walltime=15,
#username=None,
project=config[resource]['project'],
access_schema=config[resource]['schema'],
queue=config[resource]['queue'],
#database_url='mongodb://138.201.86.166:27017/ee_exp_4c',
)
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the pipeline to 16. This means that 16 instances
# of each pipeline stage are executed.
#
# Execution of the 16 pipeline instances can happen concurrently or
# sequentially, depending on the resources (cores) available in the
# SingleClusterEnvironment.
ccount = RunExchange(stages=3, instances=2)
cluster.run(ccount)
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
try:
cluster.deallocate()
except:
pass
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=15,
#username=None,
project=config[resource]['project'],
access_schema = config[resource]['schema'],
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
# Allocate the resources.
cluster.allocate()
# We set the simulation 'instances' to 16 and analysis 'instances' to 1. We set the adaptive
# simulation to True and specify the simulation extraction script to be used.
cur_path = os.path.dirname(os.path.abspath(__file__))
mssa = MSSA(iterations=2, simulation_instances=16, analysis_instances=1, adaptive_simulation=True, sim_extraction_script='{0}/extract.py'.format(cur_path))
cluster.run(mssa)
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
try:
cluster.deallocate()
except:
pass
pipe = Test(ensemble_size=ENSEMBLE_SIZE + 1, pipeline_size=1)
# Create an application manager
app = AppManager(name='Adap_sampling')
# Register kernels to be used
app.register_kernels(rand_kernel)
app.register_kernels(sleep_kernel)
# Add workload to the application manager
app.add_workload(pipe)
# Create a resource handle for target machine
res = ResourceHandle(
resource="local.localhost",
cores=4,
# username=,
# project =,
# queue=,
walltime=10,
database_url='mongodb://ensembletk.imp.fu-berlin.de:27017/rp')
# Submit request for resources + wait till job becomes Active
res.allocate(wait=True)
# Run the given workload
res.run(app)
# Deallocate the resource
res.deallocate()