def getWorkloadArguments():
baseArgs = DataProcessing.getWorkloadArguments()
specArgs = {"RequestType": {"default": "MonteCarloFromGEN", "optional": True,
"attr": "requestType"},
"PrimaryDataset": {"default": None, "type": str,
"optional": True, "validate": primdataset,
"attr": "inputPrimaryDataset", "null": True},
"ConfigCacheUrl": {"default": None, "type": str,
"optional": True, "validate": None,
"attr": "configCacheUrl", "null": False},
"ConfigCacheID": {"default": None, "type": str,
"optional": False, "validate": None,
"attr": "configCacheID", "null": True},
"MCPileup": {"default": None, "type": str,
"optional": True, "validate": dataset,
"attr": "mcPileup", "null": True},
"DataPileup": {"default": None, "type": str,
"optional": True, "validate": dataset,
"attr": "dataPileup", "null": True},
"DeterministicPileup": {"default": False, "type": strToBool,
"optional": True, "validate": None,
"attr": "deterministicPileup", "null": False}}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def __call__(self, workloadName, arguments):
"""
_call_
Create a ReDigi workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# Transform the pileup as required by the CMSSW step
self.pileupConfig = parsePileupConfig(self.mcPileup, self.dataPileup)
# Adjust the pileup splitting
self.procJobSplitArgs.setdefault("deterministicPileup", self.deterministicPileup)
# Adjust the sizePerEvent, timePerEvent and memory for step two and three
if self.stepTwoTimePerEvent is None:
self.stepTwoTimePerEvent = self.timePerEvent
if self.stepTwoSizePerEvent is None:
self.stepTwoSizePerEvent = self.sizePerEvent
if self.stepTwoMemory is None:
self.stepTwoMemory = self.memory
if self.stepThreeTimePerEvent is None:
self.stepThreeTimePerEvent = self.timePerEvent
if self.stepThreeSizePerEvent is None:
self.stepThreeSizePerEvent = self.sizePerEvent
if self.stepThreeMemory is None:
self.stepThreeMemory = self.memory
return self.buildWorkload()
def __call__(self, workloadName, arguments):
"""
_call_
Create a DQMHarvest workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
self.workload = self.createWorkload()
self.workload.setDashboardActivity("harvesting")
splitArgs = {"runs_per_job": 1}
if self.dqmHarvestUnit == "multiRun":
# then it should result in a single job in the end, very high number of runs
splitArgs['runs_per_job'] = 999999
self.workload.setWorkQueueSplitPolicy("Dataset", "Harvest", splitArgs)
# also creates the logCollect job by default
self.addDQMHarvestTask(uploadProxy=self.dqmUploadProxy,
periodic_harvest_interval=self.periodicHarvestInterval,
dqmHarvestUnit=self.dqmHarvestUnit)
# setting the parameters which need to be set for all the tasks
# sets acquisitionEra, processingVersion, processingString
self.workload.setTaskPropertiesFromWorkload()
self.reportWorkflowToDashboard(self.workload.getDashboardActivity())
return self.workload
def __call__(self, workloadName, arguments):
"""
_call_
Create a ReDigi workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# Transform the pileup as required by the CMSSW step
self.pileupConfig = parsePileupConfig(self.mcPileup, self.dataPileup)
# Adjust the pileup splitting
self.procJobSplitArgs.setdefault("deterministicPileup", self.deterministicPileup)
# Adjust the sizePerEvent, timePerEvent and memory for step two and three
if self.stepTwoTimePerEvent is None:
self.stepTwoTimePerEvent = self.timePerEvent
if self.stepTwoSizePerEvent is None:
self.stepTwoSizePerEvent = self.sizePerEvent
if self.stepTwoMemory is None:
self.stepTwoMemory = self.memory
if self.stepThreeTimePerEvent is None:
self.stepThreeTimePerEvent = self.timePerEvent
if self.stepThreeSizePerEvent is None:
self.stepThreeSizePerEvent = self.sizePerEvent
if self.stepThreeMemory is None:
self.stepThreeMemory = self.memory
return self.buildWorkload()
def getWorkloadArguments():
baseArgs = DataProcessing.getWorkloadArguments()
specArgs = {
"RequestType": {
"default": "MonteCarloFromGEN",
"optional": True,
"attr": "requestType"
},
"PrimaryDataset": {
"default": None,
"type": str,
"optional": True,
"validate": primdataset,
"attr": "inputPrimaryDataset",
"null": False
},
"ConfigCacheUrl": {
"default": None,
"type": str,
"optional": True,
"validate": None,
"attr": "configCacheUrl",
"null": False
},
"ConfigCacheID": {
"default": None,
"type": str,
"optional": False,
"validate": None,
"attr": "configCacheID",
"null": True
},
"MCPileup": {
"default": None,
"type": str,
"optional": True,
"validate": dataset,
"attr": "mcPileup",
"null": True
},
"DataPileup": {
"default": None,
"type": str,
"optional": True,
"validate": dataset,
"attr": "dataPileup",
"null": True
},
"DeterministicPileup": {
"default": False,
"type": strToBool,
"optional": True,
"validate": None,
"attr": "deterministicPileup",
"null": False
}
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def __call__(self, workloadName, arguments):
