nanobind
Nuitka
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nanobind | Nuitka | |
---|---|---|
11 | 94 | |
2,028 | 10,835 | |
- | 2.9% | |
9.6 | 10.0 | |
7 days ago | 4 days ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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
Nuitka
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Py2wasm – A Python to WASM Compiler
Thanks for the feedback! I'm Syrus, main author of the work on py2wasm.
We already opened a PR into Nuitka to bring the relevant changes upstream: https://github.com/Nuitka/Nuitka/pull/2814
We envision py2wasm being a thin layer on top of Nuitka, as also commented in the article.
From what we gathered, we believe that there's usefulness on having py2wasm as a separate package, as py2wasm would also need to ship the precompiled Python distribution (3.11) for WASI (which will not be needed for the other Nuitka use cases), apart of also shipping other tools that are not directly relevant for Nuitka
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Python Is Portable
This is a good place to mention https://nuitka.net/ which aims to compile python programs into standalone binaries.
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We are under DDoS attack and we do nothing
For Python, you could make a proper deployment binary using Nuitka (in standalone mode – avoid onefile mode for this). I'm not pretending it's as easy as building a Go executable: you may have to do some manual hacking for more unusual unusual packages, and I don't think you can cross compile. I think a key element you're getting at is that Go executables have very few dependencies on OS packages, but with Python (once you've sorted the actual Python dependencies) you only need the packages used for manylinux [2], which is not too onerous.
[1] https://nuitka.net/
[2] https://peps.python.org/pep-0599/#the-manylinux2014-policy
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Faster Blogging: A Developer's Dream Setup
glee is rich in blogging features but has some drawbacks. One of the main drawbacks is its compatibility with multiple operating systems and system architectures. We lost one potential customer due to glee incompatibility in macOS. Another major issue is the deployment time. We built the first version of glee entirely in Python and used nuitka, nuitka compiles Python programs into a single executable binary file. We need to create three separate stages for creating executable binaries for Windows, Mac, and Linux in deployment, and it takes around 20 minutes to complete.
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Python 3.13 Gets a JIT
There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.
And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.
And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.
[0] https://nuitka.net/
[1] https://github.com/python/mypy/tree/master/mypyc
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Briefcase: Convert a Python project into a standalone native application
Nuitka deals pretty well with those in general: https://nuitka.net/
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Ask HN: How does Nuitka (Python compiler) work?
Hi HN,
Has anyone explored Nuitka [1] and developed understanding from a blank slate?
Is there any toy version of this, so that one can start playing with the language translation concepts?
Is there any underlying theory/inspiration upon which this project is built?
Are there any similar projects, in say other languages?
[1] https://github.com/Nuitka/Nuitka
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Why not tell people to “simply” use pyenv, poetry or anaconda
That's more of cultural problem in the Python community.
If I provide an end user software to my client written an Python (so not a backend, not a lib...), I will compile it with nuitka (https://github.com/Nuitka/Nuitka) and hide the stack trace (https://www.bitecode.dev/p/why-and-how-to-hide-the-python-st...) to provide a stand alone executable.
This means the users don't have to know it's made with Python or install anything, and it just works.
However, Python is not like Go or Rust, and providing such an installer requires more than work, so a huge part of the user base (which have a lot of non professional coders) don't have the skill, time or resources to do it.
And few people make the promotion of it.
I should write an article on that because really, nobody wants to setup python just to use a tool.
- Python cruising on back of c++
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Is cython a safe option for obfuscate a python project?
As for a simpler option, you could use a "compiler": https://github.com/Nuitka/Nuitka
What are some alternatives?
pybind11 - Seamless operability between C++11 and Python
PyInstaller - Freeze (package) Python programs into stand-alone executables
awesome-cython - A curated list of awesome Cython resources. Just a draft for now.
pyarmor - A tool used to obfuscate python scripts, bind obfuscated scripts to fixed machine or expire obfuscated scripts.
matplotlibcpp17 - Alternative to matplotlibcpp with better syntax, based on pybind
PyOxidizer - A modern Python application packaging and distribution tool
epython - EPython is a typed-subset of the Python for extending the language new builtin types and methods
py2exe - modified py2exe to support unicode paths
avendish - declarative polyamorous cross-system intermedia objects
false-positive-malware-reporting - Trying to release your software sucks, mostly because of antivirus false positives. I don't have an answer, but I do have a list of links to help get your code whitelisted.
warp - A Python framework for high performance GPU simulation and graphics
py2app