def start():
"""
Starts one of the processes based on command line arguments.
:return : the worker class that was started
"""
parser = create_parser()
args = parser.parse_args()
config_type = args.type
if args.cmd == 'uci':
disable(999999) # plz don't interfere with uci
config = Config(config_type=config_type)
setup(config, args)
logger.info(f"config type: {config_type}")
import chess_zero.lib.tf_util as ttt
if args.cmd == 'self':
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
ttt.set_session_config(allow_growth=True)
from .worker import self_play
return self_play.start(config)
elif args.cmd == 'opt':
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
ttt.set_session_config(allow_growth=True)
from .worker import optimize
return optimize.start(config)
elif args.cmd == 'eval':
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
ttt.set_session_config(allow_growth=True)
from .worker import evaluate
return evaluate.start(config)
elif args.cmd == 'sl':
from .worker import sl
return sl.start(config)
elif args.cmd == 'uci':
from .play_game import uci
return uci.start(config)
def start(config: Config):
tf_util.set_session_config(per_process_gpu_memory_fraction=0.5)
return SelfPlayWorker(config, env=ChessEnv()).start()
# 轮到人类
print('当前局面如下:')
pretty_print_panel(env.board.panel)
print()
action = input("请输入您要放置的棋子位置:")
while action not in env.board.legal_moves:
print("输入有误！请重新输入.")
action = input("请输入您要放置的棋子位置:")
env.step(action)
print('本局游戏结束！当前棋面为:')
pretty_print_panel(env.board.panel)
if env.winner == Winner.draw:
man_score = 0.5
elif env.white_won == robot_white:
man_score = 0
else:
man_score = 1
cur.append(cur_pipes)
return man_score
if __name__ == '__main__':
import chess_zero.lib.tf_util as ttt
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
ttt.set_session_config(allow_growth=True)
start()
def start(config: Config):
tf_util.set_session_config(per_process_gpu_memory_fraction=0.2)
return EvaluateWorker(config).start()
def start(config: Config):
tf_util.set_session_config(config.trainer.vram_frac)
return OptimizeWorker(config).start()
def start(config: Config):
tf_util.set_session_config(per_process_gpu_memory_fraction=0.59)
return OptimizeWorker(config).start()
def start(config: Config):
tf_util.set_session_config(per_process_gpu_memory_fraction=0.1)
return SupervisedLearningWorker(config, env=ChessEnv()).start()
def start(config: Config):
tf_util.set_session_config(config.eval.vram_frac)
return EvaluateWorker(config).start()
