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1 #!/usr/bin/env python3
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2 import multiprocessing
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3 import os
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4 import time
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5 from itertools import cycle
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6 '''
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7 functions for parallel processing of data chunks using worker function
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8 '''
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9
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10
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11 def run_multiple_pbs_jobs(cmds, status_files, qsub_params=""):
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12 '''
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13 Example of pbs_params:
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14 -l walltime=1000:00:00,nodes=1:ppn=8,mem=15G
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15 -l walltime=150:00:00,nodes=1:ppn=1
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16
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17 '''
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18 jobs = []
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19 status_function = []
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20 status_command = []
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21 for cmd, sf in zip(cmds, status_files):
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22 jobs.append(pbs_send_job(cmd, sf, qsub_params))
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23 for p in jobs:
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24 p.join()
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25 status_function.append(p.exitcode)
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26 # collect pbs run status
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27 for sf in status_files:
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28 with open(sf) as f:
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29 status_command.append(f.read().strip())
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30 status = {'function': status_function, 'command': status_command}
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31 return status
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32
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33
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34 def pbs_send_job(cmd, status_file, qsub_params):
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35 ''' send job to pbs cluster, require status file'''
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36 p = multiprocessing.Process(target=pbs_run,
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37 args=(cmd, status_file, qsub_params))
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38 p.start()
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39 return p
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40
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41
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42 def pbs_run(cmd, status_file, qsub_params):
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43 '''
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44 run shell command cmd on pbs cluster, wait for job to finish
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45 and return status
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46 '''
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47 print(status_file)
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48 error_file = status_file + ".e"
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49 # test if writable
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50 try:
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51 f = open(status_file, 'w').close()
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52 f = open(error_file, 'w').close()
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53 except IOError:
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54 print("cannot write to status files, make sure path exists")
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55 raise IOError
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56
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57 if os.path.exists(status_file):
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58 print("removing old status file")
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59 os.remove(status_file)
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60 cmd_full = ("echo '{cmd} && echo \"OK\" > {status_file} || echo \"ERROR\""
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61 " > {status_file}' | qsub -e {err}"
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62 " {qsub_params} ").format(cmd=cmd, status_file=status_file,
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63 err=error_file,
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64 qsub_params=qsub_params)
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65 os.system(cmd_full)
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66
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67 while True:
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68 if os.path.exists(status_file):
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69 break
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70 else:
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71 time.sleep(3)
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72 with open(status_file) as f:
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73 status = f.read().strip()
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74 return status
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75
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76
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77 def spawn(f):
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78 def fun(pipe, x):
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79 pipe.send(f(x))
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80 pipe.close()
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81 return fun
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82
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83
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84 def get_max_proc():
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85 '''Number of cpu to ise in ether get from config.py is available or
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86 from global PROC or from environment variable PRCO or set to system max'''
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87 try:
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88 from config import PROC as max_proc
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89 except ImportError:
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90 if "PROC" in globals():
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91 max_proc = PROC
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92 elif "PROC" in os.environ:
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93 max_proc = int(os.environ["PROC"])
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94
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95 else:
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96 max_proc = multiprocessing.cpu_count()
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97 return max_proc
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98
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99
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100 def parmap2(f, X, groups, ppn):
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101 max_proc = get_max_proc()
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102 print("running in parallel using ", max_proc, "cpu(s)")
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103 process_pool = []
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104 output = [None] * len(X)
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105 # prepare processes
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106 for x, index in zip(X, list(range(len(X)))):
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107 # status:
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108 # 0: waiting, 1: running, 2:collected
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109 process_pool.append({
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110 'status': 0,
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111 'proc': None,
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112 'pipe': None,
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113 'index': index,
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114 'group': groups[index],
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115 'ppn': ppn[index]
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116
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117 })
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118
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119 # run processes
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120 running = 0
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121 finished = 0
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122 sleep_time = 0.001
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123 while True:
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124 # count alive processes
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125 if not sleep_time:
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126 sleep_time = 0.001
