def blockResult(self, modelRun, jobMI):
# CHeck jobMI is of type MPI ...
maxRunTime = modelRun.jobParams['maxRunTime']
pollInterval = modelRun.jobParams['pollInterval']
procHandle = jobMI.procHandle
# Navigate to the model's base directory
startDir = os.getcwd()
if modelRun.basePath != startDir:
print "Changing to ModelRun's specified base path '%s'" % \
(modelRun.basePath)
os.chdir(modelRun.basePath)
if maxRunTime == None or maxRunTime <= 0:
timeOut = False
retCode = procHandle.wait()
else:
if pollInterval > maxRunTime: pollInterval = maxRunTime
totalTime = 0
timeOut = True
while totalTime <= maxRunTime:
# Note: current strategy in this loop means 'totalTime'
# recorded here will only be as accurate as size of
# pollInterval.
# Thus this is a fall-back for recording time taken.
time.sleep(pollInterval)
totalTime += pollInterval
retCode = procHandle.poll()
if retCode is not None:
timeOut = False
break
if timeOut:
# At this point, we know the process has run too long.
# From Python 2.6, change this to procHandle.kill()
print "Error: passed timeout of %s, sending quit signal." % \
(str(timedelta(seconds=maxRunTime)))
os.kill(procHandle.pid, signal.SIGQUIT)
# TODO: set finishTime
# Check status of run (eg error status)
stdOutFilename = modelRun.getStdOutFilename()
stdErrFilename = modelRun.getStdErrFilename()
if timeOut == True:
raise ModelRunTimeoutError(modelRun.name, stdOutFilename,
stdErrFilename, maxRunTime)
if retCode != 0:
raise ModelRunRegularError(modelRun.name, retCode, stdOutFilename,
stdErrFilename)
else:
# Taking advantage of os.path.join functionality to automatically
# over-ride later absolute paths.
absOutPath = os.path.join(modelRun.basePath, modelRun.outputPath)
absLogPath = os.path.join(modelRun.basePath, modelRun.logPath)
print "Model ran successfully (output saved to path %s" %\
(absOutPath),
if absLogPath != absOutPath:
print ", std out & std error to %s" % (absLogPath),
print ")."
# Now tidy things up after the run.
jobMI.stdOutFile.close()
jobMI.stdErrFile.close()
print "Doing post-run tidyup:"
modelRun.postRunCleanup()
# Construct a modelResult
mResult = ModelResult(modelRun.name, absOutPath)
mResult.jobMetaInfo = jobMI
try:
#TODO: the below should be a standard method of ModelResult
tSteps, simTime = getSimInfoFromFreqOutput(mResult.outputPath)
except ValueError:
# For now, allow runs that didn't create a freq output
tSteps, simTime = None, None
#Now collect profiler performance info.
for profiler in self.profilers:
profiler.attachPerformanceInfo(jobMI, mResult)
if modelRun.basePath != startDir:
print "Restoring initial path '%s'" % \
(startDir)
os.chdir(startDir)
return mResult
def blockResult(self, modelRun, jobMI):
# CHeck jobMI is of type MPI ...
maxRunTime = modelRun.jobParams['maxRunTime']
pollInterval = modelRun.jobParams['pollInterval']
procHandle = jobMI.procHandle
# Navigate to the model's base directory
startDir = os.getcwd()
if modelRun.basePath != startDir:
print "Changing to ModelRun's specified base path '%s'" % \
(modelRun.basePath)
os.chdir(modelRun.basePath)
if maxRunTime == None or maxRunTime <= 0:
timeOut = False
retCode = procHandle.wait()
else:
if pollInterval > maxRunTime: pollInterval = maxRunTime
totalTime = 0
timeOut = True
while totalTime <= maxRunTime:
# Note: current strategy in this loop means 'totalTime'
# recorded here will only be as accurate as size of
# pollInterval.
# Thus this is a fall-back for recording time taken.
time.sleep(pollInterval)
totalTime += pollInterval
retCode = procHandle.poll()
if retCode is not None:
timeOut = False
break
if timeOut:
# At this point, we know the process has run too long.
# From Python 2.6, change this to procHandle.kill()
print "Error: passed timeout of %s, sending quit signal." % \
(str(timedelta(seconds=maxRunTime)))
os.kill(procHandle.pid, signal.SIGQUIT)
# TODO: set finishTime
# Check status of run (eg error status)
stdOutFilename = modelRun.getStdOutFilename()
stdErrFilename = modelRun.getStdErrFilename()
if timeOut == True:
raise ModelRunTimeoutError(modelRun.name, stdOutFilename,
stdErrFilename, maxRunTime)
if retCode != 0:
raise ModelRunRegularError(modelRun.name, retCode, stdOutFilename,
stdErrFilename)
else:
# Taking advantage of os.path.join functionality to automatically
# over-ride later absolute paths.
absOutPath = os.path.join(modelRun.basePath, modelRun.outputPath)
absLogPath = os.path.join(modelRun.basePath, modelRun.logPath)
print "Model ran successfully (output saved to path %s" %\
(absOutPath),
if absLogPath != absOutPath:
print ", std out & std error to %s" % (absLogPath),
print ")."
