我是并行处理的新手。我编写了一个需要大量时间执行的脚本，因此希望它能够在CPU内核上并行运行。为此，我使用并行python模块。我已经导入了它
import pp
ppservers = ()
if len(sys.argv) > 1:
ncpus = int(sys.argv[1])
job_server = pp.Server(ncpus, ppservers=ppservers)
else:
job_server = pp.Server(ppservers=ppservers)
我想让它并行执行的功能是：
def Parallel_Solution_Combination_Method(subset):
我传递给函数的参数子集是两个染色体对象的列表。染色体对象是我在上面定义的相同脚本中构建的类。
class chromosome():
def __init__(self):
self.record_template = ''
self.record_target = ''
self.molpdf_score = 0.0
self.ga341_score = 0.0
self.dope_score = 0.0
self.normalized_dope_score = 0.0
self.flag_value = 0
self.distance_value = 0
def add_molpdf(self, molpdf):
self.molpdf_score = molpdf
def add_ga341(self, ga341):
self.ga341_score = ga341
def add_dope(self, dope):
self.dope_score = dope
def add_normalized_dope(self, normalized_dope):
self.normalized_dope_score = normalized_dope
def add_records(self, records):
self.seq_records = records
for rec in self.seq_records:
if rec.id == template_id:
self.record_template = rec.seq
elif rec.id == target_id:
self.record_target = rec.seq
def set_flag(self, flag):
self.flag_value = flag
def add_distance(self, distance):
self.distance_value = distance
子集参数包含两个染色体的列表（上面定义的类）。现在我希望它并行执行的函数（也就是Parallel_Solution_Combination_Method）调用两个函数：
find_allIndices
find_different_indicies
我稍后使用此脚本尝试并行运行该功能
jobs = [(subset, job_server.submit(Parallel_Solution_Combination_Method,
(subset,), (find_allIndices,find_different_indicies,),("random",)))
for subset in subsets
]
每当我为循环运行它时：
for subset, job in jobs:
print subset, job()
我收到此错误：
(<__main__.chromosome instance at 0x000000000A15FA88>, <__main__.chromosome instance at 0x000000000A2D4C88>)
A fatal error has occured during the function execution
Traceback (most recent call last):
File "C:\Users\mossig\AppData\Local\Continuum\Anaconda\lib\site- packages\ppworker.py", line 86, in run
__args = pickle.loads(__sargs)
AttributeError: 'module' object has no attribute 'chromosome' None
正如您可能已经注意到for循环正在打印染色体实例，但在我看来，PP无法识别染色体对象。我不知道错误是由什么引起的。我尝试在jobs_server.submit（）方法的modules参数中添加染色体类，它仍然无效并给出相同的结果。
请帮助!!谢谢。
def Parallel_Solution_Combination_Method(subset):
print 'entered parallel sol comb'
child_chromosome = chromosome()
combination_model_offset = 300
attempts = 0
while True:
template1 = subset[0].record_template
template2 = subset[1].record_template
template_child = template1
template_gap1 = find_allIndices(template1, '-')
template_gap2 = find_allIndices(template2, '-')
if(len(template_gap1) !=0 and len(template_gap2) != 0):
template_gap_difference = find_different_indicies(template_gap1, template_gap2)
if(len(template_gap_difference) != 0):
template_slice_point = random.choice(template_gap_difference)
if(template_gap2[template_slice_point -1] < template_gap1[template_slice_point]):
#swap template1 template2 values as well as their respective gap indices
#so that in crossover the gaps would not collide with each other.
temp_template = template1
temp_gap = template_gap1
template1 = template2
template2 = temp_template
template_gap1 = template_gap2
template_gap2 = temp_gap
#the crossing over takes the first part of the child sequence to be up until
#the crossing point without including it. this way it ensures that the resulting
#child sequence is different from both of the parents by at least one point.
child_template_gap = template_gap1[:template_slice_point]+template_gap2[template_slice_point:]
child_gap_part1 = child_template_gap[:template_slice_point]
child_gap_part2 = child_template_gap[template_slice_point:]
if template_slice_point == 0:
template_child = template2
else:
template_child = template1[:template_gap1[template_slice_point]]
template_residues_part1 = str(template_child).translate(None, '-')
template_residues_part2 = str(template2).translate(None, '-')
template_residues_part2 = template_residues_part2[len(template_residues_part1):]
for i in range(template_gap1[template_slice_point-1], len(template1)):
if i in child_gap_part2:
template_child = template_child + '-'
else:
template_child = template_child + template_residues_part2[0:1]
template_residues_part2 = template_residues_part2[1:]
target1 = subset[0].record_target
target2 = subset[1].record_target
target_child = target1
target_gap1 = find_allIndices(target1, '-')
target_gap2 = find_allIndices(target2, '-')
if(len(target_gap1) !=0 and len(target_gap2) != 0):
target_gap_difference = find_different_indicies(target_gap1, target_gap2)
if(len(target_gap_difference) !=0):
target_slice_point = random.choice(target_gap_difference)
if(target_gap2[target_slice_point -1] < target_gap1[target_slice_point]):
#swap template1 template2 values as well as their respective gap indices
#so that in crossover the gaps would not collide with each other.
temp_target = target1
temp_gap = target_gap1
target1 = target2
target2 = temp_target
target_gap1 = target_gap2
target_gap2 = temp_gap
#the crossing over takes the first part of the child sequence to be up until
#the crossing point without including it. this way it ensures that the resulting
#child sequence is different from both of the parents by at least one point.
child_target_gap = target_gap1[:target_slice_point]+target_gap2[target_slice_point:]
child_gap_part1 = child_target_gap[:target_slice_point]
child_gap_part2 = child_target_gap[target_slice_point:]
if target_slice_point == 0:
target_child = target2
else:
target_child = target1[:target_gap1[target_slice_point]]
target_residues_part1 = str(target_child).translate(None, '-')
target_residues_part2 = str(target2).translate(None, '-')
target_residues_part2 = target_residues_part2[len(target_residues_part1):]
for i in range(target_gap1[target_slice_point-1], len(target1)):
if i in child_gap_part2:
target_child = target_child + '-'
else:
target_child = target_child + target_residues_part2[0:1]
target_residues_part2 = target_residues_part2[1:]
if not [False for y in Reference_Set if y.record_template == template_child and y.record_target == target_child] or attempts <= 100:
break
attempts +=1
child_chromosome.record_template = template_child
child_chromosome.record_target = target_child
generate_PIR(template_header, template_description, child_chromosome.record_template, target_header,target_description, child_chromosome.record_target)
output_values = start_model(template_id, target_id,'PIR_input.ali', combination_model_offset)
child_chromosome.molpdf_score = output_values['molpdf']
mdl = complete_pdb(env, '1BBH.B99990300.pdb')
child_chromosome.normalized_dope_score = mdl.assess_normalized_dope()
return child_chromosome
我也试着在以后运行它：
fn = pp.Template(job_server, Parallel_Solution_Combination_Method,
(find_allIndices,find_different_indicies, generate_PIR,
start_model,),("random", "modeller", "Bio",)
)
job1 = fn.submit(subsets[0])
result = job1()
我收到了这个错误：
A fatal error has occured during the function execution
Traceback (most recent call last):
File "C:\Users\mossig\AppData\Local\Continuum\Anaconda\lib\site-packages\ppworker.py", line 86, in run
__args = pickle.loads(__sargs)
AttributeError: 'module' object has no attribute 'chromosome'