# output_node_name="logit_layer/MatMul",
# max_batch_size=40000)
# move_scoring_model_path = "/srv/tmp/move_scoring_1/pre_commit_test_1/1528363445"
# save_model_as_graphdef_for_serving(
# model_path=move_scoring_model_path,
# output_model_path=move_scoring_model_path,
# output_filename="tensorrt_move_scoring_graph.pb",
# output_node_name="GatherNd_2",
# # trt_memory_fraction=.45,
# max_batch_size=40000)
BOARD_EVAL_GRAPHDEF_FILENAME = "/srv/tmp/encoder_evaluation_helper/sf_data_attempts/sf_data_crazy_network_13/1529028437/tensorrt_eval_graph.pb"
MOVE_SCORING_GRAPHDEF_FILENAME = "/srv/tmp/move_scoring_1/pre_commit_test_1/1528363445/tensorrt_move_scoring_graph.pb"
BOARD_PREDICTOR, MOVE_PREDICTOR, PREDICTOR_CLOSER = get_inference_functions(
BOARD_EVAL_GRAPHDEF_FILENAME, MOVE_SCORING_GRAPHDEF_FILENAME)
first_move_scoring_testing_filename = "/srv/databases/chess_engine/one_rand_per_board_data/move_scoring_testing_set_1.npy"
batch_first_engine = BatchFirstEngine(4, BOARD_PREDICTOR, MOVE_PREDICTOR,
first_move_scoring_testing_filename)
stockfish_engine = StockFishEngine(
"stockfish-8-linux/Linux/stockfish_8_x64")
random_engine = RandomEngine()
for j in range(1):
play_one_game(batch_first_engine, random_engine, True)
PREDICTOR_CLOSER()
pgn_file_paths[:-1],
to_collect_filters=[during_search_n_man_filter_creator(6)],
data_writer=board_eval_data_writer_creator(
file_ratios,
for_deep_pink_loss_use,
comparison_move_generator=standard_comparison_move_generator,
print_frequency=10000),
num_first_moves_to_skip=5,
output_filenames=final_dataset_filenames)
print("Time taken to create databases:", time.time() - start_time)
BOARD_EVAL_GRAPHDEF_FILE = "/srv/tmp/encoder_evaluation/normal_next_try_4_regulated/1528279891/tensorrt_eval_graph.pb"
TEMP_STR = "/srv/tmp/move_scoring_1/pre_commit_test_1/1528279921/tensorrt_move_scoring_graph.pb"
BOARD_PREDICTOR, _, BOARD_PREDICTOR_CLOSER = get_inference_functions(
BOARD_EVAL_GRAPHDEF_FILE, TEMP_STR)
def cur_eval_fn(node_array):
return BOARD_PREDICTOR(*struct_array_to_ann_inputs(
create_struct_array_from_jitclasses(node_array))).squeeze(axis=1)
def dummy_eval_fn(node_array):
return np.zeros(len(node_array), dtype=np.float32)
# board_eval_writer = board_eval_data_writer_creator(
# [1],
# [True],
# comparison_move_generator=standard_comparison_move_generator,
# print_frequency=100000)
#
#
game_length_filter_creator(170),
excessive_promotion_filter_creator(3)
],
to_collect_filters=[during_search_n_man_filter_creator(3)],
data_writer=board_eval_data_writer_creator(
file_ratios,
for_deep_pink_loss_use,
comparison_move_generator=standard_comparison_move_generator,
print_frequency=10000),
num_first_moves_to_skip=4,
output_filenames=temp_dataset_filenames) #=#final_dataset_filenames)
print("Time taken to create databases:", time.time() - start_time)
BOARD_EVAL_GRAPHDEF_FILENAME = "/srv/tmp/encoder_evaluation_helper/sf_data_attempts/encoder_new_data_2.6/1529409998/tensorrt_eval_graph.pb"
BOARD_PREDICTOR, _, PREDICTOR_CLOSER = get_inference_functions(
BOARD_EVAL_GRAPHDEF_FILENAME, None, session_gpu_memory=.1)
def cur_eval_fn(node_array):
return BOARD_PREDICTOR(
*struct_array_to_ann_inputs(node_array)).squeeze(axis=1)
def dummy_eval_fn(node_array):
return np.zeros(len(node_array), dtype=np.float32)
# STOCKFISH_LOCATION = "/home/sam/PycharmProjects/ChessAI/stockfish-8-linux/Linux/stockfish_8_x64"
#
#
# board_eval_writer = board_eval_data_writer_creator(
# [1],
# [True],
# comparison_move_generator=stockfish_move_generator_creator(STOCKFISH_LOCATION, 1, 1), #Using very little resources so that it won't find a better move than the GM had
OUTPUT_NODE_NAMES = ["%s/%s"%(prefix,name) for name, prefix in zip(OUTPUT_NODE_NAMES, PREFIXES)]
TRT_OUTPUT_FILENAME = "COMBINED_TRT_TEST_314.pbtxt"
# save_trt_graphdef(
# OUTPUT_MODEL_PATH + "/" + REMAPPED_INPUT_NAME,
# OUTPUT_MODEL_PATH,
# TRT_OUTPUT_FILENAME,
# OUTPUT_NODE_NAMES,
# trt_memory_fraction=.65,
# max_batch_size=int(1.25*MAX_SEARCH_BATCH_SIZE),
# write_as_text=True)
MOVE_SCORING_TEST_FILENAME = "/srv/databases/lichess/lichess_db_standard_rated_2018-07_first_100k_games.npy"
ZERO_VALUE_BOARD_FILENAME = "/srv/databases/has_zero_valued_board/combined_zero_boards.npy"
BOARD_PREDICTOR, MOVE_PREDICTOR, PREDICTOR_CLOSER = get_inference_functions(OUTPUT_MODEL_PATH + "/" + TRT_OUTPUT_FILENAME, session_gpu_memory=.2)
search_depth = 4
batch_first_engine = BatchFirstEngine(
search_depth,
BOARD_PREDICTOR,
MOVE_PREDICTOR,
bin_database_file="deeper_network_1.npy",
max_batch_size=MAX_SEARCH_BATCH_SIZE,
saved_zero_shift_file="no_dilations_inception_1.npy",
)
ethereal_engine = UCIEngine("Ethereal-11.00/src/Ethereal", move_time=10, num_threads=1, print_search_info=True)