UBUMLaaS

Machine Learning as a Service (MLaaS) platform based on ADMIRABLE and BEST-AI research groups methods.

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This application is described as one of the result of two different projects (with objectives partially overlapping, and being this application in the intersection of the objectives):

1. Project "Algoritmos de ensembles para problemas de salidas múltiples - nuevos desarrollos y aplicaciones" from "Ministerio de Economía y Competitividad" (reference: TIN2015-67534-P)
2. Project "Minería de datos para la mejora del mantenimiento y disponibilidad de máquinas de altas presiones" from "Consejería de Educación de la Junta de Castilla y León" (reference: BU085P17)

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Installation (Linux)

1. Clone this repository

   $ git clone https://github.com/JoseBarbero/UBUMLaaS.git
2. Go to UBUMLaaS repository's folder

   $ cd UBUMLaaS
3. Create a conda environment

   $ conda env create -f UBUMLaaS_env.yml
4. Activate environment

   $ conda activate UBUMLaaS
5. Modify env_variables.sh with properly values

   export SECRET_KEY=<app secret key>
   export EMAIL_AC=<email>
   export EMAIL_PASS=<email-password>
   export EMAIL_URL=<email-url>
   export FLASK_ENV=development #development or production
   LIBFOLDER=/absolute/path/to/UBUMLaaS
6. With the conda environment UBUMLaaS, execute the script to export environment variables when activate conda env.

   $ source env_vars_to_conda.sh
7. Create database

   $ mv data_base.sqlite ubumlaas/data.sqlite
   Opt: Download a database and put it in ./ubumlaas/
8. Install Redis-Server

   $ sudo apt install redis-server
   $ sudo service redis-server start
   $ sudo systemctl enable redis-server #If you want to initialize the service in startup
   Caution: Close all workers of RQ before stop redis-server

Execution

1. Inside the UBUMLaaS repository's folder, activate conda environment if not activated.

   $ conda activate UBUMLaaS
2. Execute to run the server

   python app.py

Update database

1. Execute migrate.py

   $ python migrate.py

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