8 ms·

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

260 points by clessg 2y ago

bnchrch 2y ago
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.
davidw 2y ago
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.
- jsiva 2y ago
  I can't say I know much about AI stuff or BEAM. But my best guess is that elixir native ML should integrate well with OTP's distributed computing capabilities. As an outsider to the elixir ecosystem, I've seen glimpses of elixir ML here and there but no mention of attempting to bridge the python ML ecosystem into elixir.
  - deleted 2y ago
    [deleted]
- PaulStatezny 2y ago
  One factor may be that a few years back the language creator (José Valim, also the author of this article) announced that the language is basically "completed", and that they would shift focus to other things like developer tooling and other projects outside of the language itself. José is quite prolific, so I think it's natural that he moves on to things like this. It's hard to know what reception will be like until you build it.
  - davidw 2y ago
    From a strictly "marketing" point of view, if you want to grow the language and ecosystem, it seems the successful move is to stake out a place where you're likely to win. I think often this happens more or less by accident rather than conscious design - but think of something like PHP which made it really easy to whip up quick web pages, or Rails which drive Ruby adoption for a better, more structured, but still dynamic and quick web programming experience. And I suppose part of those happy accidents are people just hacking on something they think is cool, so I wouldn't stress too much about the "marketing aspect". I'm just curious what drove it.
    - jolux 2y ago
      My guess is that Jose and the core team are both personally interested in the big wave of ML stuff we've been experiencing recently and also want to demonstrate that Elixir is a viable platform for doing this work to teams which have adopted Elixir and are interested in ML but don't want to add a bunch of Python into their codebase.
      - barrell 2y ago
        I would also guess that Elixir led to some crazy things they didn’t even imagine possible with web, like phoenixs live view [1]. Even if they don’t have explicit ideas of how it will impact ML, it’ll be really interesting to try. [1]: https://phoenixframework.org/blog/phoenix-liveview-1.0-released https://phoenixframework.org/blog/phoenix-liveview-1.0-relea...
    - josevalim 2y ago
      I have always considered helping the community grow into a diverse ecosystem to be my main responsibility (the Python community being a great example here). This particular effort started because some people got together and realized that we could do it! Do it in a way that felt part of Elixir and not just a bunch of bindings to C libraries. We honestly never had the expectation that we had to beat Python (otherwise we would simply not have started). Early on, we were not even sure if we could be better at one single thing. However, 3 years later, we do have features that would be quite hard or impossible to implement in Python. For example: * Nx Serving - https://hexdocs.pm/nx/Nx.Serving.html https://hexdocs.pm/nx/Nx.Serving.html - allows you to serve machine learning models, across nodes and GPUs, with concurrency, batching, and partitioning, and it has zero dependencies * Livebook - https://livebook.dev https://livebook.dev - brings truly reproducible workflows (hard to achieve in Python due to mutability), smart cells, and other fresh ideas * A more cohesive ecosystem - Nx, Scholar, Explorer, etc all play together, zero-copy and all, because they are the only players in town Of course, there are also things that Python can do, that we cannot. The most obvious ones being: * In Python, integration with C code is easier, and that matters a lot in this space. Python also allows C to call Python, and that's just not possible in the Erlang VM * Huge ecosystem, everything happens in Python first At the end of the day, what drives me is that the Erlang VM offers a unique set of features, and combining them with different problems have historically lead to interesting and elegant solutions. Which drives more people to join, experiment, run in production, and create new things.
- dpflan 2y ago
  Jose may show up here and answer your questions... https://news.ycombinator.com/user?id=josevalim https://news.ycombinator.com/user?id=josevalim
