{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"CuPy provides a NumPy and SciPy-compatible array library that runs computations on NVIDIA GPUs via CUDA 13.x, allowing you to accelerate numerical code with minimal API changes.","skillfed_tags":["gpu-computing","cuda","array-library"],"use_cases":["Accelerate large-scale matrix operations and linear algebra for machine learning or scientific simulations.","Speed up image processing pipelines by running convolutions and transformations on GPU.","Run iterative numerical algorithms such as optimization or PDE solvers that benefit from GPU parallelism.","Prototype GPU-accelerated code in Python before moving to lower-level implementations.","Process high-dimensional data arrays in applications where CPU throughput is a bottleneck."],"what_it_does":"CuPy is a GPU-accelerated array computing library that mirrors NumPy and SciPy's API, allowing you to run numerical computations on NVIDIA GPUs via CUDA 13.x. It is designed for scientists and engineers who want to leverage GPU parallelism without rewriting their numerical code from scratch. The package depends on numpy and cuda-pathfinder, and is distributed as precompiled wheels for Python 3.10 through 3.14 on Linux and Windows.\n\nYou install it either by ensuring CUDA Toolkit 13.x is already on your system, or by using the [ctk] tag to have pip bundle the necessary CUDA runtime components. Once installed, you can replace NumPy arrays with CuPy arrays and call the same functions, with computation happening on the GPU instead of the CPU.","worth_installing":"Yes, if you have NVIDIA GPU hardware and CUDA 13.x available, and your workload involves large-scale numerical computation that would benefit from GPU acceleration. The package is production-stable, actively maintained, and has no known vulnerabilities. Install friction is moderate\u2014you must have CUDA 13.x or use the [ctk] tag\u2014but the API compatibility with NumPy makes adoption straightforward for existing code."},"id":"cupy-cuda13x","links":{"html":"https://skillfed.io/packages/cupy-cuda13x","md":"https://skillfed.io/packages/cupy-cuda13x.md","pypi":"https://pypi.org/project/cupy-cuda13x/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-01","license_spdx":"MIT","license_treatment":"permissive","name":"cupy-cuda13x","python_support":"supports_current","summary":"CuPy: NumPy & SciPy for GPU"},"popularity":{"monthly_downloads":620674,"position":5724,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"14.1.1"}