Project Description
I have all my photos on a private NAS running nextcloud.
This NAS has an ARM CPU and 1GB of RAM, which means I cannot run the face recognition plugin because it requires a GPU, 2 GB of RAM, and PDLib is not available for this arch (I know I could build it and package it ... but doesn't sound fun ;) )
However, I have a Coral TPU connected to a USB port (Thanks to my super friend Marc!):
https://coral.ai/products/accelerator
Where I could run Tensorflow Lite... you see where this is going, don't you?
Goal for this Hackweek
The goal is to run face recognition on the Coral TPU using tensorflow lite and then using the nextcloud API to tag the images.
Resources
This project is part of:
Hack Week 20
Activity
Comments
Be the first to comment!
Similar Projects
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
Automated Test Report reviewer by oscar-barrios
Description
In SUMA/Uyuni team we spend a lot of time reviewing test reports, analyzing each of the test cases failing, checking if the test is a flaky test, checking logs, etc.
Goals
Speed up the review by automating some parts through AI, in a way that we can consume some summary of that report that could be meaningful for the reviewer.
Resources
No idea about the resources yet, but we will make use of:
- HTML/JSON Report (text + screenshots)
- The Test Suite Status GithHub board (via API)
- The environment tested (via SSH)
- The test framework code (via files)
AI for product management by a_jaeger
Description
Learn about AI and how it can help myself
What are the jobs that a PM does where AI can help - and how?
Goals
- Investigate how AI can help with different tasks
- Check out different AI tools, which one is best for which job
- Summarize learning
Resources
- Reading some blog posts by PMs that looked into it
- Popular and less popular AI tools
Work is done SUSE internally at https://confluence.suse.com/display/~a_jaeger/Hackweek+25+-+AI+for+a+PM and subpages.
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.
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
Save pytorch models in OCI registries by jguilhermevanz
Description
A prerequisite for running applications in a cloud environment is the presence of a container registry. Another common scenario is users performing machine learning workloads in such environments. However, these types of workloads require dedicated infrastructure to run properly. We can leverage these two facts to help users save resources by storing their machine learning models in OCI registries, similar to how we handle some WebAssembly modules. This approach will save users the resources typically required for a machine learning model repository for the applications they need to run.
Goals
Allow PyTorch users to save and load machine learning models in OCI registries.
Resources