Project Description
Build a tool that provides a GUI for Terraform and is able to spin up machines directly on KVM. The user should not be required to have any Terraform skills and should be guided through the GUI when he wants to create new Terraform scripts. Furthermore it would be helpful if the tool could export its configuration to HCL.
Goal for this Hackweek
Having at least a prototype that can spin up simple machine configurations.
Resources
The libivrt provider for Terraform should be used:
https://github.com/dmacvicar/terraform-provider-libvirt
This project is part of:
Hack Week 20
Activity
Comments
-
almost 4 years ago by dominic_vieira | Reply
Have you looked at the Blue Horizon project? This was created by the Public Cloud Engineering Team - https://github.com/SUSE-Enceladus/blue-horizon
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almost 4 years ago by kevinklinger | Reply
@dominic_vieira I think the difference is that Blue Horizon requires that you have the desired scripts already coded. This GUI I'm thinking of would be something to guide the user through the process of creating Terraform scripts.
But thanks for the link
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almost 4 years ago by pagarcia | Reply
If you don't want to build the UI from scratch but rather focus on the HCL-generation problem, this might come handy: https://appsmith.com/ https://github.com/appsmithorg/appsmith
Add new widgets for Terraform-related concepts, then generate HCL from that.
The inverse (HCL to UI) is even more interesting :-)
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Join the Gitter channel! https://gitter.im/uyuni-project/hackweek
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SUSE AI Meets the Game Board by moio
Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!
Results: Infrastructure Achievements
We successfully built and automated a containerized stack to support our AI experiments. This included:
- a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
- Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
./deploy.sh
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, above) with GPU acceleration (nvtop
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Results: Game Design Insights
Our project focused on modeling and analyzing two card games of our own design within the TAG framework:
- Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
- AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
- Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
- more about Bamboo on Dario's site
- more about R3 on Silvio's site (italian, translation coming)
- more about Totoro on Silvio's site
A family picture of our card games in progress. From the top: Bamboo, Totoro, R3
Results: Learning, Collaboration, and Innovation
Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:
- "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
- AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
- GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
- Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.
Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!
The Context: AI + Board Games