{"name":"CuPy: NumPy & SciPy for GPU-Accelerated Computing in Python","description":"CuPy is a powerful Python array library that provides NumPy and SciPy-compatible interfaces for GPU-accelerated computing. It enables users to seamlessly run existing numerical code on NVIDIA CUDA or AMD ROCm platforms with minimal changes. This tool also offers direct access to low-level CUDA features for advanced performance tuning and high-performance scientific computing.","github":"https://github.com/cupy/cupy","url":"https://osrepos.com/repo/cupy-cupy","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/cupy-cupy","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/cupy-cupy.md","json":"https://osrepos.com/repo/cupy-cupy.json","topics":["cupy","numpy","scipy","gpu-computing","cuda","rocm","python","scientific-computing"],"keywords":["cupy","numpy","scipy","gpu-computing","cuda","rocm","python","scientific-computing"],"stars":null,"summary":"CuPy is a powerful Python array library that provides NumPy and SciPy-compatible interfaces for GPU-accelerated computing. It enables users to seamlessly run existing numerical code on NVIDIA CUDA or AMD ROCm platforms with minimal changes. This tool also offers direct access to low-level CUDA features for advanced performance tuning and high-performance scientific computing.","content":"## Introduction\n\nCuPy is an open-source Python array library designed for high-performance GPU-accelerated computing. It offers a NumPy and SciPy-compatible interface, allowing developers to easily port existing numerical code to leverage the power of GPUs. CuPy acts as a drop-in replacement, making it straightforward to accelerate scientific computing and data processing tasks on NVIDIA CUDA or AMD ROCm platforms. Beyond its compatibility, CuPy also provides direct access to low-level CUDA features, enabling advanced users to fine-tune performance and integrate with existing CUDA C/C++ programs.\n\n## Installation\n\nCuPy can be installed using `pip` or `conda`. Choose the appropriate package based on your GPU platform and CUDA/ROCm version.\n\n### Pip\n\nBinary packages (wheels) are available on PyPI for Linux and Windows.\n\n*   **For CUDA 12.x:**\n    bash\n    pip install cupy-cuda12x\n    \n*   **For CUDA 13.x:**\n    bash\n    pip install cupy-cuda13x\n    \n*   **For ROCm 7.0 (experimental):**\n    bash\n    pip install cupy-rocm-7-0\n    \n\n### Conda\n\nBinary packages are also available on Conda-Forge.\n\n*   **General CUDA installation:**\n    bash\n    conda install -c conda-forge cupy\n    \n*   **To specify a CUDA version (e.g., 12.0):**\n    bash\n    conda install -c conda-forge cupy cuda-version=12.0\n    \n\n## Examples\n\nHere's a quick example demonstrating CuPy's NumPy-like syntax for GPU operations:\n\npython\nimport cupy as cp\n\n# Create a CuPy array on the GPU\nx = cp.arange(6).reshape(2, 3).astype('f')\nprint(\"CuPy array x:\")\nprint(x)\n\n# Perform a sum operation on the GPU\nsum_result = x.sum(axis=1)\nprint(\"\\nSum along axis 1:\")\nprint(sum_result)\n\n\n**Output:**\n\nCuPy array x:\n[[ 0.  1.  2.]\n [ 3.  4.  5.]]\nSum along axis 1:\n[ 3. 12.]\n\n\nThis example shows how CuPy arrays behave similarly to NumPy arrays, but computations are executed on the GPU.\n\n## Why Use CuPy?\n\nCuPy offers several compelling advantages for developers working with numerical computations:\n\n*   **GPU Acceleration:** Leverage the massive parallel processing power of GPUs to significantly speed up computationally intensive tasks, outperforming CPU-only solutions for large datasets.\n*   **NumPy/SciPy Compatibility:** Enjoy a familiar API that mirrors NumPy and SciPy, minimizing the learning curve and facilitating the migration of existing codebases to GPU environments.\n*   **Low-Level CUDA Access:** For advanced users, CuPy provides direct interfaces to CUDA features like RawKernels, Streams, and Runtime APIs, allowing for fine-grained control and optimization of GPU operations.\n*   **Broad Platform Support:** CuPy supports both NVIDIA CUDA and AMD ROCm platforms, offering flexibility across different hardware environments.\n*   **Active Community and Development:** Backed by Preferred Networks and a vibrant community, CuPy is continuously evolving with new features and improvements.\n\n## Links\n\n*   [CuPy Official Website](https://cupy.dev/)\n*   [CuPy Documentation](https://docs.cupy.dev/en/stable/)\n*   [Installation Guide](https://docs.cupy.dev/en/stable/install.html)\n*   [GitHub Repository](https://github.com/cupy/cupy)\n*   [API Reference](https://docs.cupy.dev/en/stable/reference/)\n*   [Tutorial](https://docs.cupy.dev/en/stable/user_guide/basic.html)","metrics":{"detailViews":6,"githubClicks":10},"dates":{"published":null,"modified":"2025-12-29T16:01:26.000Z"}}