Project Description
As Generative AI is everywhere around, we want to research its possibilities, how it can help SUSE, its employees and customers. The initial idea is to build solution based on Amazon Bedrock, to integrate our asset management tools and to be able to query the data and get the answers using human-like text.
Goal for this Hackweek
Populate all available data from SUSE Asset management tools (integrate with Jira Insight, Racktables, CloudAccountMetadata, Cloudquery,...) into the foundational model (e.g. Amazon Titan). Then make the foundational model able to answer simple queries like how many VMs are running in PRG2 or who are the owners of EC2 instances of t2 family.
This is only one of the ideas for GenAI we have. Most probably we will try to cover also another scenarios. If you are interested or you have any other idea how to utilize foundational models, let us know.
Resources
- https://aws.amazon.com/bedrock/
- https://github.com/aws-samples/amazon-bedrock-workshop
- Specifically for this hackweek was created AWS Account
ITPE Gen IA Dev (047178302800)accessible via Okta - whoever is interested, please contact me (or raise JiraSD ticket to be added to CLZ: ITPE Gen IA Dev) and use region us-west-2 (don't mind the typo, heh). - we have booked AWS engineer, expert on Bedrock on 2023-11-06 (1-5pm CET, meeting link) - anyone interested can join
Keywords
AI, GenAI, GenerativeAI, AWS, Amazon Bedrock, Amazon Titan, Asset Management
Looking for hackers with the skills:
ai genai generativeai aws amazontitan assetmanagement bedrock
This project is part of:
Hack Week 23
Activity
Comments
-
-
about 1 year ago by mpiala | Reply
and recording of the workshop: Gen AI with AWS-20231106_130308-Meeting Recording.mp4
-
about 1 year ago by vadim | Reply
@mpiala now that we all have some hands on experience with bedrock I suggest that you create a few workgroup sessions for tomorrow / Thursday and invite contributors. I'd split the work into three workstreams:
- Create infrastructure / pipelines that would deploy the project in a reproducible way
- Create a crawler that would parse the source data and populate a vector database (probably a lambda that can be triggered by cloudwatch)
- Create a backend that would query the vector DB, run inference and integrate with slack
For vector DB we can use something off the shelf, like pinecone.io - later we can move it to Athena or something else.
Similar Projects
Use AI tools to convert legacy perl scripts to bash by nadvornik
Description
Use AI tools to convert legacy perl scripts to bash
Goals
Uyuni project contains legacy perl scripts used for setup. The perl dependency could be removed, to reduce the container size. The goal of this project is to research use of AI tools for this task.
Resources
Results:
Aider is not the right tool for this. It works ok for small changes, but not for complete rewrite from one language to another.
I got better results with direct API use from script.
COOTWbot by ngetahun
Project Description
At SCC, we have a rotating task of COOTW (Commanding Office of the Week). This task involves responding to customer requests from jira and slack help channels, monitoring production systems and doing small chores. Usually, we have documentation to help the COOTW answer questions and quickly find fixes. Most of these are distributed across github, trello and SUSE Support documentation. The aim of this project is to explore the magic of LLMs and create a conversational bot.
Goal for this Hackweek
- Build data ingestion
Data source:
- SUSE KB docs
- scc github docs
- scc trello knowledge board
Test out new RAG architecture
https://gitlab.suse.de/ngetahun/cootwbot
Use local/private LLM for semantic knowledge search by digitaltomm
Description
Use a local LLM, based on SUSE AI (ollama, openwebui) to power geeko search (public instance: https://geeko.port0.org/).
Goals
Build a SUSE internal instance of https://geeko.port0.org/ that can operate on internal resources, crawling confluence.suse.com, gitlab.suse.de, etc.
Resources
Repo: https://github.com/digitaltom/semantic-knowledge-search
Public instance: https://geeko.port0.org/
Results
Internal instance:
I have an internal test instance running which has indexed a couple of internal wiki pages from the SCC team. It's using the ollama (llama3.1:8b) backend of suse-ai.openplatform.suse.com to create embedding vectors for indexed resources and to create a chat response. The semantic search for documents is done with a vector search inside of sqlite, using sqlite-vec.
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 and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)
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
Learn how to integrate Elixir and Phoenix Liveview with LLMs by ninopaparo
Description
Learn how to integrate Elixir and Phoenix Liveview with LLMs by building an application that can provide answers to user queries based on a corpus of custom-trained data.
Goals
Develop an Elixir application via the Phoenix framework that:
- Employs Retrieval Augmented Generation (RAG) techniques
- Supports the integration and utilization of various Large Language Models (LLMs).
- Is designed with extensibility and adaptability in mind to accommodate future enhancements and modifications.
Resources
- https://elixir-lang.org/
- https://www.phoenixframework.org/
- https://github.com/elixir-nx/bumblebee
- https://ollama.com/
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 and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)
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