are-we-learning-yet
rust-numpy
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are-we-learning-yet
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This year I tried solving AoC using Rust, here are my impressions coming from Python!
Also http://arewelearningyet.com
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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?
Hey OP, you might want to check this site out: http://arewelearningyet.com
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Is rust good for mathematical computing?
Note that you can update the page (adding packages or updating descriptions) via those github issues: https://github.com/anowell/are-we-learning-yet/issues
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I wanted to share my experience of Rust as a deep learning researcher
Not sure if you’ve encountered it, but you should be aware of http://arewelearningyet.com
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Announcing neuronika 0.1.0, a deep learning framework in Rust
Just make a PR: https://github.com/anowell/are-we-learning-yet
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
What are some alternatives?
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
RustPython - A Python Interpreter written in Rust
neuronika - Tensors and dynamic neural networks in pure Rust.
julia - The Julia Programming Language
wgpu - Cross-platform, safe, pure-rust graphics api.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
book - The Rust Programming Language
rayon - Rayon: A data parallelism library for Rust
rust-bert - Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Awesome-Rust-MachineLearning - This repository is a list of machine learning libraries written in Rust. It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers, etc. 🦀
PyO3 - Rust bindings for the Python interpreter