mxe VS pybind11

Cross compiler 64bit minGW along with MXE environment

I used to use MXE [1] to compile fully static Windows binaries on Linux VMs hosted with Travis. It needed to crane in everything though, so it was a source of bottlenecks from time to time. I was also uncertain about the provenance of a lot of the dependencies in that toolchain. So when Travis died I took the opportunity to move Windows builds back to gnu with msys2, all over GH Actions. These are actually comparatively snappy and I’m reasonably satisfied with it.

[1] https://mxe.cc/

MXE readily supports GCC 12 as a plugin (just a configuration line): https://github.com/mxe/mxe/tree/master/plugins/gcc12

Also, if you don't have any other significant dependencies, getting the development tools on Windows is not that hard with the Qt installer. Alternatively, there is MXE.

MXE (https://mxe.cc/) is a great cross compiler environment (on linux) that uses mingw.

You can install Docker to build on any Linux distro, and if one of those is Ubuntu then you can use https://github.com/mxe/mxe to further build for Windows. Or VirtualBox + Ubuntu + MXE.

I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.

I have a C++ function computing a large tensor which I would like to return to Python as a NumPy array via pybind11.

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

pybind11 makes it easy to call C++ from Python if you want to mix.

If you've never used Pybind before these pybind tests[1] and this repo[2] have good examples you can crib to get started (in addition to the docs). Once you handle passing/returning/creating the main data types (list, tuple, dict, set, numpy array) the first time, then it's mostly smooth sailing.

Pybind offers a lot of functionality, but core "good parts" I've found useful are (a) use a numpy array in Python and pass it to a C++ method to work on, (b) pass your python data structure to pybind and then do work on it in C++ (some copy overhead), and (c) Make a class/struct in C++ and expose it to Python (so no copying overhead and you can create nice cache-aware structs, etc.).

[1] https://github.com/pybind/pybind11/blob/master/tests/test_py...

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.

Also I can highly recommend starting using C++ to augment your Python code, i.e. find the parts that are slow or undoable in Python and write those in C++ then expose them as Python functions. You can use https://github.com/pybind/pybind11 to call C++ code from Python.