I once had a bad dream.

I started good, a sunny day. I had just fixed an issue and push it to my fork, in order to create a Pull Request. I was happy. It felt awesome to have found a fix so elegant. Two lines of code.

But then, something happened. A cloud appear in the sky and partially hide the sun. github triggered the end to end test suite. You could see the waiting icon, you could almost hear the engines starting, ... a lightning and a thunder appeared in the sky, the sky turned dark and the e2e test started in our private jenkins server.

1 hour passed by... 2 hours .... the e2e tests still running ... 4 hours, not even 50%. Finally, I had to go, get some dinner, get some rest, so I decided to look at it the next day.

Sleep was not good. Dreaming the tests would fail and the deadline be missed .... I couldn't sleep no more so I wake up early in the morning, before sunrise. The computer still opened was lightning up the room. Out, the storm turned out to be a heavy rain. And the tests did not passed! 4 hours and half after starting them, so half an hour after leaving for dinner, and there was a fail test.

Finding out what went wrong took 2 hours...it was not even related to the code, but an infrastructure issue. A timeout when connecting to a mirror which was being restarted when the tests run. Apparently we hit a maintenance window.

Damn! So let's just "restart" the tests. This time, I decided to put an alarm 4 hours after and switch to another task, just to avoid the anxiety of the previous evening looking at the results. 4 hours passed by, and the tests are good. It is not raining anymore, a git of sun is filtering throw the window, hope is in the air. 4 more hours, and the tests still have not failed. Going to dinner and bed again and putting the alarm early in the morning. First thing in the morning I looked at the tests ... running again, feels good. All tests have passed and there is only one final "cleanup" state. Shower, breakfeast, sun is shining again!

Finally, all is green, so let's go and push the merge button ... oh oh ... clouds hide the sun again ... where is the merge button?? no way, there is a conflict with the code in master and I can't merge!!! I can't merge!!! shocked, I looked into the history ... John merged a PR overnight that was touching the same file ... hopeless I start crying on my desk...

Scared?

This is not so different on what could happen if we were running the whole e2e test suite in the SUSE Manager (uyuni) Pull Requests. However, not running any e2e tests has also a bad consequence. We find the issues after code has been merged in master, and then we spend days looking at what could have caused this, reverting commits, and starting again.

However, if we could run a subset of the e2e tests, we could shorten the time it takes and so run them at the PR level, so no broken code could get merged.

The thing is , how do we select which tests to run?

This project is about using Machine Learning to do predictive test selection, thus selecting which tests to run based on the history of previous test runs.

Inspired by: https://engineering.fb.com/2018/11/21/developer-tools/predictive-test-selection/

Looking for hackers with the skills:

ml ci qa ai

This project is part of:

Hack Week 20

Activity

  • almost 5 years ago: PSuarezHernandez liked this project.
  • almost 5 years ago: llansky3 liked this project.
  • almost 5 years ago: ories liked this project.
  • almost 5 years ago: j_renner liked this project.
  • almost 5 years ago: RDiasMateus liked this project.
  • almost 5 years ago: pagarcia liked this project.
  • almost 5 years ago: jordimassaguerpla added keyword "ci" to this project.
  • almost 5 years ago: jordimassaguerpla added keyword "qa" to this project.
  • almost 5 years ago: jordimassaguerpla added keyword "ai" to this project.
  • almost 5 years ago: jordimassaguerpla added keyword "ml" to this project.
  • almost 5 years ago: jordimassaguerpla originated this project.

  • Comments

    • llansky3
      almost 5 years ago by llansky3 | Reply

      This could have some wider potential - clever picking of tests to shorten the time yet maximize the findings (and learning that in time based on test execution history to predict). I can see how that saves tons of money in cases where test on a real industrial machines need to be executed (e.g. cost of running engine on a test bed is 5-10k$ per day so there is huge difference if you need to run for 1 or 3 days)

    Similar Projects

    Kubernetes-Based ML Lifecycle Automation by lmiranda

    Description

    This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.

    The pipeline will automate the lifecycle of a machine learning model, including:

    • Data ingestion/collection
    • Model training as a Kubernetes Job
    • Model artifact storage in an S3-compatible registry (e.g. Minio)
    • A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
    • A lightweight inference service that loads and serves the latest model
    • Monitoring of model performance and service health through Prometheus/Grafana

    The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.

    Goals

    By the end of Hack Week, the project should:

    1. Produce a fully functional ML pipeline running on Kubernetes with:

      • Data collection job
      • Training job container
      • Storage and versioning of trained models
      • Automated deployment of new model versions
      • Model inference API service
      • Basic monitoring dashboards
    2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

    3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

    4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

    Resources

    Project Repository

    Updates

    1. Training pipeline and datasets
    2. Inference Service py


    Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

    Description

    Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

    This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

    The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

    Goals

    By the end of Hack Week, we aim to have a single, working Python script that:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources

    Outcome


    Backporting patches using LLM by jankara

    Description

    Backporting Linux kernel fixes (either for CVE issues or as part of general git-fixes workflow) is boring and mostly mechanical work (dealing with changes in context, renamed variables, new helper functions etc.). The idea of this project is to explore usage of LLM for backporting Linux kernel commits to SUSE kernels using LLM.

    Goals

    • Create safe environment allowing LLM to run and backport patches without exposing the whole filesystem to it (for privacy and security reasons).
    • Write prompt that will guide LLM through the backporting process. Fine tune it based on experimental results.
    • Explore success rate of LLMs when backporting various patches.

    Resources

    • Docker
    • Gemini CLI

    Repository

    Current version of the container with some instructions for use are at: https://gitlab.suse.de/jankara/gemini-cli-backporter


    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo


    Description

    The Problem

    Running LLMs can get expensive and complex pretty quickly.

    Today there are typically two choices:

    1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
    2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

    What if there was a middle ground?

    What if infrastructure scaled itself instead of making you scale it?

    Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?

    Project Repository: github.com/alexander-demicev/llmserverless


    What This Project Does

    A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

    A complete, self-scaling LLM infrastructure that:

    • Scales to zero when idle (no idle costs)
    • Scales up automatically when requests come in
    • Adds more nodes when needed, removes them when demand drops
    • Runs on any infrastructure - laptop, bare metal, or cloud

    Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

    How It Works

    A combination of open source tools working together:

    Flow:

    • Users interact with OpenWebUI (chat interface)
    • Requests go to LiteLLM Gateway
    • LiteLLM routes requests to:
      • Ollama (Knative) for local model inference (auto-scales pods)
      • Or cloud APIs for fallback


    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


    Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo

    Description

    Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.

    Goals

    Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:

    • Gain insight into the latest AI trends, tools, and architectural concepts.
    • Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).

    Resources

    • Red Hat AI Topic Articles

      • https://www.redhat.com/en/topics/ai
    • Kubeflow Documentation

      • https://www.kubeflow.org/docs/
    • Q4 2025 CNCF Technology Landscape Radar report:

      • https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
      • https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
    • Agent-to-Agent (A2A) Protocol

      • https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/