a project by pdostal
Rancher maintains 2 Kubernetes distribution, both production grade:
- k3s: Single binary containing containerd backend. Can be run as server or agent on each node of cluster. SQLite is used by default instead of ETCD. MySQL and Postgres also supported. No prerequisity. Intended for edge computing.
- RKE: Deployed remotely over SSH on top of Docker which is the only required component of the underlying OS. Standard ETCD database in container. Each node and its roles are defined in YAML. Resource hungry, datacenter grade.
Each scenario needs:
1) Support Server: As Internet gateway, DHCP and DNS server. 2) Master node for the control node and it's database 3) 4) Worker nodes joining the cluster.
Resources:
- [k3s webpage]: https://k3s.io/
- [k3s github repository]: https://github.com/k3s-io/k3s
- [rke1 github repository]: https://github.com/rancher/rke
- [rke1 cluster.yaml]: https://rancher.com/docs/rke/latest/en/example-yamls/#minimal-cluster-yml-example
- [rke1 deploy guide]: https://rancher.com/docs/rke/latest/en/installation/#prepare-the-nodes-for-the-kubernetes-cluster
This project was inspired by the awesome [SUSE @Home] workshop: https://github.com/SUSE/suse-at-home
TO-DO:
- Run more tests on top of the Kubernetes cluster.
- Implement the same for rke2 (not in production yet).
This project is part of:
Hack Week 20
Activity
Comments
Similar Projects
openQA log viewer by mpagot
Description
*** Warning: Are You at Risk for VOMIT? ***
Do you find yourself staring at a screen, your eyes glossing over as thousands of lines of text scroll by? Do you feel a wave of text-based nausea when someone asks you to "just check the logs"?
You may be suffering from VOMIT (Verbose Output Mental Irritation Toxicity).
This dangerous, work-induced ailment is triggered by exposure to an overwhelming quantity of log data, especially from parallel systems. The human brain, not designed to mentally process 12 simultaneous autoinst-log.txt files, enters a state of toxic shock. It rejects the "Verbose Output," making it impossible to find the one critical error line buried in a 50,000-line sea of "INFO: doing a thing."
Before you're forced to rm -rf /var/log in a fit of desperation, we present the digital antacid.
No panic: we have The openQA Log Visualizer
This is the UI antidote for handling toxic log environments. It bravely dives into the chaotic, multi-machine mess of your openQA test runs, finds all the related, verbose logs, and force-feeds them into a parser.
Goals
Work on the existing POC openqa-log-visualizer about few specific tasks:
- add support for more type of logs
- extend the configuration file syntax beyond the actual one
- work on log parsing performance
Find some beta-tester and collect feedback and ideas about features
If time allow for it evaluate other UI frameworks and solutions (something more simple to distribute and run, maybe more low level to gain in performance).
Resources
openQA tests needles elaboration using AI image recognition by mdati
Description
In the openQA test framework, to identify the status of a target SUT image, a screenshots of GUI or CLI-terminal images,
the needles framework scans the many pictures in its repository, having associated a given set of tags (strings), selecting specific smaller parts of each available image. For the needles management actually we need to keep stored many screenshots, variants of GUI and CLI-terminal images, eachone accompanied by a dedicated set of data references (json).
A smarter framework, using image recognition based on AI or other image elaborations tools, nowadays widely available, could improve the matching process and hopefully reduce time and errors, during the images verification and detection process.
Goals
Main scope of this idea is to match a non-text status of a running openQA test, an image of a shell console or application-GUI screenshot, using less time and resources and with less errors in data preparation and use, than the actual openQA needles framework; that is:
- having a given SUT (system under test) GUI or CLI-terminal screenshot, with a local distribution of pixels or text commands related to a running test status,
- we want to identify a desired target, e.g. a screen image status or data/commands context,
- based on AI/ML-pretrained archives containing object or other proper elaboration tools,
- possibly able to identify also object not present in the archive, i.e. by means of AI/ML mechanisms.
- the matching result should be then adapted to continue working in the openQA test, likewise and in place of the same result that would have been produced by the original openQA needles framework.
- We expect an improvement of the matching-time(less time), reliability of the expected result(less error) and simplification of archive maintenance in adding/removing objects(smaller DB and less actions).
Hackweek step
POC:
- study the available tools
- prepare a plan for the process to build
- write and build a draft application
- prepare the data archive from a subset of needles
- initialize/pre-train the base archive
- select a screenshot from the subset, removing/changing some part
- run the POC application
- expect the image type is identified in a good %.
Resources
first step of this project is quite identification of useful resources for the scope; some possibilities are:
- SUSE AI and other ML tools (i.e. Tensorflow)
- Tools able to manage images
- RPA test tools (like i.e. Robot framework)
- other.
MCP Perl SDK by kraih
Description
We've been using the MCP Perl SDK to connect openQA with AI. And while the basics are working pretty well, the SDK is not fully spec compliant yet. So let's change that!
Goals
- Support for Resources
- All response types (Audio, Resource Links, Embedded Resources...)
- Tool/Prompt/Resource update notifications
- Dynamic Tool/Prompt/Resource lists
- New authentication mechanisms
Resources