nanobind
SWIG
| nanobind | SWIG | |
|---|---|---|
| 11 | 25 | |
| 2,042 | 5,499 | |
| - | 0.7% | |
| 9.6 | 9.7 | |
| 3 days ago | 8 days ago | |
| C++ | C++ | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nanobind
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Progress on No-GIL CPython
Take a look at https://github.com/wjakob/nanobind
> More concretely, benchmarks show up to ~4× faster compile time, ~5× smaller binaries, and ~10× lower runtime overheads compared to pybind11.
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Advanced Python Mastery – A Course by David Beazley
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.
- Swig – Connect C/C++ programs with high-level programming languages
- Nanobind: Tiny and efficient C++/Python bindings
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Create Python bindings for my C++ code with PyBind11
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.
- Nanobind – Seamless operability between C++17 and Python
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Cython Is 20
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
- GitHub - wjakob/nanobind: nanobind — Seamless operability between C++17 and Python
SWIG
- Swig – Connect C/C++ programs with high-level programming languages
- Using Lua with C++
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Purego – A library for calling C functions from Go without Cgo
How is this any different than a mature tool such as SWIG (https://www.swig.org/)?
I've used SWIG extensively with Python to call C code and import C headers for testing/tooling purposes.
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How does Golang communicate with C++?
For pure C, CGO. For C++ they are likely creating shims with Swig: https://www.swig.org/
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I feel really dumb whenever I take a coding test for a job
I mostly write in C and C++ so for language bindings I use Swig a lot. Say Im creating a machine learning library in C++, its very easy to create a Python API that can call the C++ classes and methods using Swig. iirc, I am using the same swig interface file to create bindings for Python, OCaml, R and even Fortran. Feel free to DM me if you got any more questions or anything!
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Is there a way to use a c++ project in a python project?
Swig can make c++ types and functions available to python.
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Boxflow - A universal layout engine written in Zig
The likes of SWIG is often used to link C library-like code to 11-ish other widely used languages.
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Hi, I have this program in C which I have to convert in Java(Android code), so that it could be used for decoding the output obtained from a Simulated program. Please help.
Maybe you could use swig to create a wrapper for Java.
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How do SWIG and LLVM compare as language ecosystems?
But, you might find these links interesting: * https://github.com/swig/swig/issues/918 * https://github.com/kaby76/swigged.llvm
What are some alternatives?
pybind11 - Seamless operability between C++11 and Python
cffi
awesome-cython - A curated list of awesome Cython resources. Just a draft for now.
CppSharp - Tools and libraries to glue C/C++ APIs to high-level languages
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
Cython - The most widely used Python to C compiler
matplotlibcpp17 - Alternative to matplotlibcpp with better syntax, based on pybind
djinni
epython - EPython is a typed-subset of the Python for extending the language new builtin types and methods
JavaCPP - The missing bridge between Java and native C++
avendish - declarative polyamorous cross-system intermedia objects
JNA - Java Native Access