maturin
Numba
Our great sponsors
maturin | Numba | |
---|---|---|
37 | 124 | |
3,211 | 9,404 | |
5.2% | 1.6% | |
9.4 | 9.9 | |
9 days ago | 9 days ago | |
Rust | 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.
maturin
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In Rust for Python: A Match from Heaven
This story unfolds as a captivating journey where the agile Flounder, representing the Python programming language, navigates the vast seas of coding under the wise guidance of Sebastian, symbolizing Rust. Central to their adventure are three powerful tridents: cargo, PyO3, and maturin.
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Feedback from calling Rust from Python
-- Maturin on GitHub
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Some Reasons to Avoid Cython
My new favorite way to write very fast libraries for Python is to just use Rust and Maturin:
https://github.com/PyO3/maturin
It basically automates everything for you. If you use it with Github actions, it will compile wheels for you on each release for every platform and python version you want, and even upload them to PyPi (pip) for you. Everything feels very modern and well thought out. People really care about good tooling in the Rust world.
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Which programming language to focus on for my PhD journey in bioinformatics?
Python first, you will be able to experiment quickly with the notebooks. Then maybe write (or rewrite) some modules in Rust that you can expose as python modules, with py03 and maturin. Feel free to publish useful packages on both crates.io and pypi.org, so you can contribute to Python and Rust ecosystems.
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python to rust migration
Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.
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Ask HN: Is it worth it for me to learn Go or Rust as a Data Engineer?
It's relatively easy to extend Python with project like Py03[0] and Maturin[1]. Polars[2] is the perfect example of that.
It's not easy to push coworkers/companies to use an unfamiliar language. Rust isn't fast to learn. You need very good arguments and a good usecase to make it works.
I doubt that learning Rust will help you more that learning more about the data engineers tools, so this isn't really "worth" your time.
[0] -- https://pyo3.rs/v0.18.3/
[1] -- https://github.com/PyO3/maturin
[2] -- https://www.pola.rs/
- Rust CLI app installable via PIP?
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Blog Post: Making Python 100x faster with less than 100 lines of Rust
In this case, PyO3/maturin does all the setup and getting the module into Python. They also have docs going into a lot more depth on this.
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Is Rust faster than Python out of the box
Lastly if you're willing to introduce Rust, I'd consider a gradual approach using native libraries built in rust with PYO3. Check the maturin guide that helps you to streamline the build process of native libraries : https://github.com/PyO3/maturin . From there you could try to find hotspots in your python app and replace those with a native implementation.
- sccache now supports GHA as backend
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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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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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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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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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.
- Numba Supports Python 3.11
What are some alternatives?
Poetry - Python packaging and dependency management made easy
NetworkX - Network Analysis in Python
setuptools-rust - Setuptools plugin for Rust support
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
termux-packaging - Termux packaging tools.
Dask - Parallel computing with task scheduling
PyOxidizer - A modern Python application packaging and distribution tool
cupy - NumPy & SciPy for GPU
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
pybind11 - Seamless operability between C++11 and Python
SymPy - A computer algebra system written in pure Python