{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"},{"label":"Visualization","url":"https://skillfed.io/packages/category/scientific-engineering-visualization"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"PyCUDA provides Pythonic bindings to Nvidia's CUDA API, letting you write GPU-accelerated code in Python with automatic memory management, error handling, and access to CUDA's full driver API.","skillfed_tags":["gpu-computing","cuda","high-performance"],"use_cases":["Accelerate numerical simulations and scientific computing by offloading compute-intensive kernels to GPU.","Build custom CUDA kernels for machine learning preprocessing or inference steps not covered by high-level frameworks.","Prototype GPU algorithms in Python before optimizing them in C++ or CUDA.","Interoperate between Python code and existing CUDA libraries or OpenGL graphics pipelines.","Manage GPU memory and multi-GPU coordination in research or data-processing pipelines."],"what_it_does":"PyCUDA is a Python wrapper around Nvidia's CUDA driver API that lets you write GPU-accelerated code without leaving Python. It abstracts away low-level CUDA complexity while exposing the full power of the driver API when needed. The package uses RAII (Resource Acquisition Is Initialization) to tie GPU memory and context cleanup to Python object lifetimes, eliminating manual resource management and preventing leaks. It includes high-level abstractions like SourceModule for compiling CUDA kernels and GPUArray for working with GPU memory, plus automatic error translation from CUDA errors into Python exceptions.\n\nThe core layer is written in C++, so the convenience abstractions carry minimal overhead. PyCUDA depends on pytools, platformdirs, and mako for code generation and utility functions. It requires Python 3.8+ and an Nvidia GPU with the CUDA toolkit installed; installation involves compiling C++ code, making setup more involved than pure-Python packages. The project is actively maintained with a long history since 2008 and carries no known security vulnerabilities.","worth_installing":"Yes, if you have an Nvidia GPU and need direct, low-level access to CUDA from Python. The high install friction (compiled C++ component, CUDA toolkit requirement) and active maintenance make it suitable for serious GPU computing work. Skip it if you need quick prototyping without system-level setup, or if you can use higher-level frameworks like CuPy or PyTorch instead."},"id":"pycuda","links":{"html":"https://skillfed.io/packages/pycuda","md":"https://skillfed.io/packages/pycuda.md","pypi":"https://pypi.org/project/pycuda/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-15","license_spdx":null,"license_treatment":"permissive","name":"pycuda","python_support":"supports_current","summary":"Python wrapper for Nvidia CUDA"},"popularity":{"monthly_downloads":86454,"position":13862,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.1"}