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

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

#61

Post by joaogui1 »

andy_ppp wrote: post #15
davidw wrote: post #3

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 prog…

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.

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.

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

#62

Post by josevalim »

blueblahvlah wrote: post #60
josevalim wrote: post #52

Earlier quoted context omitted.

> People have to realise these are mostly for hobby. It is really hard to get these working with other libraries. I have been dealing with this type of feedback for almost 20 years at this point: "This library doesn't work for my needs, so the conclusion is that it is hobby/not useful/etc for everyone". In your experience, we are not there yet (and we may never be), but the generalization does not hold. Take Explorer…

Thank you Jose, for your 2 decades of relentless work. I have not disparaged any maintainers or their work. I get it from your comments that these projects are in early days of adoption and development. From my perspective, just because Chris Grainger does data pipelines in production does not prove the project is for everyone. It means it worked out for him. May be seeing more updates, papers, interviews, podcasts a…

That's precisely my point. Because Chris runs it in production, it doesn't mean the project is production-ready for everyone. And the fact you ran into roadblocks, doesn't mean they are "mostly for hobby" or are "half-baked". Your initial comment lacked this nuance and rushed into generalizations, which I did my best to clarify.

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

#63

Post by seanmor5 »

blueblahvlah wrote: post #58
seanmor5 wrote: post #41

Earlier quoted context omitted.

To be fair to the creators of “half baked” libraries, you’re comparing our work to frameworks with several years head start with the backing of trillion dollar organizations (excluding polars). To have convinced ANYBODY to successfully make the switch is, to me, both somewhat of a miracle and an indication that there is a legitimate market for this work. I think it also dispels the hobby claim As for your issues with…

Sean Moriarity, please use your elixir knowledge to write a user defined function or a plugin in explorer. It's easy to attack other people and assume they are ignorant.

It has always been possible to do custom transformations via Series.transform. And here is a PR adding it to dataframes: https://github.com/elixir-explorer/explorer/pull/912 - the PR uses existing APIs, it was possible before, it just makes it more convenient. I am sure more will be added later

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

#64

Post by seanmor5 »

elicksaur wrote: post #47
seanmor5 wrote: post #41

Earlier quoted context omitted.

To be fair to the creators of “half baked” libraries, you’re comparing our work to frameworks with several years head start with the backing of trillion dollar organizations (excluding polars). To have convinced ANYBODY to successfully make the switch is, to me, both somewhat of a miracle and an indication that there is a legitimate market for this work. I think it also dispels the hobby claim As for your issues with…

Telling someone they’re ignorant probably doesn’t get them to use your tech. Not the person you’re replying to; I use elixir everyday at work. The biggest barrier to adoption of these libraries I can see is that there is not a wealth of resources to solve the actual problem the person is experiencing. Googling “dyplr verbs in python” there’s a towards data science post that’s the 6th result. It has step by step code…

I appreciate your response. I should not have replied in the manner that I did. I think there are many ways to make the argument against using these libraries, and all of the maintainers would agree it doesn't make sense in many situations. I am fine with these arguments, but I don't think it warrants referring to the work we've done as half-baked or hobby projects. Even if not the intention, I think it implies a lack of effort and professionalism which comes across as more disparaging than simply pointing out the gaps.

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

#66

Post by cess11 »

cess11 wrote: post #49
ricketycricket wrote: post #26

Earlier quoted context omitted.

> 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?

This one: 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/discussion...

The tokenizer generator has risen again.

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

#67

Post by davidw »

josevalim wrote: post #57
davidw wrote: post #11

Earlier quoted context omitted.

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 dyn…

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…

Thanks for the great answer! And thanks for your work with Elixir. As a long-time Erlang user, I was impressed at how it wasn't just a slightly nicer syntax, but included genuine innovations and improvements.

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

#68

Post by dpflan »

josevalim wrote: post #55
dpflan wrote: post #35

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. """

Thank you.

No problem, I see that it's fixed, glad I could help!
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