Arraymancer VS nimib

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!

Jupyter notebook is indeed very important. It mainly provides data scientists with two things: a literate programming environment (mixing text, code and outputs) and a way to hold state of data in memory (so that you can perform computation interactively).

As a different take to literate programming we have created a library and an ecosystem around it: https://github.com/pietroppeter/nimib

For holding state a Nim repl (which is on the roadmap as secondary priority after completing incremental compilation) is definitely an option.

Another option could be to create a library framework for caching (or be able to serialize and deserialize quickly) large data and objects. One way to see it, could be to build something similar to streamlit cache (streamlit indeed provides great interactivity)

As a reminder, at Nim Conf back in October 2022 Andreas presented Nim 2.0 in this video https://www.youtube.com/watch?v=aDi50K_Id_k&list=PLxLdEZg8DR...

Hearing again I cannot chuckle when Araq says: Nim v1 is good at everything, Nim v2 is supposed to be better at everything.

Back then it was supposed to come out in 2022 and indeed a RC1 came out in Dec. In the blogpost for RC1 you find the desciption of all new features: https://nim-lang.org/blog/2022/12/21/version-20-rc.html

This longer time is because extra care is being taken into having a smooth transitions (for example important libraries have been tested to work on nim v2, e.g. we made sure nimib was working with v2 in early Feb: https://github.com/pietroppeter/nimib/releases/tag/v0.3.6)

https://github.com/pietroppeter/nimib

Based on that and using a book theme, scinim getting started documentation is being built, e.g.: