Project Description
MONAI Deploy aims to become the de-facto standard for developing packaging, testing, deploying, and running medical AI applications in clinical production. MONAI Deploy creates a set of intermediate steps where researchers and physicians can build confidence in the techniques and approaches used with AI — allowing for an iterative workflow.
Contributors to MONAI include Nvidia, Mayo Clinic, King's College London, NHS, Standford University, ... and many others.
The core piece of MONAI Deploy is the Monai Application Package (MAP), which contains the ML model in a "runnable state". It is implemented as a container, and built with docker-nvidia. This is relevant because you need a GPU to build the MAP. You'll see later why this is relevant. This container can be later run on a k8s cluster.
Before you create a MAP, you need to train the ML models. They are trained with MONAI Core, another framework which is a piece of the whole MONAI "puzzle". Those models can be published in the MONAI Model Zoo. They are published using a very specific format, which is called "a bundle".
In the monai deploy app sdk project in github, you can see several examples on how to package a "model bundle" into a MAP. Plus in the documentation you can find a step by step guide on how to build them, meaning how to create the MAP (the container). Examples, both code and documentation, use the ML models in the MONAI model Zoo.
MONAI model Zoo is free, you can search for models and use them for your research. However, there is not such a repository for MAPs, even the docs and examples show how to build those models into MAPS.
And this is the motivation of the project, to create this "link", and release into a registry, at least one MAP based on a model in MONAI Model Zoo.
Goal for this Hackweek
The specific goal is to implement a Continuous Integration workflow that builds a MAP (Monai Application Package), based on the example in code and documentation. Specifically, it is to implement a github action workflow that releases it into github container registry.
Implementation
The github action workflow will be added to a fork of the monai-deploy-app-sdk project, given we will be using the code that is already in the examples directory. Later a Pull Request can be created to the upstream project.
A limitation of this project is that we need to run the github action in a GPU node. ASFAIK github does not support that, so we need to run this on an external runner. For that I will be using MS Azure cloud to host a vm with GPU. For 3 reasons: first, it should be faster to clone from github from azure; second, I will try to use the free 90 days; third, I want to get familiar with Azure.
Finally, most probably I will use terraform to deploy the node in Azure.
This way, every time we want to release a new model in the MAP format, we will deploy a vm in Azure, do the build with the GPU, release into the github container registry, and remove the vm.
Resources
https://monai.io/ https://monai.io/model-zoo.html https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/gettingstarted/tutorials/monaibundle_app.html https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples/apps
Looking for hackers with the skills:
ml mlops ai artificial-intelligence gpu azure cloud monai medical containers github_actions github-ci ci
This project is part of:
Hack Week 23
Activity
Comments
-
over 1 year ago by jordimassaguerpla | Reply
I was able to create a terraform file and a workflow file but then I was not able to make the build work.
Here the terraform file:
https://github.com/jordimassaguerpla/monai-deploy-app-sdk/blob/main/main.tf
Here the workflow file:
https://github.com/jordimassaguerpla/monai-deploy-app-sdk/blob/main/.github/workflows/buildandpush_models.yml
-
over 1 year ago by jordimassaguerpla | Reply
I think the issue is that it tries to load the container, but I had not installed nvidia-docker2, and thus it can't load the container.
-
about 1 year ago by jordimassaguerpla | Reply
Here the fix for libseccomp, so nvidia-container-toolkit can be installed: https://build.opensuse.org/request/show/1128309 Here the fix for nvidia-holoscan: https://github.com/nvidia-holoscan/holoscan-sdk/pull/14
With these 2 fixes and by increasing the Disc in Azure to 64GB, I was able to build the ML model as a container :)
-
about 1 year ago by jordimassaguerpla | Reply
And voilà, here the MONAI Application Package ready to be used:
https://github.com/jordimassaguerpla/monai-deploy-app-sdk/pkgs/container/monai-deploy-app-sdk%2Fsimple_app-x64-workstation-dgpu-linux-amd64
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
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
ghostwrAIter - a local AI assisted tool for helping with support cases by paolodepa
Description
This project is meant to fight the loneliness of the support team members, providing them an AI assistant (hopefully) capable of scraping supportconfigs in a RAG fashion, trying to answer specific questions.
