# Dispatches a fixed number of tasks to computational nodes
# mpirun -n 4 python 03-dispatch.py
from pyopus.parallel.cooperative import cOS
from pyopus.parallel.mpi import MPI
from funclib import jobGenerator, jobProcessor, jobCollector
if __name__=='__main__':
# Set up MPI
cOS.setVM(MPI())
results=cOS.dispatch(
jobList=((jobProcessor, [value]) for value in xrange(100)),
remote=True
)
print("Results: "+str(results))
# Finish, need to do this if MPI is used
cOS.finalize()
finalF[iFunc][iPopSize][iRun]=fBest
except:
print "Finished"
if __name__=='__main__':
cOS.setVM(MPI(startupDir=os.getcwd()))
# Prepare results storage
finalF=zeros((len(funcIndicesList), len(popSizeList), nRun))
# Dispatch jobs
cOS.dispatch(
jobList=jobGenerator(),
collector=jobCollector(finalF),
remote=True
)
# Prepare function names
names=[]
dims=[]
for i in funcIndicesList:
prob=glbc.GlobalBCsuite[i]()
names.append(prob.name)
dims.append(prob.n)
# Store summary
summary={
'funcIndices': funcIndicesList,
'names': names,
createRandomBigCircuitMatrix(copy(createRandomValueVector())))
if globalVars.insertAdam:
hotGen.pool[
0] = AE.adam #Insert an already designed circuit to optimise.
hotGen.pool[
0].fullRedundancyMatrix = fullRedundancyBigCircuitMatrix(
AE.adam.BigCircuitMatrix)
print "Adam-circuit inserted into population."
raw_input("...initial population created.")
#---EVALUATE & SORT INITIAL POPULATION---#
print "EVALUATING GENERATION %d" % generationNum
stw0 = time()
results = cOS.dispatch(jobList=((PROBLEM,
[hotGen.pool[i], generationNum, i, True])
for i in range(0, len(hotGen.pool))),
remote=True)
results = np.array(results)
#Put together objects and objectivesScores
for i in range(0, len(hotGen.pool)):
hotGen.pool[i].objectivesScore = np.transpose(results[:, 0])[i]
stw1 = time()
print "Evaluation of initial population lasted for %f s" % (stw1 - stw0)
#Sort population
staSort = time()
faster_nondominated_sort(hotGen)
endSort = time()
print "Fast_nondominated_sort finished in %.2f s." % (endSort - staSort)
# Dispatches tasks to computational nodes until specified result is reached or exceeded
# This example also demonstrates the use of a collector.
# mpirun -n 4 python 04-dyndispatch.py
from pyopus.parallel.cooperative import cOS
from pyopus.parallel.mpi import MPI
from funclib import dynJobGenerator, jobProcessor, jobCollector
if __name__ == '__main__':
# Set up MPI
cOS.setVM(MPI())
# This list will be filled with results
results = []
cOS.dispatch(
jobList=dynJobGenerator(start=0,
step=1), # Start at 0, increase by one
collector=jobCollector(results, stopAtResult=150),
remote=True)
print("Results: " + str(results))
# Finish, need to do this if MPI is used
cOS.finalize()
bestScoresList = data[2]
result = data[3]
bestI = data[4]
#datadirname = data[5]
BigMatrixSize = data[6]
POP_SIZE = data[7]
averageScoresList = data[8]
#generations.append(generation)
hotGen = deepcopy(generation)
raw_input("...old population resurrected.")
else:
print "Creating initial population. Press any to proceed..."
NEWindividuals = cOS.dispatch(
jobList=((dispatchRandomCircuitObjectGeneration, [i])
for i in range(0, POP_SIZE)),
remote=True)
for i in range(0, POP_SIZE):
hotGen.add_individual(NEWindividuals[i])
if insertAdam:
hotGen.pool[
0] = AE.adam #Insert an already designed circuit to optimise.
hotGen.pool[
0].fullRedundancyMatrix = fullRedundancyBigCircuitMatrix(
AE.adam.BigCircuitMatrix)
print "Adam-circuit inserted into population."
raw_input("...initial population created.")
#---CREATE INITIAL POPULATION---#
#---EVALUATE & SORT INITIAL POPULATION---#
# Dispatches tasks to computational nodes until specified result is reached or exceeded
# This example also demonstrates the use of a collector.
# mpirun -n 4 python 04-dyndispatch.py
from pyopus.parallel.cooperative import cOS
from pyopus.parallel.mpi import MPI
from funclib import dynJobGenerator, jobProcessor, jobCollector
if __name__=='__main__':
# Set up MPI
cOS.setVM(MPI())
# This list will be filled with results
results=[]
cOS.dispatch(
jobList=dynJobGenerator(start=0, step=1), # Start at 0, increase by one
collector=jobCollector(results, stopAtResult=150),
remote=True
)
print("Results: "+str(results))
# Finish, need to do this if MPI is used
cOS.finalize()