INim VS Arraymancer

- `nim secret`[0]: code is interpreted with Nim's vm, it is limited to compile-time (e.g. no C/C++).

- `nlvm r`[1]: nlvm backend supports JIT compilation and repl-like interface was added in latest release.

- `inim`[2]: supports all nim code, but it's not really a true repl. It adds code to a file and recompiles it (you can see the source file with `ctrl+x`). I'd recommend to use it with clang compiler, because it's a bit faster than gcc in my experience. It's the best option right now. And I use it almost daily.

First two options have rudimentary input system. But you can get command history and left-right navigation by wrapping them with rlwrap (should be preinstalled on most *nixes): `rlwrap nim secret`, `rlwrap nlvm r`.

There are plans to support incremental compilation for the next Nim release (it is currently broken). That would improve inim and other repls experience significantly.

[0]: https://nim-lang.org/docs/manual.html#restrictions-on-compil...

[1]: https://forum.nim-lang.org/t/10697

[2]: https://github.com/inim-repl/INim

Go must be terrible to google too! Nim's easy to google though, it's how I got to rust really. I'm actually the maintainer of inim, the nim repl: https://github.com/inim-repl/INim

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 !

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

- 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.

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 ...

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.

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

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!