nim-libp2p
libp2p implementation in Nim (by vacp2p)
Arraymancer
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends (by mratsim)
nim-libp2p | Arraymancer | |
---|---|---|
1 | 21 | |
236 | 1,307 | |
-0.4% | - | |
8.9 | 8.2 | |
1 day ago | 3 days ago | |
Nim | Nim | |
MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
nim-libp2p
Posts with mentions or reviews of nim-libp2p.
We have used some of these posts to build our list of alternatives
and similar projects.
Arraymancer
Posts with mentions or reviews of Arraymancer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-28.
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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 !
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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
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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?
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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 ...
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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.
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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)
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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!