Meshroom VS cudf

Take a look at Meshroom from https://alicevision.org/

It's a pretty decent tool, although not sure if videos work well.

I once tried to build a mesh from an aerial video by extracting certain frames and the result was ok-ish

You'll need to get some practice with photogrammetry or other 3D scanning to get the appropriate shapes for each animal, this should be more or less something the right size with adequate straps and padding - https://alicevision.org is a good place to learn about photogrammetry.

Have you tried Alice Vision? https://alicevision.org

Huh. https://github.com/alicevision/meshroom/issues/1650 suggests you can get the camera positions into blender, and then putting pyramids views in, and then get the interested area.

Another alternative for ptgui is https://alicevision.org and it is free

A new version of Meshroom released last month, so I decided to compare the performance and results of the two with the same dataset and mostly the same settings.

Photogrametrie https://alicevision.org/

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.).