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World of Software > Computing > ML-LIB: Machine Learning Library Proposed For The Linux Kernel
Computing

ML-LIB: Machine Learning Library Proposed For The Linux Kernel

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Last updated: 2026/02/06 at 2:48 PM
News Room Published 6 February 2026
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ML-LIB: Machine Learning Library Proposed For The Linux Kernel
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Sent out today as a request for comments (RFC) by a Linux kernel engineer employed by IBM is a machine learning library for the Linux kernel. The intent is on plugging in running ML models to the Linux kernel that could be used for system performance optimizations and various other purposes.

Viacheslav Dubeyko who is employed by IBM as a Linux kernel developer sent out an RFC patch series today that aims to introduce a machine learning library for the Linux kernel. Dubeyko explains of the problem statement leading to the creation of this machine learning library:

“There are already research works and industry efforts to employ ML approaches for configuration and optimization the Linux kernel. However, introduction of ML approaches in Linux kernel is not so simple and straightforward way. There are multiple problems and unanswered questions on this road. First of all, any ML model requires the floating-point operations (FPU) for running. But there is no direct use of FPUs in kernel space. Also, ML model requires training phase that can be a reason of significant performance degradation of Linux kernel. Even inference phase could be problematic from the performance point of view on kernel side. The using of ML approaches in Linux kernel is inevitable step. But, how can we use ML approaches in Linux kernel? Which infrastructure do we need to adopt ML models in Linux kernel?”

With the proposal, the machine learning model can be represented by a process/thread running in user-space while relying on machine learning model proxy on the kernel space side for different subsystems.

The ML_LIB Kconfig help text explains:

“Machine Learning (ML) library has goal to provide the interaction and communication of ML models in user-space with kernel subsystems. It implements the basic code primitives that builds the way of ML models integration into Linux kernel functionality.”

There are various open design elements still and other unanswered questions around employing machine learning within the Linux kernel. The RFC proposal is sure to be a contentious topic on the mailing list as is most AI/ML matters. Those interested in this initial RFC patch series for this machine learning library for the Linux kernel can see it on the LKML.

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