Post Syndicated from corbet original https://lwn.net/Articles/1057569/
For those wanting more machine learning in the kernel, Viacheslav Dubeyko
has posted a
new in-kernel library for that purpose.
What is the goal of using ML models in Linux kernel? The main goal
is to employ ML models for elaboration of a logic of particular
Linux kernel subsystem based on processing data or/and an efficient
subsystem configuration based on internal state of subsystem. As a
result, it needs: (1) collect data for training, (2) execute ML
model training phase, (3) test trained ML model, (4) use ML model
for executing the inference phase. The ML model inference can be
used for recommendation of Linux kernel subsystem configuration
or/and for injecting a synthesized subsystem logic into kernel
space (for example, eBPF logic).
It is rigorously undocumented
and there are no real users, so it’s not entirely clear what the purpose
is, but there are undoubtedly interesting things that could be done with
it.