# or the number of nodes (nNode) with the number of processor per nodes (ppn)
# since nCpu = ppn * nNode, only two of those 3 variables need to be specified.
# If you already specified the JD_PPN environment variable, you only have to specify nNode or nCpu.
# nNode is often preferable since it is more portable from one cluster to another.
nNode = 1
walltime = 60*10 # some clusters requires a walltime to accept job submission
# you can specify a different queue from the script parameters. For example 'debug' (if available on the current cluster)
if len(sys.argv) > 1: queue = sys.argv[1]
else: queue = None
n = int(1e8) # summation will go from 0 to n
experiment = newExperiment('sum') # you always need to create an experiment first
print(experiment.folder)
job = experiment.newJob('sum') # an experiment usually have several jobs, in our case, we'll only have one
job.setConf(nNode=nNode, mpi=True, walltime = walltime, queue=queue) # set the configuration file
# Create the callable object from function and argument list and write it on disk
job.setFunction( parallelSum, end=n )
# obtain the dispatcher as specified by the environment variable
# $DISPATCHER_TYPE = [ PbsDispatcher | SequentialDispatcher | SgeDispatcher ]
# some dispatcher requires a project id which can be specified by the variable $PROJECT_ID
d = dispatcher.getHostDispatcher()
d.submitExperiment(experiment)
# If you already specified the JD_PPN environment variable, you only have to specify nNode or nCpu.
# nNode is often preferable since it is more portable from one cluster to another.
nNode = 1
nJob = 2
walltime = 60*30 # some clusters requires a walltime to be between some minimum and maximum limits
# you can specify a different queue from the script parameters. For example 'debug' (if available on the current cluster)
if len(sys.argv) > 1: queue = sys.argv[1]
else: queue = None
# number of samples for approximating pi on each job
nSamples = 500000
experiment = newExperiment('approxPi')
print(experiment.folder)
for i in range(nJob):
job = experiment.newJob( 'job-%d'%i )
job.setConf(nNode = nNode, mpi=True, walltime = walltime, queue=queue) # set the configuration file
job.setFunction( approxPi, nSamples )
# obtain the dispatcher as specified by the environment variable
# $DISPATCHER_TYPE = [ PbsDispatcher | SequentialDispatcher | SgeDispatcher ]
# some dispatcher requires a project id which can be specified by the variable $PROJECT_ID
d = dispatcher.getHostDispatcher()
# The same problem happens if you define your function in the __main__ module
from pyjobber.example.dotProfile.dotProfile import dotProfileList
# In general, the dispatcher, need to know either the number of cores requested (nCpu)
# or the number of nodes (nNode) with the number of processor per nodes (ppn)
# since nCpu = ppn * nNode, only two of those 3 variables need to be specified.
# If you already specified the JD_PPN environment variable, you only have to specify nNode or nCpu.
# nNode is often preferable since it is more portable from one cluster to another.
nNode = 1
walltime = 60*30 # some clusters requires a walltime to be between some minimum and maximum limits
experiment = newExperiment('dotProfile')
print(experiment.folder)
job = experiment.newJob( 'job' )
job.setConf(nNode = nNode, mpi=True, walltime = walltime) # set the configuration file
job.setFunction( dotProfileList, 100,100,100, 1e8)
# obtain the dispatcher as specified by the environment variable
# $DISPATCHER_TYPE = [ PbsDispatcher | SequentialDispatcher | SgeDispatcher ]
# some dispatcher requires a project id which can be specified by the variable $PROJECT_ID
d = dispatcher.getHostDispatcher()
d.submitExperiment(experiment)
