def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
chess_model = PlayWithHuman(config)
env = ChessEnv().reset()
human_is_black = random() < 0.5
chess_model.start_game(human_is_black)
while not env.done:
if env.board.turn == chess.BLACK:
if not human_is_black:
action = chess_model.move_by_ai(env)
print("IA moves to: " + action)
else:
action = chess_model.move_by_human(env)
print("You move to: " + action)
else:
if human_is_black:
action = chess_model.move_by_ai(env)
print("IA moves to: " + action)
else:
action = chess_model.move_by_human(env)
print("You move to: " + action)
board, info = env.step(action)
env.render()
print("Board fen = " + board.fen())
print("\nEnd of the game.")
print("Game result:")
print(env.board.result())
def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
me_player = None
env = ChessEnv().reset()
app = Flask(__name__)
model = ChessModel(config)
if not load_best_model_weight(model):
raise RuntimeError("Best model not found!")
player = ChessPlayer(config, model.get_pipes(config.play.search_threads))
@app.route('/play', methods=["GET", "POST"])
def play():
data = request.get_json()
print(data["position"])
env.update(data["position"])
env.step(data["moves"], False)
bestmove = player.action(env, False)
return jsonify(bestmove)
app.run(host="0.0.0.0", port="8080")
def __init__(self):
# Tensorflow session
import tensorflow as tf
log.debug("Initializing Tensorflow session...")
tf_session_config = tf.ConfigProto()
tf_session_config.gpu_options.allow_growth = True
self.sess = tf.Session(config=tf_session_config)
from chess_zero.config import Config, PlayWithHumanConfig
from chess_zero.env.chess_env import ChessEnv
self.chess_env_class = ChessEnv
default_config = Config()
PlayWithHumanConfig().update_play_config(default_config.play)
self.alpha_player = self.get_player_from_model(default_config)
def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
me_player = None
env = ChessEnv().reset()
while True:
line = input()
words = line.rstrip().split(" ", 1)
if words[0] == "uci":
print("id name ChessZero")
print("id author ChessZero")
print("uciok")
elif words[0] == "isready":
if not me_player:
me_player = get_player(config)
print("readyok")
elif words[0] == "ucinewgame":
env.reset()
elif words[0] == "position":
words = words[1].split(" ", 1)
if words[0] == "startpos":
env.reset()
else:
if words[0] == "fen": # skip extraneous word
words = words[1].split(' ', 1)
fen = words[0]
for _ in range(5):
words = words[1].split(' ', 1)
fen += " " + words[0]
env.update(fen)
if len(words) > 1:
words = words[1].split(" ", 1)
if words[0] == "moves":
for w in words[1].split(" "):
env.step(w, False)
elif words[0] == "go":
if not me_player:
me_player = get_player(config)
action = me_player.action(env, False)
print(f"bestmove {action}")
elif words[0] == "stop":
pass
elif words[0] == "quit":
break
def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
config.play.thinking_loop = 1
chess_model = None
env = ChessEnv().reset()
while True:
line = input()
words = line.rstrip().split(" ", 1)
if words[0] == "uci":
print("id name ChessZero")
print("id author ChessZero")
print("uciok")
elif words[0] == "isready":
if chess_model is None:
chess_model = PlayWithHuman(config)
print("readyok")
elif words[0] == "ucinewgame":
env.reset()
elif words[0] == "position":
words = words[1].split(" ", 1)
if words[0] == "startpos":
env.reset()
else:
fen = words[0]
for _ in range(5):
words = words[1].split(' ', 1)
fen += " "+words[0]
env.update(fen)
if len(words) > 1:
words = words[1].split(" ", 1)
if words[0] == "moves":
for w in words[1].split(" "):
env.step(w, False)
elif words[0] == "go":
action = chess_model.move_by_ai(env)
print(f"bestmove {action}")
elif words[0] == "stop":
pass
elif words[0] == "quit":
break
def start(config: Config):
PlayWithHumanConfig().update_play_config(config.play)
chess_model = PlayWithEngine(config)
env = ChessEnv().reset()
human_is_black = random() < 0.5
chess_model.start_game(human_is_black)
while not env.done:
if (env.board.turn == chess.BLACK) == human_is_black:
action = chess_model.move_by_opponent(env)
print("You move to: " + action)
else:
action = chess_model.move_by_ai(env)
print("AI moves to: " + action)
board, info = env.step(action)
env.render()
print("Board FEN = " + board.fen())
print("\nEnd of the game.") #spaces after this?
print("Game result:") #and this?
print(env.board.result())
def create(self):
# Initial Alpha Zero setup
default_config = Config()
PlayWithHumanConfig().update_play_config(default_config.play)
return self.get_player_from_model(default_config)
# The gRPC serve function.
#
# Params:
# max_workers: pool of threads to execute calls asynchronously
# port: gRPC server port
#
# Add all your classes to the server here.
# (from generated .py files by protobuf compiler)
def serve(max_workers=10, port=7777):
server = grpc.server(futures.ThreadPoolExecutor(max_workers=max_workers))
grpc_bt_grpc.add_AlphaZeroServicer_to_server(AlphaZeroServicer(), server)
server.add_insecure_port("[::]:{}".format(port))
return server
if __name__ == "__main__":
"""
Runs the gRPC server to communicate with the Snet Daemon.
"""
# Initial Alpha Zero setup
default_config = Config()
PlayWithHumanConfig().update_play_config(default_config.play)
ALPHA_ZERO_PLAYER = get_player_from_model(default_config)
parser = service.common.common_parser(__file__)
args = parser.parse_args(sys.argv[1:])
service.common.main_loop(serve, args)