cluster = SingleClusterEnvironment(
resource=RPconfig.REMOTE_HOST,
cores=RPconfig.PILOTSIZE,
walltime=RPconfig.WALLTIME,
username=RPconfig.UNAME, # username
project=RPconfig.ALLOCATION, # project
queue=RPconfig.QUEUE,
database_url=RPconfig.DBURL,
)
cluster.shared_data = [
Kconfig.md_input_file,
Kconfig.lsdm_config_file,
Kconfig.top_file,
Kconfig.mdp_file,
"{0}/spliter.py".format(Kconfig.misc_loc),
"{0}/gro.py".format(Kconfig.misc_loc),
"{0}/pre_analyze.py".format(Kconfig.misc_loc),
"{0}/post_analyze.py".format(Kconfig.misc_loc),
"{0}/selection.py".format(Kconfig.misc_loc),
"{0}/reweighting.py".format(Kconfig.misc_loc),
]
cluster.allocate()
# We set the 'instances' of the simulation step to 16. This means that 16
# instances of the simulation are executed every iteration.
# We set the 'instances' of the analysis step to 1. This means that only
# one instance of the analysis is executed for each iteration
cur_path = os.path.dirname(os.path.abspath(__file__))
randomsa = Gromacs_LSDMap(
iterations=Kconfig.num_iterations,
coordinates.tolist()[2]))
data.close()
cluster = SingleClusterEnvironment(
resource="xsede.comet",
cores=core_count,
walltime=90,
username="******",
project="unc100",
#queue='debug',
database_url=
"mongodb://*****:*****@ds019678.mlab.com:19678/pilot_test")
cluster.shared_data = [
'/home/sean/midas/leaflet_finder/Vanilla/input.txt'
]
# Allocate the resources.
cluster.allocate()
instance_count = int(math.ceil(float(traj_count) / float(window_size)))
print "instance total is " + str(instance_count)
leaflet = leaflet(iterations=1,
simulation_instances=instance_count,
analysis_instances=1)
cluster.run(leaflet)
#cluster.profile(leaflet)
data.write('%s,%s,%s\n'%(coordinates.tolist()[0],coordinates.tolist()[1],coordinates.tolist()[2]))
data.close()
cluster = SingleClusterEnvironment(
resource="xsede.comet",
cores=core_count,
walltime=60,
username="******",
project="unc100",
#queue='debug',
database_url="mongodb://*****:*****@ds019678.mlab.com:19678/pilot_test"
)
cluster.shared_data =[
'/home/sean/midas/leaflet_finder/Vanilla/input.txt'
]
# Allocate the resources.
cluster.allocate()
#stage input data???
#make list of every window combination, to be used in atomDist
#for i in range(0,traj_count,window_size):
# for j in range(i, traj_count-1,window_size):
# list_elem = [i,j]
# window_list.append(list_elem)
# number of cores and runtime.
cluster = SingleClusterEnvironment(
resource=RPconfig.REMOTE_HOST,
cores=RPconfig.PILOTSIZE,
walltime=RPconfig.WALLTIME,
username = RPconfig.UNAME, #username
project = RPconfig.ALLOCATION, #project
queue = RPconfig.QUEUE,
database_url = RPconfig.DBURL,
# access_schema = config[RPconfig.REMOTE_HOST]['schema'] # This is so to support different access methods - gsissh, ssh - remove this if always running using ssh
)
cluster.shared_data = [
Kconfig.initial_crd_file,
Kconfig.md_input_file,
Kconfig.minimization_input_file,
Kconfig.top_file,
]
cluster.allocate()
coco_amber_static = Extasy_CocoAmber_Static(maxiterations=Kconfig.num_iterations, simulation_instances=Kconfig.num_CUs, analysis_instances=1)
cluster.run(coco_amber_static)
cluster.deallocate()
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
cluster = SingleClusterEnvironment(
resource=RPconfig.REMOTE_HOST,
cores=RPconfig.PILOTSIZE,
walltime=RPconfig.WALLTIME,
username = RPconfig.UNAME, #username
project = RPconfig.ALLOCATION, #project
queue = RPconfig.QUEUE,
database_url = RPconfig.DBURL,
# access_schema = config[RPconfig.REMOTE_HOST]['schema'] # This is so to support different access methods - gsissh, ssh - remove this if always running using ssh
)
cluster.shared_data = [
Kconfig.initial_crd_file,
Kconfig.md_input_file,
Kconfig.minimization_input_file,
Kconfig.top_file,
'{0}/postexec.py'.format(Kconfig.helper_scripts)
]
cluster.allocate()
coco_amber_static = Extasy_CocoAmber_Static(maxiterations=Kconfig.num_iterations, simulation_instances=Kconfig.num_CUs, analysis_instances=Kconfig.num_CUs/64)
cluster.run(coco_amber_static)
cluster.deallocate()
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
resource=RPconfig.REMOTE_HOST,
cores=RPconfig.PILOTSIZE,
walltime=RPconfig.WALLTIME,
username = RPconfig.UNAME, #username
project = RPconfig.ALLOCATION, #project
queue = RPconfig.QUEUE,
database_url = RPconfig.DBURL
)
cluster.shared_data = [
Kconfig.md_input_file,
Kconfig.lsdm_config_file,
Kconfig.top_file,
Kconfig.mdp_file,
'{0}/spliter.py'.format(Kconfig.helper_scripts),
'{0}/gro.py'.format(Kconfig.helper_scripts),
'{0}/run.py'.format(Kconfig.helper_scripts),
'{0}/pre_analyze.py'.format(Kconfig.helper_scripts),
'{0}/post_analyze.py'.format(Kconfig.helper_scripts),
'{0}/selection.py'.format(Kconfig.helper_scripts),
'{0}/reweighting.py'.format(Kconfig.helper_scripts)
]
if Kconfig.ndx_file is not None:
cluster.shared_data.append(Kconfig.ndx_file)
cluster.allocate()
# We set the 'instances' of the simulation step to 16. This means that 16
# instances of the simulation are executed every iteration.
# We set the 'instances' of the analysis step to 1. This means that only
# Create a new static execution context with one resource and a fixed
# number of cores and runtime.
cluster = SingleClusterEnvironment(
resource=RPconfig.REMOTE_HOST,
cores=RPconfig.PILOTSIZE,
walltime=RPconfig.WALLTIME,
username=RPconfig.UNAME, #username
project=RPconfig.ALLOCATION, #project
queue=RPconfig.QUEUE,
database_url=RPconfig.DBURL)
cluster.shared_data = [
Kconfig.md_input_file, Kconfig.lsdm_config_file, Kconfig.top_file,
Kconfig.mdp_file, '{0}/spliter.py'.format(Kconfig.misc_loc),
'{0}/gro.py'.format(Kconfig.misc_loc),
'{0}/pre_analyze.py'.format(Kconfig.misc_loc),
'{0}/post_analyze.py'.format(Kconfig.misc_loc),
'{0}/selection.py'.format(Kconfig.misc_loc),
'{0}/reweighting.py'.format(Kconfig.misc_loc)
]
cluster.allocate()
# We set the 'instances' of the simulation step to 16. This means that 16
# instances of the simulation are executed every iteration.
# We set the 'instances' of the analysis step to 1. This means that only
# one instance of the analysis is executed for each iteration
cur_path = os.path.dirname(os.path.abspath(__file__))
randomsa = Gromacs_LSDMap(
iterations=Kconfig.num_iterations,
simulation_instances=Kconfig.num_CUs,
