Deployment/Setup of all ommr4all services
You can setup OMMR4all using docker and docker-compose.
- Download and install
docker-ceanddocker-composefor your platform. - Download the
docker-compose.ymlfile. - Open
docker-compose.ymland replace${STORAGE}and the${PORT}to your wishes (e.g., use/opt/ommr4all-storageand8001). - Build the container and bring it up:
docker-compose up -d- Create a super user:
docker-compose run /opt/ommr4all/ommr4all-deploy-venv/bin/python /opt/ommr4all/ommr4all-deploy/modules/ommr4all-server/manage.py createsuperuserdocker-compose pulldocker-compuse up
You can run docker image prune -f to clean all previous versions or older images that are currently unused.
Follow the instructions in the Dockerfile.
You can also setup a gitlab-runner for automatic deployment (Clone the project on github.com with CI-integration), create a runner with either
deployment-production: redeploy if a new (version) tag was addeddeployment-master: redeploy if the master is updated
These instructions are not complete yet.
- Download and install all requirements (node>=10, >=python3.6)
- Install the IDEs (IntelliJ, or PyCharm and WebStorm)
- Create a virtual environment, activate it, and install your desired tensorflow version (e.g.,
pip install tensorflow_gpu<2) - Install all python submodules (located in the
modulesdirecture) but the server:python setup.py install. - Install the server
requirements.txt:pip install -r requirements.txt. - Open the ommr4all-client directory in WebStorm and launch the
Angluar CLI Server. - Open the ommr4all-sever directory in PyCharm and launch the
Django Server. - In WebStorm launch the
Angular Applicationwhich will open a browser.