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Source channel @githubtrending · Post #14985 · Jul 22

#c_lang#cuda#cuda_driver_api#cuda_kernels#cuda_opengl You can use the CUDA Samples from NVIDIA to learn and test CUDA Toolkit 12.9 features by downloading them from GitHub or as a ZIP file. These samples show how to use CUDA for GPU programming, including utilities, concepts, libraries, and performance optimization. You build them with CMake on Linux, Windows, or Tegra devices, and can run tests automatically with a provided Python script. This helps you understand CUDA programming, debug GPU code, and optimize your applications for better performance on NVIDIA GPUs. It’s a practical way to develop and improve GPU-accelerated software efficiently. https://github.com/NVIDIA/cuda-samples

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djangoproject

@djangoproject · Post #521 · 12/13/2017, 07:57 PM

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https://www.infoworld.com/article/3230202/python/6-essential-libraries-for-every-python-developer.html 6 essential libraries for every Python developer Whether you're compiling Python for speed, building native UIs for Python desktop apps, or refining existing Python code, these Python projects have you covered Python has seen wide adoption across industries and disciplines by dint of being easy to work with. But it has also been aided by a wealth of third-party projects—libraries, add-ons, and complementary development efforts—that extend the language to an ever widening range of use cases. #PyPy #CFFI(C Foreign Function Interface library) #PyInstaller #PBR(Python Build Reasonableness) #WxPython #Mypy

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