pybind11

I can’t help mentioning my own project - https://github.com/aldanor/ipybind - which provides a lightweight Jupyter interface to pybind11 [1], allowing to quickly sketch working C++ extensions in the notebook; all features of pybind11 like numpy support are immediately available. Been using it myself quite a lot for prototyping C++/Python code. [1] https://github.com/pybind/pybind11

I find pybind11 [1] to be perfect for my C++ code. There's so little boilerplate, and I get RAII-guaranteed memory safety and all the speed my C++ development can bring. For example, the binding of an accelerated HyperLogLog implementation only requires tiny amount of work, plus a line in my Makefile: PYBIND11_MODULE(_hll, m) { m.doc() = "pybind11-powered HyperLogLog"; // optional module docstring py::class_<hll_t> (m, "hll") .def(py::init<size_t>()) .def("clear", &hll_t::clear, "Clear all entries.") .def("resize", &hll_t::resize, "Change old size to a new size.") .def("sum", &hll_t::sum, "Add up results.") .def("report", &hll_t::report, "Emit estimated cardinality. Performs sum if not performed, but sum must be recalculated if further entries are added.") .def("add

pybind11 is the nicest foreign function interface I've encountered: https://github.com/pybind/pybind11

pybind11 is a similar library, but for interfacing with C++ from Python: https://github.com/pybind/pybind11

We've had good luck with some academic code bases in production -- ETH Zurich puts out some great code [1,2]. [1] https://github.com/libigl/libigl [2] https://github.com/pybind/pybind11

If you're into both C++ and Python, I recommend pybind11 [1] - Cython is a crutch and moreover C++ is not a first class citizen in it. For reasonably large codebases Cython just doesn't scale and debugging is a total nightmare. Disclaimer: I'm a contributor to pybind11. [1] https://github.com/pybind/pybind11

Metaprogramming such as pybind11 does is neat. Thanks for the pointer to the project. It still looks like manually written C extension modules to me based off of what I see in https://github.com/pybind/pybind11/blob/master/docs/basics.r.... Just much shorter code with a lot of the hard to get right details taken care of you. Good! Better than the status quo. But it doesn't abstract the problem away very much. (no doubt some will consider that a feature) What if I also wanted my wrapper available on a non CPython VM? The proper way to use PyPy is not via its fake CPython API support (slow and memory hungry). Imagine if a PyPy Generator were added to CLIF. It'd use the same interface definition Parser but generate an entirely different set of code. In PyPy's case that would probably be a C library wrapping the C++ for generated c