function2 VS pybind11

Fixed size function wrappers are not only limited and also a waste of space. Use a configurable function wrapper like fu2 with a stack allocator and set small object optimizer capacity to 0.

std::function wasn't cutting it for me, but I ended up going with fu2::function. It comes with a lot of features (almost too many), but the header size and compile times are comparable to , and it fit my needs pretty well.

Thank you, function2 is also used heavily in Idle as a type-erasure backend for continuable that is the primary asynchronous task abstraction.

You can write your own std::function, too, nor is it the only STL type that can take a capturing lambda (std::packaged_task for example).

A capturing lambda is just a class with an operator(). It's complicated to do what std::function does, but fully possible.

In fact, custom std::function replacements have better lambda support than std::function itself. Such as unique_function in https://github.com/Naios/function2 which can handle non-copyable lambdas.

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.