def client_loop(
model_manager: polygames.ModelManager,
start_time: float,
context: tube.Context,
execution_params: ExecutionParams,
) -> None:
model_manager.start()
max_time = execution_params.max_time
while max_time is None or time.time() < start_time + max_time:
time.sleep(60)
print("Resource usage:")
print(utils.get_res_usage_str())
print("Context stats:")
print(context.get_stats_str())
def client_loop(
assembler: tube.ChannelAssembler,
start_time: float,
context: tube.Context,
execution_params: ExecutionParams,
) -> None:
assembler.start()
max_time = execution_params.max_time
while max_time is None or time.time() < start_time + max_time:
time.sleep(60)
print("Resource usage:")
print(utils.get_res_usage_str())
print("Context stats:")
print(context.get_stats_str())
def train_model(
command_history: utils.CommandHistory,
start_time: float,
model: torch.jit.ScriptModule,
device: torch.device,
ddpmodel,
optim: torch.optim.Optimizer,
context: tube.Context,
model_manager: polygames.ModelManager,
get_train_reward: Callable[[], List[int]],
game_params: GameParams,
model_params: ModelParams,
optim_params: OptimParams,
simulation_params: SimulationParams,
execution_params: ExecutionParams,
epoch: int = 0,
) -> None:
info = zutils.get_game_info(game_params)
c, h, w = info["feature_size"][:3]
rc, rh, rw = info["raw_feature_size"][:3]
c_prime, h_prime, w_prime = info["action_size"][:3]
predicts = (2 if game_params.predict_end_state else
0) + game_params.predict_n_states
batchsizes = {
"s": [c, h, w],
"v": [3 if getattr(model, "logit_value", False) else 1],
"pred_v": [1],
"pi": [c_prime, h_prime, w_prime],
"pi_mask": [c_prime, h_prime, w_prime]
}
if game_params.player == "forward":
batchsizes["action_pi"] = [c_prime, h_prime, w_prime]
if predicts > 0:
batchsizes["predict_pi"] = [rc * predicts, rh, rw]
batchsizes["predict_pi_mask"] = [rc * predicts, rh, rw]
if getattr(model, "rnn_state_shape", None) is not None:
batchsizes["rnn_state_mask"] = [1]
if execution_params.rnn_seqlen > 0:
for k, v in batchsizes.items():
batchsizes[k] = [execution_params.rnn_seqlen, *v]
if getattr(model, "rnn_state_shape", None) is not None:
batchsizes["rnn_initial_state"] = model.rnn_state_shape
rank = 0
if ddpmodel:
rank = torch.distributed.get_rank()
executor = ThreadPoolExecutor(max_workers=1)
savefuture = None
stat = utils.MultiCounter(execution_params.checkpoint_dir)
max_time = execution_params.max_time
init_epoch = epoch
while max_time is None or time.time() < start_time + max_time:
if epoch - init_epoch >= optim_params.num_epoch:
break
epoch += 1
if rank == 0 and epoch % execution_params.saving_period == 0:
model_manager.add_tournament_model("e%d" % (epoch),
model.state_dict())
savestart = time.time()
if savefuture is not None:
savefuture.result()
savefuture = utils.save_checkpoint(
command_history=command_history,
epoch=epoch,
model=model,
optim=optim,
game_params=game_params,
model_params=model_params,
optim_params=optim_params,
simulation_params=simulation_params,
execution_params=execution_params,
executor=executor)
print("checkpoint saved in %gs" % (time.time() - savestart))
_train_epoch(
model=model,
device=device,
ddpmodel=ddpmodel,
batchsizes=batchsizes,
optim=optim,
model_manager=model_manager,
stat=stat,
epoch=epoch,
optim_params=optim_params,
sync_period=simulation_params.sync_period,
)
# resource usage stats
print("Resource usage:")
print(utils.get_res_usage_str())
print("Context stats:")
print(context.get_stats_str())
# train result
print(
">>>train: epoch: %d, %s" %
(epoch, utils.Result(get_train_reward()).log()),
flush=True,
)
if savefuture is not None:
savefuture.result()
# checkpoint last state
utils.save_checkpoint(
command_history=command_history,
epoch=epoch,
model=model,
optim=optim,
game_params=game_params,
model_params=model_params,
optim_params=optim_params,
simulation_params=simulation_params,
execution_params=execution_params,
)
def train_model(
command_history: utils.CommandHistory,
start_time: float,
train_device: torch.device,
model: torch.jit.ScriptModule,
model_path: Path,
ddpmodel,
optim: torch.optim.Optimizer,
context: tube.Context,
assembler: tube.ChannelAssembler,
get_train_reward: Callable[[], List[int]],
game_params: GameParams,
model_params: ModelParams,
optim_params: OptimParams,
simulation_params: SimulationParams,
execution_params: ExecutionParams,
epoch: int = 0,
) -> None:
stat = utils.MultiCounter(execution_params.checkpoint_dir)
max_time = execution_params.max_time
init_epoch = epoch
while max_time is None or time.time() < start_time + max_time:
if epoch - init_epoch >= optim_params.num_epoch:
break
epoch += 1
if not (epoch - init_epoch) % execution_params.saving_period:
assembler.add_tournament_model("e%d" % (epoch), model.state_dict())
utils.save_checkpoint(
command_history=command_history,
epoch=epoch,
model=model,
optim=optim,
assembler=assembler,
game_params=game_params,
model_params=model_params,
optim_params=optim_params,
simulation_params=simulation_params,
execution_params=execution_params,
)
_train_epoch(
train_device=train_device,
model=model,
ddpmodel=ddpmodel,
model_path=model_path,
optim=optim,
assembler=assembler,
stat=stat,
epoch=epoch,
optim_params=optim_params,
sync_period=simulation_params.sync_period,
)
# resource usage stats
print("Resource usage:")
print(utils.get_res_usage_str())
print("Context stats:")
print(context.get_stats_str())
# train result
print(
">>>train: epoch: %d, %s" %
(epoch, utils.Result(get_train_reward()).log()),
flush=True,
)
# checkpoint last state
utils.save_checkpoint(
command_history=command_history,
epoch=epoch,
model=model,
optim=optim,
assembler=assembler,
game_params=game_params,
model_params=model_params,
optim_params=optim_params,
simulation_params=simulation_params,
execution_params=execution_params,
)