I just ran a 2006 Java code to detect and read car license plate, and it ran in the first run on Java 17. The code is 17 years old and runs just fine without any issues. I think Java folks messed up AI/ML space due to licensing. But I still think it's best PL for AI/ML.
From Python to Elixir Machine Learning
31–40 of 89 posts
Re: From Python to Elixir Machine Learning
#32I wish there wouldn’t be such a song and dance about “moving away from Python”. There’s nothing wrong with creating ML tools in Elixir, but it’s always Python is slow, Python has no concurrency support, blah blah
I come from Ruby but the reactions can be similar, happy to give my data point. The thing is Elixir is really good at an increasing number of things. If you need to write a HTTP proxy in the middle of your application, since Elixir processes & incoming HTTP workers are cheap, you do not need to go evented: it just works. If you need to have reactive web apps with automated changes pushed to the client, it's the same:…
And nerves makes it easy to do IoT stuff / almost-embedded
Re: From Python to Elixir Machine Learning
#33I just ran a 2006 Java code to detect and read car license plate, and it ran in the first run on Java 17. The code is 17 years old and runs just fine without any issues. I think Java folks messed up AI/ML space due to licensing. But I still think it's best PL for AI/ML.
What makes it the best language for AI and ML? I've never heard that take so just curious your thoughts. It's nice that legacy code still runs but that's usually not people's concern in ML.
Re: From Python to Elixir Machine Learning
#34Instead of moving to Elixir I believe it makes more sense to wait/move to Mojo when it's ready: https://www.modular.com/mojo
Given how Swift for Tensorflow went, how openai was equally pushing for it, I would rather take a "wait and see" approach to Mojo.
Do you mean OpenAI or do you maybe mean Fast.ai?
Re: From Python to Elixir Machine Learning
#35Re: From Python to Elixir Machine Learning
#36I wish there wouldn’t be such a song and dance about “moving away from Python”. There’s nothing wrong with creating ML tools in Elixir, but it’s always Python is slow, Python has no concurrency support, blah blah
I come from Ruby but the reactions can be similar, happy to give my data point. The thing is Elixir is really good at an increasing number of things. If you need to write a HTTP proxy in the middle of your application, since Elixir processes & incoming HTTP workers are cheap, you do not need to go evented: it just works. If you need to have reactive web apps with automated changes pushed to the client, it's the same:…
still not easy at the most important thing of all: being approachable, instrumented and intuitive to people less dedicated to programming.
I say this as someone that likes elixir, but after seeing it failing miserably at my org, I'm very skeptical it can be thrown around like a spring or node or django project. It needs real support from the org and requires module design skills that are not present in most random devs from a random org.
Re: From Python to Elixir Machine Learning
#37Something not mentioned is Explorer ( https://hexdocs.pm/explorer/Explorer.html ) which is a very nice dataframe library backed by Rusts Polars https://www.pola.rs/ with great integrations for Livebook https://livebook.dev/ .
I found explorer quite frustrating. I've used polars in python and loved it, but I brought in some financial data and couldn't strip off "£" from the start of a string so I could go on to cast it to a number. As far as I could tell I would have to bring that data into elixir, do the text processing and put it back into explorer, which to me defeated the whole point of a dataframe library. I imagine it's good for prec…
You can use str.slice or str.extract to clean the data:
https://pola-rs.github.io/polars/py-polars/html/reference/ex...
Re: From Python to Elixir Machine Learning
#38Earlier quoted context omitted.
I come from Ruby but the reactions can be similar, happy to give my data point. The thing is Elixir is really good at an increasing number of things. If you need to write a HTTP proxy in the middle of your application, since Elixir processes & incoming HTTP workers are cheap, you do not need to go evented: it just works. If you need to have reactive web apps with automated changes pushed to the client, it's the same:…
still not easy at the most important thing of all: being approachable, instrumented and intuitive to people less dedicated to programming. I say this as someone that likes elixir, but after seeing it failing miserably at my org, I'm very skeptical it can be thrown around like a spring or node or django project. It needs real support from the org and requires module design skills that are not present in most random de…
Could you expand on what you mean by this?
Module design doesn't seem any harder than class design in JS or Python.
Do you mean the language is generally harder for non-developers? Or that Elixir is harder for JS/Python developers to pick up and write good code? Or something else?
Writing well designed Elixir code does seem to require a fairly different approach from most common OO languages, at least at a surface level. (Although IMO that's more because you can copy OO patterns you've seen before without thinking much about why they're good patterns than because good design in Elixir is much different from OO)
Re: From Python to Elixir Machine Learning
#39Earlier quoted context omitted.
ML is not an island, it is part of a much broader "data science" universe that is currently served fairly well (still imperfectly) by the Python/C++ stack and is not easy to replicate. Throwing BEAM or FP acronyms around won't really strike a chord with people working with data and models. Mojo will (as per promise) tap into the wider ecosystem. Other platforms are more than welcome to try but this ultimately require…
There are very real limitations to the tooling approach summarized as "slightly more ergonomic APIs to underlying C/C++ code" that is currently dominant in Python qua ML. One of the biggest is of course extensibility, which, if difficult enough (as is the case in most Python ML tools unless you're extending only at the relatively slow Python layer) drastically hinders progress in making tooling better while keeping p…
I would not necessarily disagree with any of these points, just sceptical that these are enough to create the substantial investment by diverse actors to create real alternatives. The example of Julia non-adoption is very important to keep in mind.
Python was not designed for ML, it happened to it, the way Android happened to Java etc. Loosely speaking the Mojo project serves a function similar to that of Kotlin in the Android mobile world. Trying to remedy some recognized friction points while maintaining the benefits of a widely established ecosystem.
Obviously not holding a crystal ball: if the ML hype mutates into something more permanent and very widely embedded across different verticals (not just the big tech sponsored pytorch / tensorflow platforms and use cases) and if the Python/C++ combo becomes a recognized bottleneck then the conditions might spark another approach.
Re: From Python to Elixir Machine Learning
#40Show me I can save a considerable amount of time training a 70b parameter huggingface model by switching to Elixir, then we can talk about me learning a new language.