Goals
- Setup an Ollama backend, spinning one (or more??) code-focused LLMs selected by license, performance and quality of the results between:
- deepseek-coder-v2
- dolphin-mistral
- starcoder2
- (...others??)
- Setup a Web UI for it, choosing an easily extensible and customizable option between:
- Extend the solution in order to be able to:
- Add ZIU/Concord shared folders to its RAG context
- Add BZ cases, splitted in comments to its RAG context
- A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query BZ
- Add specific packages picking them from IBS repos
- A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query IBS
- A plus would be to desume the packages of interest and the right channel and version to be picked from the added BZ cases
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
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.
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)
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
Mortgage Plan Analyzer by RMestre
https://github.com/rjpmestre/mortgage-plan-analyzer
Project Description
Many people face challenges when trying to renegotiate their mortgages with different banks. They receive offers from multiple lenders and struggle to compare them effectively. Each proposal may have slightly different terms and data presentation, making it hard to make informed decisions. Additionally, understanding the impact of various taxes and variables can be complex. The Mortgage Plan Analyzer project aims to address these issues.
Project Overview:
The Mortgage Plan Analyzer is a web-based tool built using PHP, Laravel, Livewire, and AdminLTE/bootstrap. It provides a user-friendly platform for individuals to input basic specifications about their mortgage, adjust taxes and variables, and obtain short-term projections for each proposal. Users can also compare multiple mortgage offers side by side, enabling them to make informed decisions about their mortgage renegotiation.
Why Start This Project:
I found myself in this position and most tools I found around are either for marketing/selling purposes or not flexible enough. As i was starting getting lost in a jungle of spreadsheets i thought I could just create a tool to help me and others that may be experiencing the same struggles to provide clarity and transparency in the decision-making process.
Hackweek 25 ideas (to refine still :) )
- Euribor Trends in Projections
- - Use historical Euribor data to model optimistic and pessimistic scenarios for variable-rate loans.
- Use the annual summaries (installments, amortizations, etc) and run some analysis to highlight key differences, like short-term savings vs. long-term costs
- Financial plan can be hard/boring to follow. Create a simple viewing mode that summarizes monthly values and their annual sums.
Hackweek 24 update
- Improved summaries graphs by adding:
- - Line graph;
- - Accumulated line graph;
- - Set the range to short/mid/long term;
- - Highlight best simulation and value per year;
- Improve the general behaviour of the forms:
- - Simulations name setting;
- - Cloning simulations;
- - Adjust update timing on input changes;
- Show/Hide big tables;
- Support multi languages (added english);
- Added examples;
- Adjustments to fonts and sizes;
- Fixed loading screen;
- Dependencies adjustments;
Hackweek 23 initial release
- Developed a base site that:
- - Allows adding up to 3 simulations;
- - Create financial plans;
- - Simulations comparison graph for the first 4 years;
- Created Github project @ https://github.com/rjpmestre/mortgage-plan-analyzer ;
- Launched a demo instance using Oracle Cloud Free Tier currently @ http://138.3.251.182/
Resources
- Banco de Portugal: Main simulator all portuguese banks have to follow ( https://clientebancario.bportugal.pt/credito-habitacao )
- Laravel: A PHP web application framework for building robust and scalable applications. ( https://laravel.com/ )
- Livewire: A Laravel library for building dynamic interfaces without writing JavaScript. ( https://livewire.laravel.com/ )
- AdminLTE: A responsive admin dashboard template for creating a visually appealing interface. ( https://adminlte.io/ )
ADS-B receiver with MicroOS by epaolantonio
I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time?
There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090
) and web frontend (tar1090
).
Goals
- Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
- Make it easy to install
- Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)
Resources
- 1x Small Board Computer capable of running MicroOS
- 1x RTL2832U DVB-T dongle
- 1x MicroSD card
- https://github.com/antirez/dump1090
- https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
- https://github.com/wiedehopf/tar1090
Project status (2024-11-22)
So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb
(as a replacement for dump1090
), tar1090
, tar1090-db
and mlat-client
(not used yet).
Current status:
- Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
- Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
- Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
- Local web interface (tar1090) to easily visualize the results
- Cockpit pre-configured to ease maintenance
What's missing:
- MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
- FlightAware support
Give it a go at https://g7.github.io/adsbreceiver/ !
