nx
Elixir
Our great sponsors
nx | Elixir | |
---|---|---|
36 | 133 | |
2,460 | 23,162 | |
1.4% | 2.3% | |
9.4 | 9.8 | |
17 days ago | 6 days ago | |
Elixir | Elixir | |
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nx
-
Unpacking Elixir: Concurrency
Does nx not work for you? https://github.com/elixir-nx/nx/tree/main/nx#readme
-
A LiveView Is a Process
It is historically not great at number computing. This is being addressed by a relatively new project called Nx. https://github.com/elixir-nx/nx
It is not the right choice for CPU intensive tasks like graphics, HFT, etc. Some companies have used Rust to write native extensions for those kinds of problems. https://discord.com/blog/using-rust-to-scale-elixir-for-11-m...
- How does Elixir stack up to Julia in the future of writing machine-learning software?
-
Data wrangling in Elixir with Explorer, the power of Rust, the elegance of R
José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.
My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).
In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:
* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box
* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks
* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to 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!
-
Elixir and Rust is a good mix
> I guess, why not use Rust entirely instead of as a FFI into Elixir or other backend language?
Because Rust brings none of the benefits of the BEAM ecosystem to the table.
I was an early Elixir adopter, not working currently as an Elixir developer, but I have deployed one of the largest Elixir applications for a private company in my country.
I know it has limits, but the language itself is only a small part of the whole.
Take ML, Jose Valim and Sean Moriarity have studied the problem, made a plan to tackle it and started solving it piece by piece [1] in a tightly integrated manner, it feels natural, as if Elixir always had those capabilities in a way that no other language does and to put the icing on the cake the community released Livebook [2] to interactively explore code and use the new tools in the simplest way possible, something that Python notebooks only dream of being capable of, after a decade of progress
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.
And 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.
The opposite 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
-
Distributed² Machine Learning Notebooks with Elixir and Livebook
(including docs and tests!): https://github.com/elixir-nx/nx/pull/1090
I'll be glad to answer questions about Nx or anything from Livebook's launch week!
-
Why Python keeps growing, explained
I think that experiment is taking shape with Elixir:
https://github.com/elixir-nx/nx
-
Does Nx use a Metal in the Backend ?
However the issue here at Nx https://github.com/elixir-nx/nx/issues/490 is already closed.
-
Do I need to use Elixir from Go perspective?
Outside of that, Elixir can be used for data pipelines, audio-video processing, and it is making inroads on Machine Learning with projects like Livebook, Nx, and Bumblebee.
- Elixir – HUGE Release Coming Soon
Elixir
-
Perfect Elixir: Environment Setup
I’m on MacOS and erlang.org, elixir-lang.org, and postgresql.org all suggest installation via Homebrew, which is a very popular package manager for MacOS.
-
Reliability in Legacy Software
But regardless of their reasons, they'll note that the service is easily meeting its SLOs. It was written in a highly performant, if idiosyncratic language, and uses patterns which give it a high level of resilience and the ability to recover from many situations automatically. The service is steady as a rock, and left to its own devices will more or less chug along indefinitely once deployed.
-
Top Paying Programming Technologies 2024
6. Elixir - $96,381
-
What's New in Elixir 1.16
The Elixir 1.16 release candidate is out now, and it comes with some compelling improvements to diagnostics, documentation, and a few other enhancements that make Elixir an even better choice for developers.
- Definindo item ativo no menu no Phoenix Framework usando Short-circuit Evaluation
-
Elixir v1.16 Released
You can find more examples in the PR https://github.com/elixir-lang/elixir/pull/13106.
-
Meet entr, the standalone file watcher
As you might have guessed, one of the main use cases for entr is to rerun tests whenever files change. I'm an Elixir engineer, and I use entr to run mix test continuously whenever I save an Elixir file.
-
Good Bye CRUD APIs, Hello Sync: Realtime PostgreSQL with ElectricSQL
The diagram demonstrates the communication pathway between the browser and the Postgres database through the Electric service. Essentially, Electric Sync Service, an Elixir application, orchestrates active-active data replication between the user's local DB and Postgres.
-
Building Apps with Tauri and Elixir
The Elixir programming language is no stranger to desktop applications as the language actually supports building them out of the box. It uses wxWidgets: a C++ library that lets developers create applications for Windows, macOS, Linux and other platforms with a single code base. But wxWidgets has a very complex API, and doesn’t solve issues that usually come with desktop applications around packaging.
-
Show HN: Podsee – AI tool for podcast listeners
Hi everyone, I just launched Podsee(https://pods.ee) for podcast listeners, lovers. You can search and listen to podcasts at Podsee. What makes it different is that you can get the AI transcript for an episode.
It started as a side project after I resigned my job one year ago. As a programmer, I love Elixir (http://elixir-lang.org/) and Phoenix LiveView(https://github.com/phoenixframework/phoenix_live_view), and want to make a product with it. So I build Podsee.
I'm planning to add more AI features to it, like summarize the episode audio, episode to comics, etc.
I'd love to invite you all to try out the product and would appreciate hearing your feedback! Thanks!
What are some alternatives?
gleam - ⭐️ A friendly language for building type-safe, scalable systems!
rust - Empowering everyone to build reliable and efficient software.
axon - Nx-powered Neural Networks
solidity - Solidity, the Smart Contract Programming Language
dplyr - dplyr: A grammar of data manipulation
crystal - The Crystal Programming Language
explorer - An open source block explorer
rust - Rust for the xtensa architecture. Built in targets for the ESP32 and ESP8266
fib - Performance Benchmark of top Github languages
Akka - Build highly concurrent, distributed, and resilient message-driven applications on the JVM
meander - Tools for transparent data transformation
React - The library for web and native user interfaces.