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127 for i in process_pool:
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128 if i['status'] == 1 and not (i['proc'].exitcode is None):
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129 sleep_time = 0.0
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130 # was running now finished --> collect
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131 i['status'] = 2
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132 running -= 1
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133 finished += 1
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134 output[i['index']] = collect(i['proc'], i['pipe'])
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135 del i['pipe']
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136 del i['proc']
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137 if i['status'] == 0 and running < max_proc:
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138 # waiting and free --> run
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139 # check if this group can be run
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140 running_groups = [pp['group']
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141 for pp in process_pool if pp['status'] == 1]
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142 # check max load of concurent runs:
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143 current_load = sum([pp['ppn']
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144 for pp in process_pool if pp['status'] == 1])
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145 cond1 = (i['ppn'] + current_load) <= 1
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146 cond2 = not i['group'] in running_groups
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147 if cond1 and cond2:
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148 sleep_time = 0.0
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149 try:
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150 i['pipe'] = multiprocessing.Pipe()
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151 except OSError as e:
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152 print('exception occured:',e)
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153 continue
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154 i['proc'] = multiprocessing.Process(
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155 target=spawn(f),
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156 args=(i['pipe'][1], X[i['index']]),
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157 name=str(i['index']))
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158 i['proc'].start()
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159 i['status'] = 1
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160 running += 1
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161 if finished == len(process_pool):
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162 break
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163 if sleep_time:
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164 # sleep only if nothing changed in the last cycle
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165 time.sleep(sleep_time)
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166 # sleep time gradually increase to 1 sec
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167 sleep_time = min(2 * sleep_time, 1)
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168 return output
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169
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170
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171 def print_status(pp):
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172 states = ['waiting', 'running', 'collected']
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173 print("___________________________________")
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174 print("jobid status group ppn exitcode")
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175 print("===================================")
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176 for i in pp:
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177 print(
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178 i['index'], " ",
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179 states[i['status']], " ",
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180 i['group'], " ",
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181 i['ppn'], " ",
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182 i['proc'].exitcode
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183 )
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184
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185
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186 def collect(pf, pp):
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187 if pf.pid and not pf.exitcode and not pf.is_alive():
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188 returnvalue = pp[0].recv()
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189 pf.join()
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190 pp[0].close()
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191 pp[1].close()
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192 return returnvalue
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193 elif pf.exitcode:
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194 print("job finished with exit code {}".format(pf.exitcode))
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195 pf.join()
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196 pp[0].close()
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197 pp[1].close()
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198 return None
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199 # return None
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200 else:
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201 raise Exception('not collected')
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202
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203
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204 def parmap(f, X):
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205
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206 max_proc = get_max_proc()
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207
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208 pipe = []
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209 proc = []
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210 returnvalue = {}
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211
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212 for x, index in zip(X, list(range(len(X)))):
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213 pipe.append(multiprocessing.Pipe())
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214 proc.append(multiprocessing.Process(target=spawn(f),
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215 args=(pipe[-1][1], x), name=str(index)))
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216 p = proc[-1]
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217 # count alive processes
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218 while True:
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219 running = 0
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220 for i in proc:
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221 if i.is_alive():
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222 running += 1
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223 # print "running:"+str(running)
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224 if running < max_proc:
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225 break
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226 else:
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227 time.sleep(0.1)
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228 p.start()
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229 # print "process started:"+str(p.pid)
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230 # check for finished
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231
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232 for pf, pp, index in zip(proc, pipe, range(len(pipe))):
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233 if pf.pid and not pf.exitcode and not pf.is_alive() and (pf.name not in returnvalue):
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234 pf.join()
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235 returnvalue[str(pf.name)] = pp[0].recv()
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236 pp[0].close()
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237 pp[1].close()
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238 # proc must be garbage collected - to free all file connection
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239 del proc[index]
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240 del pipe[index]
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241
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242 # collect the rest:
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243 [pf.join() for pf in proc]
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244 for pf, pp in zip(proc, pipe):
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245 if pf.pid and not pf.exitcode and not pf.is_alive() and (pf.name not in returnvalue):
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246 returnvalue[str(pf.name)] = pp[0].recv()
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247 pp[0].close()