Project links
- https://g7.github.io/adsbreceiver/
- https://github.com/g7/adsbreceiver
- https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver
Port the classic browser game HackTheNet to PHP 8 by dgedon
Description
The classic browser game HackTheNet from 2004 still runs on PHP 4/5 and MySQL 5 and needs a port to PHP 8 and e.g. MariaDB.
Goals
- Port the game to PHP 8 and MariaDB 11
- Create a container where the game server can simply be started/stopped
Resources
- https://github.com/nodeg/hackthenet
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
Enable the containerized Uyuni server to run on different host OS by j_renner
Description
The Uyuni server is provided as a container, but we still require it to run on Leap Micro? This is not how people expect to use containerized applications, so it would be great if we tested other host OSs and enabled them by providing builds of necessary tools for (e.g. mgradm). Interesting candidates should be:
- openSUSE Leap
- Cent OS 7
- Ubuntu
- ???
Goals
Make it really easy for anyone to run the Uyuni containerized server on whatever OS they want (with support for containers of course).
Improve Development Environment on Uyuni by mbussolotto
Description
Currently create a dev environment on Uyuni might be complicated. The steps are:
- add the correct repo
- download packages
- configure your IDE (checkstyle, format rules, sonarlint....)
- setup debug environment
- ...
The current doc can be improved: some information are hard to be find out, some others are completely missing.
Dev Container might solve this situation.
Goals
Uyuni development in no time:
- using VSCode:
- setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
- dev container should contains all dependencies
- setup debug environment
- implement a GitHub Workspace solution
- re-write documentation
Lots of pieces are already implemented: we need to connect them in a consistent solution.
Resources
- https://github.com/uyuni-project/uyuni/wiki
ddflare: (Dynamic)DNS management via Cloudflare API in Kubernetes by fgiudici
Description
ddflare is a project started a couple of weeks ago to provide DDNS management using v4 Cloudflare APIs: Cloudflare offers management via APIs and access tokens, so it is possible to register a domain and implement a DynDNS client without any other external service but their API.
Since ddflare allows to set any IP to any domain name, one could manage multiple A and ALIAS domain records. Wouldn't be cool to allow full DNS control from the project and integrate it with your Kubernetes cluster?
Goals
Main goals are:
- add containerized image for ddflare
- extend ddflare to be able to add and remove DNS records (and not just update existing ones)
- add documentation, covering also a sample pod deployment for Kubernetes
- write a ddflare Kubernetes operator to enable domain management via Kubernetes resources (using kubebuilder)
Available tasks and improvements tracked on ddflare github.
Resources
- https://github.com/fgiudici/ddflare
- https://developers.cloudflare.com/api/
- https://book.kubebuilder.io
Automate PR process by idplscalabrini
Description
This project is to streamline and enhance the pr review process by adding automation for identifying some issues like missing comments, identifying sensitive information in the PRs like credentials. etc. By leveraging GitHub Actions and golang hooks we can focus more on high-level reviews
Goals
- Automate lints and code validations on Github actions
- Automate code validation on hook
- Implement a bot to pre-review the PRs
Resources
Golang hooks and Github actions
Automate PR process by idplscalabrini
Description
This project is to streamline and enhance the pr review process by adding automation for identifying some issues like missing comments, identifying sensitive information in the PRs like credentials. etc. By leveraging GitHub Actions and golang hooks we can focus more on high-level reviews
Goals
- Automate lints and code validations on Github actions
- Automate code validation on hook
- Implement a bot to pre-review the PRs
Resources
Golang hooks and Github actions
Drag Race - comparative performance testing for pull requests by balanza
Description
«Sophia, a backend developer, submitted a pull request with optimizations for a critical database query. Once she pushed her code, an automated load test ran, comparing her query against the main branch. Moments later, she saw a new comment automatically added to her PR: the comparison results showed reduced execution time and improved efficiency. Smiling, Sophia messaged her team, “Performance gains confirmed!”»
Goals
- To have a convenient and ergonomic framework to describe test scenarios, including environment and seed;
- to compare results from different tests
- to have a GitHub action that executes such tests on a CI environment
Resources
The MVP will be built on top of Preevy and K6.