"""
simple dependency example,
"""
import batchOpenMPI
#defining function
def f_org(x) :
return x ** 2
def g_org(x) :
return x + 1
def h_org(x) :
return x - 0.2
# creating batch functions
f = batchOpenMPI.batchFunction(f_org)
g = batchOpenMPI.batchFunction(g_org)
h = batchOpenMPI.batchFunction(h_org)
batchOpenMPI.begin_MPI_loop()
# building processing que
bi = g.addtoBatch(3,2)
print(f.addtoBatch(bi))
print(h.addtoBatch(bi))
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
# now actuall code
print (
"""
f(x) = x ** 2
g(x) = x + 1
def _DE_batch(x):
return DE(tfun_id=x[0],
Np=int(x[1]),
Cr=x[2],
F=x[3],
evals=x[4],
randomSeed=x[5],
tfun_d=prob_D,
DE_x=0,
DE_y=1,
printLevel=0)
DE_batch = batchOpenMPI.batchFunction(_DE_batch)
def DE_CPV_validity_checks(CPV_array, OFE_budget):
'check tuning constraints'
N, Cr, F = CPV_array
if OFE_budget < N:
return False, 'OFE_budget < N'
if N < 5:
return False, 'N < 5'
if Cr < 0 or Cr > 1:
return False, 'Cr not in [0,1]'
if F < 0:
return False, 'F < 0'
return True, ""
def __init__(self, batchFun, prob_id):
self.batchFun = batchFun
self.prob_id = prob_id
def prep_input( self, CPV_tuple, OFE_budgets, randomSeed):
return [ self.prob_id ] + CPV_tuple.tolist() + [ max(OFE_budgets), randomSeed ]
def __call__(self, CPV_tuple, OFE_budgets, randomSeed):
F, OFEs_made = self.batchFun( self.prep_input( CPV_tuple, OFE_budgets, randomSeed))
F = F - cec_tp_mins[self.prob_id]
return get_F_vals_at_specified_OFE_budgets(F, OFEs_made, OFE_budgets)
def addtoBatch(self, CPV_tuple, OFE_budgets, randomSeed):
self.batchFun.addtoBatch(self.prep_input( CPV_tuple, OFE_budgets, randomSeed))
def _DE_batch(x):
return DE(tfun_id=x[0], Np=int(x[1]), Cr=x[2], F=x[3], evals=x[4], randomSeed=x[5],
tfun_d=prob_D, DE_x=0, DE_y=1, printLevel=0)
DE_batch = batchOpenMPI.batchFunction( _DE_batch )
def DE_CPV_validity_checks(CPV_array, OFE_budget):
'check tuning constraints'
N, Cr, F = CPV_array
if OFE_budget < N :
return False, 'OFE_budget < N'
if N < 5:
return False, 'N < 5'
if Cr < 0 or Cr > 1 :
return False, 'Cr not in [0,1]'
if F < 0:
return False, 'F < 0'
return True, ""
DE_CPV_lb = numpy.array([ 5, 0.0, 0.0 ]) #lower initilization bound
"""
example of how2use batchOpenMPI, with lastProcessBatch=True to avoid process being tied down unessarcyilly
"""
import batchOpenMPI, time
def randomPause_base(pause) :
time.sleep(pause)
randomPause = batchOpenMPI.batchFunction(randomPause_base)
batchOpenMPI.begin_MPI_loop()
print('lastProcessBatch=True example/test')
pauses = [1,3]
for pause in pauses :
randomPause.addtoBatch(pause)
batchOpenMPI.processBatch(lastProcessBatch=True)
for pause in pauses:
randomPause(pause)
batchOpenMPI.end_MPI_loop(print_stats=True)
print('The first worker should have exited before the last worker, and spent less time processing')
"""
example with 'calls_expected' in addtoBatch used
"""
import batchOpenMPI
def f_mult(x) :
return x*2.0
f = batchOpenMPI.batchFunction(f_mult) #creating function wrapper
batchOpenMPI.begin_MPI_loop() # both the workers and the master process run the same code up until here
f.addtoBatch(4,calls_expected=4)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = [f(4),f(4),f(4)]
print(res)
#another test
f.addtoBatch(1)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = f(1), f(1)
batchOpenMPI.end_MPI_loop(print_stats=True) #releases workers
print("*** jobs executed by workers should be 2 ,(5 calls made),jobs uncollected should = 1, jobs_master=1")
"""
example with multiRef
"""
import batchOpenMPI
def f_mult(x) :
return x*2.0
f = batchOpenMPI.batchFunction(f_mult,multiRef=True) #creating function wrapper