"""
_call_
Create a ReReco workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# Arrange the skims in a skimConfig object (i.e. a list of skim configurations)
self.skimConfigs = []
skimIndex = 1
while "SkimName%s" % skimIndex in arguments:
skimConfig = {}
skimConfig["SkimName"] = arguments["SkimName%s" % skimIndex]
skimConfig["SkimInput"] = arguments["SkimInput%s" % skimIndex]
skimConfig["ConfigCacheID"] = arguments["Skim%sConfigCacheID" % skimIndex]
skimConfig["TimePerEvent"] = float(arguments.get("SkimTimePerEvent%s" % skimIndex, self.timePerEvent))
skimConfig["SizePerEvent"] = float(arguments.get("SkimSizePerEvent%s" % skimIndex, self.sizePerEvent))
skimConfig["Memory"] = float(arguments.get("SkimMemory%s" % skimIndex, self.memory))
skimConfig["SkimJobSplitAlgo"] = arguments.get("SkimSplittingAlgo%s" % skimIndex, "FileBased")
skimConfig["SkimJobSplitArgs"] = {"include_parents" : True}
if skimConfig["SkimJobSplitAlgo"] == "FileBased":
skimConfig["SkimJobSplitArgs"]["files_per_job"] = int(arguments.get("SkimFilesPerJob%s" % skimIndex, 1))
elif skimConfig["SkimJobSplitAlgo"] == "EventBased" or skimConfig["SkimJobSplitAlgo"] == "EventAwareLumiBased":
skimConfig["SkimJobSplitArgs"]["events_per_job"] = int(arguments.get("SkimEventsPerJob%s" % skimIndex, int((8.0 * 3600.0) / skimConfig["TimePerEvent"])))
if skimConfig["SkimJobSplitAlgo"] == "EventAwareLumiBased":
skimConfig["SkimJobSplitAlgo"]["max_events_per_lumi"] = 20000
elif skimConfig["SkimJobSplitAlgo"] == "LumiBased":
skimConfig["SkimJobSplitArgs"["lumis_per_job"]] = int(arguments.get("SkimLumisPerJob%s" % skimIndex, 8))
self.skimConfigs.append(skimConfig)
skimIndex += 1
return self.buildWorkload()
def __call__(self, workloadName, arguments):
"""
_call_
Create a DQMHarvest workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
self.workload = self.createWorkload()
self.workload.setDashboardActivity("harvesting")
splitArgs = {"runs_per_job": 1}
if self.dqmHarvestUnit == "multiRun":
# then it should result in a single job in the end, very high number of runs
splitArgs['runs_per_job'] = 999999
self.workload.setWorkQueueSplitPolicy("Dataset", "Harvest", splitArgs)
# also creates the logCollect job by default
self.addDQMHarvestTask(
uploadProxy=self.dqmUploadProxy,
periodic_harvest_interval=self.periodicHarvestInterval,
dqmHarvestUnit=self.dqmHarvestUnit)
# setting the parameters which need to be set for all the tasks
# sets acquisitionEra, processingVersion, processingString
self.workload.setTaskPropertiesFromWorkload()
self.reportWorkflowToDashboard(self.workload.getDashboardActivity())
return self.workload
def getWorkloadCreateArgs():
baseArgs = DataProcessing.getWorkloadCreateArgs()
specArgs = {"RequestType": {"default": "ReDigi", "optional": False},
"StepOneOutputModuleName": {"null": True},
"StepTwoOutputModuleName": {"null": True},
"ConfigCacheID": {"optional": True, "null": True},
"StepOneConfigCacheID": {"optional": False, "null": True},
"StepTwoConfigCacheID": {"null": True},
"StepThreeConfigCacheID": {"null": True},
"KeepStepOneOutput": {"default": True, "type": strToBool, "null": False},
"KeepStepTwoOutput": {"default": True, "type": strToBool, "null": False},
"StepTwoTimePerEvent": {"type": float, "null": True,
"validate": lambda x: x > 0},
"StepThreeTimePerEvent": {"type": float, "null": True,
"validate": lambda x: x > 0},
"StepTwoSizePerEvent": {"type": float, "null": True,
"validate": lambda x: x > 0},
"StepThreeSizePerEvent": {"type": float, "null": True,
"validate": lambda x: x > 0},
"StepTwoMemory": {"type": float, "null": True,
"validate": lambda x: x > 0},
"StepThreeMemory": {"type": float, "null": True,
"validate": lambda x: x > 0},
"MCPileup": {"validate": dataset, "attr": "mcPileup", "null": True},
"DataPileup": {"null": True, "validate": dataset},
"DeterministicPileup": {"default": False, "type": strToBool, "null": False}}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def __call__(self, workloadName, arguments):
"""
_call_
Create a ReReco workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# Arrange the skims in a skimConfig object (i.e. a list of skim configurations)
self.skimConfigs = []
skimIndex = 1
while "SkimName%s" % skimIndex in arguments:
skimConfig = {}
skimConfig["SkimName"] = arguments["SkimName%s" % skimIndex]
skimConfig["SkimInput"] = arguments["SkimInput%s" % skimIndex]
skimConfig["ConfigCacheID"] = arguments["Skim%sConfigCacheID" % skimIndex]
skimConfig["TimePerEvent"] = float(arguments.get("SkimTimePerEvent%s" % skimIndex, self.timePerEvent))
skimConfig["SizePerEvent"] = float(arguments.get("SkimSizePerEvent%s" % skimIndex, self.sizePerEvent))
skimConfig["Memory"] = float(arguments.get("SkimMemory%s" % skimIndex, self.memory))
skimConfig["SkimJobSplitAlgo"] = arguments.get("SkimSplittingAlgo%s" % skimIndex, "FileBased")
