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  1. Wishful thinking and missleading ! With scikit-learn, python and python based app, nothing is fast ! Even if installed all those “fast libraries and CUDA support” python does not use ANY HARDWARE ACCELERATIB AVAILABLE IN THE SYSTEM and PARALELL PROCESING done on CPU WORKS in SEQUENTIAL MODE ONLY ! How is this FAST ??? Maybe on some alien CPU’s not known to the rest of mankind !

    1. Hi Mark! Thanks for sharing your perspective. It’s true that vanilla Python and certain CPU-based libraries can be slow when you’re dealing with massive datasets. However, tools like RAPIDS (cuDF, cuML, cuGraph) and CuPy do actively leverage GPU hardware acceleration through NVIDIA CUDA under the hood. Although the user-facing API is in Python, the performance-critical parts are written in C++ and CUDA, which can provide substantial speedups—particularly on NVIDIA GPUs.

      You might want to try a quick experiment if you have a compatible GPU: run a GPU-accelerated operation (like a big matrix multiplication in CuPy) and compare it to the same operation in CPU-based NumPy. The difference can be dramatic. Of course, performance also depends on factors like setup, environment, and data size, but many users report significant gains in workflows using RAPIDS.

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