def __init__(self,
t_prof,
eval_methods,
chief_cls,
eval_agent_cls,
n_iterations=None,
iteration_to_import=None,
name_to_import=None):
"""
Args:
t_prof (TrainingProfile)
eval_methods (dict): dict of {evaluator1_name: frequency, ...} Currently supported evaluators
are "br", "rlbr", and "lbr"
chief_cls (ChiefBase subclass): class, not instance
n_iterations (int) number of iterations to run. If None, runs forever
iteration_to_import (int): step/iteration to import
name_to_import (str): name of the run to import
"""
super().__init__(t_prof=t_prof)
if self._t_prof.CLUSTER:
self._ray.init_cluster(redis_address=t_prof.redis_head_adr)
else:
self._ray.init_local()
file_util.do_pickle(obj=t_prof,
file_name=t_prof.name,
path=t_prof.path_trainingprofiles)
self.n_iterations = n_iterations
self._step_to_import = iteration_to_import
self._name_to_import = name_to_import
if self._t_prof.DISTRIBUTED:
from PokerRL.eval.lbr.DistLBRMaster import DistLBRMaster as LBRMaster
from PokerRL.eval.rl_br.DistRLBRMaster import DistRLBRMaster as RLBRMaster
from PokerRL.eval.rl_br.workers.ps.Dist_RLBR_ParameterServer import \
Dist_RLBR_ParameterServer as RLBRParameterServer
from PokerRL.eval.rl_br.workers.la.Dist_RLBR_LearnerActor import Dist_RLBR_LearnerActor as RLBRLearnerActor
from PokerRL.eval.lbr.DistLBRWorker import DistLBRWorker as LBRWorker
from PokerRL.eval.br.DistBRMaster import DistBRMaster as BRMaster
from PokerRL.eval.head_to_head.DistHead2HeadMaster import DistHead2HeadMaster as Head2HeadMaster
else:
from PokerRL.eval.lbr.LocalLBRMaster import LocalLBRMaster as LBRMaster
from PokerRL.eval.rl_br.LocalRLBRMaster import LocalRLBRMaster as RLBRMaster
from PokerRL.eval.rl_br.workers.ps.Local_RLBR_ParameterServer import \
Local_RLBR_ParameterServer as RLBRParameterServer
from PokerRL.eval.rl_br.workers.la.Local_RLBR_LearnerActor import \
Local_RLBR_LearnerActor as RLBRLearnerActor
from PokerRL.eval.lbr.LocalLBRWorker import LocalLBRWorker as LBRWorker
from PokerRL.eval.br.LocalBRMaster import LocalBRMaster as BRMaster
from PokerRL.eval.head_to_head.LocalHead2HeadMaster import LocalHead2HeadMaster as Head2HeadMaster
# safety measure to avoid overwriting logs when reloading
if name_to_import is not None and iteration_to_import is not None and name_to_import == t_prof.name:
t_prof.name += "_"
print("Creating Chief...")
self.chief_handle = self._ray.create_worker(chief_cls, t_prof)
self.eval_masters = {}
if "br" in list(eval_methods.keys()):
print("Creating BR Evaluator...")
self.eval_masters["br"] = (
self._ray.create_worker(BRMaster, t_prof, self.chief_handle,
eval_agent_cls),
eval_methods["br"] # freq
)
if "h2h" in list(eval_methods.keys()):
print("Creating Head-to-Head Mode Evaluator...")
self.eval_masters["h2h"] = (
self._ray.create_worker(Head2HeadMaster, t_prof,
self.chief_handle, eval_agent_cls),
eval_methods["h2h"] # freq
)
if "lbr" in list(eval_methods.keys()):
print("Creating LBR Evaluator...")
