Project Description
The goal is to have a language model, that is able to answer technical questions on Uyuni. Uyuni documentation is too large for in-context processing, so finetuning is the way to go.
Goal for this Hackweek
Finetune a model based on llama-2-7b.
Resources
No Hackers yet
This project is part of:
Hack Week 23
Comments
Be the first to comment!
Similar Projects
Try AI training with ROCm and LoRA by bmwiedemann
Description
I want to setup a Radeon RX 9600 XT 16 GB at home with ROCm on Slowroll.
Goals
I want to test how fast AI inference can get with the GPU and if I can use LoRA to re-train an existing free model for some task.
Resources
- https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html
- https://build.opensuse.org/project/show/science:GPU:ROCm
- https://src.opensuse.org/ROCm/
- https://www.suse.com/c/lora-fine-tuning-llms-for-text-classification/
Results
got inference working with llama.cpp:
export LLAMACPP_ROCM_ARCH=gfx1200
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$LLAMACPP_ROCM_ARCH \
-DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
-Dhipblas_DIR=/usr/lib64/cmake/hipblaslt/ \
&& cmake --build build --config Release -j8
m=models/gpt-oss-20b-mxfp4.gguf
cd $P/llama.cpp && build/bin/llama-server --model $m --threads 8 --port 8005 --host 0.0.0.0 --device ROCm0 --n-gpu-layers 999
Without the --device option it faulted. Maybe because my APU also appears there?
I updated/fixed various related packages: https://src.opensuse.org/ROCm/rocm-examples/pulls/1 https://src.opensuse.org/ROCm/hipblaslt/pulls/1 SR 1320959
benchmark
I benchmarked inference with llama.cpp + gpt-oss-20b-mxfp4.gguf and ROCm offloading to a Radeon RX 9060 XT 16GB. I varied the number of layers that went to the GPU:
- 0 layers 14.49 tokens/s (8 CPU cores)
- 9 layers 17.79 tokens/s 34% VRAM
- 15 layers 22.39 tokens/s 51% VRAM
- 20 layers 27.49 tokens/s 64% VRAM
- 24 layers 41.18 tokens/s 74% VRAM
- 25+ layers 86.63 tokens/s 75% VRAM (only 200% CPU load)
So there is a significant performance-boost if the whole model fits into the GPU's VRAM.
MCP Trace Suite by r1chard-lyu
Description
This project plans to create an MCP Trace Suite, a system that consolidates commonly used Linux debugging tools such as bpftrace, perf, and ftrace.
The suite is implemented as an MCP Server. This architecture allows an AI agent to leverage the server to diagnose Linux issues and perform targeted system debugging by remotely executing and retrieving tracing data from these powerful tools.
- Repo: https://github.com/r1chard-lyu/systracesuite
- Demo: Slides
Goals
Build an MCP Server that can integrate various Linux debugging and tracing tools, including bpftrace, perf, ftrace, strace, and others, with support for future expansion of additional tools.
Perform testing by intentionally creating bugs or issues that impact system performance, allowing an AI agent to analyze the root cause and identify the underlying problem.
Resources
- Gemini CLI: https://geminicli.com/
- eBPF: https://ebpf.io/
- bpftrace: https://github.com/bpftrace/bpftrace/
- perf: https://perfwiki.github.io/main/
- ftrace: https://github.com/r1chard-lyu/tracium/
AI-Powered Unit Test Automation for Agama by joseivanlopez
The Agama project is a multi-language Linux installer that leverages the distinct strengths of several key technologies:
- Rust: Used for the back-end services and the core HTTP API, providing performance and safety.
- TypeScript (React/PatternFly): Powers the modern web user interface (UI), ensuring a consistent and responsive user experience.
- Ruby: Integrates existing, robust YaST libraries (e.g.,
yast-storage-ng) to reuse established functionality.
The Problem: Testing Overhead
Developing and maintaining code across these three languages requires a significant, tedious effort in writing, reviewing, and updating unit tests for each component. This high cost of testing is a drain on developer resources and can slow down the project's evolution.
The Solution: AI-Driven Automation
This project aims to eliminate the manual overhead of unit testing by exploring and integrating AI-driven code generation tools. We will investigate how AI can:
- Automatically generate new unit tests as code is developed.
- Intelligently correct and update existing unit tests when the application code changes.
By automating this crucial but monotonous task, we can free developers to focus on feature implementation and significantly improve the speed and maintainability of the Agama codebase.
Goals
- Proof of Concept: Successfully integrate and demonstrate an authorized AI tool (e.g.,
gemini-cli) to automatically generate unit tests. - Workflow Integration: Define and document a new unit test automation workflow that seamlessly integrates the selected AI tool into the existing Agama development pipeline.
- Knowledge Sharing: Establish a set of best practices for using AI in code generation, sharing the learned expertise with the broader team.
