Using pyfmi with multiprocessing for simulation of Modelica FMUs - python-2.7

Question

I am trying to simulate multiple Modelica FMUs in parallel using python/pyfmi and multiprocessing. However I am not able to return any pyfmi FMI objects from the subprocesses once the FMUs are initialized. It seems that pyfmi FMI objects (e.g. pyfmi.fmi.FMUModelCS2 or pyfmi.fmi.FMUState2) are not pickable. I also tried dill to pickle, which doesn't work for me eather. With dill the objects are picklable though, meaning no error, but somehow corrupted if I try to reload them afterwards. Does anyone have an idea of how to solve this issue? Thanks!

Answer 1

The problem is that pyfmi.fmiFMUModelCS2 is a Cython class dependent on external libraries which makes it unpickable. So it is not possible unfortunately.
If you want to use multiprocessing the only way forward that I see is that you first create the processes and then load the FMUs into the separate processes. In this way you do not need to pickle the classes.

Answer 2

I faced a similar problem when I created EstimationPy. I ended up creating a wrapper for running parallel simulation of the same FMU using multiple processes.
I suggest you to look at the implementation here
https://github.com/lbl-srg/EstimationPy/blob/master/estimationpy/fmu_utils/fmu_pool.py
And to the example http://lbl-srg.github.io/EstimationPy/modules/examples/first_order.html#run-multiple-simulations

Answer 3

The pathos module allows multiprocessing with a similar interface as the multiprocessing but relies on dill instead of pickle for serialisation.
The Pool method works for parallel execution of model.simulate, provided that results are handled in memory:
n_core = 2
n_simulation = 10
# ====
import pyfmi
model = pyfmi.load_fmu(path_fmu)
def worker(*args):
model.reset()
print "================> %d" % args[0]
return model.simulate(options=dict(result_handling="memory"))["y"]
from pathos.multiprocessing import Pool
pool = Pool(n_core)
out = pool.map(worker, range(n_simulation))
pool.close()
pool.join()
Note in the above snippet that it is necessary to handle results in memory : options=dict(result_handling="memory").
The default is to use temporary files which works for when the amount of simulations is small.
However, the longer the queue, the higher the chance to get something like
Exception in thread Thread-27:
Traceback (most recent call last):
File "/home/USER/anaconda2/lib/python2.7/threading.py", line 801, in __bootstrap_inner
self.run()
File "/home/USER/anaconda2/lib/python2.7/threading.py", line 754, in run
self.__target(*self.__args, **self.__kwargs)
File "/home/USER/anaconda2/lib/python2.7/site-packages/multiprocess/pool.py", line 389, in _handle_results
task = get()
File "/home/USER/anaconda2/lib/python2.7/site-packages/dill/dill.py", line 260, in loads
return load(file)
File "/home/USER/anaconda2/lib/python2.7/site-packages/dill/dill.py", line 250, in load
obj = pik.load()
File "/home/USER/anaconda2/lib/python2.7/pickle.py", line 864, in load
dispatch[key](self)
File "/home/USER/anaconda2/lib/python2.7/pickle.py", line 1139, in load_reduce
value = func(*args)
TypeError: __init__() takes exactly 2 arguments (1 given)
which I fail to grasp.