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
project=config[resource]['project'],
access_schema = config[resource]['schema'],
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
os.system('/bin/echo Welcome! > input_file.txt')
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the BagofTasks to 16. This means that 16 instances
# of each BagofTasks step are executed.
app = MyApp(stages=1,instances=16)
cluster.run(app)
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
try:
# Deallocate the resources.
cluster.deallocate()
except:
pass
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 both the the simulation and the analysis stage 'instances' to 16.
# This means that 16 instances of the simulation stage and 16 instances of
# the analysis stage are executed every iteration.
randomsa = RandomSA(maxiterations=1, simulation_instances=16, analysis_instances=16)
cluster.run(randomsa)
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
# After execution has finished, we print some statistical information
# extracted from the analysis results that were transferred back.
for it in range(1, randomsa.iterations+1):
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
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the BagofTasks to 16. This means that 16 instances
# of each BagofTasks stage are executed.
#
# Execution of the 16 BagofTasks instances can happen concurrently or
# sequentially, depending on the resources (cores) available in the
# SingleClusterEnvironment.
ccount = CharCount(stages=3,instances=16)
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:
# Deallocate the resources.
cluster.deallocate()
except:
pass
# Print the checksums
# Create a new resource handle with one resource and a fixed
# number of cores and runtime.
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 both the the simulation and the analysis stage 'instances' to 8.
msma = MSMA(iterations=2, simulation_instances=8, analysis_instances=8)
cluster.run(msma)
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
resource=resource,
cores=config[resource]["cores"],
walltime=60,
username='******',
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()
# Set the 'instances' of the BagofTasks to 16. This means that 16 instances
# of each BagofTasks step are executed.
app = MyApp(stages=1, instances=1)
cluster.run(app)
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
try:
# Deallocate the resources.
cluster.deallocate()
except:
pass
# Allocate the resources.
cluster.allocate()
# creating RE pattern object
re_pattern = RePattern(workdir_local)
# set number of replicas
re_pattern.replicas = 8
# set number of cycles
re_pattern.nr_cycles = 3
# initializing replica objects
replicas = re_pattern.initialize_replicas()
re_pattern.add_replicas( replicas )
# run RE simulation
cluster.run(re_pattern, force_plugin="replica_exchange.static_pattern_2")
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()
cores=RPconfig.PILOTSIZE,
walltime=RPconfig.WALLTIME,
username=RPconfig.UNAME, #username
project=RPconfig.ALLOCATION, #project
queue=RPconfig.QUEUE,
database_url=RPconfig.DBURL,
access_schema='gsissh')
cluster.shared_data = [
Kconfig.initial_crd_file, Kconfig.grompp_1_mdp,
Kconfig.grompp_2_mdp, Kconfig.grompp_3_mdp,
Kconfig.grompp_1_itp_file, Kconfig.grompp_2_itp_file,
Kconfig.top_file, Kconfig.restr_file
]
cluster.allocate()
coco_gromacs_static = Extasy_CocoGromacs_Static(
maxiterMods=Kconfig.num_iterations,
simulation_instances=Kconfig.num_CUs,
analysis_instances=1)
cluster.run(coco_gromacs_static)
cluster.deallocate()
except EnsemblemdError, er:
print "The gromacs-coco ExTASY workflow not completed correctly due to an Ensemble MD Toolkit Error: {0}".format(
str(er))
raise # Just raise the execption again to get the backtrace
