rust-numpy
py2many
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rust-numpy | py2many | |
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10 | 29 | |
1,015 | 590 | |
5.1% | 2.2% | |
6.7 | 8.1 | |
5 days ago | 21 days ago | |
Rust | Python | |
BSD 2-clause "Simplified" License | MIT License |
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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
py2many
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Transpiler, a Meaningless Word
> Another problem is that there are hundreds of built-in library functions that need to be compiled from Python from C
An approach I've advocated as one of the main authors of py2many is that all of the python builtin functions be written in a subset of python[1] and then compiled into native code. This has the benefit of avoiding GIL, problems with C-API among other things.
Do checkout the examples here[2] which work out of the box for many of the 8-9 supported backends.
[1] https://github.com/py2many/py2many/blob/main/doc/langspec.md
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py2many VS kithon - a user suggested alternative
2 projects | 17 Jun 2023
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Why I'm still using Python
https://github.com/py2many/py2many/blob/main/doc/langspec.md
Reimplement a large enough, commonly used subset of python stdlib using this dialect and we may be in the business of writing cross platform apps (perhaps start with android and Ubuntu/Gnome)
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Codon: A high-performance Python compiler
For py2many, there is an informal specification here:
https://github.com/py2many/py2many/blob/main/doc/langspec.md
Would be great if all the authors of "python-like" languages get together and come up with a couple of specs.
I say a couple, because there are ones that support the python runtime (such as cython) and the ones which don't (like py2many).
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A Python-compatible statically typed language erg-lang/erg
It'd not fully solve your issue, but have you ever seen https://github.com/py2many/py2many ?
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Omyyyy/pycom: A Python compiler, down to native code, using C++
Cython doesn't consume python3 type hints and needs special type hints of its own. But it's certainly more mature than other players in the field.
What we need is a rpython suitable for app programming and a stdlib written in that dialect.
https://github.com/py2many/py2many/blob/main/doc/langspec.md
- I made a Python compiler, that can compile Python source down to fast, standalone executables.
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
No intermediate AST. To understand the various stages of transpilation and separation of language specific and independent rewriters, this file is a good starting point:
https://github.com/adsharma/py2many/blob/main/py2many/cli.py...
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Implicit Overflow Considered Harmful (and how to fix it)
Link to the test that's relevant for this discussion:
https://github.com/adsharma/py2many/blob/main/tests/cases/in...
This is an explicit deviation from python's bigint, which doesn't map very well to systemsey languages. The next logical step is to build on this to have dependent and refinement types.
Work in progress here:
https://github.com/adsharma/Typpete
What are some alternatives?
RustPython - A Python Interpreter written in Rust
pybind11 - Seamless operability between C++11 and Python
julia - The Julia Programming Language
PyO3 - Rust bindings for the Python interpreter
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
PythonNet - Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.
rayon - Rayon: A data parallelism library for Rust
PyCall.jl - Package to call Python functions from the Julia language
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
sqlglot - Python SQL Parser and Transpiler