self._lbr_workers = [
self._ray.create_worker(LBRWorker, t_prof, self.chief_handle,
eval_agent_cls)
for _ in range(self._t_prof.module_args["lbr"].n_workers)
]
self.eval_masters["lbr"] = (
self._ray.create_worker(LBRMaster, t_prof, self.chief_handle),
eval_methods["lbr"] # freq
)
self._ray.wait([
self._ray.remote(
self.eval_masters["lbr"][0].set_worker_handles,
*self._lbr_workers)
])
if "rlbr" in list(eval_methods.keys()):
print("Creating RL-BR Evaluator...")
self._rlbr_ps = self._ray.create_worker(
RLBRParameterServer,
t_prof,
self.chief_handle,
)
self._rlbr_las = [
self._ray.create_worker(RLBRLearnerActor, t_prof,
self.chief_handle, eval_agent_cls)
for _ in range(self._t_prof.module_args["rlbr"].n_las)
]
self.eval_masters["rlbr"] = (
self._ray.create_worker(RLBRMaster, t_prof, self.chief_handle,
eval_agent_cls),
eval_methods["rlbr"] # freq
)
self._ray.wait([
self._ray.remote(
self.eval_masters["rlbr"][0].set_learner_actors,
*self._rlbr_las),
])
self._ray.wait([
self._ray.remote(self.eval_masters["rlbr"][0].set_param_server,
self._rlbr_ps),
])
self.crayon = CrayonWrapper(
name=t_prof.name,
chief_handle=self.chief_handle,
path_log_storage=self._t_prof.path_log_storage,
crayon_server_address=t_prof.local_crayon_server_docker_address,
runs_distributed=t_prof.DISTRIBUTED,
runs_cluster=t_prof.CLUSTER,
)
"""
from PokerRL.cfr.LinearCFR import LinearCFR
from PokerRL.game import bet_sets
from PokerRL.game.games import DiscretizedNLLeduc
from PokerRL.rl.base_cls.workers.ChiefBase import ChiefBase
if __name__ == '__main__':
from PokerRL._.CrayonWrapper import CrayonWrapper
n_iterations = 150
name = "LinCFR_EXAMPLE"
# Passing None for t_prof will is enough for ChiefBase. We only use it to log; This CFR impl is not distributed.
chief = ChiefBase(t_prof=None)
crayon = CrayonWrapper(name=name,
path_log_storage=None,
chief_handle=chief,
runs_distributed=False,
runs_cluster=False)
cfr = LinearCFR(name=name,
game_cls=DiscretizedNLLeduc,
agent_bet_set=bet_sets.POT_ONLY,
chief_handle=chief)
for iter_id in range(n_iterations):
print("Iteration: ", iter_id)
cfr.iteration()
crayon.update_from_log_buffer()
crayon.export_all(iter_nr=iter_id)
class DriverBase(WorkerBase):
"""
The Driver runs the HighLevelAlgo and creates all workers. If running distributed, the Driver is the worker node
that launches the computation and distributes tasks between workers.
