kino VS explorer

José is adamantly championing Livebook as a tool for learning the language and teaching aspiring alchemists. In particular, he spoke about the breadth of possibilities for visualising aspects of Livebook using Kino, such as charts, graphs, and mermaid diagrams. As mentioned previously, Dockyard Academy is taking this approach with their bootcamp curriculum, and is using this suite of visualisation tools alongside smart cells to lower the barrier to entry for new developers.

I think this is a fair analogy, but maybe I'd add that LiveBooks uses MarkDown markupfor the non-code part of the notebook, make it far more readable, and with more visualizations coming in.

For example, recently an addition to add automatically generated sequence diagrams to Livebook Kino (https://github.com/livebook-dev/kino/pull/165). So generate code which shows a supervision tree, and automatically create a visual diagram of the code in one place. Pretty great for learning and explanatory material.

There's an upcoming feature in Livebook Kino that is related do automatically generated sequence diagrams for process communication: https://github.com/livebook-dev/kino/pull/165

So I posted this issue (kino#48) and implemented a new widget Kino.ImageDynamic which can be updated with Kino.ImageDynamic.push/2.

The Explorer library [0] in Elixir uses Polars underneath it.

[0] https://github.com/elixir-explorer/explorer

To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).

In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.

Already exists. Check out https://github.com/elixir-nx/explorer which provides a tidyverse-like API in Elixir using polars as the back end.

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

I hope this helps!

There's a package in the Nx ecosystem called Explorer (https://github.com/elixir-nx/explorer). It uses bindings for the rust library, polars, which is much more betterer than Pandas.

FWIW around April this year I started using erlport with python polars in a production ETL app because explorer didn't have the features I needed at the time.

Machine learning is rapidly expanding within the Elixir ecosystem, with tools such as Nx, Axon, and Explorer being used both by individuals and companies such as Amplified, as mentioned above.