{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/3"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"CuPy provides a NumPy- and SciPy-compatible array library that runs computations on NVIDIA GPUs using CUDA 12.x, letting you write GPU-accelerated numerical code with a familiar NumPy-like API.","skillfed_tags":["gpu-computing","cuda","numerical-arrays"],"use_cases":["Accelerate large matrix operations and linear algebra computations by running them on GPU instead of CPU.","Port existing NumPy code to GPU with minimal changes, using CuPy's drop-in compatible API.","Build GPU-accelerated machine learning pipelines that need fast numerical array operations.","Perform large-scale scientific simulations and data transformations that benefit from GPU parallelism.","Combine CuPy with deep learning frameworks to handle numerical preprocessing on the same GPU device."],"what_it_does":"CuPy is a GPU-accelerated array library that mirrors NumPy and SciPy's API, allowing you to write numerical code that runs on NVIDIA GPUs instead of CPUs. It's built on CUDA 12.x and lets you leverage GPU parallelism for matrix operations, linear algebra, Fourier transforms, and other scientific computing tasks without rewriting your code from scratch. The package is actively maintained, supports modern Python versions (3.10\u20133.14), and is available as precompiled wheels for Linux (x86_64 and aarch64) and Windows.\n\nYou can install it standalone if you already have CUDA Toolkit 12.x on your system, or use the [ctk] extra to have pip bundle the necessary CUDA runtime components. It depends on NumPy and cuda-pathfinder for runtime operation. The library is in production-stable status and widely used in scientific and machine-learning workflows where GPU acceleration is needed.","worth_installing":"Yes, if you have CUDA 12.x hardware and want GPU-accelerated NumPy-like operations. The package is actively maintained, permissively licensed, and widely used in production. Install friction is moderate\u2014you need CUDA Toolkit 12.x or must use the [ctk] extra\u2014but that's a one-time setup cost. No known security vulnerabilities. Not worth installing if you don't have NVIDIA GPU hardware or are locked into a different CUDA version."},"id":"cupy-cuda12x","links":{"html":"https://skillfed.io/packages/cupy-cuda12x","md":"https://skillfed.io/packages/cupy-cuda12x.md","pypi":"https://pypi.org/project/cupy-cuda12x/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-01","license_spdx":"MIT","license_treatment":"permissive","name":"cupy-cuda12x","python_support":"supports_current","summary":"CuPy: NumPy & SciPy for GPU"},"popularity":{"monthly_downloads":3215490,"position":2698,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"14.1.1"}