def parallelRun(self, verbose=True, **kwargs):
'''
parallelRun: Run the worker in parallel status and send the result message back
@param verbose: verbose mode
@type verbose: boolean
'''
from pytom.parallel.messages import StatusMessage, MessageError
from pytom.basic.exceptions import ParameterError
from pytom.basic.structures import PyTomClassError
end = False
while not end:
# get the message string
mpi_msgString = getMsgStr()
try:
msg = self.getJobMsg(mpi_msgString)
self.jobFromMsg(msg)
if verbose == True:
print(self.name + ': running...')
[resV, orientV, sumV, sqrV] = self.run(verbose, moreInfo=True)
# write the result volume back to the disk
resFilename = self.name + '_job' + str(self.jobID) + '_res.em'
orientFilename = self.name + '_job' + str(
self.jobID) + '_orient.em'
resV.write(resFilename)
orientV.write(orientFilename)
if sumV and sqrV:
sumFilename = self.name + '_job' + str(
self.jobID) + '_sum.em'
sqrFilename = self.name + '_job' + str(
self.jobID) + '_sqr.em'
sumV.write(sumFilename)
sqrV.write(sqrFilename)
from pytom.localization.structures import Volume, Orientation
res = Volume(resFilename, self.volume.subregion)
orient = Orientation(orientFilename)
# construct the result
from pytom.localization.peak_job import PeakResult
result = PeakResult(res, orient, self.jobID)
# sent the result back
if verbose == True:
print(self.name + ': sending back result')
result.send(self.mpi_id, self.backTo)
except (MessageError, PyTomClassError, ParameterError):
try:
# get the message as StatusMessage and finish
msg = StatusMessage('', '')
msg.fromStr(mpi_msgString)
if msg.getStatus() == 'End':
end = True
if verbose == True:
print(self.name + ': ending...')
except (MessageError, PyTomClassError, ParameterError):
print(
'Error parsing message. Message either unknown or invalid.'
)
assert False
def parallelRun(self,
job,
splitX=0,
splitY=0,
splitZ=0,
verbose=True,
gpuID=-1):
"""
parallelRun: Parallel run the job on the computer cluster.
@param job: job
@type job: L{pytom.localization.peak_job.PeakJob}
@param splitX: split part along the x dimension
@type splitX: integer
@param splitY: split part along the y dimension
@type splitY: integer
@param splitZ: split part along the z dimension
@type splitZ: integer
"""
import pytom_mpi
if self.mpi_id == 0: # send the first message
# if not pytom_mpi.isInitialised():
# pytom_mpi.init()
job.members = pytom_mpi.size()
print('job members', job.members)
job.send(0, 0)
print("\n")
self.gpuID = gpuID
end = False
while not end:
# get the message string
mpi_msgString = getMsgStr()
msgType = self.getMsgType(mpi_msgString)
if msgType == 2: # Job msg
msg = self.getJobMsg(mpi_msgString)
job = self.jobFromMsg(msg) # set members
if self.mpi_id == 0:
self.distributeJobs(job, splitX, splitY, splitZ)
else:
self.distributeJobs(job)
result = self.run(verbose, gpuID=gpuID)
self.summarize(result, self.jobID)
elif msgType == 1: # Result msg
msg = self.getResMsg(mpi_msgString)
res = self.resFromMsg(msg)
if verbose == True:
print(self.name + ": processing result from worker " +
msg.getSender())
resV = res.result.getVolume()
resO = res.orient.getVolume()
jobID = res.jobID
self.summarize([resV, resO], jobID)
elif msgType == 0: # Status msg
# get the message as StatusMessage and finish
from pytom.parallel.messages import StatusMessage
msg = StatusMessage('', '')
msg.fromStr(mpi_msgString)
if msg.getStatus() == 'End':
end = True
if verbose == True:
print(self.name + ': end')
else: # Error
raise RuntimeError("False message type!")
if self.mpi_id == 0:
# delete the temporary files on the disk
import os
files = os.listdir(self.dstDir)
for name in files:
if 'job' in name and '.em' in name and not 'sum' in name and not 'sqr' in name:
os.remove(self.dstDir + '/' + name)
if self.gpuID:
gpuflag = ''
else:
gpuflag = ''
# rename the result files name
os.rename(
self.dstDir + '/' + 'node_0_res.em', self.dstDir + '/' +
'scores_{}{}.em'.format(self.suffix, gpuflag))
os.rename(
self.dstDir + '/' + 'node_0_orient.em', self.dstDir + '/' +
'angles_{}{}.em'.format(self.suffix, gpuflag))
self.clean() # clean itself
pytom_mpi.finalise()