- andy_ppp 2y ago
  I actually think the BEAM is an ideal environment for machine learning, sharding things across machines. The only thing I’m not sure of is if PyTorch etc. are more optimised than XLA the backend Axon uses… would be good to see some performance comparisons of a big LLM running on both. For everything else I’d suggest Elixir was a better experience.
  - joaogui1 2y ago
    XLA tends tends to be better optimized for TPUs, Pytorch is better with GPUs, but I believe you can choose a backend when using Nx.
- regulation_d 2y ago
  We use Elixir for our primary application, with a fair amount of Python code to manage our ML pipelines. But we also need real-time inference and it's really convenient/performant to be able to just do that in-app. So I, for one, am very grateful for the work that's been done provide the level of tooling in Elixir. It has worked quite well for us.
- cess11 2y ago
  It's very serious. The BEAM had a problem in that it lacked solid number-crunching libraries, so some folks solved that, and when they did distributed ML-capabilities kind of just fell out as a neat bonus so some folks did that too. So now it's integrated into the basic Livebook, just boot it and go to that example and you have a transcriber or whatever as boilerplate to play around with. Want something else from Huggingface? Just switch out a couple of strings referencing that model and all the rest is sorted for you, except if the tokenizer or whatever doesn't follow a particular format but in that case you just upload it to some free web service and make a PR with the result and reference that version hash specifically and it'll work. Python is awful for sharding, concurrency, distribution, that kind of thing. With the BEAM you can trivially cluster with an instance on a dedicated GPU-outfitted machine that runs the LLM models or what have you and there you have named processes that control process pools for running queries and they'll be immediately available to any BEAM that clusters with it. Fine, you'll do some VPN or something that requires a bit of experience with networking, but compared to building a robust, distributed system in Python it's easy mode. I don't know what the goals are, but I perceive the Nx/Bumblebee/BEAM platform as obviously better than Python for building production systems. There might be advantages to Python when creating and training models, I'm not sure, but if you already have the models and need to serve more than one, and want the latency to be low so the characteristically slow response feels a little faster, and don't already have a big Kubernetes system for running many Python applications in a distributed manner, then this is for you and it'll be better than good enough until you've created a rather large success.
  - ricketycricket 2y ago
    > except if the tokenizer or whatever doesn't follow a particular format but in that case you just upload it to some free web service and make a PR with the result and reference that version hash specifically and it'll work. May I ask to which service you are referring?
    - cess11 2y ago
      This one: https://jonatanklosko-bumblebee-tools.hf.space/apps/tokenizer-generator https://jonatanklosko-bumblebee-tools.hf.space/apps/tokenize... It's linked in the Bumblebee README. Seems broken at the moment, maybe the PR it made is more informative: https://huggingface.co/Neprox/STT-Swedish-Whisper/discussions/3 https://huggingface.co/Neprox/STT-Swedish-Whisper/discussion...
- TJSomething 2y ago
  I feel like a big audience would be people moving away from Spark.
  - vvpan 2y ago
    Can you provide a little more context, I am curious.
bluevlahblah 2y ago
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
- victorbjorklund 2y ago
  At this point is isnt trying to convert a happy python user like you. Rather to give tools to teams whose app is already in Elixir and the devs knows Elixir. Instead of bringing in Python to the mix you can use Elixir
  - elicksaur 2y ago
    Elixir dev you are describing and we use Python for ML stuff because it’s easier to work with the more documented frameworks. These libraries just aren’t “there” yet, and I think it’s important to be honest about that. I’ve tried extending/converting to them, and it just wasn’t worth it yet.
    - josevalim 2y ago