skimConfig["SkimJobSplitArgs"] = {"include_parents" : True}
if skimConfig["SkimJobSplitAlgo"] == "FileBased":
skimConfig["SkimJobSplitArgs"]["files_per_job"] = int(arguments.get("SkimFilesPerJob%s" % skimIndex, 1))
elif skimConfig["SkimJobSplitAlgo"] == "EventBased" or skimConfig["SkimJobSplitAlgo"] == "EventAwareLumiBased":
skimConfig["SkimJobSplitArgs"]["events_per_job"] = int(arguments.get("SkimEventsPerJob%s" % skimIndex, int((8.0 * 3600.0) / skimConfig["TimePerEvent"])))
if skimConfig["SkimJobSplitAlgo"] == "EventAwareLumiBased":
skimConfig["SkimJobSplitAlgo"]["max_events_per_lumi"] = 20000
elif skimConfig["SkimJobSplitAlgo"] == "LumiBased":
skimConfig["SkimJobSplitArgs"["lumis_per_job"]] = int(arguments.get("SkimLumisPerJob%s" % skimIndex, 8))
self.skimConfigs.append(skimConfig)
skimIndex += 1
return self.buildWorkload()
def __call__(self, workloadName, arguments):
"""
_call_
Create a MonteCarloFromGEN workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
return self.buildWorkload()
def __call__(self, workloadName, arguments):
"""
_call_
Create a MonteCarloFromGEN workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
return self.buildWorkload()
def getWorkloadAssignArgs():
baseArgs = DataProcessing.getWorkloadAssignArgs()
specArgs = {
"Override": {"default": {"eos-lfn-prefix": "root://eoscms.cern.ch//eos/cms/store/logs/prod/recent/PromptReco"},
"type": dict},
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def getWorkloadAssignArgs():
baseArgs = DataProcessing.getWorkloadAssignArgs()
specArgs = {
"Override": {"default": {"eos-lfn-prefix": "root://eoscms.cern.ch//eos/cms/store/logs/prod/recent/PromptReco"},
"type": dict},
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def getWorkloadArguments():
baseArgs = DataProcessing.getWorkloadArguments()
specArgs = {"RequestType" : {"default" : "ReDigi", "optional" : True,
"attr" : "requestType"},
"StepOneOutputModuleName" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepOneOutputModuleName", "null" : True},
"StepTwoOutputModuleName" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepTwoOutputModuleName", "null" : True},
"ConfigCacheID": {"default" : None, "optional": True, "null": True},
"StepOneConfigCacheID" : {"default" : None, "type" : str,
"optional" : False, "validate" : None,
"attr" : "stepOneConfigCacheID", "null" : True},
"StepTwoConfigCacheID" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepTwoConfigCacheID", "null" : True},
"StepThreeConfigCacheID" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepThreeConfigCacheID", "null" : True},
"KeepStepOneOutput" : {"default" : True, "type" : strToBool,
"optional" : True, "validate" : None,
"attr" : "keepStepOneOutput", "null" : False},
"KeepStepTwoOutput" : {"default" : True, "type" : strToBool,
"optional" : True, "validate" : None,
"attr" : "keepStepTwoOutput", "null" : False},
"StepTwoTimePerEvent" : {"default" : 1, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepTwoTimePerEvent", "null" : False},
"StepThreeTimePerEvent" : {"default" : 1, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepThreeTimePerEvent", "null" : False},
"StepTwoSizePerEvent" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepTwoSizePerEvent", "null" : True},
"StepThreeSizePerEvent" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepThreeSizePerEvent", "null" : True},
"StepTwoMemory" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepTwoMemory", "null" : True},
"StepThreeMemory" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepThreeMemory", "null" : True},
"MCPileup" : {"default" : None, "type" : str,
"optional" : True, "validate" : dataset,
"attr" : "mcPileup", "null" : True},
"DataPileup" : {"default" : None, "type" : str,
"optional" : True, "validate" : dataset,
"attr" : "dataPileup", "null" : True},
"DeterministicPileup" : {"default" : False, "type" : strToBool,
"optional" : True, "validate" : None,
"attr" : "deterministicPileup", "null" : False}}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def getWorkloadCreateArgs():
baseArgs = DataProcessing.getWorkloadCreateArgs()
specArgs = {"RequestType": {"default": "DQMHarvest", "optional": True},
"ConfigCacheID": {"optional": True, "null": True},
"DQMConfigCacheID": {"optional": False, "attr": "dqmConfigCacheID"},
"DQMUploadUrl": {"optional": False, "attr": "dqmUploadUrl"},
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def getWorkloadCreateArgs():
baseArgs = DataProcessing.getWorkloadCreateArgs()
specArgs = {"RequestType" : {"default" : "ReReco", "optional" : False},
"TransientOutputModules" : {"default" : [], "type" : makeList,
"attr" : "transientModules", "null" : False}
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def validateSchema(self, schema):
"""
_validateSchema_
Standard DataProcessing schema validation.