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248 pp[1].close()
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249 # convert to list in input correct order
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250 returnvalue = [returnvalue[str(i)] for i in range(len(X))]
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251 return returnvalue
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252
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253
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254 def parallel2(command, *args, groups=None, ppn=None):
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255 ''' same as parallel but groups are used to identifie mutually
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256 exclusive jobs, jobs with the same goup id are never run together
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257 ppn params is 'load' of the job - sum of loads cannot exceed 1
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258 '''
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259 # check args, expand if necessary
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260 args = list(args)
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261 N = [len(i) for i in args] # lengths of lists
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262 Mx = max(N)
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263 if len(set(N)) == 1:
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264 # all good
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265 pass
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266 elif set(N) == set([1, Mx]):
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267 # expand args of length 1
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268 for i in range(len(args)):
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269 if len(args[i]) == 1:
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270 args[i] = args[i] * Mx
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271 else:
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272 raise ValueError
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273 if not groups:
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274 groups = range(Mx)
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275 elif len(groups) != Mx:
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276 print("length of groups must be same as number of job or None")
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277 raise ValueError
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278
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279 if not ppn:
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280 ppn = [0] * Mx
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281 elif len(ppn) != Mx:
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282 print("length of ppn must be same as number of job or None")
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283 raise ValueError
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284 elif max(ppn) > 1 and min(ppn):
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285 print("ppn values must be in 0 - 1 range")
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286 raise ValueError
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287 # convert argument to suitable format - 'transpose'
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288 argsTuples = list(zip(*args))
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289 args = [list(i) for i in argsTuples]
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290
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291 # multiprocessing.Pool()
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292
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293 def command_star(args):
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294 return(command(*args))
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295
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296 x = parmap2(command_star, argsTuples, groups, ppn)
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297 return x
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298
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299
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300 def parallel(command, *args):
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301 ''' Execute command in parallel using multiprocessing
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302 command is the function to be executed
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303 args is list of list of arguments
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304 execution is :
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305 command(args[0][0],args[1][0],args[2][0],args[3][0],....)
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306 command(args[0][1],args[1][1],args[2][1],args[3][1],....)
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307 command(args[0][2],args[1][2],args[2][2],args[3][2],....)
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308 ...
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309 output of command is returned as list
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310 '''
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311 # check args, expand if necessary
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312 args = list(args)
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313 N = [len(i) for i in args] # lengths of lists
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314 Mx = max(N)
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315 if len(set(N)) == 1:
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316 # all good
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317 pass
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318 elif set(N) == set([1, Mx]):
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319 # expand args of length 1
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320 for i in range(len(args)):
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321 if len(args[i]) == 1:
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322 args[i] = args[i] * Mx
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323 else:
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324 raise ValueError
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325
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326 # convert argument to suitable format - 'transpose'
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327 argsTuples = list(zip(*args))
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328 args = [list(i) for i in argsTuples]
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329
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330 multiprocessing.Pool()
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331
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332 def command_star(args):
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333 return(command(*args))
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334
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335 x = parmap(command_star, argsTuples)
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336 return x
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337
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338
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339 def worker(*a):
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340 x = 0
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341 y = 0
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342 for i in a:
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343 if i == 1.1:
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344 print("raising exception")
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345 s = 1 / 0
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346 y += i
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347 for j in range(10):
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348 x += i
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349 for j in range(100000):
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350 x = 1.0 / (float(j) + 1.0)
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351 return(y)
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352
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353 # test
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354 if __name__ == "__main__":
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355 # x = parallel2(worker, [1], [2], [3], [4], [1], [1, 2, 3, 7, 10, 1.1, 20, 30, 40, 10, 30, 20, 40, 50, 50], [
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356 # 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 4, 3, 2])
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357
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358 x = parallel2(
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359 worker, [1], [2], [3], [4], [1],
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360 [1, 2, 3, 7, 10, 1.2, 20, 30, 40, 10, 30, 20, 40, 50, 50],
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361 [3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 5, 6, 4, 3, 2],
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362 groups=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
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363 ppn=[0.6, 0.6, 0.2, 0.6, 0.2, 0.2, 0.4,
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364 0.1, 0.1, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1]
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365 )
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366 print(x)
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