I currently have a functioning and blazing fast model written in C++ and CUDA. However, I'd like to use Scipy.minimize to fit the model to some experimental data. I was hoping it would be easy, but when I try to describe my problem to Google, it keeps telling me that I want to extend Python with C++, the documentation of which makes me want to cry.
I used to use PyCUDA back in the day, but I don't think I am able to wrap the entire model, so I need a little nudge in the right direction.
What I'm trying to accomplish.
from scipy.optimize import minimize
#Initial starting parameters
ics = [ 1, 0, 0 ... 0]
#The objective function is called from my model, which is a built .exe that will accept parameters as maybe a string and somehow output something
model = os.popen("directory/mymodel.exe" + params)
difference = minimize(model, ics, method='Nelder-Mead')
Is something like this possible? I'm having a hard time getting around the interface between C++ and Python and how I can make my .exe generate data interpretable by python so that Scipy can adjust the parameters and iterate.