"""
DataProcessing.validateSchema(self, schema)
self.validateConfigCacheExists(configID=schema["DQMConfigCacheID"],
configCacheUrl=schema['ConfigCacheUrl'],
couchDBName=schema["CouchDBName"],
getOutputModules=False)
def validateSchema(self, schema):
"""
_validateSchema_
Standard DataProcessing schema validation.
"""
DataProcessing.validateSchema(self, schema)
self.validateConfigCacheExists(configID=schema["DQMConfigCacheID"],
configCacheUrl=schema['ConfigCacheUrl'],
couchDBName=schema["CouchDBName"],
getOutputModules=False)
def validateSchema(self, schema):
"""
_validateSchema_
Check for required fields, and some skim facts
"""
DataProcessing.validateSchema(self, schema)
mainOutputModules = list(
self.validateConfigCacheExists(
configID=schema["ConfigCacheID"],
configCacheUrl=schema['ConfigCacheUrl'],
couchDBName=schema["CouchDBName"],
getOutputModules=True))
# Skim facts have to be validated outside the usual master validation
skimSchema = {
k: v
for (k, v) in viewitems(schema) if k.startswith("Skim")
}
skimArguments = self.getSkimArguments()
skimIndex = 1
skimInputs = set()
while "SkimName%s" % skimIndex in schema:
instanceArguments = {}
for argument in skimArguments:
realArg = argument.replace("#N", str(skimIndex))
instanceArguments[realArg] = skimArguments[argument]
try:
validateArgumentsCreate(skimSchema, instanceArguments)
except Exception as ex:
self.raiseValidationException(str(ex))
self.validateConfigCacheExists(
configID=schema["Skim%sConfigCacheID" % skimIndex],
configCacheUrl=schema['ConfigCacheUrl'],
couchDBName=schema["CouchDBName"],
getOutputModules=False)
if schema["SkimInput%s" % skimIndex] not in mainOutputModules:
error = "Processing config does not have the following output module: %s." % schema[
"SkimInput%s" % skimIndex]
self.raiseValidationException(msg=error)
skimInputs.add(schema["SkimInput%s" % skimIndex])
skimIndex += 1
# Validate that the transient output modules are used in a skim task
if "TransientOutputModules" in schema:
diffSet = set(schema["TransientOutputModules"]) - skimInputs
if diffSet:
self.raiseValidationException(
msg=
"A transient output module was specified but no skim was defined for it"
)
def validateSchema(self, schema):
"""
_validateSchema_
Standard StdBase schema validation, plus verification
of the ConfigCacheID
"""
DataProcessing.validateSchema(self, schema)
couchUrl = schema.get("ConfigCacheUrl", None) or schema["CouchURL"]
self.validateConfigCacheExists(configID=schema["ConfigCacheID"],
couchURL=couchUrl,
couchDBName=schema["CouchDBName"])
return
def validateSchema(self, schema):
"""
_validateSchema_
Standard StdBase schema validation, plus verification
of the ConfigCacheID
"""
DataProcessing.validateSchema(self, schema)
couchUrl = schema.get("ConfigCacheUrl", None) or schema["CouchURL"]
self.validateConfigCacheExists(configID=schema["ConfigCacheID"],
couchURL=couchUrl,
couchDBName=schema["CouchDBName"])
return
def __call__(self, workloadName, arguments):
"""
_call_
Create a MonteCarloFromGEN workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# Transform the pileup as required by the CMSSW step
self.pileupConfig = parsePileupConfig(self.mcPileup, self.dataPileup)
# Adjust the pileup splitting
self.procJobSplitArgs.setdefault("deterministicPileup", self.deterministicPileup)
return self.buildWorkload()
def __call__(self, workloadName, arguments):
"""
_call_
Create a MonteCarloFromGEN workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# Transform the pileup as required by the CMSSW step
self.pileupConfig = parsePileupConfig(self.mcPileup, self.dataPileup)
# Adjust the pileup splitting
self.procJobSplitArgs.setdefault("deterministicPileup", self.deterministicPileup)
return self.buildWorkload()
def __init__(self):
"""
__init__
Setup parameters that will be later overwritten in the call,
otherwise pylint will complain about them.