# Now tidy things up after the run.
jobMI.stdOutFile.close()
jobMI.stdErrFile.close()
print "Doing post-run tidyup:"
modelRun.postRunCleanup()
# Construct a modelResult
mResult = ModelResult(modelRun.name, absOutPath)
mResult.jobMetaInfo = jobMI
try:
#TODO: the below should be a standard method of ModelResult
tSteps, simTime = getSimInfoFromFreqOutput(mResult.outputPath)
except ValueError:
# For now, allow runs that didn't create a freq output
tSteps, simTime = None, None
#Now collect profiler performance info.
for profiler in self.profilers:
profiler.attachPerformanceInfo(jobMI, mResult)
if modelRun.basePath != startDir:
print "Restoring initial path '%s'" % \
(startDir)
os.chdir(startDir)
return mResult
def blockResult(self, modelRun, jobMetaInfo):
# Check jobMetaInfo is of type PBS
# via self.runType = "PBS"
startDir = os.getcwd()
if modelRun.basePath != startDir:
print "Changing to ModelRun's specified base path '%s'" % \
(modelRun.basePath)
os.chdir(modelRun.basePath)
jobID = jobMetaInfo.jobId
pollInterval = modelRun.jobParams['pollInterval']
checkOutput = 0
# NB: unlike with the MPI Job Runner, we don't check the "maxJobTime" here:- since that was encoded
# in the PBS Walltime used. Wait as long as necessary for job to be queued, run, and completed
# in PBS system.
pbsWaitTime = 0
gotResult = False
pbsError = False
while gotResult == False:
time.sleep(pollInterval)
pbsWaitTime += pollInterval
# check PBS job output ... (eg using qstat on jobID)
qstat = os.popen("qstat " + jobID).readlines()
qstatus = "%s" % (qstat)
# when the job has been submitted and we query the job ID we should get something like:
# if the job has ended:
#qstat: Unknown Job Id 3506.tweedle
# OR
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 E batch
# if the job has not commenced running or is still running:
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 Q batch
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 R batch
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 S batch
# if the job has not been able to be run:
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 C batch
# So if we break the command line up into an array of words separated by spaces:
qstatus = qstatus.split(" ")
#jobName and modelName MUST be THE SAME
for ii in range(len(qstatus)):
if qstatus[ii] == "Unknown":
print "job has already run\n"
gotResult = True
elif qstatus[ii] == "R":
print "job is still running\n"
elif qstatus[ii] == "Q":
print "job is queued\n"
elif qstatus[ii] == "C":
print "job is cancelled\n"
gotResult = True
pbsError = True
elif qstatus[ii] == "E":
print "job has ended\n"
gotResult = True
# Check status of run (eg error status)
# TODO: archive PBS file in modelRun output directory.
# TODO: connect/copy PBS stdout/error files to standard expected names.
stdOutFilename = modelRun.getStdOutFilename()
stdErrFilename = modelRun.getStdErrFilename()
#qdel = os.popen("qdel "+jobID).readlines()
if pbsError:
raise ModelRunRegularError(modelRun.name, -1, stdOutFilename,
stdErrFilename)
else:
absOutPath = os.path.join(modelRun.basePath, modelRun.outputPath)
absLogPath = os.path.join(modelRun.basePath, modelRun.logPath)
# TODO: Move and rename output and error files created by PBS,
# ... to stdOutFilename, stdErrFilename
# check PBS output file and make sure there's something in it
jobName = "%s" % (modelRun.name)
jobid = jobID.split(".")