"""
def __init__(self,
t_prof,
eval_methods,
chief_cls,
eval_agent_cls,
n_iterations=None,
iteration_to_import=None,
name_to_import=None):
"""
Args:
t_prof (TrainingProfile)
eval_methods (dict): dict of {evaluator1_name: frequency, ...} Currently supported evaluators
are "br", "rlbr", and "lbr"
chief_cls (ChiefBase subclass): class, not instance
n_iterations (int) number of iterations to run. If None, runs forever
iteration_to_import (int): step/iteration to import
name_to_import (str): name of the run to import
"""
super().__init__(t_prof=t_prof)
if self._t_prof.CLUSTER:
self._ray.init_cluster(redis_address=t_prof.redis_head_adr)
else:
self._ray.init_local()
file_util.do_pickle(obj=t_prof,
file_name=t_prof.name,
path=t_prof.path_trainingprofiles)
self.n_iterations = n_iterations
self._step_to_import = iteration_to_import
self._name_to_import = name_to_import
if self._t_prof.DISTRIBUTED:
from PokerRL.eval.lbr.DistLBRMaster import DistLBRMaster as LBRMaster
from PokerRL.eval.rl_br.DistRLBRMaster import DistRLBRMaster as RLBRMaster
from PokerRL.eval.rl_br.workers.ps.Dist_RLBR_ParameterServer import \
Dist_RLBR_ParameterServer as RLBRParameterServer
from PokerRL.eval.rl_br.workers.la.Dist_RLBR_LearnerActor import Dist_RLBR_LearnerActor as RLBRLearnerActor
from PokerRL.eval.lbr.DistLBRWorker import DistLBRWorker as LBRWorker
from PokerRL.eval.br.DistBRMaster import DistBRMaster as BRMaster
from PokerRL.eval.head_to_head.DistHead2HeadMaster import DistHead2HeadMaster as Head2HeadMaster
else:
from PokerRL.eval.lbr.LocalLBRMaster import LocalLBRMaster as LBRMaster
from PokerRL.eval.rl_br.LocalRLBRMaster import LocalRLBRMaster as RLBRMaster
from PokerRL.eval.rl_br.workers.ps.Local_RLBR_ParameterServer import \
Local_RLBR_ParameterServer as RLBRParameterServer
from PokerRL.eval.rl_br.workers.la.Local_RLBR_LearnerActor import \
Local_RLBR_LearnerActor as RLBRLearnerActor
from PokerRL.eval.lbr.LocalLBRWorker import LocalLBRWorker as LBRWorker
from PokerRL.eval.br.LocalBRMaster import LocalBRMaster as BRMaster
from PokerRL.eval.head_to_head.LocalHead2HeadMaster import LocalHead2HeadMaster as Head2HeadMaster
# safety measure to avoid overwriting logs when reloading
if name_to_import is not None and iteration_to_import is not None and name_to_import == t_prof.name:
t_prof.name += "_"
print("Creating Chief...")
self.chief_handle = self._ray.create_worker(chief_cls, t_prof)
self.eval_masters = {}
if "br" in list(eval_methods.keys()):
print("Creating BR Evaluator...")
self.eval_masters["br"] = (
self._ray.create_worker(BRMaster, t_prof, self.chief_handle,
eval_agent_cls),
eval_methods["br"] # freq
)
if "h2h" in list(eval_methods.keys()):
print("Creating Head-to-Head Mode Evaluator...")
self.eval_masters["h2h"] = (
self._ray.create_worker(Head2HeadMaster, t_prof,
self.chief_handle, eval_agent_cls),
eval_methods["h2h"] # freq
)
if "lbr" in list(eval_methods.keys()):
print("Creating LBR Evaluator...")
self._lbr_workers = [
self._ray.create_worker(LBRWorker, t_prof, self.chief_handle,
eval_agent_cls)
for _ in range(self._t_prof.module_args["lbr"].n_workers)
]
self.eval_masters["lbr"] = (
self._ray.create_worker(LBRMaster, t_prof, self.chief_handle),
eval_methods["lbr"] # freq
)
self._ray.wait([
self._ray.remote(
self.eval_masters["lbr"][0].set_worker_handles,
*self._lbr_workers)
])
if "rlbr" in list(eval_methods.keys()):
print("Creating RL-BR Evaluator...")
