livebook
Arraymancer
livebook | Arraymancer | |
---|---|---|
80 | 21 | |
4,425 | 1,307 | |
2.1% | - | |
9.8 | 8.2 | |
4 days ago | 6 days ago | |
Elixir | Nim | |
Apache License 2.0 | 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.
livebook
-
Super simple validated structs in Elixir
To get started you need a running instance of Livebook
- Arraymancer – Deep Learning Nim Library
-
Setup Nx lib and EXLA to run NX/AXON with CUDA
LiveBook site
-
Interactive Code Cells
I prefer functional programming with Livebook[1] for this type of thing. Once you run a cell, it can be published right into a web component as well.
[1] - https://livebook.dev
-
What software should I use as an alternative to Microsoft OneNote?
If you're a coder, Livebook might be worth a look too. I certainly have my eyes on it.
-
Advent of Code Day 5
Would highly recommend looking at Jose's use of livebook to answer these. It makes testing easier. It's old but still relevant. Video link inside
- Advent of Code 2023 is nigh
-
Racket branch of Chez Scheme merging with mainline Chez Scheme
That's hard to say. Racket is a rather complete language, as is F# and Elixir. And F# and Racket are extremely capable multi-paradigm languages, supporting basically any paradigm. Elixir is a bit more restricted in terms of its paradigms, but that's a feature oftentimes, and it also makes up for it with its process framework and deep VM support from the BEAM.
I would say that the key difference is that F# and Elixir are backed by industry whereas Racket is primarily backed via academia. Thus, the incentives and goals are more aligned for F# and Elixir to be used in industrial settings.
Also, both F# and Elixir gain a lot from their host VMs in the CLR and BEAM. Overall, F# is the cleanest language of the three, as it is easy to write concise imperative, functional, or OOP code and has easy asynchronous facilities. Elixir supports macros, and although Racket's macro system is far more advanced, I don't think it really provides any measurable utility over Elixir's. I would also say that F# and Elixir's documentation is better than Racket's. Racket has a lot of documentation, but it can be a little terse at times. And Elixir definitely has the most active, vibrant, and complete ecosystem of all three languages, as well as job market.
The last thing is that F# and Elixir have extremely good notebook implementations in Polyglot Notebooks (https://marketplace.visualstudio.com/items?itemName=ms-dotne...) and Livebook (https://livebook.dev/), respectively. I would say both of these exceed the standard Python Jupyter notebook, and Racket doesn't have anything like Polyglot Notebooks or Livebook. (As an aside, it's possible for someone to implement a Racket kernel for Polyglot Notebooks, so maybe that's a good side project for me.)
So for me, over time, it has slowly whittled down to F# and Elixir being my two languages that I reach for to handle effectively any project. Racket just doesn't pull me in that direction, and I would say that Racket is a bit too locked to DrRacket. I tried doing some GUI stuff in Racket, and despite it having an already built framework, I have actually found it easier to write my own due to bugs found and the poor performance of Racket Draw.
-
Runme – Interactive Runbooks Built with Markdown
This looks very similar to LiveBook¹. It is purely Elixir/BEAM based, but is quite polished and seems like a perfect workflow tool that is also able to expose these workflows (simply called livebooks) as web apps that some functional, non-technical person can execute on his/her own.
1: https://livebook.dev/
- Livebook: Automate code and data workflows with interactive notebooks
Arraymancer
-
Arraymancer – Deep Learning Nim Library
It is a small DSL written using macros at https://github.com/mratsim/Arraymancer/blob/master/src/array....
Nim has pretty great meta-programming capabilities and arraymancer employs some cool features like emitting cuda-kernels on the fly using standard templates depending on backend !
-
Go, Python, Rust, and production AI applications
Nim has also a powerful deep learning library called Arraymancer. It's selling point is that you don't have to rewrite your code from research to production. It's used in various machine learning projects, but one recent one that caught my eye was https://github.com/amkrajewski/nimCSO "Composition Space Optimization"
https://github.com/mratsim/Arraymancer
-
D Programming Language
- https://github.com/mratsim/Arraymancer/blob/master/src/array...
It's worth noting that nim async/await transformation is fully implemented as a library in macros.
- Prospects of utilising Nim in scientific computation?
-
How to write performant Nim?
https://github.com/mratsim/Arraymancer 11. « Premature optimisation is the root of all evil », Donald Knuth, The art of computer Programming It would be quite useful that someone writes one with examples for all these recommendations and more ...
-
Deeplearning in Nim?
In particular for deep learning as bobsyourunkl already mentioned there is arraymancer on the one hand and also flambeau on the other. The latter is a Nim wrapper around libtorch (i.e. the PyTorch C++ backend). It is missing things (to be wrapped by adding a few lines) and has some rough edges, but if one needs to get stuff done, it's possible.
-
Mastering Nim – now available on Amazon
how are u compiling (optimization, custom compilation flags etc.?) In my case https://github.com/mratsim/Arraymancer big project compile under your 4.2s so or you have like 10k+ lines of codes with macros or you just pass some debug flags to compiler :D
- Nim Version 1.6.6 Released
- The counter-intuitive rise of Python in scientific computing (2020)
-
Computer Programming with Nim
We have both raw wrappers for BLAS:
https://github.com/andreaferretti/nimblas
as well as LAPACK:
https://github.com/andreaferretti/nimlapack
For an example, consider calling the least squares routine `dgelsd` in arraymancer:
https://github.com/mratsim/Arraymancer/blob/master/src/array...
wrapped up in a nicer user facing API.
Feel free to hop onto matrix, if you have more questions!
What are some alternatives?
kino - Client-driven interactive widgets for Livebook
nimtorch - PyTorch - Python + Nim
awesome-advent-of-code - A collection of awesome resources related to the yearly Advent of Code challenge.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
interactive - .NET Interactive combines the power of .NET with many other languages to create notebooks, REPLs, and embedded coding experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before.
nimble - Package manager for the Nim programming language.
Genie.jl - 🧞The highly productive Julia web framework
awesome-tensor-compilers - A list of awesome compiler projects and papers for tensor computation and deep learning.
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
nvim-treesitter - Nvim Treesitter configurations and abstraction layer
axon - Nx-powered Neural Networks
prologue - Powerful and flexible web framework written in Nim