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_24.02.yml
# or
mamba env create -f conda/rsc_rapids_24.04.yml

PyPI

As of version 0.4.0 rapids-singlecell is now 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:

pip install 'rapids-singlecell[rapids11]' --extra-index-url=https://pypi.nvidia.com #CUDA11.X
pip install 'rapids-singlecell[rapids12]' --extra-index-url=https://pypi.nvidia.com #CUDA12

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

To view a full guide how to set up a fully functioned single cell GPU accelerated conda environment visit GPU_SingleCell_Setup

GPU-Memory and System Requirements

rapids-singlecell relays for most computation on the GPU. A GPU with sufficient VRAM is therefore required to handle large datasets. With a RTX 3090 it’s possible to analyze 200000 cells without any issues. With an A100 80GB it is even possible to analyze more than 1000000. For even larger datasets, rmm is required to oversubscribe GPU memory into host memory, similar to SWAP memory. However, using managed_memory can result in a performance penalty, but this is still preferable to CPU runtimes.

The upper limit for GPU-based AnnData is a .nnz (non-zero elements) value of 2**31-1 (2147483647). This constraint is due to the maximum indptr (index pointer array for compressed sparse format) size that cupy currently supports for sparse matrices.