self._rlbr_ps = self._ray.create_worker(
RLBRParameterServer,
t_prof,
self.chief_handle,
)
self._rlbr_las = [
self._ray.create_worker(RLBRLearnerActor, t_prof,
self.chief_handle, eval_agent_cls)
for _ in range(self._t_prof.module_args["rlbr"].n_las)
]
self.eval_masters["rlbr"] = (
self._ray.create_worker(RLBRMaster, t_prof, self.chief_handle,
eval_agent_cls),
eval_methods["rlbr"] # freq
)
self._ray.wait([
self._ray.remote(
self.eval_masters["rlbr"][0].set_learner_actors,
*self._rlbr_las),
])
self._ray.wait([
self._ray.remote(self.eval_masters["rlbr"][0].set_param_server,
self._rlbr_ps),
])
self.crayon = CrayonWrapper(
name=t_prof.name,
chief_handle=self.chief_handle,
path_log_storage=self._t_prof.path_log_storage,
crayon_server_address=t_prof.local_crayon_server_docker_address,
runs_distributed=t_prof.DISTRIBUTED,
runs_cluster=t_prof.CLUSTER,
)
def _maybe_load_checkpoint_init(self):
if self._step_to_import is None:
self._cfr_iter = 0
else:
assert self._step_to_import is not None
print("Loading checkpoint ", self._step_to_import)
self._cfr_iter = self._step_to_import + 1
self.load_checkpoint(step=self._step_to_import,
name_to_load=self._name_to_import)
print("Loaded from iter: ", self._step_to_import)
def _delete_past_checkpoints(self, steps_not_to_delete):
_dir = ospj(self._t_prof.path_checkpoint, self._t_prof.name)
all_dir_names = file_util.get_all_dirs_in_dir(_dir)
dir_names_to_delete = [
e for e in all_dir_names
if e not in [str(s) for s in steps_not_to_delete]
]
for step in dir_names_to_delete:
shutil.rmtree(ospj(_dir, step))
def _delete_past_log_files(self, steps_not_to_delete):
_dir = ospj(self._t_prof.path_log_storage, self._t_prof.name)
all_dir_names = file_util.get_all_dirs_in_dir(_dir)
dir_names_to_delete = [
e for e in all_dir_names
if e not in [str(s) for s in steps_not_to_delete]
]
for step in dir_names_to_delete:
shutil.rmtree(ospj(_dir, step))
def evaluate(self):
"""
puts whole network on wait while the parameters are synced, but evaluates while training the next iteration(s)
"""
evaluators_to_run = []
for kind in list(self.eval_masters.keys()):
ev = self.eval_masters[kind][0]
freq = self.eval_masters[kind][1]
if self._cfr_iter % freq == 0:
evaluators_to_run.append(ev)
print("Evaluating vs.", kind.upper())
self._ray.wait([self._ray.remote(ev.update_weights)])
for ev in evaluators_to_run:
self._ray.remote(ev.evaluate, self._cfr_iter)
def save_logs(self):
"""
Pulls all new logs from the LogBuffer and adds them to crayon (Tensorboard wrapper). It also saves them to disk.
"""
self.crayon.update_from_log_buffer()
self.crayon.export_all(iter_nr=self._cfr_iter)
# Delete past version
s = [self._cfr_iter]
if self._cfr_iter > self._t_prof.log_export_freq + 1:
s.append(self._cfr_iter - self._t_prof.log_export_freq)
self._delete_past_log_files(steps_not_to_delete=s)
def periodically_export_eval_agent(self):
if self._cfr_iter % self._t_prof.eval_agent_export_freq == 0:
self.export_eval_agent()
def periodically_checkpoint(self):
if self._cfr_iter % self._t_prof.checkpoint_freq == 0:
print("Saving Checkpoint")
self.checkpoint(curr_step=self._cfr_iter)
def export_eval_agent(self):
print("Exporting agent")
self._ray.wait(
[self._ray.remote(self.chief_handle.export_agent, self._cfr_iter)])
# ____________________________________________________ Override ____________________________________________________
def run(self):
"""
Calling this function should start up your algorithm.
"""
raise NotImplementedError
def checkpoint(self, curr_step):
"""
Calling this function should trigger "".checkpoint(curr_step=curr_step)"" on all workers in the network.
"""
raise NotImplementedError
def load_checkpoint(self, name_to_load, step):
"""
Calling this function should trigger all workers to load ""name_to_load"" at iteration ""step"" by calling
worker.load_checkpoint(name_to_load=name_to_load, step=step)
"""
raise NotImplementedError