      > These libraries just aren’t “there” yet, and I think it’s important to be honest about that. Exactly. These projects are still during early adoption stage. While it is production-ready and many are doing so, getting there requires improving documentation, discussing roadmaps with maintainers about a feature you may need (perhaps even contributing it), giving presentations (at your company or at events), and so forth. This can be exciting to some, but a distraction for others. There is certainly plenty of work ahead.
    - deleted 2y ago
      [deleted]
- ch4s3 2y ago
  I don't think a lot of people are using these in production yet, but you can't become fully baked without spending some time being half baked. Sometimes though it's nice to have most of your stuff in one language.
mrdoops 2y ago
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 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 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.
- 6gvONxR4sf7o 2y ago
  I've been really curious about BEAM languages but never made the leap. How well does it manage heterogeneous compute? I'm used to other languages making me define what happens on CPU vs GPU and defining cross-machine talk around those kinds of considerations. What parts of that does elixir (and company) allow me to not write? Is there a good balance between abstractions when it comes to still maybe wanting control over what goes where (heteregeneity)? Super curious and kinda looking for an excuse here :)
  - ricketycricket 2y ago
    May not answer all your questions, but this may be a good starting point: https://hexdocs.pm/nx/Nx.Defn.html https://hexdocs.pm/nx/Nx.Defn.html
  - elbasti 2y ago
    The BEAM is pretty high level, and it's REALLY good at managing distributed compute at the thread or device level. If you have a parallelizeable workflow, it's very easy to make it (properly!) parallel locally, where by "properly" I mean having supervision trees, sane restart behavior, etc. And once you have that you can extend that parallelism to different nodes in a network (with the same sanity around supervision and discovery) basically for free. Like, one-line-of-code for free. Nonetheless, it's all message-passing, and so pretty high level. AFAIK it's not designed for parallelizing compute at GPU scale. That being said, if you have multiple GPUs and multiple machines that have to coordinate between them, Elixir/Erlang is pretty much perfect.
melodyogonna 2y ago
MLIR enables so much potential to systems that use it
- tonyhb 2y ago
  MLIR is cool and has an exciting future for sure.
behnamoh 2y ago
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.
- cess11 2y ago
  ASDF is easy to use once you've learned a few of the basic commands, and that way you'll have an easy time when new versions come out and want to check out new features. Like the built-in JSON parser, the gradual typing when it drops, things like that. If you built something useful you might not want to upgrade it just to look at the new stuff, and if you built nothing and just drop into iex a couple of times per year it'll still be easier to pull in the latest BEAM and Elixir versions and play around than figuring out if Homebrew has the new version, and if so, whether it installs nicely over the old or not. I don't think I've got the LSP running, might check tomorrow. It's OK, the IDE autocompletes some things and for me development basically happens in the REPL and then gets pasted into tests and the project anyway. And iex has good autocomplete and help functions and so on. Edit: A hurdle might be to install the GUI libraries needed for Observer, but you'll probably be able to search out an incantation for your operating system once you're into doing stuff with process trees.
  - behnamoh 2y ago
    I think the Python dev style I adopted can't be easily ported to Elixir. In Python, I rely heavily on an LSP because I want to fiddle with a lot of functions/classes located deeply in libraries. In VSCode, I simply press CMD and click on any function (or in Neovim, I `gf` or `gd` it). I thought it'd make even more sense in Elixir because apparently everything is a module. Am I missing something? How do you use iex efficiently?
dpflan 2y ago
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. """
- josevalim 2y ago
  Thank you.
  - dpflan 2y ago
    No problem, I see that it's fixed, glad I could help!
deleted 2y ago
[deleted]
djaouen 2y ago
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 https://arxiv.org/abs/2306.06391
Dowwie 2y ago
I'd like to see DSPy ported to Elixir, utilizing all of the best patterns available.