"""
DataProcessing.__init__(self)
self.stepTwoMemory = None
self.stepTwoSizePerEvent = None
self.stepTwoTimePerEvent = None
self.stepThreeMemory = None
self.stepThreeSizePerEvent = None
self.stepThreeTimePerEvent = None
def __init__(self):
"""
__init__
Setup parameters that will be later overwritten in the call,
otherwise pylint will complain about them.
"""
DataProcessing.__init__(self)
self.stepTwoMemory = None
self.stepTwoSizePerEvent = None
self.stepTwoTimePerEvent = None
self.stepThreeMemory = None
self.stepThreeSizePerEvent = None
self.stepThreeTimePerEvent = None
def validateSchema(self, schema):
"""
_validateSchema_
Standard StdBase schema validation, plus verification
of the ConfigCacheID
"""
DataProcessing.validateSchema(self, schema)
self.validateConfigCacheExists(configID=schema["ConfigCacheID"],
configCacheUrl=schema['ConfigCacheUrl'],
couchDBName=schema["CouchDBName"],
getOutputModules=False)
return
def validateSchema(self, schema):
"""
_validateSchema_
Check for required fields, and some skim facts
"""
DataProcessing.validateSchema(self, schema)
couchUrl = schema.get("ConfigCacheUrl", None) or schema["CouchURL"]
mainOutputModules = self.validateConfigCacheExists(
configID=schema["ConfigCacheID"],
couchURL=couchUrl,
couchDBName=schema["CouchDBName"],
getOutputModules=True).keys()
# Skim facts have to be validated outside the usual master validation
skimArguments = self.getSkimArguments()
skimIndex = 1
skimInputs = set()
while "SkimName%s" % skimIndex in schema:
instanceArguments = {}
for argument in skimArguments.keys():
realArg = argument.replace("#N", str(skimIndex))
instanceArguments[realArg] = skimArguments[argument]
msg = validateArgumentsCreate(schema, instanceArguments)
if msg is not None:
self.raiseValidationException(msg)
self.validateConfigCacheExists(
configID=schema["Skim%sConfigCacheID" % skimIndex],
couchURL=couchUrl,
couchDBName=schema["CouchDBName"])
if schema["SkimInput%s" % skimIndex] not in mainOutputModules:
error = "Processing config does not have the following output module: %s." % schema[
"SkimInput%s" % skimIndex]
self.raiseValidationException(msg=error)
skimInputs.add(schema["SkimInput%s" % skimIndex])
skimIndex += 1
# Validate that the transient output modules are used in a skim task
if "TransientOutputModules" in schema:
diffSet = set(schema["TransientOutputModules"]) - skimInputs
if diffSet:
self.raiseValidationException(
msg=
"A transient output module was specified but no skim was defined for it"
)
def getWorkloadArguments():
baseArgs = DataProcessing.getWorkloadArguments()
specArgs = {"PrimaryDataset" : {"default" : None, "type" : str,
"optional" : True, "validate" : primdataset,
"attr" : "inputPrimaryDataset", "null" : False},
"ConfigCacheID" : {"default" : None, "type" : str,
"optional" : False, "validate" : None,
"attr" : "configCacheID", "null" : False}}
baseArgs.update(specArgs)
return baseArgs
def validateSchema(self, schema):
"""
_validateSchema_
Check for required fields, and some skim facts
"""
DataProcessing.validateSchema(self, schema)
mainOutputModules = self.validateConfigCacheExists(configID = schema["ConfigCacheID"],
configCacheUrl = schema['ConfigCacheUrl'],
couchDBName = schema["CouchDBName"],
getOutputModules = True).keys()
# Skim facts have to be validated outside the usual master validation
skimSchema = {k: v for (k, v) in schema.iteritems() if k.startswith("Skim")}
skimArguments = self.getSkimArguments()
skimIndex = 1
skimInputs = set()
while "SkimName%s" % skimIndex in schema:
instanceArguments = {}
for argument in skimArguments:
realArg = argument.replace("#N", str(skimIndex))
instanceArguments[realArg] = skimArguments[argument]
try:
validateArgumentsCreate(skimSchema, instanceArguments)
except Exception as ex:
self.raiseValidationException(str(ex))
self.validateConfigCacheExists(configID = schema["Skim%sConfigCacheID" % skimIndex],
configCacheUrl = schema['ConfigCacheUrl'],
couchDBName = schema["CouchDBName"],
getOutputModules=False)
if schema["SkimInput%s" % skimIndex] not in mainOutputModules:
error = "Processing config does not have the following output module: %s." % schema["SkimInput%s" % skimIndex]
self.raiseValidationException(msg = error)
skimInputs.add(schema["SkimInput%s" % skimIndex])
skimIndex += 1
# Validate that the transient output modules are used in a skim task
if "TransientOutputModules" in schema:
diffSet = set(schema["TransientOutputModules"]) - skimInputs
if diffSet:
self.raiseValidationException(msg = "A transient output module was specified but no skim was defined for it")
def __call__(self, workloadName, arguments):
"""
_call_
Create a ReReco workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# These are mostly place holders because the job splitting algo and
# parameters will be updated after the workflow has been created.
self.procJobSplitArgs = {}
if self.procJobSplitAlgo in ["EventBased", "EventAwareLumiBased"]:
if self.eventsPerJob is None:
self.eventsPerJob = int((8.0 * 3600.0) / self.timePerEvent)
self.procJobSplitArgs["events_per_job"] = self.eventsPerJob
if self.procJobSplitAlgo == "EventAwareLumiBased":
self.procJobSplitArgs[
"job_time_limit"] = 96 * 3600 # 4 days in seconds
self.procJobSplitArgs["allowCreationFailure"] = False
elif self.procJobSplitAlgo == "LumiBased":
self.procJobSplitArgs["lumis_per_job"] = self.lumisPerJob
elif self.procJobSplitAlgo == "FileBased":
self.procJobSplitArgs["files_per_job"] = self.filesPerJob
self.skimJobSplitArgs = {}
if self.skimJobSplitAlgo in ["EventBased", "EventAwareLumiBased"]:
if self.eventsPerJob is None:
self.eventsPerJob = int((8.0 * 3600.0) / self.timePerEvent)
if self.skimJobSplitAlgo == "EventAwareLumiBased":
self.skimJobSplitArgs["job_time_limit"] = 48 * 3600 # 2 days
self.skimJobSplitArgs["allowCreationFailure"] = False
self.skimJobSplitArgs["events_per_job"] = self.eventsPerJob
elif self.skimJobSplitAlgo == "LumiBased":
self.skimJobSplitArgs["lumis_per_job"] = self.lumisPerJob
elif self.skimJobSplitAlgo == "FileBased":
self.skimJobSplitArgs["files_per_job"] = self.filesPerJob
self.skimJobSplitArgs = arguments.get("SkimJobSplitArgs", {
"files_per_job": 1,
"include_parents": True
})
return self.buildWorkload()
def getWorkloadArguments():
baseArgs = DataProcessing.getWorkloadArguments()
specArgs = {"TransientOutputModules" : {"default" : [], "type" : makeList,
"optional" : True, "validate" : None,
"attr" : "transientModules", "null" : False},
"ConfigCacheID" : {"default" : None, "type" : str,
"optional" : False, "validate" : None,
"attr" : "configCacheID", "null" : False}}
baseArgs.update(specArgs)
return baseArgs
def getWorkloadArguments():
baseArgs = DataProcessing.getWorkloadArguments()
specArgs = {"TransientOutputModules" : {"default" : [], "type" : makeList,
"optional" : True, "validate" : None,
"attr" : "transientModules", "null" : False},
"ConfigCacheID" : {"default" : None, "type" : str,
"optional" : False, "validate" : None,
"attr" : "configCacheID", "null" : False}}
baseArgs.update(specArgs)
return baseArgs
def validateSchema(self, schema):
"""
_validateSchema_
Check for required fields, and some skim facts
"""
DataProcessing.validateSchema(self, schema)
couchUrl = schema.get("ConfigCacheUrl", None) or schema["CouchURL"]
mainOutputModules = self.validateConfigCacheExists(configID = schema["ConfigCacheID"],
couchURL = couchUrl,
couchDBName = schema["CouchDBName"],
getOutputModules = True).keys()
# Skim facts have to be validated outside the usual master validation
skimArguments = self.getSkimArguments()
skimIndex = 1
skimInputs = set()
while "SkimName%s" % skimIndex in schema:
instanceArguments = {}
for argument in skimArguments.keys():
realArg = argument.replace("#N", str(skimIndex))
instanceArguments[realArg] = skimArguments[argument]
msg = validateArguments(schema, instanceArguments)
if msg is not None:
self.raiseValidationException(msg)
self.validateConfigCacheExists(configID = schema["Skim%sConfigCacheID" % skimIndex],
couchURL = couchUrl,
couchDBName = schema["CouchDBName"])
if schema["SkimInput%s" % skimIndex] not in mainOutputModules:
error = "Processing config does not have the following output module: %s." % schema["SkimInput%s" % skimIndex]
self.raiseValidationException(msg = error)
skimInputs.add(schema["SkimInput%s" % skimIndex])
skimIndex += 1
# Validate that the transient output modules are used in a skim task
if "TransientOutputModules" in schema:
diffSet = set(schema["TransientOutputModules"]) - skimInputs
if diffSet:
self.raiseValidationException(msg = "A transient output module was specified but no skim was defined for it")
def getWorkloadArguments():
baseArgs = DataProcessing.getWorkloadArguments()
specArgs = {"StepOneOutputModuleName" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepOneOutputModuleName", "null" : False},
"StepTwoOutputModuleName" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepTwoOutputModuleName", "null" : False},
"StepOneConfigCacheID" : {"default" : None, "type" : str,
"optional" : False, "validate" : None,
"attr" : "stepOneConfigCacheID", "null" : False},
"StepTwoConfigCacheID" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepTwoConfigCacheID", "null" : False},
"StepThreeConfigCacheID" : {"default" : None, "type" : str,
"optional" : True, "validate" : None,
"attr" : "stepThreeConfigCacheID", "null" : False},
"KeepStepOneOutput" : {"default" : True, "type" : strToBool,
"optional" : True, "validate" : None,
"attr" : "keepStepOneOutput", "null" : False},
"KeepStepTwoOutput" : {"default" : True, "type" : strToBool,
"optional" : True, "validate" : None,
"attr" : "keepStepTwoOutput", "null" : False},
"StepTwoTimePerEvent" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepTwoTimePerEvent", "null" : False},
"StepThreeTimePerEvent" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepThreeTimePerEvent", "null" : False},
"StepTwoSizePerEvent" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepTwoSizePerEvent", "null" : False},
"StepThreeSizePerEvent" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepThreeSizePerEvent", "null" : False},
"StepTwoMemory" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepTwoMemory", "null" : False},
"StepThreeMemory" : {"default" : None, "type" : float,
"optional" : True, "validate" : lambda x : x > 0,
"attr" : "stepThreeMemory", "null" : False},
"MCPileup" : {"default" : None, "type" : str,
"optional" : True, "validate" : dataset,
"attr" : "mcPileup", "null" : False},
"DataPileup" : {"default" : None, "type" : str,
"optional" : True, "validate" : dataset,
"attr" : "dataPileup", "null" : False},
"DeterministicPileup" : {"default" : False, "type" : strToBool,
"optional" : True, "validate" : None,
"attr" : "deterministicPileup", "null" : False}}
baseArgs.update(specArgs)
return baseArgs
def getWorkloadCreateArgs():
baseArgs = DataProcessing.getWorkloadCreateArgs()
specArgs = {"RequestType": {"default": "PromptReco", "optional": True},
"ConfigCacheID": {"optional": True, "null": True},
"Scenario": {"default": None, "optional": False,
"attr": "procScenario", "null": False},
"ProcessingString": {"default": "", "validate": procstringT0},
"WriteTiers": {"default": ["RECO", "AOD", "DQM", "ALCARECO"],
"type": makeList, "optional": False, "null": False},
"AlcaSkims": {"default": ["TkAlCosmics0T", "MuAlGlobalCosmics", "HcalCalHOCosmics"],
"type": makeList, "optional": False, "null": False},
"PhysicsSkims": {"default": [], "type": makeList,
"optional": True, "null": False},
"InitCommand": {"default": None, "optional": True, "null": True},
"EnvPath": {"default": None, "optional": True, "null": True},
"BinPath": {"default": None, "optional": True, "null": True},
"DoLogCollect": {"default": True, "type": strToBool,
"optional": True, "null": False},
"SplittingAlgo": {"default": "EventAwareLumiBased", "null": False,
"validate": lambda x: x in ["EventBased", "LumiBased",
"EventAwareLumiBased", "FileBased"],
"attr": "procJobSplitAlgo"},
"EventsPerJob": {"default": 500, "type": int, "validate": lambda x: x > 0,
"null": False},
"SkimSplittingAlgo": {"default": "FileBased", "null": False,
"validate": lambda x: x in ["EventBased", "LumiBased",
"EventAwareLumiBased", "FileBased"],
"attr": "skimJobSplitAlgo"},
"SkimEventsPerJob": {"default": 500, "type": int, "validate": lambda x: x > 0,
"null": False},
"SkimLumisPerJob": {"default": 8, "type": int, "validate": lambda x: x > 0,
"null": False},
"SkimFilesPerJob": {"default": 1, "type": int, "validate": lambda x: x > 0,
"null": False},
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def getWorkloadCreateArgs():
baseArgs = DataProcessing.getWorkloadCreateArgs()
specArgs = {"RequestType": {"default": "PromptReco", "optional": True},
"ConfigCacheID": {"optional": True, "null": True},
"Scenario": {"default": None, "optional": False,
"attr": "procScenario", "null": False},
"ProcessingString": {"default": "", "validate": procstringT0},
"WriteTiers": {"default": ["RECO", "AOD", "DQM", "ALCARECO"],
"type": makeList, "optional": False, "null": False},
"AlcaSkims": {"default": ["TkAlCosmics0T", "MuAlGlobalCosmics", "HcalCalHOCosmics"],
"type": makeList, "optional": False, "null": False},
"PhysicsSkims": {"default": [], "type": makeList,
"optional": True, "null": False},
"InitCommand": {"default": None, "optional": True, "null": True},
"EnvPath": {"default": None, "optional": True, "null": True},
"BinPath": {"default": None, "optional": True, "null": True},
"DoLogCollect": {"default": True, "type": strToBool,
"optional": True, "null": False},
"SplittingAlgo": {"default": "EventAwareLumiBased", "null": False,
"validate": lambda x: x in ["EventBased", "LumiBased",
"EventAwareLumiBased", "FileBased"],
"attr": "procJobSplitAlgo"},
"EventsPerJob": {"default": 500, "type": int, "validate": lambda x: x > 0,
"null": False},
"SkimSplittingAlgo": {"default": "FileBased", "null": False,
"validate": lambda x: x in ["EventBased", "LumiBased",
"EventAwareLumiBased", "FileBased"],
"attr": "skimJobSplitAlgo"},
"SkimEventsPerJob": {"default": 500, "type": int, "validate": lambda x: x > 0,
"null": False},
"SkimLumisPerJob": {"default": 8, "type": int, "validate": lambda x: x > 0,
"null": False},
"SkimFilesPerJob": {"default": 1, "type": int, "validate": lambda x: x > 0,
"null": False},
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs
def __call__(self, workloadName, arguments):
"""
_call_
Create a ReReco workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# These are mostly place holders because the job splitting algo and
