from functional import Functional
# Reads program options.
parser = OptionParser()
parser.add_option( "--save", dest="saveme", help="Pickle job folder.",\
metavar="FILE", type="str")
parser.add_option( "--load", dest="loadme", help="Loads a pickled job folder.", \
metavar="FILE", type="str")
parser.add_option( "--pools", dest="pools", help="Number of mpi pools.", \
metavar="N", type="int", default=1)
parser.add_option( "--pbs", dest="pbs", help="Directory where to write pbs stuff.", \
metavar="DIRECTORY", type="str")
(options, args) = parser.parse_args()
# takes care of mpi pools
local_comm = world.split(world.rank % options.pools)
# creates a functional instance.
functional = Functional()
# Creates dictionary
if options.loadme is None or options.saveme is not None:
job_dictionary = jobs.JobFolder()
for caps in ["A", "B", "C", "D"]:
capsjob = job_dictionary / caps
if caps == "B":
capsjob.vasp = functional
capsjob.args = "beast", 666
capsjob["indict"] = "indicted"
def main(system_params=None):
from boost.mpi import world, broadcast
from pylada.vasp.extract import Extract, ExtractGW
from pylada.vasp.specie import U, nlep
from pylada.vasp import Vasp
import os
from os import path
from .nlep import Objective
from pylada.vasp.nlep.postprocessing import find_best, load_test, prepare_analog_fit
print("mpi rank %d of %d" % (world.rank, world.size))
# Things which need to be put into dictionary of input script.
# global_dict = { "Extract": Extract, "ExtractGW": ExtractGW, "Vasp": Vasp, "nlep": nlep, "U": U }
# read file governing run. other input files should be generic
run_input = load_run_input()
run_control = RunControl(run_input)
load_from_analogs = run_input.load_from_analogs
# create systems
from .systems import setup_systems
systems = setup_systems(run_input, system_params)
if (run_input.nbjobs == -1):
run_control.nbjobs = world.size
assert world.size >= run_control.nbjobs, "Too few procs granted, or too many jobs requested."
job_comm = world
vasp_comm = world
color = world.rank % run_control.nbjobs
job_comm = world.split(color)
vasp_comm = job_comm
if (job_comm.size > 1):
vasp_comm = job_comm.split(job_comm.rank)
create_directories(run_control, systems, job_comm, color)
# barrier to be sure all necessary directories exist
world.barrier()
# creates objective functions from systems
for s in systems.systems:
print(s.outdir)
s.objective = Objective(s.input.vasp, s.input.dft_in, s.input.gw_in,
outdir=s.outdir, comm=vasp_comm, units=run_input.units)
s.evals_file = file("%s/evals" % s.outdir, "w")
systems.evals_file = file("%s/evals" % run_control.outdir, "w")
# setup correspondence between vector "x" and list of species (ie at least
# one subpart shared by > 1 systems):
systems.setup_species()
# barrier to be sure all necessary directories exist
world.barrier()
# only serial vasp so far, but lmmin is a parallel fitter
if (job_comm.rank != 0 and run_input.optimizer != "lmmin"):
print("rank %d superfluous, returning; beware barrier" % world.rank)
return
print("world rank %d job %d local rank %d working dir %s" % (world.rank, color, job_comm.rank, run_control.outdir))
job_main(job_comm, run_input, systems, vasp_comm)
from pylada.opt.changedir import Changedir
from pylada.opt import Redirect
# pseudo files
pseudo_files = [ "maskr", "vq.Ge", "vq.Si", "vq.SiGe.Ge", "vq.SiGe.Si", "vwr.pso" ]
# directories where jobs are at.
jobs = ["VBM", "Gamma", "X", "L", "W1", "W2"]
testdir = "test_input"
workdir = "work"
if world.rank == 0 and (not exists(workdir)): makedirs(workdir)
# creates local comm split into N groups.
N = 2
color = world.rank % N
local_comm = world.split(color)
# launch escan for different jobs.
for i, dir in enumerate(jobs):
# splits job according to color.
if i % N != color: continue
# creates working directory with all input files.
workhere = join(workdir, dir)
if local_comm.rank == 0:
if exists(workhere): rmtree(workhere) # deletes everything if already there.
makedirs(workhere)
# symlink potential files
testhere = join(testdir, "pseudos")
for file in pseudo_files: copy(join(testhere, file), workhere)
# symlinks input files.