Contribution & Resources
We are seeking contributors interested in AI-powered development and improving developer efficiency. Whether you have previous experience with code generation tools or are eager to learn, your participation is highly valuable.
If you want to dive deep into AI for software quality, please reach out and join the effort!
- Authorized AI Tools: Tools supported by SUSE (e.g.,
gemini-cli) - Focus Areas: Rust, TypeScript, and Ruby components within the Agama project.
Interesting Links
Uyuni Health-check Grafana AI Troubleshooter by ygutierrez
Description
This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.
Goals
- Investigate if and how the
grafana-llm-appplug-in can be used within the Uyuni Health-check tool. - Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
- Evaluate support for local LLMs and external APIs through the plugin.
- Evaluate if and how the Uyuni MCP server could be integrated as another source of information.
Resources
Multi-agent AI assistant for Linux troubleshooting by doreilly
Description
Explore multi-agent architecture as a way to avoid MCP context rot.
Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.
Goals
Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.
Example prompts/responses:
user$ the system seems slow
assistant$ process foo with pid 12345 is using 1000% cpu ...
user$ I can't connect to the apache webserver
assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...
Resources
Language Python. The Python ADK is more mature than Golang.
https://google.github.io/adk-docs/
https://github.com/djoreilly/linux-helper
Set Uyuni to manage edge clusters at scale by RDiasMateus
Description
Prepare a Poc on how to use MLM to manage edge clusters. Those cluster are normally equal across each location, and we have a large number of them.
The goal is to produce a set of sets/best practices/scripts to help users manage this kind of setup.
Goals
step 1: Manual set-up
Goal: Have a running application in k3s and be able to update it using System Update Controler (SUC)
- Deploy Micro 6.2 machine
Deploy k3s - single node
- https://docs.k3s.io/quick-start
Build/find a simple web application (static page)
- Build/find a helmchart to deploy the application
Deploy the application on the k3s cluster
Install App updates through helm update
Install OS updates using MLM
step 2: Automate day 1
Goal: Trigger the application deployment and update from MLM
- Salt states For application (with static data)
- Deploy the application helmchart, if not present
- install app updates through helmchart parameters
- Link it to GIT
- Define how to link the state to the machines (based in some pillar data? Using configuration channels by importing the state? Naming convention?)
- Use git update to trigger helmchart app update
- Recurrent state applying configuration channel?
step 3: Multi-node cluster
Goal: Use SUC to update a multi-node cluster.
- Create a multi-node cluster
- Deploy application
- call the helm update/install only on control plane?
- Install App updates through helm update
- Prepare a SUC for OS update (k3s also? How?)
- https://github.com/rancher/system-upgrade-controller
- https://documentation.suse.com/cloudnative/k3s/latest/en/upgrades/automated.html
- Update/deploy the SUC?
- Update/deploy the SUC CRD with the update procedure
Enhance setup wizard for Uyuni by PSuarezHernandez
Description
This project wants to enhance the intial setup on Uyuni after its installation, so it's easier for a user to start using with it.
Uyuni currently uses "uyuni-tools" (mgradm) as the installation entrypoint, to trigger the installation of Uyuni in the given host, but does not really perform an initial setup, for instance:
- user creation
- adding products / channels
- generating bootstrap repos
- create activation keys
- ...
Goals
- Provide initial setup wizard as part of mgradm uyuni installation
Resources
Uyuni Health-check Grafana AI Troubleshooter by ygutierrez
Description
This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.
Goals
- Investigate if and how the
grafana-llm-appplug-in can be used within the Uyuni Health-check tool. - Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
- Evaluate support for local LLMs and external APIs through the plugin.
- Evaluate if and how the Uyuni MCP server could be integrated as another source of information.
Resources
Set Up an Ephemeral Uyuni Instance by mbussolotto
Description
To test, check, and verify the latest changes in the master branch, we want to easily set up an ephemeral environment.
Goals
- Create an ephemeral environment manually
Create an ephemeral environment automatically
Resources
https://github.com/uyuni-project/uyuni
https://www.uyuni-project.org/uyuni-docs/en/uyuni/index.html
Ansible to Salt integration by vizhestkov
Description
We already have initial integration of Ansible in Salt with the possibility to run playbooks from the salt-master on the salt-minion used as an Ansible Control node.
In this project I want to check if it possible to make Ansible working on the transport of Salt. Basically run playbooks with Ansible through existing established Salt (ZeroMQ) transport and not using ssh at all.
It could be a good solution for the end users to reuse Ansible playbooks or run Ansible modules they got used to with no effort of complex configuration with existing Salt (or Uyuni/SUSE Multi Linux Manager) infrastructure.
Goals
- [v] Prepare the testing environment with Salt and Ansible installed
- [v] Discover Ansible codebase to figure out possible ways of integration
- [v] Create Salt/Uyuni inventory module
- [v] Make basic modules to work with no using separate ssh connection, but reusing existing Salt connection
- [v] Test some most basic playbooks
Resources