This release is a pre-release and may not be stable for production use.
rapids-singlecell: GPU-Accelerated Single-Cell Analysis within scverse®
rapids-singlecell provides GPU-accelerated single-cell analysis with an AnnData-first API. It is largely compatible with Scanpy and includes selected functionality from Squidpy, decoupler, and pertpy. Computations use CuPy and NVIDIA CUDA-X Data Science (formerly RAPIDS) for performance on large datasets.
- GPU acceleration: Common single-cell workflows on
AnnDatarun on the GPU. - Ecosystem compatibility: Works with Scanpy APIs; includes pieces from Squidpy, decoupler, and pertpy.
- Simple installation: Available via Conda and PyPI.
- Version-matched agent skill: Released pip packages include an analysis skill for Codex, Claude, and other agents.
Agent skill
Released packages include the matching model-neutral analysis skill:
rapids-singlecell-install-skills --agent codex
rapids-singlecell-install-skills --agent claude
rapids-singlecell-install-skills --agent claude-science # or: --agent agents
rapids-singlecell-check-kernel # before starting Jupyter
Use --dest /path/to/skills/rapids-singlecell for another agent.
See the installation guide for installation and preflight details.
Documentation
For more information please have a look through the documentation
Citation
If you use this tool, please cite:
Please cite the relevant tools if used: decoupler for decoupler functions, squidpy for spatial analysis, and pertpy for perturbation analysis.
rapids-singlecell is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. If you like scverse® and want to support our mission, please consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.
1 maintainer
Metadata
1 maintainer
Release files for rapids-singlecell-cu12 0.19.0rc2
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Built distributions (wheels)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| rapids_singlecell_cu12-0.19.0rc2-cp313-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.13 | abi3 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details for rapids_singlecell_cu12-0.19.0rc2-cp313-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl |
| rapids_singlecell_cu12-0.19.0rc2-cp313-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl | CPython 3.13 | abi3 | Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64 | Details for rapids_singlecell_cu12-0.19.0rc2-cp313-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl |
Total release size: 49.9 MB
Release files / rapids_singlecell_cu12-0.19.0rc2-cp313-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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|---|---|
| Size | 25.1 MB |
| Tags | CPython 3.13 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
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| Download URL | rapids_singlecell_cu12-0.19.0rc2-cp313-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl |
|---|---|
| Size | 24.8 MB |
| Tags | CPython 3.13 Linux glibc 2.26+ ARM64 Linux glibc 2.28+ ARM64 abi3 |
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SHA-256 checksum How to use checksums |
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twine/7.0.0 CPython/3.13.16
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