#from TensorMol import * from TensorMol import MSet, JOULEPERHARTREE import argparse as arg from multiprocessing import Queue, Process, Manager, Pool from matplotlib import pyplot as plt parser = arg.ArgumentParser( description= 'Grep qm area from an Amber MDcrd trajory to make training dataset!') parser.add_argument('-i', '--input') args = parser.parse_args() jsonfile = args.input os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' if __name__ == "__main__": UpdateGPARAMS(jsonfile) LoadModel() if not os.path.exists("./results"): os.system("mkdir ./results") for i in range(len(GPARAMS.Dataset_setting.Inputdatasetlist)): TMPSet = MSet(GPARAMS.Dataset_setting.Inputdatasetlist[i]) TMPSet.Load() f1 = open( './results/' + GPARAMS.Dataset_setting.Inputdatasetlist[i] + '.result', 'w') f2 = open( './results/' + GPARAMS.Dataset_setting.Inputdatasetlist[i] + '_e.csv', 'w') f3 = open( './results/' + GPARAMS.Dataset_setting.Inputdatasetlist[i] + '_f.csv', 'w') f4 = open(
if __name__ == "__main__": manager = Manager() QMQueue = manager.Queue() DataQueue = manager.Queue() GPUQueue = manager.Queue() NetstrucQueue = manager.Queue() if os.path.exists('./networks/lastsave'): os.system("rm ./networks/lastsave/* -r") os.system("cp *.ESOINN Sfactor.in ./networks/lastsave ") UpdateGPARAMS(jsonfile) for i in GPARAMS.Compute_setting.Gpulist: GPUQueue.put(i) for stage in range(GPARAMS.Train_setting.Trainstage,\ GPARAMS.Train_setting.Stagenum+GPARAMS.Train_setting.Trainstage): LoadModel() #==Main MD process with productor and Consumer model== ProductPool = Pool(len(GPARAMS.Compute_setting.Gpulist)) Resultlist = [] for i in range(len(GPARAMS.System_setting)): result = ProductPool.apply_async(productor, (i, QMQueue, GPUQueue)) Resultlist.append(result) ProductPool.close() for i in range(len(GPARAMS.System_setting)): tmp = Resultlist[i].get() print(tmp) Consumer_Process = Process(target=consumer, args=(QMQueue, )) Consumer_Process.start() ProductPool.terminate() ProductPool.join()