nx

course Livebook (https://livebook.dev) Old but interesting video https://www.youtube.com/watch?v=g3oyh3g1AtQ (Bumblebee: GPT2, Stable Diffusion, and more in Elixir) A talk on the upcoming Elixir conference (https://2024.elixirconf.com/schedule/#schedules) is actually titled "Livebook in the cloud: GPUs and clustered workflows in seconds".

o 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!

that Python notebooks only dream of being capable of, after a decade of progress But they do not not stop there, the documentation is always of very high quality, even for stuff not coming from the core developers, and they also regularly release educational material that is worth a hundred times a gain in speed. They've set a very high quality standard and I noticed how much it is important only when I stopped programming daily in Elixir and went back to other more hyped or establish ecosystems. That's not to say that Elixir is superior as a language, but that the ecosystem is flourishing and the community is able to extract the 100% of the benefits from the tools and create new marvellously crafted ones, that push the limits forward every time, in such a simple manner, that it looks like magic. Going back to Rust, you can write Rust if you need speed or for whatever reason you feel it's the right tool for the job, it's totally integrated [3][4], again in a way that many other languages can only dream of, and it's in fact the reason I've learned Rust in the first place. I must also say that the work done by the Rust community looks refreshing as well, if you look at the way rustler works it was very well thought and made writing NIFs, something that seemed arcane and distant, only for the proverbial mad professor to try, a breeze. Kudos to them. But the opposite IMO is not true, if you write Rust, you write Rust, and that's it. You can't take advantage of the many features the BEAM offers, OTP, hot code reloading, full inspection of running systems, distribution, scalability, fault tolerance, soft real time etc. etc. etc. But of course if you don't see any advantage in them, it means you probably don't need them (one other option is that you still don't know you want them :] ). In that case Rust is as good as any other language, but for a backend, even though I gently despise it, Java (or Kotlin) might be a better option. [1] https://github.com/elixir-nx/nx https://github.com/elixir-nx/axon [2] https://livebook.dev/ [3] https://github.com/rusterlium/rustler [4] https://dashbit.co/blog/rustler-precompiled

ble. As long as users of the DSL can do everything they need to do without any need for "breaking out" of the DSL's little world, they don't need to be aware that they could e.g. make fully-namespaced calls to regular stdlib functions of your runtime.