rust-numpy
pybind11
Our great sponsors
rust-numpy | pybind11 | |
---|---|---|
10 | 42 | |
1,015 | 14,741 | |
5.1% | 1.7% | |
6.7 | 8.7 | |
7 days ago | 5 days ago | |
Rust | C++ | |
BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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
-
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
-
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.
-
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.
-
[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.
-
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)
-
Feasibility of Using a Python Image Super Resolution Library in My Rust App
This example maybe helpful.
-
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...)
-
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.
-
PyO3: Rust Bindings for the Python Interpreter
https://github.com/PyO3/rust-numpy
pybind11
-
Experience using crow as web server
I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.
- Swig – Connect C/C++ programs with high-level programming languages
-
returning numpy arrays via pybind11
I have a C++ function computing a large tensor which I would like to return to Python as a NumPy array via pybind11.
-
I created smooth_lines python module, great for drawing software
This is based on the Google Ink Stroke Modeler C++ library, and using pybind11 to make it available on python.
-
Facial Landmark Detection with C++
pybind11 makes it easy to call C++ from Python if you want to mix.
-
Python’s Multiprocessing Performance Problem
If you've never used Pybind before these pybind tests[1] and this repo[2] have good examples you can crib to get started (in addition to the docs). Once you handle passing/returning/creating the main data types (list, tuple, dict, set, numpy array) the first time, then it's mostly smooth sailing.
Pybind offers a lot of functionality, but core "good parts" I've found useful are (a) use a numpy array in Python and pass it to a C++ method to work on, (b) pass your python data structure to pybind and then do work on it in C++ (some copy overhead), and (c) Make a class/struct in C++ and expose it to Python (so no copying overhead and you can create nice cache-aware structs, etc.).
[1] https://github.com/pybind/pybind11/blob/master/tests/test_py...
- Making Python Web Application with C++ Backend
-
Using pybind11 with minGW to cross compile pyhton module for Windows
I have a python module for which the logic is written in C++ and I use pybind11 to expose the objects and functions to Python.
-
IPC communication between rust, c++, and python
Reading from Python requires a wrapper, using pybind11 this is fairly done.
-
[ADVICE] Python to C++
Also I can highly recommend starting using C++ to augment your Python code, i.e. find the parts that are slow or undoable in Python and write those in C++ then expose them as Python functions. You can use https://github.com/pybind/pybind11 to call C++ code from Python.
What are some alternatives?
RustPython - A Python Interpreter written in Rust
PyO3 - Rust bindings for the Python interpreter
julia - The Julia Programming Language
nanobind - nanobind: tiny and efficient C++/Python bindings
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Optional Argument in C++ - Named Optional Arguments in C++17
rayon - Rayon: A data parallelism library for Rust
setuptools-rust - Setuptools plugin for Rust support
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
PEGTL - Parsing Expression Grammar Template Library
sol2 - Sol3 (sol2 v3.0) - a C++ <-> Lua API wrapper with advanced features and top notch performance - is here, and it's great! Documentation: