pierogis
CheeseShop
pierogis | CheeseShop | |
---|---|---|
7 | 2 | |
119 | 1 | |
0.8% | - | |
2.6 | 3.8 | |
about 2 years ago | 8 months ago | |
Python | Rust | |
GNU Affero General Public License v3.0 | MIT 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.
pierogis
- pierogis/pierogis a framework for image and animation processing
- Pierogis – Python/rust image and animation processing
-
avalanche sorted and quantized
made with pierogis
-
cold
pierogis -> now with animations
-
tunnel (000000 ffffff 65615C EBECE5 322E28 9CC0CE 5E4E3C 4B7BA3)
pierogis 0.2.0 is live
-
wave
process all of the pngs in ./frames using pyrogis and put them into ./cooked
-
PyO3: Rust Bindings for the Python Interpreter
https://github.com/pierogis/pierogis
CheeseShop
-
Apache Spark UDFs in Rust
By comparison, PyO3 handles virtually all that boilerplate, so your Rust functions can accept and return many native Rust types and everything just works (for example). Or maybe I'm missing some fundamental difference with how JVM data are handled versus Python.
-
PyO3: Rust Bindings for the Python Interpreter
At work, I'm using PyO3 for a project that churns through a lot of data (step 1) and does some pattern mining (step 2). This is the second generation of the project and is on-demand compared with the large, batch project in Spark that it is replacing. The Rust+Python project has really good performance, and using Rust for the core logic is such a joy compared with Scala or Python that a lot of other pieces are written in.
Learning PyO3, I cobbled together a sample project[0] to demonstrate how some functionality works. It's a little outdated (uses PyO3 0.11.0 compared with the current 0.13.1) and doesn't show everything, but I think it's reasonably clear.
One thing I noticed is that passing very large data from Rust and into Python's memory space is a bit of a challenge. I haven't quite grokked who owns what when and how memory gets correctly dropped, but I think the issues I've had are with the amount of RAM used at any moment and not with any memory leaks.
[0] https://github.com/aeshirey/CheeseShop
What are some alternatives?
Datamosher-Pro - A GUI based powerful automatic datamoshing application for free! Easily apply this trippy glitch effect in your videos. Contains 30+ cool glitch effects!
ffi-overhead - comparing the c ffi (foreign function interface) overhead on various programming languages
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
whatlang-pyo3 - Python Binding for Rust WhatLang, a language detection library
setuptools-rust - Setuptools plugin for Rust support
dtparse - Fast datetime parser for Python written in Rust
pythran - Ahead of Time compiler for numeric kernels
rayon - Rayon: A data parallelism library for Rust
py2many - Transpiler of Python to many other languages