get you started.

conda env create -f conda/rsc_rapids_25.12.yml #default CUDA-13.0 PYTHON-3.13
# or
mamba env create -f conda/rsc_rapids_25.10.yml #default CUDA-12.9 PYTHON-3.13

RAPIDS currently doesn't support `channel_priority: strict`; use `channel_priority: flexible` instead

PyPI

rapids-singlecell is also on PyPI.

pip install rapids-singlecell

The default installer doesn't cover RAPIDS nor CuPy. Information on how to install RAPIDS & CuPy can be found here.

If you want to use RAPIDS new PyPI packages, the whole library with all dependencies can be install with:

uv pip install 'rapids-singlecell[rapids12]' --extra-index-url=https://pypi.nvidia.com #CUDA12
uv pip install 'rapids-singlecell[rapids13]' --extra-index-url=https://pypi.nvidia.com #CUDA13

It is important to ensure that the CUDA environment is set up correctly so that RAPIDS and CuPy can locate the necessary libraries.

If you are using `python=3.12` with `uv`, you might need to add the `--index-strategy=unsafe-best-match` flag to ensure compatibility.

Docker

We also offer a Docker container for rapids-singlecell. This container includes all the necessary dependencies, making it even easier to get started with rapids-singlecell.

To use the Docker container, first, ensure that you have Docker installed on your system and that Docker supports the NVIDIA Container Toolkit. Then, you can pull our Docker image using the following command:

docker pull ghcr.io/scverse/rapids_singlecell:latest

To run the Docker container, use the following command:

docker run --rm --gpus all ghcr.io/scverse/rapids_singlecell:latest

The docker containers also work with apptainer (or singularity) on an HPC system.

First pull the container and wrap it in a .sif file:

apptainer pull rsc.sif ghcr.io/scverse/rapids_singlecell:latest

Then run the following command to execute the container:

apptainer run --nv rsc.sif

System requirements

Most computations run on the GPU. See the Memory Management page for hardware guidance, managed memory, and known limits:

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