Python dispatch Examples

Example #1

File: 03-dispatch.py Project: ustaros-ai/pyopus

# 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()

Example #2

File: depop.py Project: ustaros-ai/pyopus

			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, 

Example #3

                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)

Example #4

# 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()

Example #5

        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---#

Example #6

File: 04-dyndispatch.py Project: xanderhsia/pyopus

# 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()