def rl_loop():
"""Run the reinforcement learning loop
This is meant to be more of an integration test than a realistic way to run
the reinforcement learning.
"""
# TODO(brilee): move these all into appropriate local_flags file.
# monkeypatch the hyperparams so that we get a quickly executing network.
flags.FLAGS.conv_width = 8
flags.FLAGS.fc_width = 16
flags.FLAGS.trunk_layers = 1
flags.FLAGS.train_batch_size = 16
flags.FLAGS.shuffle_buffer_size = 1000
dual_net.EXAMPLES_PER_GENERATION = 64
flags.FLAGS.num_readouts = 10
with tempfile.TemporaryDirectory() as base_dir:
flags.FLAGS.base_dir = base_dir
working_dir = os.path.join(base_dir, 'models_in_training')
flags.FLAGS.model_dir = working_dir
model_save_path = os.path.join(base_dir, 'models', '000000-bootstrap')
local_eb_dir = os.path.join(base_dir, 'scratch')
next_model_save_file = os.path.join(base_dir, 'models',
'000001-nextmodel')
selfplay_dir = os.path.join(base_dir, 'data', 'selfplay')
model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap')
gather_dir = os.path.join(base_dir, 'data', 'training_chunks')
holdout_dir = os.path.join(base_dir, 'data', 'holdout',
'000000-bootstrap')
sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap')
os.makedirs(os.path.join(base_dir, 'data'), exist_ok=True)
print("Creating random initial weights...")
main.bootstrap(working_dir, model_save_path)
print("Playing some games...")
# Do two selfplay runs to test gather functionality
main.selfplay(load_file=model_save_path,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0)
main.selfplay(load_file=model_save_path,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0)
# Do one holdout run to test validation
main.selfplay(load_file=model_save_path,
holdout_dir=holdout_dir,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=100)
print("See sgf files here?")
sgf_listing = subprocess.check_output(["ls", "-l", sgf_dir + "/full"])
print(sgf_listing.decode("utf-8"))
print("Gathering game output...")
eb.make_chunk_for(output_dir=gather_dir,
local_dir=local_eb_dir,
game_dir=selfplay_dir,
model_num=1,
positions=dual_net.EXAMPLES_PER_GENERATION,
threads=8,
samples_per_game=200)
print("Training on gathered game data...")
main.train_dir(gather_dir, next_model_save_file)
print("Trying validate on 'holdout' game...")
main.validate(working_dir, holdout_dir)
print("Verifying that new checkpoint is playable...")
main.selfplay(load_file=next_model_save_file,
holdout_dir=holdout_dir,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir)
def rl_loop():
"""Run the reinforcement learning loop
This is meant to be more of an integration test than a realistic way to run
the reinforcement learning.
"""
# TODO(brilee): move these all into appropriate local_flags file.
# monkeypatch the hyperparams so that we get a quickly executing network.
dual_net.get_default_hyperparams = lambda **kwargs: {
'k': 8,
'fc_width': 16,
'num_shared_layers': 1,
'l2_strength': 1e-4,
'momentum': 0.9
}
dual_net.TRAIN_BATCH_SIZE = 16
dual_net.EXAMPLES_PER_GENERATION = 64
# monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions.
preprocessing.SHUFFLE_BUFFER_SIZE = 1000
flags.FLAGS.num_readouts = 10
with tempfile.TemporaryDirectory() as base_dir:
working_dir = os.path.join(base_dir, 'models_in_training')
model_save_path = os.path.join(base_dir, 'models', '000000-bootstrap')
local_eb_dir = os.path.join(base_dir, 'scratch')
next_model_save_file = os.path.join(base_dir, 'models',
'000001-nextmodel')
selfplay_dir = os.path.join(base_dir, 'data', 'selfplay')
model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap')
gather_dir = os.path.join(base_dir, 'data', 'training_chunks')
holdout_dir = os.path.join(base_dir, 'data', 'holdout',
'000000-bootstrap')
sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap')
os.makedirs(os.path.join(base_dir, 'data'), exist_ok=True)
print("Creating random initial weights...")
main.bootstrap(working_dir, model_save_path)
print("Playing some games...")
# Do two selfplay runs to test gather functionality
main.selfplay(load_file=model_save_path,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0)
main.selfplay(load_file=model_save_path,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0)
# Do one holdout run to test validation
main.selfplay(load_file=model_save_path,
holdout_dir=holdout_dir,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=100)
print("See sgf files here?")
sgf_listing = subprocess.check_output(["ls", "-l", sgf_dir + "/full"])
print(sgf_listing.decode("utf-8"))
print("Gathering game output...")
eb.make_chunk_for(output_dir=gather_dir,
local_dir=local_eb_dir,
game_dir=selfplay_dir,
model_num=1,
positions=dual_net.EXAMPLES_PER_GENERATION,
threads=8,
samples_per_game=200)
print("Training on gathered game data...")
main.train_dir(working_dir,
gather_dir,
next_model_save_file,
generation_num=1)
print("Trying validate on 'holdout' game...")
main.validate(working_dir, holdout_dir)
print("Verifying that new checkpoint is playable...")
main.selfplay(load_file=next_model_save_file,
holdout_dir=holdout_dir,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir)