def createJob(self):
job = Job()
job.setName(self.__stepName)
job.setOutputSandbox(['*log'])
job.setExecutable('/usr/bin/wget',
arguments='"{0}/{1}"'.format(URL_ROOT,
self.__executable))
job.setExecutable('/bin/chmod',
arguments='+x "{0}"'.format(self.__executable))
arguments = '"{0}" "{1}" "{2}" "{3}" "{4}" "{5}" @{{JOB_ID}}'.format(
self.__softwareVersion, self.__application, self.__outputPath,
self.__outputPattern, self.__outputSE, self.__outputMode)
if self.__extraArgs:
arguments += ' ' + self.__extraArgs
job.setExecutable(self.__executable, arguments=arguments)
# failover for failed jobs
job.setExecutable('/bin/ls -l',
modulesList=['Script', 'FailoverRequest'])
if self.__inputData:
job.setInputData(self.__inputData)
if self.__site:
job.setDestination(self.__site)
if self.__bannedsite:
job.setBannedSites(self.__bannedsite)
job.setOutputSandbox(['app.out', 'app.err', 'Script3_CodeOutput.log'])
self.__job = job
def prepareTransformationTasks(self,transBody,taskDict,owner='',ownerGroup=''):
if (not owner) or (not ownerGroup):
res = getProxyInfo(False,False)
if not res['OK']:
return res
proxyInfo = res['Value']
owner = proxyInfo['username']
ownerGroup = proxyInfo['group']
oJob = Job(transBody)
for taskNumber in sortList(taskDict.keys()):
paramsDict = taskDict[taskNumber]
transID = paramsDict['TransformationID']
self.log.verbose('Setting job owner:group to %s:%s' % (owner,ownerGroup))
oJob.setOwner(owner)
oJob.setOwnerGroup(ownerGroup)
transGroup = str(transID).zfill(8)
self.log.verbose('Adding default transformation group of %s' % (transGroup))
oJob.setJobGroup(transGroup)
constructedName = str(transID).zfill(8)+'_'+str(taskNumber).zfill(8)
self.log.verbose('Setting task name to %s' % constructedName)
oJob.setName(constructedName)
oJob._setParamValue('PRODUCTION_ID',str(transID).zfill(8))
oJob._setParamValue('JOB_ID',str(taskNumber).zfill(8))
inputData = None
for paramName,paramValue in paramsDict.items():
self.log.verbose('TransID: %s, TaskID: %s, ParamName: %s, ParamValue: %s' %(transID,taskNumber,paramName,paramValue))
if paramName=='InputData':
if paramValue:
self.log.verbose('Setting input data to %s' %paramValue)
oJob.setInputData(paramValue)
elif paramName=='Site':
if paramValue:
self.log.verbose('Setting allocated site to: %s' %(paramValue))
oJob.setDestination(paramValue)
elif paramValue:
self.log.verbose('Setting %s to %s' % (paramName,paramValue))
oJob._addJDLParameter(paramName,paramValue)
hospitalTrans = [int(x) for x in gConfig.getValue("/Operations/Hospital/Transformations",[])]
if int(transID) in hospitalTrans:
hospitalSite = gConfig.getValue("/Operations/Hospital/HospitalSite",'DIRAC.JobDebugger.ch')
hospitalCEs = gConfig.getValue("/Operations/Hospital/HospitalCEs",[])
oJob.setType('Hospital')
oJob.setDestination(hospitalSite)
oJob.setInputDataPolicy('download',dataScheduling=False)
if hospitalCEs:
oJob._addJDLParameter('GridRequiredCEs',hospitalCEs)
taskDict[taskNumber]['TaskObject'] = ''
res = self.getOutputData({'Job':oJob._toXML(),'TransformationID':transID,'TaskID':taskNumber,'InputData':inputData})
if not res ['OK']:
self.log.error("Failed to generate output data",res['Message'])
continue
for name,output in res['Value'].items():
oJob._addJDLParameter(name,string.join(output,';'))
taskDict[taskNumber]['TaskObject'] = Job(oJob._toXML())
return S_OK(taskDict)
def prepareTransformationTasks(self,transBody,taskDict,owner='',ownerGroup=''):
if (not owner) or (not ownerGroup):
res = getProxyInfo(False,False)
if not res['OK']:
return res
proxyInfo = res['Value']
owner = proxyInfo['username']
ownerGroup = proxyInfo['group']
oJob = Job(transBody)
for taskNumber in sortList(taskDict.keys()):
paramsDict = taskDict[taskNumber]
transID = paramsDict['TransformationID']
self.log.verbose('Setting job owner:group to %s:%s' % (owner,ownerGroup))
oJob.setOwner(owner)
oJob.setOwnerGroup(ownerGroup)
transGroup = str(transID).zfill(8)
self.log.verbose('Adding default transformation group of %s' % (transGroup))
oJob.setJobGroup(transGroup)
constructedName = str(transID).zfill(8)+'_'+str(taskNumber).zfill(8)
self.log.verbose('Setting task name to %s' % constructedName)
oJob.setName(constructedName)
oJob._setParamValue('PRODUCTION_ID',str(transID).zfill(8))
oJob._setParamValue('JOB_ID',str(taskNumber).zfill(8))
inputData = None
for paramName,paramValue in paramsDict.items():
self.log.verbose('TransID: %s, TaskID: %s, ParamName: %s, ParamValue: %s' %(transID,taskNumber,paramName,paramValue))
if paramName=='InputData':
if paramValue:
self.log.verbose('Setting input data to %s' %paramValue)
oJob.setInputData(paramValue)
elif paramName=='Site':
if paramValue:
self.log.verbose('Setting allocated site to: %s' %(paramValue))
oJob.setDestination(paramValue)
elif paramValue:
self.log.verbose('Setting %s to %s' % (paramName,paramValue))
oJob._addJDLParameter(paramName,paramValue)
hospitalTrans = [int(x) for x in gConfig.getValue("/Operations/Hospital/Transformations",[])]
if int(transID) in hospitalTrans:
hospitalSite = gConfig.getValue("/Operations/Hospital/HospitalSite",'DIRAC.JobDebugger.ch')
hospitalCEs = gConfig.getValue("/Operations/Hospital/HospitalCEs",[])
oJob.setType('Hospital')
oJob.setDestination(hospitalSite)
oJob.setInputDataPolicy('download',dataScheduling=False)
if hospitalCEs:
oJob._addJDLParameter('GridRequiredCEs',hospitalCEs)
taskDict[taskNumber]['TaskObject'] = ''
res = self.getOutputData({'Job':oJob._toXML(),'TransformationID':transID,'TaskID':taskNumber,'InputData':inputData})
if not res ['OK']:
self.log.error("Failed to generate output data",res['Message'])
continue
for name,output in res['Value'].items():
oJob._addJDLParameter(name,string.join(output,';'))
taskDict[taskNumber]['TaskObject'] = Job(oJob._toXML())
return S_OK(taskDict)
def read_hessjob(args=None):
from DIRAC.Interfaces.API.Dirac import Dirac
from DIRAC.Interfaces.API.Job import Job
if (len(args) != 1):
Script.showHelp()
version = args[0]
user_script = './read_hess2dst.sh'
sim_file = 'simtel_file.list'
infileLFNList = [
'/vo.cta.in2p3.fr/MC/PROD2/Config_310113/prod-2_21122012_corsika/gamma/prod-2_06052013_simtel_STD/Data/002xxx/gamma_20.0_180.0_alt2662.0_run002997.simtel.gz',
'/vo.cta.in2p3.fr/MC/PROD2/Config_310113/prod-2_21122012_corsika/gamma/prod-2_06052013_simtel_STD/Data/002xxx/gamma_20.0_180.0_alt2662.0_run002998.simtel.gz'
]
f = open(sim_file, 'w')
for infileLFN in infileLFNList:
filein = os.path.basename(infileLFN)
f.write(filein)
f.write('\n')
f.close()
j = Job()
j.setInputData(infileLFNList)
options = []
options = [sim_file]
executablestr = "%s %s %s" % (version, user_script, ' '.join(options))
j.setExecutable('./cta-read_hess.py', executablestr)
j.setInputSandbox(['cta-read_hess.py', user_script, sim_file])
j.setOutputSandbox(['read_hess.log'])
j.setOutputData(['*dst.gz'])
j.setName(user_script)
j.setCPUTime(100000)
Script.gLogger.info(j._toJDL())
Dirac().submit(j)
def run_test_job(args):
simtel_files = load_files_from_list(args[0])
#simtel_files = ["/vo.cta.in2p3.fr/MC/PROD3/LaPalma/proton/simtel/1260/Data/071xxx/proton_40deg_180deg_run71001___cta-prod3-lapalma3-2147m-LaPalma.simtel.gz",
#"/vo.cta.in2p3.fr/MC/PROD3/LaPalma/proton/simtel/1260/Data/070xxx/proton_40deg_180deg_run70502___cta-prod3-lapalma3-2147m-LaPalma.simtel.gz"]
dirac = Dirac()
j = Job()
j.setCPUTime(500)
j.setInputData(simtel_files[0])
j.setExecutable('echo', 'Hello World!')
j.setName('Hello World')
res = dirac.submit(j)
print('Submission Result: {}'.format(res))
return res
def simteljob(args = None ):
from DIRAC.Interfaces.API.Dirac import Dirac
from DIRAC.Interfaces.API.Job import Job
if (len(args)!=1):
Script.showHelp()
version = args[0]
user_script = './run_simtel.sh'
infileLFNList = ['/vo.cta.in2p3.fr/MC/PROD2/Config_120213/prod-2_21122012_corsika/proton/Data/044xxx/proton_20.0_180.0_alt2662.0_run044019.corsika.gz',
'/vo.cta.in2p3.fr/MC/PROD2/Config_120213/prod-2_21122012_corsika/proton/Data/044xxx/proton_20.0_180.0_alt2662.0_run044085.corsika.gz']
for infileLFN in infileLFNList:
filein = os.path.basename(infileLFN)
j = Job()
j.setInputSandbox( ['cta-simtel.py', user_script] )
j.setInputData(infileLFN)
user_args = []
user_args = [filein]
executablestr = "%s %s %s" % ( version, user_script, ' '.join( user_args ) )
j.setExecutable('./cta-simtel.py', executablestr)
sim_out = 'Data/sim_telarray/cta-ultra5/0.0deg/Data/*.simtel.gz'
log_out = 'Data/sim_telarray/cta-ultra5/0.0deg/Log/*.log.gz'
hist_out = 'Data/sim_telarray/cta-ultra5/0.0deg/Histograms/*.hdata.gz'
j.setOutputData([sim_out,log_out,hist_out])
j.setOutputSandbox('simtel.log')
j.setName(user_script)
j.setCPUTime(100000)
Script.gLogger.info( j._toJDL() )
Dirac().submit( j )
def basicTest(self):
j = Job()
j.setCPUTime(50000)
j.setExecutable(
'/Users/stuart/dirac/workspace/DIRAC3/DIRAC/Interfaces/API/test/myPythonScript.py'
)
# j.setExecutable('/bin/echo hello')
j.setOwner('paterson')
j.setType('test')
j.setName('MyJobName')
#j.setAncestorDepth(1)
j.setInputSandbox([
'/Users/stuart/dirac/workspace/DIRAC3/DIRAC/Interfaces/API/test/DV.opts',
'/Users/stuart/dirac/workspace/DIRAC3/DIRAC/Interfaces/API/test/DV2.opts'
])
j.setOutputSandbox(['firstfile.txt', 'anotherfile.root'])
j.setInputData([
'/lhcb/production/DC04/v2/DST/00000742_00003493_11.dst',
'/lhcb/production/DC04/v2/DST/00000742_00003493_10.dst'
])
j.setOutputData(['my.dst', 'myfile.log'])
j.setDestination('LCG.CERN.ch')
j.setPlatform('LCG')
j.setSystemConfig('x86_64-slc5-gcc43-opt')
j.setSoftwareTags(['VO-lhcb-Brunel-v30r17', 'VO-lhcb-Boole-v12r10'])
#print j._toJDL()
#print j.printObj()
xml = j._toXML()
testFile = 'jobDescription.xml'
if os.path.exists(testFile):
os.remove(testFile)
xmlfile = open(testFile, 'w')
xmlfile.write(xml)
xmlfile.close()
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Creating code for the workflow'
print j.createCode()
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Executing the workflow'
j.execute()
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Trying to run the same workflow from generated XML file'
workflow = fromXMLFile(testFile)
code = workflow.createCode()
print code
workflow.execute()
def basicTest(self):
j = Job()
j.setCPUTime(50000)
j.setExecutable('/Users/stuart/dirac/workspace/DIRAC3/DIRAC/Interfaces/API/test/myPythonScript.py')
# j.setExecutable('/bin/echo hello')
j.setOwner('paterson')
j.setType('test')
j.setName('MyJobName')
#j.setAncestorDepth(1)
j.setInputSandbox(['/Users/stuart/dirac/workspace/DIRAC3/DIRAC/Interfaces/API/test/DV.opts','/Users/stuart/dirac/workspace/DIRAC3/DIRAC/Interfaces/API/test/DV2.opts'])
j.setOutputSandbox(['firstfile.txt','anotherfile.root'])
j.setInputData(['/lhcb/production/DC04/v2/DST/00000742_00003493_11.dst',
'/lhcb/production/DC04/v2/DST/00000742_00003493_10.dst'])
j.setOutputData(['my.dst','myfile.log'])
j.setDestination('LCG.CERN.ch')
j.setPlatform('LCG')
j.setSystemConfig('x86_64-slc5-gcc43-opt')
j.setSoftwareTags(['VO-lhcb-Brunel-v30r17','VO-lhcb-Boole-v12r10'])
#print j._toJDL()
#print j.printObj()
xml = j._toXML()
testFile = 'jobDescription.xml'
if os.path.exists(testFile):
os.remove(testFile)
xmlfile = open(testFile,'w')
xmlfile.write(xml)
xmlfile.close()
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Creating code for the workflow'
print j.createCode()
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Executing the workflow'
j.execute()
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Trying to run the same workflow from generated XML file'
workflow = fromXMLFile(testFile)
code = workflow.createCode()
print code
workflow.execute()
def setInputData(self,
lfns,
bkClient=None,
runNumber=None,
persistencyType=None):
""" Add the input data and the run number, if available
"""
if not lfns:
self.log.warn(
"no lfns passed in setInputData, was that intentional?")
return S_OK("Nothing to do")
res = Job.setInputData(self, lfns)
if not res['OK']:
return res
if not runNumber or not persistencyType:
if not bkClient:
bkClient = BookkeepingClient()
if not runNumber:
res = bkClient.getFileMetadata(lfns)
if not res['OK']:
return res
runNumbers = []
for fileMeta in res['Value']['Successful'].itervalues():
try:
if fileMeta['RunNumber'] not in runNumbers and fileMeta[
'RunNumber'] is not None:
runNumbers.append(fileMeta['RunNumber'])
except KeyError:
continue
if len(runNumbers) > 1:
runNumber = 'Multiple'
elif len(runNumbers) == 1:
runNumber = str(runNumbers[0])
else:
runNumber = 'Unknown'
if not persistencyType:
res = bkClient.getFileTypeVersion(lfns)
if not res['OK']:
return res
typeVersions = res['Value']
if not typeVersions:
self.log.verbose('The requested files do not exist in the BKK')
typeVersion = ''
else:
self.log.verbose('Found file types %s for LFNs: %s' %
(typeVersions.values(), typeVersions.keys()))
typeVersionsList = list(set(typeVersions.values()))
if len(typeVersionsList) == 1:
typeVersion = typeVersionsList[0]
else:
typeVersion = ''
self._addParameter(self.workflow, 'runNumber', 'JDL', runNumber,
'Input run number')
self._addParameter(self.workflow, 'persistency', 'String', typeVersion,
'Persistency type of the inputs')
return S_OK()
def main(dataset, chunksize, test):
'''
The DATASET argument is a list of paths to MC files on the grid. Like the output of
cta-prod3-dump-dataset for example. See also
https://forge.in2p3.fr/projects/cta_dirac/wiki/CTA-DIRAC_MC_PROD3_Status
Keep in mind that for some effing reason this needs to be executed within this weird 'dirac'
environment which comes with its own glibc, python and pip. I guess the real Mr. Dirac would turn in his grave.
'''
dirac = Dirac()
with open(dataset) as f:
simtel_files = f.readlines()
print('Analysing {}'.format(len(simtel_files)))
server_list = [
"TORINO-USER", "CYF-STORM-USER", "CYF-STORM-Disk", "M3PEC-Disk",
"OBSPM-Disk", "POLGRID-Disk", "FRASCATI-USER", "LAL-Disk",
"CIEMAT-Disk", "CIEMAT-USER", "CPPM-Disk", "LAL-USER", "CYFRONET-Disk",
"DESY-ZN-USER", "M3PEC-USER", "LPNHE-Disk", "LPNHE-USER", "LAPP-USER",
"LAPP-Disk"
]
desy_server = 'DESY-ZN-USER'
servers_with_miniconda = [
'LCG.IN2P3-CC.fr', 'LCG.DESY-ZEUTHEN.de', 'LCG.CNAF.it', 'LCG.GRIF.fr',
'LCG.CYFRONET.pl', 'LCG.Prague.cz', 'LCG.CIEMAT.es'
]
chunks = np.array_split(sorted(simtel_files),
int(len(simtel_files) / chunksize))
print('Got a total of {} chunks'.format(len(chunks)))
for c, simtel_filenames in tqdm(enumerate(
chunks[0:2])): # send just 2 jobs for now.
# convert chunk to a list of strings. becasue this dirac thing cant take numpy arrays
simtel_filenames = [
str(s).strip() for s in simtel_filenames if 'SCT' not in s
]
print('Starting processing for chunk {}'.format(c))
print(simtel_filenames)
j = Job()
# set runtime to 0.5h
j.setCPUTime(30 * 60)
j.setName('cta_preprocessing_{}'.format(c))
j.setInputData(simtel_filenames)
j.setOutputData(['./processing_output/*.hdf5'],
outputSE=None,
outputPath='cta_preprocessing/')
j.setInputSandbox(
['../process_simtel.py', './install_dependencies.py'])
j.setOutputSandbox(['cta_preprocessing.log'])
j.setExecutable('./job_script.sh')
# These servers seem to have mini conda installed
# destination = np.random.choice(servers_with_miniconda)
j.setDestination(servers_with_miniconda)
value = dirac.submit(j)
print('Number {} Submission Result: {}'.format(c, value))
print("Input data list:\n")
print(inputdata_list)
for i in range(0, total_pixels, chunks):
id_start = i
id_end = i + chunks
dirac = Dirac()
j.setName('CS Faraday Rotation Measurement Reconstruction - Pixels from ' +
str(id_start) + ' to ' + str(id_end - 1))
j.setPlatform('EL7')
j.setTag([str(nprocs) + 'Processors'])
j.setDestination(SitesList)
j.setExecutable('RMSynthesis2.sh',
arguments=str(nprocs) + ' ' + str(id_start) + ' ' +
str(id_end) + ' ' + str(expmnt))
# Input data
j.setInputData(inputdata_list)
j.setInputSandbox([
'RMSynthesis2.sh', 'run2.sh',
'prmon_1.0.1_x86_64-static-gnu72-opt.tar.gz'
])
# Output data
j.setOutputSandbox([
'StdOut', 'StdErr',
'outputtxt_' + str(id_start) + '_' + str(id_end - 1) + '.txt',
'prmon' + str(id_start) + '_' + str(id_end - 1) + '.txt'
])
o_data_file = lfn + 'second/results_experiment_' + str(
expmnt) + '/' + 'LOS_' + str(id_start) + '_to_' + str(id_end -
1) + '.npy'
try:
output_process = subprocess.check_output('dirac-dms-remove-files ' +
dexit(1)
sites = result[ 'Value' ]
j.setDestination(sites)
if not opts.stagein is None:
input_stage_files = []
# we do add. input staging
files = opts.stagein.split(",")
for f in files:
if f.startswith("LFN"):
input_stage_files.append(f)
else:
input_stage_files+=extract_file(f)
for f in input_stage_files:
if not f.startswith("LFN"):
gLogger.error("*ERROR* required inputfiles to be defined through LFN, could not find LFN in %s"%f)
dexit(1)
j.setInputData(input_stage_files)
if opts.debug:
gLogger.notice('*DEBUG* just showing the JDL of the job to be submitted')
gLogger.notice(j._toJDL())
d = Dirac(True,"myRepo.rep")
res = d.submit(j)
if not res['OK']:
gLogger.error("Error during Job Submission ",res['Message'])
dexit(1)
JobID = res['Value']
gLogger.notice("Your job %s (\"%s\") has been submitted."%(str(JobID),executable))
def main():
"""
Launch job on the GRID
"""
# this thing pilots everything related to the GRID
dirac = Dirac()
if switches["output_type"] in "TRAINING":
print("Preparing submission for TRAINING data")
elif switches["output_type"] in "DL2":
print("Preparing submission for DL2 data")
else:
print("You have to choose either TRAINING or DL2 as output type!")
sys.exit()
# Read configuration file
cfg = load_config(switches["config_file"])
# Analysis
config_path = cfg["General"]["config_path"]
config_file = cfg["General"]["config_file"]
mode = cfg["General"]["mode"] # One mode naw
particle = cfg["General"]["particle"]
estimate_energy = cfg["General"]["estimate_energy"]
force_tailcut_for_extended_cleaning = cfg["General"][
"force_tailcut_for_extended_cleaning"]
# Take parameters from the analysis configuration file
ana_cfg = load_config(os.path.join(config_path, config_file))
config_name = ana_cfg["General"]["config_name"]
cam_id_list = ana_cfg["General"]["cam_id_list"]
# Regressor and classifier methods
regressor_method = ana_cfg["EnergyRegressor"]["method_name"]
classifier_method = ana_cfg["GammaHadronClassifier"]["method_name"]
# Someone might want to create DL2 without score or energy estimation
if regressor_method in ["None", "none", None]:
use_regressor = False
else:
use_regressor = True
if classifier_method in ["None", "none", None]:
use_classifier = False
else:
use_classifier = True
# GRID
outdir = os.path.join(cfg["GRID"]["outdir"], config_name)
n_file_per_job = cfg["GRID"]["n_file_per_job"]
n_jobs_max = cfg["GRID"]["n_jobs_max"]
model_dir = cfg["GRID"]["model_dir"]
training_dir_energy = cfg["GRID"]["training_dir_energy"]
training_dir_classification = cfg["GRID"]["training_dir_classification"]
dl2_dir = cfg["GRID"]["dl2_dir"]
home_grid = cfg["GRID"]["home_grid"]
user_name = cfg["GRID"]["user_name"]
banned_sites = cfg["GRID"]["banned_sites"]
# HACK
if force_tailcut_for_extended_cleaning is True:
print("Force tail cuts for extended cleaning!!!")
# Prepare command to launch script
source_ctapipe = "source /cvmfs/cta.in2p3.fr/software/conda/dev/setupConda.sh"
source_ctapipe += " && conda activate ctapipe_v0.11.0"
if switches["output_type"] in "TRAINING":
execute = "data_training.py"
script_args = [
"--config_file={}".format(config_file),
"--estimate_energy={}".format(str(estimate_energy)),
"--regressor_config={}.yaml".format(regressor_method),
"--regressor_dir=./",
"--outfile {outfile}",
"--indir ./ --infile_list={infile_name}",
"--max_events={}".format(switches["max_events"]),
"--{mode}",
"--cam_ids {}".format(cam_id_list),
]
output_filename_template = "TRAINING"
elif switches["output_type"] in "DL2":
execute = "write_dl2.py"
script_args = [
"--config_file={}".format(config_file),
"--regressor_config={}.yaml".format(regressor_method),
"--regressor_dir=./",
"--classifier_config={}.yaml".format(classifier_method),
"--classifier_dir=./",
"--outfile {outfile}",
"--indir ./ --infile_list={infile_name}",
"--max_events={}".format(switches["max_events"]),
"--{mode}",
"--force_tailcut_for_extended_cleaning={}".format(
force_tailcut_for_extended_cleaning),
"--cam_ids {}".format(cam_id_list),
]
output_filename_template = "DL2"
# Make the script save also the full calibrated images if required
if switches["save_images"] is True:
script_args.append("--save_images")
# Make the script print debug information if required
if switches["debug_script"] is True:
script_args.append("--debug")
cmd = [source_ctapipe, "&&", "./" + execute]
cmd += script_args
pilot_args_write = " ".join(cmd)
# For table merging if multiple runs
pilot_args_merge = " ".join([
source_ctapipe,
"&&",
"./merge_tables.py",
"--template_file_name",
"{in_name}",
"--outfile",
"{out_name}",
])
prod3b_filelist = dict()
if estimate_energy is False and switches["output_type"] in "TRAINING":
prod3b_filelist["gamma"] = cfg["EnergyRegressor"]["gamma_list"]
elif estimate_energy is True and switches["output_type"] in "TRAINING":
prod3b_filelist["gamma"] = cfg["GammaHadronClassifier"]["gamma_list"]
prod3b_filelist["proton"] = cfg["GammaHadronClassifier"]["proton_list"]
elif switches["output_type"] in "DL2":
prod3b_filelist["gamma"] = cfg["Performance"]["gamma_list"]
prod3b_filelist["proton"] = cfg["Performance"]["proton_list"]
prod3b_filelist["electron"] = cfg["Performance"]["electron_list"]
# from IPython import embed
# embed()
# Split list of files according to stoprage elements
with open(prod3b_filelist[particle]) as f:
filelist = f.readlines()
filelist = ["{}".format(_.replace("\n", "")) for _ in filelist]
res = dirac.splitInputData(filelist, n_file_per_job)
list_run_to_loop_on = res["Value"]
# define a template name for the file that's going to be written out.
# the placeholder braces are going to get set during the file-loop
output_filename = output_filename_template
output_path = outdir
if estimate_energy is False and switches["output_type"] in "TRAINING":
output_path += "/{}/".format(training_dir_energy)
step = "energy"
if estimate_energy is True and switches["output_type"] in "TRAINING":
output_path += "/{}/".format(training_dir_classification)
step = "classification"
if switches["output_type"] in "DL2":
if force_tailcut_for_extended_cleaning is False:
output_path += "/{}/".format(dl2_dir)
else:
output_path += "/{}_force_tc_extended_cleaning/".format(dl2_dir)
step = ""
output_filename += "_{}.h5"
# sets all the local files that are going to be uploaded with the job
# plus the pickled classifier
# if file name starts with `LFN:`, it will be copied from the GRID
input_sandbox = [
# Utility to assign one job to one command...
os.path.expandvars("$GRID/pilot.sh"),
os.path.expandvars("$PROTOPIPE/protopipe/"),
os.path.expandvars("$GRID/merge_tables.py"),
# python wrapper for the mr_filter wavelet cleaning
# os.path.expandvars("$PYWI/pywi/"),
# os.path.expandvars("$PYWICTA/pywicta/"),
# script that is being run
os.path.expandvars("$PROTOPIPE/protopipe/scripts/" + execute),
# Configuration file
os.path.expandvars(os.path.join(config_path, config_file)),
]
models_to_upload = []
configs_to_upload = []
if estimate_energy is True and switches["output_type"] in "TRAINING":
config_path_template = "LFN:" + os.path.join(home_grid, outdir,
model_dir, "{}.yaml")
config_to_upload = config_path_template.format(regressor_method)
model_path_template = "LFN:" + os.path.join(
home_grid, outdir, model_dir, "regressor_{}_{}.pkl.gz")
for cam_id in cam_id_list:
model_to_upload = model_path_template.format(
cam_id, regressor_method) # TBC
print("The following model(s) will be uploaded to the GRID:")
print(model_to_upload)
models_to_upload.append(model_to_upload)
print(
"The following configs(s) for such models will be uploaded to the GRID:"
)
print(config_to_upload)
configs_to_upload.append(config_to_upload)
# input_sandbox.append(model_to_upload)
elif estimate_energy is False and switches["output_type"] in "TRAINING":
pass
else: # Charge also classifer for DL2
model_type_list = ["regressor", "classifier"]
model_method_list = [regressor_method, classifier_method]
config_path_template = "LFN:" + os.path.join(home_grid, outdir,
model_dir, "{}.yaml")
model_path_template = "LFN:" + os.path.join(
home_grid, outdir, model_dir, "{}_{}_{}.pkl.gz")
if force_tailcut_for_extended_cleaning is True:
force_mode = mode.replace("wave", "tail")
print("################")
print(force_mode)
else:
force_mode = mode
for idx, model_type in enumerate(model_type_list):
print(
"The following configuration file will be uploaded to the GRID:"
)
config_to_upload = config_path_template.format(
model_method_list[idx])
print(config_to_upload)
configs_to_upload.append(config_to_upload) # upload only 1 copy
print(
"The following model(s) related to such configuration file will be uploaded to the GRID:"
)
for cam_id in cam_id_list:
if model_type in "regressor" and use_regressor is False:
print("Do not upload regressor model on GRID!!!")
continue
if model_type in "classifier" and use_classifier is False:
print("Do not upload classifier model on GRID!!!")
continue
model_to_upload = model_path_template.format(
model_type_list[idx], cam_id, model_method_list[idx])
print(model_to_upload)
models_to_upload.append(model_to_upload)
# input_sandbox.append(model_to_upload)
# summary before submitting
print("\nDEBUG> running as:")
print(pilot_args_write)
print("\nDEBUG> with input_sandbox:")
print(input_sandbox)
print("\nDEBUG> with output file:")
print(output_filename.format("{job_name}"))
print("\nDEBUG> Particles:")
print(particle)
print("\nDEBUG> Energy estimation:")
print(estimate_energy)
# ######## ## ## ## ## ## ## #### ## ## ######
# ## ## ## ## ### ## ### ## ## ### ## ## ##
# ## ## ## ## #### ## #### ## ## #### ## ##
# ######## ## ## ## ## ## ## ## ## ## ## ## ## ## ####
# ## ## ## ## ## #### ## #### ## ## #### ## ##
# ## ## ## ## ## ### ## ### ## ## ### ## ##
# ## ## ####### ## ## ## ## #### ## ## ######
# list of files on the GRID SE space
# not submitting jobs where we already have the output
batcmd = "dirac-dms-user-lfns --BaseDir {}".format(
os.path.join(home_grid, output_path))
result = subprocess.check_output(batcmd, shell=True)
try:
grid_filelist = open(result.split()[-1]).read()
except IOError:
raise IOError("ERROR> cannot read GRID filelist...")
# get jobs from today and yesterday...
days = []
for i in range(2): # how many days do you want to look back?
days.append(
(datetime.date.today() - datetime.timedelta(days=i)).isoformat())
# get list of run_tokens that are currently running / waiting
running_ids = set()
running_names = []
for status in ["Waiting", "Running", "Checking"]:
for day in days:
try:
[
running_ids.add(id) for id in dirac.selectJobs(
status=status, date=day, owner=user_name)["Value"]
]
except KeyError:
pass
n_jobs = len(running_ids)
if n_jobs > 0:
print("Scanning {} running/waiting jobs... please wait...".format(
n_jobs))
for i, id in enumerate(running_ids):
if ((100 * i) / n_jobs) % 5 == 0:
print("\r{} %".format(((20 * i) / n_jobs) * 5)),
jobname = dirac.getJobAttributes(id)["Value"]["JobName"]
running_names.append(jobname)
else:
print("\n... done")
for bunch in list_run_to_loop_on:
# for bunch in bunches_of_run:
# from IPython import embed
# embed()
# this selects the `runxxx` part of the first and last file in the run
# list and joins them with a dash so that we get a nice identifier in
# the output file name.
# if there is only one file in the list, use only that one
# run_token = re.split('_', bunch[+0])[3] # JLK JLK
run_token = re.split("_", bunch[0])[3]
if len(bunch) > 1:
run_token = "-".join([run_token, re.split("_", bunch[-1])[3]])
print("-" * 50)
print("-" * 50)
# setting output name
output_filenames = dict()
if switches["output_type"] in "DL2":
job_name = "protopipe_{}_{}_{}_{}_{}".format(
config_name, switches["output_type"], particle, run_token,
mode)
output_filenames[mode] = output_filename.format("_".join(
[particle, mode, run_token]))
else:
job_name = "protopipe_{}_{}_{}_{}_{}_{}".format(
config_name, switches["output_type"], step, particle,
run_token, mode)
output_filenames[mode] = output_filename.format("_".join(
[step, particle, mode, run_token]))
# if job already running / waiting, skip
if job_name in running_names:
print("\n WARNING> {} still running\n".format(job_name))
continue
print("Output file name: {}".format(output_filenames[mode]))
# if file already in GRID storage, skip
# (you cannot overwrite it there, delete it and resubmit)
# (assumes tail and wave will always be written out together)
already_exist = False
file_on_grid = os.path.join(output_path, output_filenames[mode])
print("DEBUG> check for existing file on GRID...")
if file_on_grid in grid_filelist:
print("\n WARNING> {} already on GRID SE\n".format(job_name))
continue
if n_jobs_max == 0:
print("WARNING> maximum number of jobs to submit reached")
print("WARNING> breaking loop now")
break
else:
n_jobs_max -= 1
j = Job()
# runtime in seconds times 8 (CPU normalisation factor)
j.setCPUTime(6 * 3600 * 8)
j.setName(job_name)
j.setInputSandbox(input_sandbox)
if banned_sites:
j.setBannedSites(banned_sites)
# Add simtel files as input data
j.setInputData(bunch)
for run_file in bunch:
file_token = re.split("_", run_file)[3]
# wait for a random number of seconds (up to five minutes) before
# starting to add a bit more entropy in the starting times of the
# dirac queries.
# if too many jobs try in parallel to access the SEs,
# the interface crashes
# #sleep = random.randint(0, 20) # seconds
# #j.setExecutable('sleep', str(sleep))
# JLK: Try to stop doing that
# consecutively downloads the data files, processes them,
# deletes the input
# and goes on to the next input file;
# afterwards, the output files are merged
# j.setExecutable('dirac-dms-get-file', "LFN:" + run_file)
# source the miniconda ctapipe environment and
# run the python script with all its arguments
if switches["output_type"] in "DL2":
output_filename_temp = output_filename.format("_".join(
[particle, mode, file_token]))
if switches["output_type"] in "TRAINING":
output_filename_temp = output_filename.format("_".join(
[step, particle, mode, file_token]))
j.setExecutable(
"./pilot.sh",
pilot_args_write.format(
outfile=output_filename_temp,
infile_name=os.path.basename(run_file),
mode=mode,
),
)
# remove the current file to clear space
j.setExecutable("rm", os.path.basename(run_file))
# simple `ls` for good measure
j.setExecutable("ls", "-lh")
# if there is more than one file per job, merge the output tables
if len(bunch) > 1:
names = []
names.append(("*_{}_".format(particle), output_filenames[mode]))
for in_name, out_name in names:
print("in_name: {}, out_name: {}".format(in_name, out_name))
j.setExecutable(
"./pilot.sh",
pilot_args_merge.format(in_name=in_name,
out_name=out_name),
)
print("DEBUG> args append: {}".format(
pilot_args_merge.format(in_name=in_name,
out_name=out_name)))
bunch.extend(models_to_upload)
bunch.extend(configs_to_upload)
j.setInputData(bunch)
print("Input data set to job = {}".format(bunch))
outputs = []
outputs.append(output_filenames[mode])
print("Output file path: {}{}".format(output_path,
output_filenames[mode]))
j.setOutputData(outputs, outputSE=None, outputPath=output_path)
# check if we should somehow stop doing what we are doing
if switches["dry"] is True:
print("\nThis is a DRY RUN! -- NO job has been submitted!")
print("Name of the job: {}".format(job_name))
print("Name of the output file: {}".format(outputs))
print("Output path from GRID home: {}".format(output_path))
break
# this sends the job to the GRID and uploads all the
# files into the input sandbox in the process
print("\nSUBMITTING job with the following INPUT SANDBOX:")
print(input_sandbox)
print("Submission RESULT: {}\n".format(dirac.submitJob(j)["Value"]))
# break if this is only a test submission
if switches["test"] is True:
print("This is a TEST RUN! -- Only ONE job will be submitted!")
print("Name of the job: {}".format(job_name))
print("Name of the output file: {}".format(outputs))
print("Output path from GRID home: {}".format(output_path))
break
# since there are two nested loops, need to break again
if switches["test"] is True:
break
try:
os.remove("datapipe.tar.gz")
os.remove("modules.tar.gz")
except:
pass
# Upload analysis configuration file for provenance
SE_LIST = ['CC-IN2P3-USER', 'DESY-ZN-USER', 'CNAF-USER', 'CEA-USER']
analysis_config_local = os.path.join(config_path, config_file)
# the configuration file is uploaded to the data directory because
# the training samples (as well as their cleaning settings) are independent
analysis_config_dirac = os.path.join(home_grid, output_path, config_file)
print("Uploading {} to {}...".format(analysis_config_local,
analysis_config_dirac))
if switches["dry"] is False:
# Upload this file to all Dirac Storage Elements in SE_LIST
for se in SE_LIST:
# the uploaded config file overwrites any old copy
ana_cfg_upload_cmd = "dirac-dms-add-file -f {} {} {}".format(
analysis_config_dirac, analysis_config_local, se)
ana_cfg_upload_result = subprocess.check_output(ana_cfg_upload_cmd,
shell=True)
print(ana_cfg_upload_result)
else:
print("This is a DRY RUN! -- analysis.yaml has NOT been uploaded.")
print("\nall done -- exiting now")
exit()