rust-numpy
pythran
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rust-numpy | pythran | |
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10 | 7 | |
1,015 | 1,964 | |
5.1% | - | |
6.7 | 8.0 | |
6 days ago | 14 days ago | |
Rust | C++ | |
BSD 2-clause "Simplified" License | 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.
rust-numpy
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Numba: A High Performance Python Compiler
On the contrary, it can use and interface with numpy quite easily: https://github.com/PyO3/rust-numpy
- Carefully exploring Rust as a Python developer
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Hmm
Once I figured out the right tools, it was easy. Its just "maturin new". It automatically converts python floats and strings. Numpy arrays come through as a special Pyarray type, that you need to unwrap, but that's just one builtin function. Using pyo3, maturin and numpy, https://github.com/PyO3/rust-numpy it's fairly easy.
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Man, I love this language.
If I'm understanding this documentation correctly then you may be able to pass the numpy array directly with func(df['col'].to_numpy) which may save some conversion.
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[D] Is Rust stable/mature enough to be used for production ML? Is making Rust-based python wrappers a good choice for performance heavy uses and internal ML dependencies in 2021?
Otherwise, though, Rust is an excellent choice. The many advantages of Rust (great package manager, memory safety, modern language features, ...) are already well documented so I won't repeat them here. Specifically for writing Python libraries, check out PyO3, maturin, and rust-numpy, which allow for seamless integration with the Python scientific computing ecosystem. Dockerizing/packaging is a non-issue, with the aforementioned libraries you can easily publish Rust libraries as pip packages or compile them from source as part of your docker build. We have several successful production deployments of Rust code at OpenAI, and I have personally found it to be a joy to work with.
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Writing Rust libraries for the Python scientific computing ecosystem
Integration with numpy uses the rust-numpy crate: Example of method that accepts numpy arrays as arguments Example of a method that returns a numpy array to Python (this performs a copy, there ought to be a way to avoid it but the current implementation has been plenty fast for my use case so far)
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Feasibility of Using a Python Image Super Resolution Library in My Rust App
This example maybe helpful.
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Julia is the better language for extending Python
Given that it's via pyO3, you could even pass the numpy arrays using https://github.com/PyO3/rust-numpy and get ndarrays at the other side.
Same no copy, slightly more user friendly approach.
Further criticism of the actual approach - even if we didn't do zero copy, there's no preallocation for the vector despite the size being known upfront, and nested vectors are very slow by default.
So you could speed up the entire thing by passing it to ndarray, and then running a single call to sum over the 2D array you'd find at the other end. (https://docs.rs/ndarray/0.15.1/ndarray/struct.ArrayBase.html...)
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Parsing PDF Documents in Rust
I believe converting between pandas Series (e.g. columns) and numpy ndarrays can be pretty cheap, right? Once they're in that format, you can use rust to work directly on the numpy memory buffer with rust-numpy. Otherwise, feather is a format designed for IPC of columnar data; pyarrow is in pandas (might be an optional dependency) and may be pretty quick for that, and rust has an arrow implementation too.
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PyO3: Rust Bindings for the Python Interpreter
https://github.com/PyO3/rust-numpy
pythran
- Codon: Python Compiler
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How Python virtual environments work
Numpy and Scipy are good reasons. Unfortunately Scipy does not even compile on FreeBSD lately, and I have opened three issues about it against Scipy and Pythran (and the fix was with xsimd).
https://github.com/serge-sans-paille/pythran/issues/2070
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S6: A standalone JIT compiler library for CPython
In someone lands here seeking a maintained compiler for Python, there's a lot, on top of my head:
- Pythran (https://pythran.readthedocs.io) (ahead of time compiler)
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Accelerate Python code 100x by import taichi as ti
Yes, I mean Pythran ( https://github.com/serge-sans-paille/pythran ). Thank you.
Was Nuitka better? Pythran is quite simple to install and use in Jupyter.
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Omyyyy/pycom: A Python compiler, down to native code, using C++
The only project that compares 1:1 is Pythran: https://github.com/serge-sans-paille/pythran
Pythran is fairly nice, and it really does work. I tried it last year and it compiles down to modifiable templated C++. I was able to use it to build Python for a highly specialized environment.
All the others compile down to dynamically linked binaries, and that just puts them in the "other" box.
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OpenAI Codex Python to C++ Code Generator
You might want to contact the author of Pythran [1], maybe something can be learned from what they do.
[1] https://github.com/serge-sans-paille/pythran/commits/master
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PyO3: Rust Bindings for the Python Interpreter
[1] https://github.com/serge-sans-paille/pythran
What are some alternatives?
RustPython - A Python Interpreter written in Rust
setuptools-rust - Setuptools plugin for Rust support
julia - The Julia Programming Language
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
codex_py2cpp - Converts python code into c++ by using OpenAI CODEX.
rayon - Rayon: A data parallelism library for Rust
shedskin - Shed Skin is a restricted-Python-to-C++ compiler. Read the introduction below to learn about the restrictions.
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
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.
PyO3 - Rust bindings for the Python interpreter
pybind11 - Seamless operability between C++11 and Python