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elixir-nx
scholar
Elixir2022–2024
elixir-nx / scholar

scholar

Traditional machine learning on top of Nx

AI & Machine LearningLearning & Reference
Explore on GitHubRead Hacker News discussions
3Distinct people
4Mentions
0Explicit recommenders
498GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Traditional machine learning on top of Nx

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month4 dated mentions

Observed mentions: 2022-12-09 06:40:32 UTC to 2024-08-27 10:10:24 UTC.

012022-12-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 20222023-01-01 00:00:00 UTC to 2023-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-01 00:00:00 UTC to 2023-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 20232023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 20222022-12-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
Jan 20232023-01-01 00:00:00 UTC2023-02-01 00:00:00 UTC000
Feb 20232023-02-01 00:00:00 UTC2023-03-01 00:00:00 UTC000
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC110
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC101
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC000
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC000
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC000
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC000
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC000
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC000
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC000
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC000
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC000
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC000
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC000
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC101

What Hacker News says

4 sources
thibaut_barrere·Aug 27, 2024
Since you wrote "using something other than Python" and not necessarily only Ruby, definitely look into Livebook and Elixir, and the whole ecosystem around it, including: - https://github.com/elixir-nx/axon Nx-powered Neural Networks - https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir - https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx - https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration) - https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir - https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers) - https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx And of
Read the rest
course Livebook (https://livebook.dev) Old but interesting video https://www.youtube.com/watch?v=g3oyh3g1AtQ (Bumblebee: GPT2, Stable Diffusion, and more in Elixir) A talk on the upcoming Elixir conference (https://2024.elixirconf.com/schedule/#schedules) is actually titled "Livebook in the cloud: GPUs and clustered workflows in seconds".
In Ruby in Jupyter Notebook
josevalim·Apr 14, 2023
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 t
Read the rest
o 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!
In Data wrangling in Elixir with Explorer, the power of Rust, the elegance of R
josevalim·Dec 9, 2022
I have written a bit about potential benefits in the initial announcement of Nx [0] and then on its first release [1]. In a nutshell, there has been trends in Python (such as JAX and Thinc.ai) that argue functional programming can provide better abstractions and more composable building blocks for deep learning libraries. And I believe Elixir, as a functional language with Lisp-style macros, is in an excellent position to exploit that - as seen in "numerical definitions" which compile a subset of Elixir to the CPU/GPU. I also think the Erlang VM, with its distribution and network capabilities, can provide exciting developments in the realm of federated and distributed learning. We aren't exploring those aspects yet but we are getting closer to having the foundation to do so. Regarding ML ops, I believe one main advantage is explained in t
Read the rest
his video announcement. When deploying a ML model with Nx, you can embed the model within your applications: you don't need a 3rd-party service because we batch and route requests from multiple cores and multiple nodes withing Erlang/Elixir. This can be specially beneficial for projects like Nerves [5] and we will see how it evolves in the long term (as we _just_ announced it). Finally, one of the benefits on starting from scratch after Python has paved the way is that we can learn from its ecosystem and provide a unified experience. You can think of Nx as Numpy+JAX+TFServing all in one place and we hope that doing so streamlines the developer experience. This also means libraries like Scholar [2] (which aims to serve a similar role as SciPy) and Meow [3] (for Genetic Algorithms) get to use the same abstractions and compile to the CPU/GPU. The latter can show an order of magnitude improvement over other currently used frameworks [4]. [0]: https://dashbit.co/blog/nx-numerical-elixir-is-now-publicly-... [1]: https://dashbit.co/blog/elixir-and-machine-learning-nx-v0.1 [2]: https://github.com/elixir-nx/scholar/ [3]: https://github.com/jonatanklosko/meow [4]: https://dl.acm.org/doi/10.1145/3512290.3528753 [5]: https://www.nerves-project.org/
In Bumblebee: GPT2, Stable Diffusion, and More in Elixir

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