def rl_loop_eval():
"""Run the reinforcement learning loop
This tries to create a realistic way to run the reinforcement learning with
all default parameters.
"""
(_, new_model) = get_latest_model()
qmeas.start_time('puzzle')
new_model_path = os.path.join(MODELS_DIR, new_model)
sgf_files = [
'./benchmark_sgf/9x9_pro_YKSH.sgf',
'./benchmark_sgf/9x9_pro_IYMD.sgf',
'./benchmark_sgf/9x9_pro_YSIY.sgf',
'./benchmark_sgf/9x9_pro_IYHN.sgf',
]
result, total_pct = predict_games.report_for_puzzles_parallel(
new_model_path, sgf_files, 2, tries_per_move=1)
#result, total_pct = predict_games.report_for_puzzles(new_model_path, sgf_files, 2, tries_per_move=1)
print('accuracy = ', total_pct)
print('result = ', result)
mlperf_log.minigo_print(key=mlperf_log.EVAL_ACCURACY,
value={
"epoch": iteration,
"value": total_pct
})
mlperf_log.minigo_print(key=mlperf_log.EVAL_TARGET,
value=goparams.TERMINATION_ACCURACY)
qmeas.record('puzzle_total', total_pct)
qmeas.record('puzzle_result', repr(result))
qmeas.record('puzzle_summary', {
'results': repr(result),
'total_pct': total_pct,
'model': new_model
})
qmeas._flush()
with open(os.path.join(BASE_DIR, new_model + '-puzzles.txt'), 'w') as f:
f.write(repr(result))
f.write('\n' + str(total_pct) + '\n')
qmeas.stop_time('puzzle')
if total_pct >= goparams.TERMINATION_ACCURACY:
print('Reaching termination accuracy; ', goparams.TERMINATION_ACCURACY)
mlperf_log.minigo_print(key=mlperf_log.RUN_STOP,
value={"success": True})
with open('TERMINATE_FLAG', 'w') as f:
f.write(repr(result))
f.write('\n' + str(total_pct) + '\n')
qmeas.end()
def rl_loop_pk():
"""Run the reinforcement learning loop
This tries to create a realistic way to run the reinforcement learning with
all default parameters.
"""
_, new_model = get_latest_model()
model_num, model_name = get_second_latest_model()
if not evaluate(model_name, new_model, verbose=0):
print('Flag bury new model')
with open('PK_FLAG', 'w') as f:
f.write("pk\n")
qmeas.end()
print('cleaning up {}'.format(SELFPLAY_BACKUP_DIR))
os.system('rm {}/*'.format(SELFPLAY_BACKUP_DIR))
qmeas.stop_time('selfplay_wait')
if __name__ == '__main__':
#tf.logging.set_verbosity(tf.logging.INFO)
qmeas.start(os.path.join(BASE_DIR, 'stats'))
# make sure seed is small enough
SEED = int(sys.argv[1]) % 65536
# make sure ITERATION is small enough because we use nanosecond as ITERATION
ITERATION = int(sys.argv[2]) % 65536
# get TF logger
log = logging.getLogger('tensorflow')
log.setLevel(logging.DEBUG)
# create formatter and add it to the handlers
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# create file handler which logs even debug messages
fh = logging.FileHandler('tensorflow.log')
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
log.addHandler(fh)
if len(sys.argv) > 3:
main_()
qmeas.end()
bury_latest_model()
if __name__ == '__main__':
#tf.logging.set_verbosity(tf.logging.INFO)
seed = int(sys.argv[1])
iteration = int(sys.argv[2])
print('Setting random seed, iteration = ', seed, iteration)
seed = hash(seed) + iteration
print("training seed: ", seed)
random.seed(seed)
tf.set_random_seed(seed)
numpy.random.seed(seed)
qmeas.start(os.path.join(BASE_DIR, 'stats'))
# get TF logger
log = logging.getLogger('tensorflow')
log.setLevel(logging.DEBUG)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# create file handler which logs even debug messages
fh = logging.FileHandler('tensorflow.log')
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
log.addHandler(fh)
rl_loop()
qmeas.end()
def rl_loop_bury():
bury_latest_model()
qmeas.end()