[medo-bear]

what do you mean a one-off project? some of these have corporate/institutional backing but thats not the point. if you know how to implement machine learning models then there should be nothing (except maybe availability of pretrained models) preventing you from using common lisp. if you dont want to dig deep and are happy using just the front end (perfectly acceptable) then python is a much better bet

all the needed ingredients are there to build a custom production class ml solution in common lisp: vectorization, cuda, blas, lapack

[p1esk]

Do any of those libraries linked above provide an example of training a resnet or bert on a GPU? I’m not looking for anything fancy, or production quality, just a basic example.

[medo-bear]

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-...

[p1esk]

There's not even a convolutional layer provided as a building block. This project looks pretty dead to me: most of the code is 8 years old, and the last code-changing commit is over 2 years old. I guess it's okay as a proof of concept for ML with Lisp, but I don't think anyone interested in ML would actually consider using it.

[piethesailorman]

In defense of common lisp: The language is ansi standard and hasn't seen core changes in decades. This means cl pagckages can be "done". It is very common in cl to have projects that haven't been touched in years, because it is highly unlikely to break, hence, "it just works". As for this specific ML conversation, I am not in this field and have nothing valuble to say about who uses what and how viable/appropriate any of the approaches(languages) are for ML.

[medo-bear]

> his means cl pagckages can be "done".

this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university and it has a convolutional layer implemented

https://github.com/marcoheisig/Petalisp

https://github.com/marcoheisig/Petalisp/blob/master/examples...

[piethesailorman]

Thank you for clarifying. I'll have to check out petalisp, I love discovreing new(to me) cl projects.

[medo-bear]

i work in ml and you asked for an example so i thought a proof of concept answer is sufficient. as i said somewhere else if you are looking for a ready made framework then this is not for you and should probably stick with pytorch, tf, and maybe even pyro. but if you are willing to engineer your own system for optimal performance while avoiding c++ then you would be served good by common lisp. common lisp for ml is suited for software engineers doing ml not so much statisticians