260 points by clessg 2y ago

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.

davidw 2y ago

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.

jsiva 2y ago
I can't say I know much about AI stuff or BEAM. But my best guess is that elixir native ML should integrate well with OTP's distributed computing capabilities. As an outsider to the elixir ecosystem, I've seen glimpses of elixir ML here and there but no mention of attempting to bridge the python ML ecosystem into elixir.
- deleted 2y ago
  [deleted]
PaulStatezny 2y ago
One factor may be that a few years back the language creator (José Valim, also the author of this article) announced that the language is basically "completed", and that they would shift focus to other things like developer tooling and other projects outside of the language itself. José is quite prolific, so I think it's natural that he moves on to things like this. It's hard to know what reception will be like until you build it.
- davidw 2y ago
  From a strictly "marketing" point of view, if you want to grow the language and ecosystem, it seems the successful move is to stake out a place where you're likely to win. I think often this happens more or less by accident rather than conscious design - but think of something like PHP which made it really easy to whip up quick web pages, or Rails which drive Ruby adoption for a better, more structured, but still dynamic and quick web programming experience. And I suppose part of those happy accidents are people just hacking on something they think is cool, so I wouldn't stress too much about the "marketing aspect". I'm just curious what drove it.
  - jolux 2y ago
    My guess is that Jose and the core team are both personally interested in the big wave of ML stuff we've been experiencing recently and also want to demonstrate that Elixir is a viable platform for doing this work to teams which have adopted Elixir and are interested in ML but don't want to add a bunch of Python into their codebase.
    - barrell 2y ago
      I would also guess that Elixir led to some crazy things they didn’t even imagine possible with web, like phoenixs live view [1]. Even if they don’t have explicit ideas of how it will impact ML, it’ll be really interesting to try. [1]: https://phoenixframework.org/blog/phoenix-liveview-1.0-released https://phoenixframework.org/blog/phoenix-liveview-1.0-relea...
  - josevalim 2y ago
    I have always considered helping the community grow into a diverse ecosystem to be my main responsibility (the Python community being a great example here). This particular effort started because some people got together and realized that we could do it! Do it in a way that felt part of Elixir and not just a bunch of bindings to C libraries. We honestly never had the expectation that we had to beat Python (otherwise we would simply not have started). Early on, we were not even sure if we could be better at one single thing. However, 3 years later, we do have features that would be quite hard or impossible to implement in Python. For example: * Nx Serving - https://hexdocs.pm/nx/Nx.Serving.html https://hexdocs.pm/nx/Nx.Serving.html - allows you to serve machine learning models, across nodes and GPUs, with concurrency, batching, and partitioning, and it has zero dependencies * Livebook - https://livebook.dev https://livebook.dev - brings truly reproducible workflows (hard to achieve in Python due to mutability), smart cells, and other fresh ideas * A more cohesive ecosystem - Nx, Scholar, Explorer, etc all play together, zero-copy and all, because they are the only players in town Of course, there are also things that Python can do, that we cannot. The most obvious ones being: * In Python, integration with C code is easier, and that matters a lot in this space. Python also allows C to call Python, and that's just not possible in the Erlang VM * Huge ecosystem, everything happens in Python first At the end of the day, what drives me is that the Erlang VM offers a unique set of features, and combining them with different problems have historically lead to interesting and elegant solutions. Which drives more people to join, experiment, run in production, and create new things.
dpflan 2y ago
Jose may show up here and answer your questions... https://news.ycombinator.com/user?id=josevalim https://news.ycombinator.com/user?id=josevalim
andy_ppp 2y ago
I actually think the BEAM is an ideal environment for machine learning, sharding things across machines. The only thing I’m not sure of is if PyTorch etc. are more optimised than XLA the backend Axon uses… would be good to see some performance comparisons of a big LLM running on both. For everything else I’d suggest Elixir was a better experience.
- joaogui1 2y ago
  XLA tends tends to be better optimized for TPUs, Pytorch is better with GPUs, but I believe you can choose a backend when using Nx.
regulation_d 2y ago
We use Elixir for our primary application, with a fair amount of Python code to manage our ML pipelines. But we also need real-time inference and it's really convenient/performant to be able to just do that in-app. So I, for one, am very grateful for the work that's been done provide the level of tooling in Elixir. It has worked quite well for us.
cess11 2y ago
It's very serious. The BEAM had a problem in that it lacked solid number-crunching libraries, so some folks solved that, and when they did distributed ML-capabilities kind of just fell out as a neat bonus so some folks did that too. So now it's integrated into the basic Livebook, just boot it and go to that example and you have a transcriber or whatever as boilerplate to play around with. Want something else from Huggingface? Just switch out a couple of strings referencing that model and all the rest is sorted for you, except if the tokenizer or whatever doesn't follow a particular format but in that case you just upload it to some free web service and make a PR with the result and reference that version hash specifically and it'll work. Python is awful for sharding, concurrency, distribution, that kind of thing. With the BEAM you can trivially cluster with an instance on a dedicated GPU-outfitted machine that runs the LLM models or what have you and there you have named processes that control process pools for running queries and they'll be immediately available to any BEAM that clusters with it. Fine, you'll do some VPN or something that requires a bit of experience with networking, but compared to building a robust, distributed system in Python it's easy mode. I don't know what the goals are, but I perceive the Nx/Bumblebee/BEAM platform as obviously better than Python for building production systems. There might be advantages to Python when creating and training models, I'm not sure, but if you already have the models and need to serve more than one, and want the latency to be low so the characteristically slow response feels a little faster, and don't already have a big Kubernetes system for running many Python applications in a distributed manner, then this is for you and it'll be better than good enough until you've created a rather large success.
- ricketycricket 2y ago
  > except if the tokenizer or whatever doesn't follow a particular format but in that case you just upload it to some free web service and make a PR with the result and reference that version hash specifically and it'll work. May I ask to which service you are referring?
  - cess11 2y ago
    This one: https://jonatanklosko-bumblebee-tools.hf.space/apps/tokenizer-generator https://jonatanklosko-bumblebee-tools.hf.space/apps/tokenize... It's linked in the Bumblebee README. Seems broken at the moment, maybe the PR it made is more informative: https://huggingface.co/Neprox/STT-Swedish-Whisper/discussions/3 https://huggingface.co/Neprox/STT-Swedish-Whisper/discussion...
TJSomething 2y ago
I feel like a big audience would be people moving away from Spark.
- vvpan 2y ago
  Can you provide a little more context, I am curious.

bluevlahblah 2y ago

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

victorbjorklund 2y ago
At this point is isnt trying to convert a happy python user like you. Rather to give tools to teams whose app is already in Elixir and the devs knows Elixir. Instead of bringing in Python to the mix you can use Elixir
- elicksaur 2y ago
  Elixir dev you are describing and we use Python for ML stuff because it’s easier to work with the more documented frameworks. These libraries just aren’t “there” yet, and I think it’s important to be honest about that. I’ve tried extending/converting to them, and it just wasn’t worth it yet.
  - josevalim 2y ago
    > These libraries just aren’t “there” yet, and I think it’s important to be honest about that. Exactly. These projects are still during early adoption stage. While it is production-ready and many are doing so, getting there requires improving documentation, discussing roadmaps with maintainers about a feature you may need (perhaps even contributing it), giving presentations (at your company or at events), and so forth. This can be exciting to some, but a distraction for others. There is certainly plenty of work ahead.
  - deleted 2y ago
    [deleted]
ch4s3 2y ago
I don't think a lot of people are using these in production yet, but you can't become fully baked without spending some time being half baked. Sometimes though it's nice to have most of your stuff in one language.

mrdoops 2y ago

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 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 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.

6gvONxR4sf7o 2y ago
I've been really curious about BEAM languages but never made the leap. How well does it manage heterogeneous compute? I'm used to other languages making me define what happens on CPU vs GPU and defining cross-machine talk around those kinds of considerations. What parts of that does elixir (and company) allow me to not write? Is there a good balance between abstractions when it comes to still maybe wanting control over what goes where (heteregeneity)? Super curious and kinda looking for an excuse here :)
- ricketycricket 2y ago
  May not answer all your questions, but this may be a good starting point: https://hexdocs.pm/nx/Nx.Defn.html https://hexdocs.pm/nx/Nx.Defn.html
- elbasti 2y ago
  The BEAM is pretty high level, and it's REALLY good at managing distributed compute at the thread or device level. If you have a parallelizeable workflow, it's very easy to make it (properly!) parallel locally, where by "properly" I mean having supervision trees, sane restart behavior, etc. And once you have that you can extend that parallelism to different nodes in a network (with the same sanity around supervision and discovery) basically for free. Like, one-line-of-code for free. Nonetheless, it's all message-passing, and so pretty high level. AFAIK it's not designed for parallelizing compute at GPU scale. That being said, if you have multiple GPUs and multiple machines that have to coordinate between them, Elixir/Erlang is pretty much perfect.

melodyogonna 2y ago

MLIR enables so much potential to systems that use it

tonyhb 2y ago
MLIR is cool and has an exciting future for sure.

behnamoh 2y ago

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.

cess11 2y ago
ASDF is easy to use once you've learned a few of the basic commands, and that way you'll have an easy time when new versions come out and want to check out new features. Like the built-in JSON parser, the gradual typing when it drops, things like that. If you built something useful you might not want to upgrade it just to look at the new stuff, and if you built nothing and just drop into iex a couple of times per year it'll still be easier to pull in the latest BEAM and Elixir versions and play around than figuring out if Homebrew has the new version, and if so, whether it installs nicely over the old or not. I don't think I've got the LSP running, might check tomorrow. It's OK, the IDE autocompletes some things and for me development basically happens in the REPL and then gets pasted into tests and the project anyway. And iex has good autocomplete and help functions and so on. Edit: A hurdle might be to install the GUI libraries needed for Observer, but you'll probably be able to search out an incantation for your operating system once you're into doing stuff with process trees.
- behnamoh 2y ago
  I think the Python dev style I adopted can't be easily ported to Elixir. In Python, I rely heavily on an LSP because I want to fiddle with a lot of functions/classes located deeply in libraries. In VSCode, I simply press CMD and click on any function (or in Neovim, I `gf` or `gd` it). I thought it'd make even more sense in Elixir because apparently everything is a module. Am I missing something? How do you use iex efficiently?

dpflan 2y ago

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. """

josevalim 2y ago
Thank you.
- dpflan 2y ago
  No problem, I see that it's fixed, glad I could help!

deleted 2y ago

[deleted]

djaouen 2y ago

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 https://arxiv.org/abs/2306.06391

Dowwie 2y ago

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