tealsql
rust-numpy
tealsql | rust-numpy | |
---|---|---|
7 | 10 | |
11 | 1,016 | |
- | 2.1% | |
8.3 | 8.0 | |
10 days ago | 15 days ago | |
Rust | Rust | |
- | BSD 2-clause "Simplified" 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.
tealsql
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Tealr 0.8 just released. Document your lua apis!
A project that uses tealr can be found at https://github.com/lenscas/tealsql/tree/master/pgteal, with online documentation available over at https://lenscas.github.io/tealsql/
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Man, I love this language.
There is also https://github.com/lenscas/tealsql which is used as kind of showcase project for tealr and tealr_doc_gen. While at the same time (hopefully) filling a pain point in the lua/teal eco system.
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What's everyone working on this week (7/2022)?
tealsql, my sql library for lua: Right now, it is being used to dog feed the changes in tealr. So, mostly improving its documentation, and preparing it to release it.
- What's everyone working on this week (6/2022)?
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What's everyone working on this week (39/2021)?
last weekend I put in some more time in tealsql, a postgresql client written in Rust for lua/teal.
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What's everyone working on this week (33/2021)?
The main focus is my sql client for teal/lua https://github.com/lenscas/tealsql , mainly getting rid of every part in the api that doesn't have a good type yet on the teal side of things (any, {any:any}, etc.
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What's everyone working on this week (29/2021)?
tealsql, an sql client for teal/lua. Current plan is to get the async api finished (right now it ignores every error but otherwise works without problems).
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?
youki - A container runtime written in Rust
RustPython - A Python Interpreter written in Rust
koto - A simple, expressive, embeddable programming language, made with Rust
julia - The Julia Programming Language
tealr - A wrapper around mlua and rlua to generate documentation and other helpers
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
rhyme-es
rayon - Rayon: A data parallelism library for Rust
synth - The Declarative Data Generator
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
txrx
PyO3 - Rust bindings for the Python interpreter