When researching and developing new algorithms to be used in the real-world production environment, what is your workflow and how do you usually do it? Do I have to prototype in Python, and then rewrite all code in C++/Rust?

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julia-language c-plus-plus-14 Julia Numpy Scientific

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  • julia

    The Julia Programming Language

    • I am using Julia https://julialang.org/ for this purpose, fast prototyping and similar run time performance as Rust/C.

  • SwiftyWasmer

    Discontinued A Swift API for the Wasmer WebAssembly Runtime

    • One approach could be to run your Python algorithms inside Pyiodide inside a webassembly runtime like https://github.com/AlwaysRightInstitute/SwiftyWasmer . That would let you deploy the Python code that you did the research in. It depends which Python libraries you use though, I don't think pyiodide supports everything, but the big ones are there - numpy, scikit-learn, pillow etc

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  • xtensor

    C++ tensors with broadcasting and lazy computing

    • You can try eigen (http://eigen.tuxfamily.org/), armadillo (http://arma.sourceforge.net/) which is based on LAPACK which is what numpy is based on and xtensor (https://github.com/QuantStack/xtensor) which I think is the closest thing to numpy you’re gonna find in c++

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