Nuitka
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Nuitka | Numba | |
---|---|---|
91 | 124 | |
10,582 | 9,350 | |
2.0% | 1.7% | |
10.0 | 9.9 | |
7 days ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
Nuitka
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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.
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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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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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Extending web applications with WebAssembly and Python
> Your comment would make sense if Python code could be compiled into x86 or ARM assembly in the first place.
It can actually be compiled (or transpiled) into C code [1] with few limitations, so I can't see why not.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "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."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
- Is there a way to use turn a project into a single executable file that doesn't require anyone to do anything like install Python before using it?
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Python-based compiler achieves orders-of-magnitude speedups
So the differences:
https://docs.exaloop.io/codon/general/differences
So more limited types (integers) and more type checking and collections have to have one kind of thing in them.
There are other python compilers though, like https://github.com/Nuitka/Nuitka
I wonder really what the advantages/disadvantages of these are?
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List of all subreddits on reddit
I also compiled it with Nuitka and was able to process a ~35 million line file in 1 hour and 22 minutes, averaging about 6,800 lines per second.
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "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."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
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Been using Python for 3 years, never used a Class.
There are also just-in-time compilers available for some Python features, that compile those parts to machine code. That includes Numba (usable as a library within CPython) and Pypy (an alternative Python implementation that includes a JIT compiler to improve performance). There’s also Cython, which is a superset of Python that allows more directly interfacing with C and C++ functions, and compiling the resulting combined code.
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Is there a language with lisp syntax but C semantics?
this was a submission from u/bpecsek and shows that lisp with sbcl can do quite well on bench-marking. but keep in mind that these sort of benchmarks can't tell you much about real world applications. moreover if you are really concerned about niche performance you need to start thinking about compilers. heck with an appropriate compiler even python can go wrooom
- [D] Yann LeCun's Hot Take about programming languages for ML
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Python Developer Seeking Input: Is it Worth Learning Rust for FFI?
- if no purpose built libraries are faster, use numba (http://numba.pydata.org/) to speed up your code. Optionally you can also use Taichi (https://www.taichi-lang.org/) instead of numba.
What are some alternatives?
PyInstaller - Freeze (package) Python programs into stand-alone executables
pyarmor - A tool used to obfuscate python scripts, bind obfuscated scripts to fixed machine or expire obfuscated scripts.
NetworkX - Network Analysis in Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
PyOxidizer - A modern Python application packaging and distribution tool
py2exe - modified py2exe to support unicode paths
Dask - Parallel computing with task scheduling
cupy - NumPy & SciPy for GPU
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
SymPy - A computer algebra system written in pure Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
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.