Skip to content

AleMer97/Makeathon-AMIGO

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

47 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ML 4 Hope

On a lovely weekend in april 2024, our team participated at the TUM.ai Makeathon and came up with this proof of concept for the AMIGO challenge.

PersonalizedMedicine

to start development you require python and a Docker Setup.

The Following example solution is build on https://github.com/FeatureCloud/app-round/

pip install virtualenv

python -m venv careforrare

# For Mac Users
source ./careforrare/bin/activate

# For Windows Users (use Powershell)
 ./careforrare/Scripts/Activate.ps1

# Install Requirements
pip install -r requirements.txt

# Develop your application with local environment you have to set local variables
# Please get in touch with the Care-For-Rare Team

# Build and push your container by facilitating makefile. Please change the name of DOCKER_IMAGE_NAME in your file
# if make does not work in your env please utilize statements in Makefile to create same results
make build

# to do a test run of your container with the following statement. In the logs you should see a server starting. When using Windows bases Systems we recognized mounting works better when triggering command directly in WSL System. 
docker run -d -v ./config.yml:/mnt/input/config.yml -v ./data/output:/mnt/output -p 9000:9000 featurecloud.ai/ml_4_hope:latest

# Trigger the start of the application states
curl --location 'http://localhost:9000/setup' --header 'Content-Type: application/json' --data '{"id": "0000000000000000","coordinator": false,"coordinatorID": "0000000000000000","clients": []}'

# Look at logs using. Make sure to close container after testing
docker logs <containerID>

# Push the new image to the registry
make push

Alternatively you are free to utilize the full functionalities of the feature-cloud api and Testbed
https://featurecloud.ai/developers

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Python 79.9%
  • Makefile 15.3%
  • Dockerfile 4.5%
  • Shell 0.3%