# cupy/cupy

NumPy & SciPy for GPU

Repository: https://github.com/cupy/cupy
Canonical: https://ross.abutalabs.com/products/cupy
Homepage: https://cupy.dev
Language: Python
License: MIT
License Family: permissive
Topics: cuda, cudnn, cublas, cusolver, nccl, python, numpy, cupy, curand, cusparse, gpu, cutensor, scipy, nvtx, nvrtc, tensor, cusparselt, rocm
Last push: 2026-08-25T19:47:29+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 3592, "days_push": 8, "days_rel": 13, "gap_med": 38, "n_releases_24m": 10}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12278, forks 1132 (observed 2026-08-28T04:10:52.241918+00:00)

## What it is
CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python, running on NVIDIA CUDA or AMD ROCm. It acts as a drop-in replacement for NumPy/SciPy code and provides low-level access to CUDA features like custom kernels, streams, and runtime APIs.

## Use cases
- accelerate numpy array computations on gpu
- run existing scipy code on gpu without rewriting
- write custom cuda kernels from python
- speed up numerical computing with gpu
- gpu-accelerated sparse matrix operations
- multi-gpu distributed array computations
- port numpy code to amd rocm gpus

## When to choose
- you have existing NumPy/SciPy code that needs GPU acceleration with minimal changes
- you need low-level CUDA access (RawKernels, streams, runtime APIs) from Python
- you want a mature, actively maintained GPU array library with broad CUDA library support (cuBLAS, cuDNN, NCCL, cuTENSOR)

## When to avoid
- you have no NVIDIA or AMD GPU available
- you need full NumPy/SciPy API coverage - some functions are missing or behave differently
- you need AMD ROCm support in production - it is still experimental
- you prefer a higher-level deep learning framework like PyTorch or JAX

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, math, gpu-computing, deep-learning
- domain: machine-learning, data-science, gpu-computing, deep-learning
- platform: windows, python
- tags: numpy, scipy, cuda, rocm, gpu-acceleration, array-library, drop-in-replacement, cudnn, nccl, algorithms, linux, gpu

## Member repositories
- cupy/cupy (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:52.241918+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:14:11.806791+00:00, confidence not recorded.
  - readme: https://github.com/cupy/cupy (fetched 2026-08-28T04:10:52.241918+00:00, sha cab45102981d)
  - homepage: https://cupy.dev (fetched 2026-08-29T08:11:33.489438+00:00, sha 861cf06fbc19)
  - site_page: https://docs.cupy.dev/ (fetched 2026-08-29T08:11:33.498377+00:00, sha c4c63a651ede)
  - site_page: https://docs.cupy.dev/en/stable/install.html (fetched 2026-08-29T08:11:33.500069+00:00, sha c0aea12fedfc)
  - registry_pypi: https://pypi.org/pypi/cupy/json (fetched 2026-08-29T08:11:33.502227+00:00, sha 24415a4bb9d8)
- Data as of 2026-08-30T08:39:29.467469+00:00.