# parameters will be updated after the workflow has been created.
self.procJobSplitArgs = {}
if self.procJobSplitAlgo == "EventBased" or self.procJobSplitAlgo == "EventAwareLumiBased":
if self.eventsPerJob is None:
self.eventsPerJob = int((8.0 * 3600.0) / self.timePerEvent)
self.procJobSplitArgs["events_per_job"] = self.eventsPerJob
if self.procJobSplitAlgo == "EventAwareLumiBased":
self.procJobSplitArgs["max_events_per_lumi"] = 100000
elif self.procJobSplitAlgo == "LumiBased":
self.procJobSplitArgs["lumis_per_job"] = self.lumisPerJob
elif self.procJobSplitAlgo == "FileBased":
self.procJobSplitArgs["files_per_job"] = self.filesPerJob
self.skimJobSplitArgs = {}
if self.skimJobSplitAlgo == "EventBased" or self.skimJobSplitAlgo == "EventAwareLumiBased":
if self.eventsPerJob is None:
self.eventsPerJob = int((8.0 * 3600.0) / self.timePerEvent)
self.skimJobSplitArgs["events_per_job"] = self.eventsPerJob
if self.skimJobSplitAlgo == "EventAwareLumiBased":
self.skimJobSplitArgs["max_events_per_lumi"] = 20000
elif self.skimJobSplitAlgo == "LumiBased":
self.skimJobSplitArgs["lumis_per_job"] = self.lumisPerJob
elif self.skimJobSplitAlgo == "FileBased":
self.skimJobSplitArgs["files_per_job"] = self.filesPerJob
self.skimJobSplitArgs = arguments.get("SkimJobSplitArgs", {
"files_per_job": 1,
"include_parents": True
})
return self.buildWorkload()
def __call__(self, workloadName, arguments):
"""
_call_
Create a ReReco workload with the given parameters.
"""
DataProcessing.__call__(self, workloadName, arguments)
# These are mostly place holders because the job splitting algo and
# parameters will be updated after the workflow has been created.
self.procJobSplitArgs = {}
if self.procJobSplitAlgo in ["EventBased", "EventAwareLumiBased"]:
if self.eventsPerJob is None:
self.eventsPerJob = int((8.0 * 3600.0) / self.timePerEvent)
self.procJobSplitArgs["events_per_job"] = self.eventsPerJob
if self.procJobSplitAlgo == "EventAwareLumiBased":
self.procJobSplitArgs["job_time_limit"] = 96 * 3600 # 4 days in seconds
elif self.procJobSplitAlgo == "LumiBased":
self.procJobSplitArgs["lumis_per_job"] = self.lumisPerJob
elif self.procJobSplitAlgo == "FileBased":
self.procJobSplitArgs["files_per_job"] = self.filesPerJob
self.skimJobSplitArgs = {}
if self.skimJobSplitAlgo in ["EventBased", "EventAwareLumiBased"]:
if self.eventsPerJob is None:
self.eventsPerJob = int((8.0 * 3600.0) / self.timePerEvent)
if self.skimJobSplitAlgo == "EventAwareLumiBased":
self.skimJobSplitArgs["job_time_limit"] = 48 * 3600 # 2 days
self.skimJobSplitArgs["events_per_job"] = self.eventsPerJob
elif self.skimJobSplitAlgo == "LumiBased":
self.skimJobSplitArgs["lumis_per_job"] = self.lumisPerJob
elif self.skimJobSplitAlgo == "FileBased":
self.skimJobSplitArgs["files_per_job"] = self.filesPerJob
self.skimJobSplitArgs = arguments.get("SkimJobSplitArgs",
{"files_per_job": 1,
"include_parents": True})
return self.buildWorkload()
def getWorkloadCreateArgs():
baseArgs = DataProcessing.getWorkloadCreateArgs()
specArgs = {
"RequestType": {
"default": "DQMHarvest",
"optional": True
},
"ConfigCacheID": {
"optional": True,
"null": True
},
"DQMConfigCacheID": {
"optional": False,
"attr": "dqmConfigCacheID"
},
"DQMUploadUrl": {
"optional": False,
"attr": "dqmUploadUrl"
},
}
baseArgs.update(specArgs)
DataProcessing.setDefaultArgumentsProperty(baseArgs)
return baseArgs