modin VS Ray

The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.

Yeah, tried Polars a couple of times: the API seems worse than Pandas to me too. eg the decision only to support autoincrementing integer indexes seems like it would make debugging "hmmm, that answer is wrong, what exactly did I select?" bugs much more annoying. Polars docs write "blazingly fast" all over them but I doubt that is a compelling point for people using single-node dataframe libraries. It isn't for me.

Modin (https://github.com/modin-project/modin) seems more promising at this point, particularly since a migration path for standing Pandas code is highly desirable.

If anyone wants a faster version of pandas it’s not hard to find, modin for example uses multiple cores to speed it up, so if you have 4 cores it’s about 4 times faster than pandas, and has the same API as pandas.

Modin should fit. It implements Pandas APIs with e.g. Ray as backend. https://github.com/modin-project/modin

If you really want speed you should try modin.pandas which makes pandas multi-threaded.

I am guessing XGB is fairly optimised as it is. If you would want to use the sklearn libraries with pandas, look into Modin

Source

22. Ray | Github | tutorial

Training times for GSM8k are mentioned here: https://github.com/ray-project/ray/tree/master/doc/source/te...

Here's a reference from RLlib that points to docs and an example, and here's one from one of my projects that includes all my own implementations

RLlib

⦿ AWS Glue Data Quality - Automatic data quality rule recommendations based on your data AWS Glue for Ray - Data integration with Ray (ray.io), a popular new open-source compute framework that helps you scale Python workloads

https://ray.io (just dropping the link)

I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.

By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.

Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).

[0] https://github.com/ray-project/ray

Adding these for anyone who had a similar question about Ray vs dask 1, 2, 3