tealr VS rust-numpy

working more on tealr more specifically tealr_doc_gen

Fixing some short comings in tealr that people discovered. Also, making hv_lua work with tealr and ideally start adding that fishfight.

Fantastic! Very happy to see this move forward at such a fast pace. We’re implementing scripting in Fish Fight via the tealr crate, which wraps mlua (and rlua).

Hopefully the last hand on https://github.com/lenscas/tealr for a new release. Mostly going over the documentation. Laying less emphasis on teal and more on "Hey, using this you can express a more typesafe api to lua and can actually easily document it.".

tealr, my wrapper around rlua and mlua to focus on (among other things) being better able to describe your api in the form of types: Getting it to a point to release a new version. Which is all about allowing people to document they API that they created for lua/teal. This documentation gets written to the definition files when generating them. In addition, you can also ask tealr to generate a instance.help(page:Option) method on your types. When doing this, the documentation gets made available through this function. By default it prints out "type level" documentation for this type, as well as what other pages are availalbe. When given a string, it shows the documentation for that page instead. Pages that belong to methods also automatically include the type signature.

This requires some new features in my Rust <-> Teal FFI library tealr https://github.com/lenscas/tealr which is now also close to getting a new release, with the only thing I want to do is fix how its TypedFunction work (right now, it always contains a lua function, even if it was made inside of Rust, which puts some needless limits on it).

After that it is time to finish mlua support for tealr . As well as making it better at dealing with api's like tealsql has made.

In case anyone is interested: Issue about it here https://github.com/lenscas/tealr/issues/17

Can you try removing the println! at https://github.com/lenscas/tealr/blob/cee2363a14c53b995a69efb2b4cbd84010905276/tealr_derive/src/lib.rs#L155?

On the contrary, it can use and interface with numpy quite easily: https://github.com/PyO3/rust-numpy

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.

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.

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.

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)

This example maybe helpful.

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...)

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.

https://github.com/PyO3/rust-numpy