batchOpenMPI.begin_MPI_loop(print_launch_messages=False) # both the workers and the master process run the same code up until here
no = range(10) + range(10) # creates [0,1,2,3,4,5,6,7,8,9] x 2
for i in no :# adding all f_inv input and queing them for parallel processing
f.addtoBatch(i)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = [] #used for storing results
for i in no :
res.append(f(i))
print(res)
batchOpenMPI.end_MPI_loop(print_stats=True) #releases workers
print("*** jobs executed by workers should be %i, out of the total of %i" % (len(no)/2,len(no)) )
"""
example of how2use batchOpenMPI,
simple example
"""
import batchOpenMPI
def f_inv_org(x) :
"function returns the inverse of a number"
return 1.0/x
f_inv = batchOpenMPI.batchFunction(f_inv_org, permissible_exceptions = [ZeroDivisionError]) #creating function wrapper
batchOpenMPI.begin_MPI_loop() # both the workers and the master process run the same code up until here
print('this example script prints in the inverse of the integers from 0 to 9')
no = range(10) # creates [0,1,2,3,4,5,6,7,8,9]
for i in no :# adding all f_inv input and queing them for parallel processing
f_inv.addtoBatch(i)
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
res = [] #used for storing results
for i in no :
try :
res.append(f_inv(i))
except ZeroDivisionError :
print("couldn't inverse " + str(i) + " due to 0 division error")
print(res)
batchOpenMPI.end_MPI_loop(print_stats=True) #release workers, and print stats
import batchOpenMPI, os
def ex_prog(x) :
"simulates an external program that writes its output to file"
f = file('results.txt','w')
f.write(str(x ** 2))
f.close()
def fun_org(x) :
ex_prog(x)
f = file('results.txt','r')
res = float(f.readline().strip())
f.close()
return res
fun = batchOpenMPI.batchFunction(fun_org) #creating function wrapper
batchOpenMPI.WorkingDir_base = 'workspace' #giving each worker a directory to workin.
batchOpenMPI.begin_MPI_loop() #split workers and master
no = range(10) #creating inputs
print("""f(x) = x ** 2
the results will be written to file, and as the result file name will be the same.
each process will be given its own workspace using batchOpenMPI.WorkingDir_base""")
print("\ninputs :" + str(no))
for i in no :# adding all f_inv input and queing them for parallel processing
fun.addtoBatch(i)
"""
simple dependency example, where an input depency raises an error
"""
import batchOpenMPI
#defining function
def f_org(x) :
return x ** 2
def g_org(x) :
return 1.0/x + 1
def h_org(x) :
return x - 0.2
# creating batch functions
f = batchOpenMPI.batchFunction(f_org)
g = batchOpenMPI.batchFunction(g_org,True,permissible_exceptions=[ZeroDivisionError])
h = batchOpenMPI.batchFunction(h_org)
batchOpenMPI.begin_MPI_loop()
# building processing que
print(f.addtoBatch(g.addtoBatch(3)))
print(h.addtoBatch(g.addtoBatch(3)))
print(f.addtoBatch(g.addtoBatch(0)))
batchOpenMPI.processBatch() #get the workers to calculate all the inputs
# now actuall code
try :
res3 = str(f(g(0)))
except ZeroDivisionError,msg :
res3 = 'no solution'
"""
batchOpenMPI.drop_previous_batch_results example
"""
import batchOpenMPI
#defining function
def f1_org(x) :
return 4.0 / (x-1) / (x-3)
def f2_org(x) :
return x + 1
# creating batch functions
f1 = batchOpenMPI.batchFunction(f1_org, permissible_exceptions = [ZeroDivisionError] )
f2 = batchOpenMPI.batchFunction(f2_org)
batchOpenMPI.begin_MPI_loop()
batchOpenMPI.drop_previous_batch_results = True
# building processing que
inputs = range(10)
print("""
y(x) = f1(x) + f2(x)
where
f1(x) = 4 / (x-1) / (x-3)
f2(x) = x + 1"")
inputs: """ + str(inputs))
print("""
as f1 will fail at 1 and 3. the results from f2 for these inputs will be uncollected.
