def train(working_dir):
model_num, model_name = fsdb.get_latest_model()
games = gfile.Glob(os.path.join(fsdb.selfplay_dir(), model_name, '*.zz'))
if len(games) < MIN_GAMES_PER_GENERATION:
print("{} doesn't have enough games to train a new model yet ({})".
format(model_name, len(games)))
print("Sleeping...")
time.sleep(10 * 60)
print("Done...")
sys.exit(1)
print("Training on gathered game data, initializing from {}".format(
model_name))
new_model_num = model_num + 1
new_model_name = shipname.generate(new_model_num)
print("New model will be {}".format(new_model_name))
training_file = os.path.join(fsdb.golden_chunk_dir(),
str(new_model_num) + '.tfrecord.zz')
while not gfile.Exists(training_file):
print("Waiting for", training_file)
time.sleep(1 * 60)
print("Using Golden File:", training_file)
save_file = os.path.join(fsdb.models_dir(), new_model_name)
try:
main.train(working_dir, [training_file],
save_file,
generation_num=model_num + 1)
except:
logging.exception("Train error")
def fill_and_wait_models(bufsize=dual_net.EXAMPLES_PER_GENERATION,
write_dir=None,
threads=8,
model_window=100,
skip_first_rsync=False):
""" Fills a ringbuffer with positions from the most recent games, then
continually rsync's and updates the buffer until a new model is promoted.
Once it detects a new model, iit then dumps its contents for training to
immediately begin on the next model.
"""
write_dir = write_dir or fsdb.golden_chunk_dir()
buf = ExampleBuffer(bufsize)
models = fsdb.get_models()[-model_window:]
if not skip_first_rsync:
with timer("Rsync"):
smart_rsync(models[-1][0] - 6)
files = tqdm(map(files_for_model, models), total=len(models))
buf.parallel_fill(list(itertools.chain(*files)), threads=threads)
print("Filled buffer, watching for new games")
while fsdb.get_latest_model()[0] == models[-1][0]:
with timer("Rsync"):
smart_rsync(models[-1][0] - 2)
new_files = tqdm(map(files_for_model, models[-2:]), total=len(models))
buf.update(list(itertools.chain(*new_files)))
time.sleep(60)
latest = fsdb.get_latest_model()
print("New model!", latest[1], "!=", models[-1][1])
print(buf)
buf.flush(os.path.join(write_dir, str(latest[0] + 1) + '.tfrecord.zz'))
def train(working_dir):
model_num, model_name = fsdb.get_latest_model()
print("Training on gathered game data, initializing from {}".format(model_name))
new_model_num = model_num + 1
new_model_name = shipname.generate(new_model_num)
print("New model will be {}".format(new_model_name))
training_file = os.path.join(
fsdb.golden_chunk_dir(), str(new_model_num) + '.tfrecord.zz')
while not gfile.Exists(training_file):
print("Waiting for", training_file)
time.sleep(1*60)
print("Using Golden File:", training_file)
try:
save_file = os.path.join(fsdb.models_dir(), new_model_name)
print("Training model")
dual_net.train(training_file)
print("Exporting model to ", save_file)
dual_net.export_model(working_dir, save_file)
except Exception as e:
import traceback
logging.error(traceback.format_exc())
print(traceback.format_exc())
logging.exception("Train error")
sys.exit(1)
def fill_and_wait_time(bufsize=dual_net.EXAMPLES_PER_GENERATION,
write_dir=None,
threads=32,
start_from=None):
start_from = start_from or dt.datetime.utcnow()
write_dir = write_dir or fsdb.golden_chunk_dir()
buf = ExampleBuffer(bufsize)
chunk_to_make, fast_write = _determine_chunk_to_make(write_dir)
hours = fsdb.get_hour_dirs()
with timer("Rsync"):
time_rsync(
min(dt.datetime.strptime(hours[-1], "%Y-%m-%d-%H/"), start_from))
start_from = dt.datetime.utcnow()
hours = fsdb.get_hour_dirs()
files = (tf.gfile.Glob(os.path.join(LOCAL_DIR, d, "*.zz"))
for d in reversed(hours)
if tf.gfile.Exists(os.path.join(LOCAL_DIR, d)))
files = itertools.islice(files, get_window_size(chunk_to_make))
models = fsdb.get_models()
buf.parallel_fill(list(itertools.chain.from_iterable(files)),
threads=threads)
print("Filled buffer, watching for new games")
while (fsdb.get_latest_model() == models[-1]
or buf.total_updates < MINIMUM_NEW_GAMES):
with timer("Rsync"):
time_rsync(start_from - dt.timedelta(minutes=60))
start_from = dt.datetime.utcnow()
hours = sorted(fsdb.get_hour_dirs(LOCAL_DIR))
new_files = list(
map(lambda d: tf.gfile.Glob(os.path.join(LOCAL_DIR, d, '*.zz')),
hours[-2:]))
buf.update(list(itertools.chain.from_iterable(new_files)))
if fast_write:
break
time.sleep(30)
if fsdb.get_latest_model() != models[-1]:
print("New model! Waiting for games. Got", buf.total_updates,
"new games so far")
latest = fsdb.get_latest_model()
print("New model!", latest[1], "!=", models[-1][1])
print(buf)
buf.flush(chunk_to_make)
