def rl_loop():
# monkeypatch the hyperparams so that we get a quickly executing network.
dual_net.get_default_hyperparams = lambda **kwargs: {
'k': 1, 'fc_width': 2, 'num_shared_layers': 1, 'l2_strength': 2e-4, 'momentum': 0.9}
with tempfile.TemporaryDirectory() as base_dir:
model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap')
selfplay_dir = os.path.join(base_dir, 'data', 'selfplay')
model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap', 'worker1')
gather_dir = os.path.join(base_dir, 'data', 'training_chunks')
sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap')
os.mkdir(os.path.join(base_dir, 'data'))
print("Creating random initial weights...")
dual_net.DualNetworkTrainer(model_save_file).bootstrap()
print("Playing some games...")
# Do two selfplay runs to test gather functionality
main.selfplay(
load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
readouts=10)
main.selfplay(
load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
readouts=10)
print("Gathering game output...")
main.gather(input_directory=selfplay_dir, output_directory=gather_dir)
print("Training on gathered game data... (ctrl+C to quit)")
main.train(gather_dir, save_file=model_save_file, num_steps=10000)
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.
"""
# 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
#monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions.
preprocessing.SHUFFLE_BUFFER_SIZE = 10000
with tempfile.TemporaryDirectory() as base_dir:
model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap')
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')
sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap')
os.mkdir(os.path.join(base_dir, 'data'))
print("Creating random initial weights...")
dual_net.DualNetworkTrainer(model_save_file).bootstrap()
print("Playing some games...")
# Do two selfplay runs to test gather functionality
main.selfplay(load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
main.selfplay(load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
print("Gathering game output...")
main.gather(input_directory=selfplay_dir, output_directory=gather_dir)
print("Training on gathered game data... (ctrl+C to quit)")
main.train(gather_dir,
save_file=model_save_file,
num_steps=10000,
logdir="logs",
verbosity=2)
def rl_loop():
# 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
#monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions.
preprocessing.SHUFFLE_BUFFER_SIZE = 10000
with tempfile.TemporaryDirectory() as base_dir:
model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap')
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')
sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap')
os.mkdir(os.path.join(base_dir, 'data'))
print("Creating random initial weights...")
dual_net.DualNetworkTrainer(model_save_file).bootstrap()
print("Playing some games...")
# Do two selfplay runs to test gather functionality
main.selfplay(
load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
main.selfplay(
load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
print("Gathering game output...")
main.gather(input_directory=selfplay_dir, output_directory=gather_dir)
print("Training on gathered game data... (ctrl+C to quit)")
main.train(gather_dir, save_file=model_save_file,
num_steps=10000, logdir="logs", verbosity=2)
def gather():
print("Gathering game output...")
main.gather(input_directory=SELFPLAY_DIR,
output_directory=TRAINING_CHUNK_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.
"""
# 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
# with tempfile.TemporaryDirectory() as base_dir:
base_dir = "/tmp/minigo"
with open('/tmp/foo', 'w') as fff:
working_dir = os.path.join(base_dir, 'models_in_training')
model_save_path = os.path.join(base_dir, 'models', '000000-bootstrap')
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)
for i in range(100):
qmeas.start_time('main-loop')
print("Playing some games...")
# Do two selfplay runs to test gather functionality
qmeas.start_time('main-loop-self-play')
for j in range(2):
main.selfplay(load_file=model_save_path,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
qmeas.stop_time('main-loop-self-play')
# Do one holdout run to test validation
qmeas.start_time('main-loop-self-play-holdout')
main.selfplay(load_file=model_save_path,
holdout_dir=holdout_dir,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=100,
readouts=10)
qmeas.stop_time('main-loop-self-play-holdout')
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...")
qmeas.start_time('main-loop-gather')
main.gather(input_directory=selfplay_dir,
output_directory=gather_dir)
qmeas.stop_time('main-loop-gather')
print("Training on gathered game data...")
qmeas.start_time('main-loop-train')
main.train(working_dir,
gather_dir,
next_model_save_file,
generation_num=1)
qmeas.stop_time('main-loop-train')
print("Trying validate on 'holdout' game...")
qmeas.start_time('main-loop-validate')
main.validate(working_dir, holdout_dir)
qmeas.stop_time('main-loop-validate')
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,
readouts=10)
qmeas.stop_time('main-loop')
qmeas._flush()
def gather():
print("Gathering game output...")
main.gather(input_directory=SELFPLAY_DIR,
output_directory=TRAINING_CHUNK_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.
"""
# 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
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')
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,
readouts=10)
main.selfplay(
load_file=model_save_path,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
# 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,
readouts=10)
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...")
main.gather(input_directory=selfplay_dir, output_directory=gather_dir)
print("Training on gathered game data...")
main.train(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,
readouts=10)
