pybind11 VS nanobind

This is based on the Google Ink Stroke Modeler C++ library, and using pybind11 to make it available on python.

I have a python module for which the logic is written in C++ and I use pybind11 to expose the objects and functions to Python.

Reading from Python requires a wrapper, using pybind11 this is fairly done.

That said, if you need to write a Python module in a compiled language, it's much easier and more fun these days to write in C++. pybind11 is extremely mature and even fun system to write Python objects in C++.

There will be specific technologies you used in your data pipelining: Parquet? Did you use Pandas to manipulate your data? Did you optimise some aspects with C++? If so did you use PyBind11 to integrate it?

git clone https://github.com/pybind/pybind11.git

I would say that pybind11 is precisely what you want here. If you don’t want to write the bindings yourself, you could try Tolc.

Pybind11 https://github.com/pybind/pybind11 is an easy way to expose c++ functions in python.

I think they should give up on Flutter for Web and just focus on mobile and desktop (and maybe extend use on Smart Watches, Smart TV, embedded instead). Too many people still remember Java Applets, google's GWT, Flash/Flex and how it ended. They are out of focus trying to support Web as well.

I had also higher expectation regarding Dart FFI - I think with their ffigen we are still only in ctypes like python bindings territory. Nowhere near something like pybind11 [0] for c++ bindings yet.

[0] https://github.com/pybind/pybind11

[1] https://github.com/mozilla/uniffi-rs

People should not take that an endorsement of Swig.

Please use ctypes, cffi or https://github.com/wjakob/nanobind

Beazley himself is amazed that it (Swig) is still in use.

Nanobind made by the creator of PyBind11, it has a similar interface, but it takes leverage of C++17 and it aims to have more efficient bindings in space and speed.

I would recommend using NanoBind, the follow up of PyBind11 by the same author (Wensel Jakob), and move as much performance critical code to C or C++. https://github.com/wjakob/nanobind

If you really care about performance called from Python, consider something like NVIDIA Warp (Preview). Warp jits and runs your code on CUDA or CPU. Although Warp targets physics simulation, geometry processing, and procedural animation, it can be used for other tasks as well. https://github.com/NVIDIA/warp

Jax is another option, by Google, jitting and vectorizing code for TPU, GPU or CPU. https://github.com/google/jax