The dedicated Optimization Problem solver.
The primary role worker is solving the optimization problem. The inherent complexity is encapsulated here so that the webserver and other collaborating systems are shielded from it. Communication with other systems is made possible through a messaging interface ( worker talks directly to database for the time being, but ideally this may change). This architectural style has the following benefits:
- Simplifies provisioning of collaborating systems
workeris flexible and language-agnostic- Simple message-oriented interface
dev and feat/* branches are encouraged to take advantage of jupyter for prototyping solutions to technical problems. Notebook checkpoint data is ignored from version control and a notebooks folder in the the project root exist for exactly this purpose.
Naturally (assuming the Anaconda stack is present) other tools may be used to solve the optimization problem. R is easily provisioned using the normal anaconda methods. There exist actively maintained solutions to integrate python and R such as rpy2 and Rserve.
see Combining the powerful worlds of Python and R as a starting point
Curious about the clock time but you don't want to sit and watch a terminal tick? Easy:
time ( python src/optimizer.py )