iceoryx VS pybind11

Also, when you want to perform some access control with access rights, you have to face sid— and ace-strings - oh they are fun. And, of course, there are all the nasty details; for instance, Windows defines macros that lead to compilation failures since they collide with internal naming. Take a look at this here, maybe it makes your efforts less painless: https://github.com/eclipse-iceoryx/iceoryx/blob/master/iceor...

You could reuse the iceoryx platform layer that enables iceoryx to run on every platform from qnx, linux, freertos, mac, windows. Maybe it can help you as well: https://github.com/eclipse-iceoryx/iceoryx/blob/master/doc/w...

Does the schema help a lot? For C++ you can get very fast without, for example with IceOryx https://github.com/eclipse-iceoryx/iceoryx

In contrast to Cap'n'Proto you get compiler optimized struct layout as benefit from using raw structs. Benchmarks are here https://iceoryx.io/v2.0.2/examples/iceperf/

full release notes: https://github.com/eclipse-iceoryx/iceoryx/blob/master/doc/website/release-notes/iceoryx-v2-0-0.md

True, a better comparison for Shadesmar would be with libraries like iceoryx and alephzero.

This is the tracking issue for Windows support https://github.com/eclipse-iceoryx/iceoryx/issues/33

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.