Chia-Plot-Status VS cudf

Not tried this but there are several about. https://github.com/grayfallstown/Chia-Plot-Status. Also check out MrPig91 on GitHub and YouTube. I used some of his code and made my own.

Chia-Plot-Status now has basic support for this chia-plotter logs to make comparing performance easier

Never trust any download source other than the official github repository https://github.com/grayfallstown/Chia-Plot-Status or the official website https://grayfallstown.github.io/Chia-Plot-Status/

As seen on https://github.com/grayfallstown/Chia-Plot-Status/graphs/contributors there is only one other person who contributed a pull request so far and that wasn't code but a documentation change.

Data reporting tools like grayfallstown/Chia-Plot-Status all the way up to mtail + prometheus + grafana for some amazing dashboards

Chia Plot Status: https://github.com/grayfallstown/Chia-Plot-Status

Available on Github

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.

Perhaps the largest for package size is the NVIDIA developed rapids toolkit https://rapids.ai/ . Even still adding things like pandas and some geospatial tools, you rapidly end up with an image well over a gigabyte, despite following cutting edge best practice with docker and python.

Yes sure, that is how OpenMP does; but on the other side: you seem to already do some basic type inference, and building an AST, no? Then you know as well the size and type of your vectors, and can execute actions in parallel if there is enough data to be worth parallelizing. Is there anyone who don't want their code to execute faster if it is possible? Those that do work in big data domain do use threads and vectorized instructions without user having to type in any directive; just import different library. Example, numpy or numpy with cuda backend, or similar GPU accelerated libraries like cudf.

Not the answer about Ray: you could use rapids.ai. I'm using it for for dataframe manipulation on GPU

To put Data Analytics on GPU Steroids, Try RAPIDS cudf https://rapids.ai/

You can scope out https://rapids.ai/. Nvidia's AI toolkits. They have some handy notebooks to poke at to get you started.

try https://rapids.ai/

As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).