On Fri, 2026-02-06 at 15:59 -0700, Jonathan Corbet wrote:
> Viacheslav Dubeyko <slava@dubeyko.com> writes:
> 
> > This patchset introduces initial vision of Machine Learning (ML) library
> > in Linux kernel. It is an effort to define the ML library API and
> > to elaborate the way of running ML models in Linux kernel.
> 
> I went looking for the documentation files ... but then I've always been
> known as an optimist.  That would be a nice thing to fill in.

Yeah, I can see your pain. :) I totally agree that documentation is necessary.
This patchset is only the first small step to share and discuss with the
community. I am sharing the initial vision of the idea and API with the hope to
have the review and discussion. If the API vision makes sense and it looks good,
then it makes sense to prepare documentation. I am planning to have the
documentation after checking the whole infrastructure with a real-life use-
case(s).

> 
> Perhaps more important, though, would be a real user for this facility.
> You must certainly have one in mind, can we see it to get a sense for
> how this library is meant to be used?

I believe there are multiple potential real-life use-cases. Currently, I have
implemented very simple testing driver that generates random numbers and,
potentially, some ML model can be trained on this "mess". :) But it's not very
interesting example. As a next step, I am considering to use this ML library
infrastructure for: (1) GC subsystem in LFS file systems (NILFS2, F2FS, SSDFS),
(2) ML-based DAMON extension with the goal to check the whole idea for real-life
use-cases.

So, let me make the next step and we will be able to discuss this ML library for
real-life use-cases. But the main approach is the collaboration of kernel
subsystem and ML model running in user-space.

Thanks,
Slava.