jobNo = jobid[0]
fileName = jobName + ".o" + jobNo
f = open(fileName, 'r')
lines = f.read()
if lines == "":
print "error in file no output obtained\n"
raise ModelRunRegularError(modelRun.name, retCode,
stdOutFilename, stdErrFilename)
else:
print "Model ran successfully (output saved to %s, std out"\
" & std error to %s." % (absOutPath, absLogPath)
print "Doing post-run tidyup:"
modelRun.postRunCleanup()
# Construct a modelResult
mResult = ModelResult(modelRun.name, absOutPath)
# Now attach appropriate Job meta info
try:
tSteps, simTime = getSimInfoFromFreqOutput(modelRun.outputPath)
except ValueError:
# For now, allow runs that didn't create a freq output
tSteps, simTime = None, None
# Perhaps functions on jobMetaInfo?
# get provenance info
# attach provenance info
# get performance info
# attach performance info
mResult.jobMetaInfo = jobMetaInfo
# Navigate to the model's base directory
if modelRun.basePath != startDir:
print "Restoring initial path '%s'" % (startDir)
os.chdir(startDir)
return mResult
def blockResult(self, modelRun, jobMetaInfo):
# Check jobMetaInfo is of type PBS
# via self.runType = "PBS"
startDir = os.getcwd()
if modelRun.basePath != startDir:
print "Changing to ModelRun's specified base path '%s'" % \
(modelRun.basePath)
os.chdir(modelRun.basePath)
jobID = jobMetaInfo.jobId
pollInterval = modelRun.jobParams['pollInterval']
checkOutput = 0
# NB: unlike with the MPI Job Runner, we don't check the "maxJobTime" here:- since that was encoded
# in the PBS Walltime used. Wait as long as necessary for job to be queued, run, and completed
# in PBS system.
pbsWaitTime = 0
gotResult = False
pbsError = False
while gotResult == False:
time.sleep(pollInterval)
pbsWaitTime += pollInterval
# check PBS job output ... (eg using qstat on jobID)
qstat = os.popen("qstat "+jobID).readlines()
qstatus = "%s" % (qstat)
# when the job has been submitted and we query the job ID we should get something like:
# if the job has ended:
#qstat: Unknown Job Id 3506.tweedle
# OR
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 E batch
# if the job has not commenced running or is still running:
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 Q batch
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 R batch
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 S batch
# if the job has not been able to be run:
#3505.tweedle cratonic30t2c3d2 WendySharples 00:15:16 C batch
# So if we break the command line up into an array of words separated by spaces:
qstatus = qstatus.split(" ")
#jobName and modelName MUST be THE SAME
for ii in range(len(qstatus)):
if qstatus[ii] == "Unknown":
print "job has already run\n"
gotResult = True
elif qstatus[ii] == "R":
print "job is still running\n"
elif qstatus[ii] == "Q":
print "job is queued\n"
elif qstatus[ii] == "C":
print "job is cancelled\n"
gotResult = True
pbsError = True
elif qstatus[ii] == "E":
print "job has ended\n"
gotResult = True
# Check status of run (eg error status)
# TODO: archive PBS file in modelRun output directory.
# TODO: connect/copy PBS stdout/error files to standard expected names.
stdOutFilename = modelRun.getStdOutFilename()
stdErrFilename = modelRun.getStdErrFilename()
#qdel = os.popen("qdel "+jobID).readlines()
if pbsError:
raise ModelRunRegularError(modelRun.name, -1, stdOutFilename,
stdErrFilename)
else:
absOutPath = os.path.join(modelRun.basePath, modelRun.outputPath)
absLogPath = os.path.join(modelRun.basePath, modelRun.logPath)
# TODO: Move and rename output and error files created by PBS,
# ... to stdOutFilename, stdErrFilename
# check PBS output file and make sure there's something in it
jobName = "%s" % (modelRun.name)
jobid = jobID.split(".")
jobNo = jobid[0]
fileName = jobName+".o"+jobNo
f = open(fileName, 'r')
lines = f.read()
if lines == "":
print "error in file no output obtained\n"
raise ModelRunRegularError(modelRun.name, retCode,
stdOutFilename, stdErrFilename)
else:
print "Model ran successfully (output saved to %s, std out"\
" & std error to %s." % (absOutPath, absLogPath)
print "Doing post-run tidyup:"
modelRun.postRunCleanup()
# Construct a modelResult
mResult = ModelResult(modelRun.name, absOutPath)
# Now attach appropriate Job meta info
try:
tSteps, simTime = getSimInfoFromFreqOutput(modelRun.outputPath)
except ValueError:
# For now, allow runs that didn't create a freq output
tSteps, simTime = None, None
# Perhaps functions on jobMetaInfo?
# get provenance info
# attach provenance info
# get performance info
# attach performance info
mResult.jobMetaInfo = jobMetaInfo
# Navigate to the model's base directory
if modelRun.basePath != startDir:
print "Restoring initial path '%s'" % (startDir)
os.chdir(startDir)
return mResult
