def main():
# launch grid
print "launching grid"
grid = launch_grid()
#publish the shared object
print "publishing sfo"
sfo = sf.sfo()
sfo_id = grid.publish(sfo)
print "communication test"
#get a worker to do a task
#proxy = grid.acquire_service(sfo_id)
#print proxy.hello().get()
#grid.release_service(proxy)
print "broadcast test"
print grid.gather(grid.broadcast(sfo_id, 'hello'))
#call init on all nodes
print "initializing model"
mkdir_p(tmpdir)
mkdir_p(outfolder)
requests = grid.broadcast(sfo_id, 'init_model', tmpdir, sequence, initpdb, restraints)
#wait til init is done
results = grid.gather(requests)
#turn off verbose noise (works because IMP.base.NONE is picklable, being an int.
grid.gather(grid.broadcast(sfo_id, 'set_checklevel', IMP.base.NONE))
grid.gather(grid.broadcast(sfo_id, 'set_loglevel', IMP.base.NONE))
# evaluate the score of the whole system (without derivatives, False flag)
print "initial energy"
grid.gather(grid.broadcast(sfo_id, 'm', 'evaluate', False))
#berendsen 300K tau=0.5ps
#perform two independent MC moves for sigma and gamma
print "initializing simulation and statistics"
grid.gather(grid.scatter(sfo_id, 'init_simulation',
zip(lambdas, tau)))
grid.gather(grid.scatter(sfo_id, 'init_stats', zip(nums, stat_rate)))
replica = ReplicaTracker(nreps, lambdas, grid, sfo_id,
rexlog=rexlog,
scheme=rex_scheme, xchg=rex_xchg,
tune_temps=tune_temps, tune_data=tune_data, templog=templog)
print "thermalization"
for i in range(n_therm):
print "\rgibbs step %d" % i,
sys.stdout.flush()
grid.gather(grid.scatter(sfo_id, 'set_inv_temp',
[ n_therm/float(i+1) * l for l in lambdas ]))
grid.gather(grid.broadcast(sfo_id, 'do_md', n_hmc_therm))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
print "start gibbs sampling loop: first relaxation"
for i in range(n_gibbs1):
print "\rgibbs step %d" % i,
sys.stdout.flush()
#print " md"
grid.gather(grid.broadcast(sfo_id, 'do_md', n_md))
#print " mc"
grid.gather(grid.broadcast(sfo_id, 'do_mc', n_mc))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
#print " swaps"
replica.tune_data['dumb_scale']=0.5
replica.replica_exchange()
#print " stats"
replica.write_rex_stats()
print "start gibbs sampling loop: second relaxation"
for i in range(n_gibbs2):
print "\rgibbs step %d" % i,
sys.stdout.flush()
#print " md"
grid.gather(grid.broadcast(sfo_id, 'do_md', n_md))
#print " mc"
grid.gather(grid.broadcast(sfo_id, 'do_mc', n_mc))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
#print " swaps"
replica.tune_data['dumb_scale']=0.2
replica.replica_exchange()
#print " stats"
replica.write_rex_stats()
print "start gibbs sampling loop: production"
for i in range(n_gibbs3):
print "\rgibbs step %d" % i,
sys.stdout.flush()
#print " md"
grid.gather(grid.broadcast(sfo_id, 'do_md', n_md))
#print " mc"
grid.gather(grid.broadcast(sfo_id, 'do_mc', n_mc))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
#print " swaps"
replica.tune_temps=False
replica.replica_exchange()
#print " stats"
replica.write_rex_stats()
print "terminating grid"
grid.terminate()
print "done."
def main():
# launch grid
print("launching grid")
grid = launch_grid()
# publish the shared object
print("publishing sfo")
sfo = sf.sfo()
sfo_id = grid.publish(sfo)
print("communication test")
# get a worker to do a task
#proxy = grid.acquire_service(sfo_id)
# print proxy.hello().get()
# grid.release_service(proxy)
print("broadcast test")
print(grid.gather(grid.broadcast(sfo_id, 'hello')))
# call init on all nodes
print("initializing model")
mkdir_p(tmpdir)
mkdir_p(outfolder)
requests = grid.broadcast(sfo_id, 'init_model', tmpdir, sequence, initpdb,
restraints)
# wait til init is done
results = grid.gather(requests)
# turn off verbose noise (works because IMP.base.NONE is picklable, being
# an int.
grid.gather(grid.broadcast(sfo_id, 'set_checklevel', IMP.base.NONE))
grid.gather(grid.broadcast(sfo_id, 'set_loglevel', IMP.base.NONE))
# evaluate the score of the whole system (without derivatives, False flag)
print("initial energy")
grid.gather(grid.broadcast(sfo_id, 'm', 'evaluate', False))
# berendsen 300K tau=0.5ps
# perform two independent MC moves for sigma and gamma
print("initializing simulation and statistics")
grid.gather(grid.scatter(sfo_id, 'init_simulation', zip(lambdas, tau)))
grid.gather(grid.scatter(sfo_id, 'init_stats', zip(nums, stat_rate)))
replica = ReplicaTracker(nreps,
lambdas,
grid,
sfo_id,
rexlog=rexlog,
scheme=rex_scheme,
xchg=rex_xchg,
tune_temps=tune_temps,
tune_data=tune_data,
templog=templog)
print("thermalization")
for i in range(n_therm):
print("\rgibbs step %d" % i, end=' ')
sys.stdout.flush()
grid.gather(
grid.scatter(sfo_id, 'set_inv_temp',
[n_therm / float(i + 1) * l for l in lambdas]))
grid.gather(grid.broadcast(sfo_id, 'do_md', n_hmc_therm))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
print("start gibbs sampling loop: first relaxation")
for i in range(n_gibbs1):
print("\rgibbs step %d" % i, end=' ')
sys.stdout.flush()
# print " md"
grid.gather(grid.broadcast(sfo_id, 'do_md', n_md))
# print " mc"
grid.gather(grid.broadcast(sfo_id, 'do_mc', n_mc))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
# print " swaps"
replica.tune_data['dumb_scale'] = 0.5
replica.replica_exchange()
# print " stats"
replica.write_rex_stats()
print("start gibbs sampling loop: second relaxation")
for i in range(n_gibbs2):
print("\rgibbs step %d" % i, end=' ')
sys.stdout.flush()
# print " md"
grid.gather(grid.broadcast(sfo_id, 'do_md', n_md))
# print " mc"
grid.gather(grid.broadcast(sfo_id, 'do_mc', n_mc))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
# print " swaps"
replica.tune_data['dumb_scale'] = 0.2
replica.replica_exchange()
# print " stats"
replica.write_rex_stats()
print("start gibbs sampling loop: production")
for i in range(n_gibbs3):
print("\rgibbs step %d" % i, end=' ')
sys.stdout.flush()
# print " md"
grid.gather(grid.broadcast(sfo_id, 'do_md', n_md))
# print " mc"
grid.gather(grid.broadcast(sfo_id, 'do_mc', n_mc))
grid.gather(grid.broadcast(sfo_id, 'write_stats'))
# print " swaps"
replica.tune_temps = False
replica.replica_exchange()
# print " stats"
replica.write_rex_stats()
print("terminating grid")
grid.terminate()
print("done.")
