rust-numpy
rust
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rust-numpy | rust | |
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10 | 9 | |
1,015 | 4,986 | |
5.1% | 2.2% | |
6.7 | 5.2 | |
8 days ago | 5 months ago | |
Rust | Rust | |
BSD 2-clause "Simplified" License | Apache License 2.0 |
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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
rust
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Have you ever wanted a library to check for 69 in a string?
You can use Tensorflow for Rust to simplify that task and avoid pain with regex. Just have the right mindset.
- Rust vs cpp for a new engineer to autonomous vehicles and robotics
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Making a better Tensorflow thanks to strong typing
What is the benefit of this compared to using bindings/a wrapper to Tensorflow, or other ML libraries written in C/C++, such as this community hosted project on tensorflow's github. If it's just for fun that is a valid enough reason imo, just curious since you describe it as a better Tensorflow because of the typing vs using the python wrapper, when there already exist ways to interact with tensorflow with both Rust and other statically typed languages, also including C++ (officially supported), C#, Haskell and Scala, as well as probably having bindings not mentioned on the documentation for more niche languages.
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Integrating machine learning models into Rust applications?
(3) You could use TensorFlow as your executor: https://github.com/tensorflow/rust
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Why Static Languages Suffer From Complexity
TensorFlow has language support for TypeScript well as Rust.
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Is PyO3 library production ready?
Thank you for the restponse! With tensorflow I am probably better of with something like; [tensorflow rust bindings](https://github.com/tensorflow/rust/tree/master/src). But I believe some useful extensions are still written in python for example; [TFDV](https://github.com/tensorflow/data-validation).. and how about scikit-learn or even something that is simpler like fb-prophet that is entirely written in python?
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How mature is the QT integration?
Tensorflow bindings exist, technically, but they're in a pretty rough state AFAIK.
- Feasibility of Using a Python Image Super Resolution Library in My Rust App
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Rusticles #10 - Wed Sep 09 2020
tensorflow/rust (Rust): Rust language bindings for TensorFlow
What are some alternatives?
RustPython - A Python Interpreter written in Rust
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
julia - The Julia Programming Language
leaf - Open Machine Intelligence Framework for Hackers. (GPU/CPU)
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
anyhow - Flexible concrete Error type built on std::error::Error
rayon - Rayon: A data parallelism library for Rust
Rustup - The Rust toolchain installer
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
rusty-machine - Machine Learning library for Rust
PyO3 - Rust bindings for the Python interpreter
solana - Web-Scale Blockchain for fast, secure, scalable, decentralized apps and marketplaces.