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Elixir and Machine Learning in 2024 so far: MLIR, Arrow, structured LLM, etc.

by clessg on 5/29/24, 12:38 PM with 63 comments

• by mrdoops on 5/29/24, 7:45 PM

  IMO the big win for Elixir/Nx/Bumblebee/etc is that you can do batched distributed inference out of the box without deploying anything separate to your app or hitting an API. Massive complexity reduction and you can more easily scale up or down. https://hexdocs.pm/nx/Nx.Serving.html#content

  And there's also a scale to 0 story for when you're not using that GPU at all: https://github.com/phoenixframework/flame

  1 language/toolchain. 1 deployable app. Real time and distributed machine learning baked in. 1 dev can go really far.

• by davidw on 5/29/24, 5:45 PM

  From a "marketing strategy" point of view, I wonder what the thinking is in investing in this stuff so heavily when Python seems to be kind of the go-to? Will they be able to create a "good enough" environment to do that kind of work with Elixir? Is it just someone or a company scratching their own itch?

  This is a genuine question - I don't know much about "AI stuff", but do know something about the economics of programming languages and I'm "intellectually curious" about what is driving this and what the goals are, rather than critical of Elixir. I love working with BEAM and miss it.

• by bnchrch on 5/29/24, 4:58 PM

  I think Elixir might have the most wonderful community out there.

  Really cool to see the concerted effort in parallel going into both ML problem space, and into introducing typing.

• by djaouen on 5/30/24, 6:36 AM

  And with Elixir's gradual type system [1] coming soon, this is looking to be quite an eventful year for José and Team. Bravo!

  [1] https://arxiv.org/abs/2306.06391

• by melodyogonna on 5/29/24, 8:19 PM

  MLIR enables so much potential to systems that use it

• by dpflan on 5/29/24, 10:15 PM

  To the author, I noticed a typo: a misspelling of "meachine" instead of "machine"

  """ These features bring Numerical Elixir and its ability to setup distributed model serving, over CPUs and GPUs, to traditional meachine learning algorithms, allowing developers and data practitioners to tackle a wider number of problems within the Elixir ecosystem. """

• by Dowwie on 5/30/24, 1:27 PM

  I'd like to see DSPy ported to Elixir, utilizing all of the best patterns available.

• by behnamoh on 5/29/24, 8:58 PM

  My experience with Elixir onboarding was meh. Spent hours trying to setup the LSP in VSCode and Neovim. Their pseudo-official LSP (elixir-ls) didn't work at all. I even made a post about it on Reddit, Github, and here. No one really knew what was going on.

  Even with Haskell you have something like ghcup and you're good to go. Not to mention Rust's amazing Cargo and Go's tooling as well.

  So far, Elixir has been even more challenging to just get up and running than Common Lisp!

  By the way, the official Elixir website recommends using Homebrew to install it. But almost everyone in the Github issues and comments says ASDF is the way to go.

• by bluevlahblah on 5/29/24, 6:10 PM

  People have to realise these are mostly for hobby. It is really hard to get these working with other libraries.

  Take explorer, it’s a mess trying to implement dplyr verbs in elixir. Anyone trying to use it is going to hit its limitations sooner or later. I tried migrating to it from polars but it is too frustrating.. gave up after some time.

  Why will people use half baked libraries instead of python ? I will stick to Keres/pytorch, polars, etc