JModelica and Concurrent Futures

I am using JModelica to optimize a model using IPOPT in the background.

I would like to run many optimizations in parallel. At the moment I am doing this using the multiprocessing module.

Right now, the code is as follows. It performs a parameter sweep over the variables T and So and writes the results to output files named for these parameters. The output files also contain a list of the parameters used in the model along with the run results.

#!/usr/local/jmodelica/bin/jm_python.sh
import itertools
import multiprocessing
import numpy as np
import time
import sys
import signal
import traceback
import StringIO
import random
import cPickle as pickle

def PrintResToFile(filename,result):
  def StripMX(x):
    return str(x).replace('MX(','').replace(')','')

  varstr = '#Variable Name={name: <10}, Unit={unit: <7}, Val={val: <10}, Col={col:< 5}, Comment="{comment}"\n'

  with open(filename,'w') as fout:
    #Print all variables at the top of the file, along with relevant information
    #about them.
    for var in result.model.getAllVariables():
      if not result.is_variable(var.getName()):
        val = result.initial(var.getName())
        col = -1
      else:
        val = "Varies"
        col = result.get_column(var.getName())

      unit = StripMX(var.getUnit())
      if not unit:
        unit = "X"

      fout.write(varstr.format(
        name    = var.getName(),
        unit    = unit,
        val     = val,
        col     = col,
        comment = StripMX(var.getAttribute('comment'))
      ))

    #Ensure that time variable is printed
    fout.write(varstr.format(
      name    = 'time',
      unit    = 's',
      val     = 'Varies',
      col     = 0,
      comment = 'None'
    ))

    #The data matrix contains only time-varying variables. So fetch all of
    #these, couple them in tuples with their column number, sort by column
    #number, and then extract the name of the variable again. This results in a
    #list of variable names which are guaranteed to be in the same order as the
    #data matrix.
    vkeys_in_order = [(result.get_column(x),x) for x in result.keys() if result.is_variable(x)]
    vkeys_in_order = map(lambda x: x[1], sorted(vkeys_in_order))

    for vk in vkeys_in_order:
      fout.write("{0:>13},".format(vk))
    fout.write("\n")

    sio = StringIO.StringIO()
    np.savetxt(sio, result.data_matrix, delimiter=',', fmt='%13.5f')
    fout.write(sio.getvalue())




def RunModel(params):
  T  = params[0]
  So = params[1]

  try:
    import pyjmi
    signal.signal(signal.SIGINT, signal.SIG_IGN)

    #For testing what happens if an error occurs
    # import random
    # if random.randint(0,100)<50:
      # raise "Test Exception"

    op = pyjmi.transfer_optimization_problem("ModelClass", "model.mop")
    op.set('a',        0.20)
    op.set('b',        1.00)
    op.set('f',        0.05)
    op.set('h',        0.05)
    op.set('S0',         So)
    op.set('finalTime',   T)

    # Set options, see: http://www.jmodelica.org/api-docs/usersguide/1.13.0/ch07s06.html
    opt_opts                                   = op.optimize_options()
    opt_opts['n_e']                            = 40
    opt_opts['IPOPT_options']['tol']           = 1e-10
    opt_opts['IPOPT_options']['output_file']   = '/z/err_'+str(T)+'_'+str(So)+'_info.dat'
    opt_opts['IPOPT_options']['linear_solver'] = 'ma27' #See: http://www.coin-or.org/Ipopt/documentation/node50.html

    res = op.optimize(options=opt_opts)

    result_file_name = 'out_'+str(T)+'_'+str(So)+'.dat'
    PrintResToFile(result_file_name, res)

    return (True,(T,So))
  except:
    ex_type, ex, tb = sys.exc_info()
    return (False,(T,So),traceback.extract_tb(tb))

try:
  fstatus = open('status','w')
except:
  print("Could not open status file!")
  sys.exit(-1)

T       = map(float,[10,20,30,40,50,60,70,80,90,100,110,120,130,140])
So      = np.arange(0.1,30.1,0.1)
tspairs = list(itertools.product(T,So))
random.shuffle(tspairs)

pool  = multiprocessing.Pool()
mapit = pool.imap_unordered(RunModel,tspairs)
pool.close()

completed = 0

while True:
  try:
    res = mapit.next(timeout=2)
    pickle.dump(res,fstatus)
    fstatus.flush()
    completed += 1
    print(res)
    print "{0: >4} of {1: >4} ({2: >4} left)".format(completed,len(tspairs),len(tspairs)-completed)
  except KeyboardInterrupt:
    pool.terminate()
    pool.join()
    sys.exit(0)
  except multiprocessing.TimeoutError:
    print "{0: >4} of {1: >4} ({2: >4} left)".format(completed,len(tspairs),len(tspairs)-completed)
  except StopIteration:
    break

Using the model:

optimization ModelClass(objective=-S(finalTime), startTime=0, finalTime=100)
  parameter Real S0 = 2;
  parameter Real F0 = 0;

  parameter Real a = 0.2;
  parameter Real b = 1;
  parameter Real f = 0.05;
  parameter Real h = 0.05;

  output Real F(start=F0, fixed=true, min=0, max=100, unit="kg");
  output Real S(start=S0, fixed=true, min=0, max=100, unit="kg");

  input Real u(min=0, max=1);
equation
  der(F) = u*(a*F+b);
  der(S) = f*F/(1+h*F)-u*(a*F+b);
end ModelClass;

Is this safe?

No, it is not safe. op.optimize() will store the optimization results with a file name derived from the model name, and then load the results to return the data, so when you try to run several optimizations at once you will get a race condition. To circumvent this, you can provide distinct result file names in opt_opts['result_file_name'].