axon

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

ted because some concepts don't require understanding, just tinkering. Python it's not easier than Ruby, shooting yourself in the foot with Python it's easier than in Ruby and yet, here we are. At least in the diatribe C vs Rust, C is actually simpler than Rust as a language and programmers have to learn fewer concepts, even though Rust is better as a language/ecosystem overall. > Everyone uses English, so you have to too That's exactly the point, the most spoken language in the World is English spoken by non native speakers. Like me. It's not British English or American English (in Europe is Russian, anyway, and Chinese and Spanish would like to have a word with you). So it is people's Esperanto, which is kinda of an abomination linguistically, but language evolve naturally it does not need a compiler or an interpreter to work. When I am abroad for holidays or conferences, I can perfectly understand what people mean when they speak "Engrish", native speakers often don't. A recent submission about it https://news.ycombinator.com/item?id=25784683 But in programming the concepts are the same in every language, so it's not like learning Finnish if you are Egyptian, it's more like learning to ride a motorbike if you can ride a bicycle. People learn Pandas and Numpy and Scikit-learn, not Python, when I learned how they worked porting the examples to Axon[1] wasn't hard. I managed to do it with the help of a colleague whose versed in learning programming and he thought Axon was more readable than Python. But then again, people usually think they know X better, feel more confident in X and will keep making the same mistakes over and over, because they don't know what they don't know and their salary do not depend on it. They have people like us fixing their code. Their salary depend on writing "I know X" on their CV, which usually is just not entirely true. Ruby just got out of fashion, but it isn't inferior to any other language out there and its ecosystem is completely fine. Reminder: TruffleRuby exists. [1] https://github.com/elixir-nx/axon edit: formatting

ave to rerun from the beginning of the branch. Iirc if you stop a long running cell, that forces a rerun as well. That can also be painful when running training loops that run for a while, but there are some pretty neat workarounds you can do using Kino. Using those workarounds does break the reproducibility guarantees though. Personally while building NN models I find that I prefer the Jupyter execution model because for NNs, rerunning cells can be really time-consuming. Being able to quickly change some variables and run a cell out of order helps while I'm exploring/experimenting. Two things I love about Livebook though are 1) the file format makes version control super easy and 2) Kino allows for real interactivity in the notebook in a way that's much harder to do in Jupyter. So in Livebook you can easily create live updating charts, images etc that show training progress or have other kinds of interactivity. If you're interested to see what my model training workflow looks like with Livebook (and I have no idea if it's the best workflow!), check out the examples below [1][2]. Overall I'd say it definitely works well, you just have to shift your mental model a bit if you're coming from Jupyter. If I were doing something where rerunning cells wasn't expensive I would probably prefer the Livebook model. [1] https://github.com/elixir-nx/axon/blob/main/notebooks/genera... [2] https://github.com/elixir-nx/axon/blob/main/notebooks/genera...

l intents and purposes) immutable. This has required a fair amount of copying when using the eager API from Polars, and because Rustler NIF resources use atomic reference counting and the GC only sweeps intermittently, there can be some pretty bad memory performance. Fortunately Polars also has a lazy API. The plan is to use that with 'peeking' for display. After that, I'd like to move into additional backends. I'm particularly keen on Ecto (database) and Apache Arrow/Ballista for distributed and OLAP work. There is also work underway for a pure Elixir backend so the library can ship without a Rust dependency. Speaking of which, there's work on prebuilt binaries underway as well. I'd love feedback on the API! I aimed for a dplyr-ish API as I think it melds better with a functional language than pandas. Generally I find dplyr more intuitive than pandas. The philosophy here is to get from brain to data as simply and intuitively as possible. Finally, contributions and any other feedback are super, super welcome. It's early days and I'm also a startup founder so I haven't been able to dedicate as much time as I'd like, but I try to get some work done and add features at least once a week. Thanks for looking! -- [1] https://github.com/elixir-nx/nx [2] https://github.com/elixir-nx/axon [3] https://github.com/pola-rs/polars [4] https://github.com/rusterlium/rustler [5] https://dplyr.tidyverse.org/