mgl
cl-cuda
mgl | cl-cuda | |
---|---|---|
15 | 5 | |
573 | 270 | |
- | - | |
3.7 | 0.0 | |
about 1 year ago | almost 3 years ago | |
Common Lisp | Common Lisp | |
MIT License | MIT License |
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mgl
- Gabor Melis - Google AI Contest Winner - Conversation and Presentation (2013) (@melisgl, author of MGL)
- MGL: A Common Lisp machine learning library
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Peter Norvig – Paradigms of AI Programming Case Studies in Common Lisp
If you are interested in machine learning, check out Gabor Melis's library: https://github.com/melisgl/mgl. It's not an area I'm super familiar with, so I can't speak to it's feature set, but I believe he used it to win a machine learning competition some years ago.
I don't think anyone's written a transformer or diffusion model with it, could be a fun challenge.
- Mgl: Common Lisp machine learning library
- what library/language combination is good for regression and classification
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New Lisp-Stat Release
although not necessarily bert or resnet the following probably has all the ingredients for what you are looking for. the author of this library is a research scientist at deepmind since 2015
https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-...
- Update: Μαθήματα πρόγραμματισμου.
- Why Hy?
cl-cuda
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Why Lisp? (2015)
> You can write a lot of macrology to get around it, but there's a point where you want actual compiler writers to be doing this
this is not the job of compiler writers (although writing macros is akin to writing a compiler but i do not think that this is what you mean). in julia the numerical programming packages are not part of the standard library and a lot of it is wrappers around C++ code especially when the drivers to the underlining hardware are closed-source [0]. also here is the similar library in common lisp [1]
[0] https://github.com/JuliaGPU/CUDA.jl
[1] https://github.com/takagi/cl-cuda
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
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Hacker News top posts: Aug 14, 2021
A Common Lisp Library to Use Nvidia CUDA\ (0 comments)
- A Common Lisp Library to Use Nvidia CUDA
-
Machine Learning in Lisp
Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.
What are some alternatives?
quilc - The optimizing Quil compiler.
numcl - Numpy clone in Common Lisp
ghdl - Binary Manager for Github Releases
criterium - Benchmarking library for clojure
py4cl - Call python from Common Lisp
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
JWM - Cross-platform window management and OS integration library for Java
hash-array-mapped-trie - A hash array mapped trie implementation in c.
http4s-native-image - Compiling an example http4s web service to a native executable using GraalVM Native Image
rewrite - Automated mass refactoring of source code.