This repository explores the possibilities of Machine Learning and AI application in binary and multiclass classification of clinical data in order to find rare diseases in children.
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/<care-for-rare-submission name>: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