def __init__(self, name, task_path, stm_queue = None, solver_id = None):
# prepare communication channels
if stm_queue is None:
self._stm_queue = multiprocessing.Queue()
else:
self._stm_queue = stm_queue
if solver_id is None:
self._solver_id = uuid.uuid4()
else:
self._solver_id = solver_id
# gather candidate runs, and select one
runs = []
answer_map = {"True": True, "False": False}
with open(task_path + ".rtd.csv") as csv_file:
reader = csv.reader(csv_file)
for (solver_name, _, run_budget, run_cost, run_answer) in reader:
if solver_name == name:
run_cost = float(run_cost)
run_budget = float(run_budget)
if run_budget >= borg.defaults.minimum_fake_run_budget:
runs.append((run_cost, answer_map[run_answer]))
(self._run_cost, self._run_answer) = cargo.grab(runs)
self._run_position = 0.0
def __call__(self, task, budget, cores = 1):
queue = multiprocessing.Queue()
solvers = []
for _ in xrange(cores):
solver_id = uuid.uuid4()
solver = cargo.grab(self._domain.solvers.values())(task, queue, solver_id)
solvers.append(solver)
try:
for solver in solvers:
solver.unpause_for(borg.unicore_cpu_budget(budget))
remaining = len(solvers)
while remaining > 0:
(solver_id, run_cpu_cost, answer, _) = queue.get()
remaining -= 1
borg.get_accountant().charge_cpu(run_cpu_cost)
if self._domain.is_final(task, answer):
return answer
return None
finally:
for solver in solvers:
solver.stop()