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# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Wrapper scripts to ensure that main.py commands are called correctly."""
import argh
import argparse
import cloud_logging
import logging
import os
import main
import shipname
import sys
import time
from utils import timer
from tensorflow import gfile
# Pull in environment variables. Run `source ./cluster/common` to set these.
BUCKET_NAME = os.environ['BUCKET_NAME']
BASE_DIR = "gs://{}".format(BUCKET_NAME)
MODELS_DIR = os.path.join(BASE_DIR, 'models')
SELFPLAY_DIR = os.path.join(BASE_DIR, 'data/selfplay')
HOLDOUT_DIR = os.path.join(BASE_DIR, 'data/holdout')
SGF_DIR = os.path.join(BASE_DIR, 'sgf')
TRAINING_CHUNK_DIR = os.path.join(BASE_DIR, 'data', 'training_chunks')
ESTIMATOR_WORKING_DIR = 'estimator_working_dir'
# How many games before the selfplay workers will stop trying to play more.
MAX_GAMES_PER_GENERATION = 10000
# What percent of games to holdout from training per generation
HOLDOUT_PCT = 0.05
def print_flags():
flags = {
'BUCKET_NAME': BUCKET_NAME,
'BASE_DIR': BASE_DIR,
'MODELS_DIR': MODELS_DIR,
'SELFPLAY_DIR': SELFPLAY_DIR,
'HOLDOUT_DIR': HOLDOUT_DIR,
'SGF_DIR': SGF_DIR,
'TRAINING_CHUNK_DIR': TRAINING_CHUNK_DIR,
'ESTIMATOR_WORKING_DIR': ESTIMATOR_WORKING_DIR,
}
print("Computed variables are:")
print('\n'.join('--{}={}'.format(flag, value)
for flag, value in flags.items()))
def get_models():
"""Finds all models, returning a list of model number and names
sorted increasing.
Returns: [(13, 000013-modelname), (17, 000017-modelname), ...etc]
"""
all_models = gfile.Glob(os.path.join(MODELS_DIR, '*.meta'))
model_filenames = [os.path.basename(m) for m in all_models]
model_numbers_names = sorted([
(shipname.detect_model_num(m), shipname.detect_model_name(m))
for m in model_filenames])
return model_numbers_names
def get_latest_model():
"""Finds the latest model, returning its model number and name
Returns: (17, 000017-modelname)
"""
return get_models()[-1]
def get_model(model_num):
models = {k: v for k, v in get_models()}
if not model_num in models:
raise ValueError("Model {} not found!".format(model_num))
return models[model_num]
def game_counts(n_back=20):
"""Prints statistics for the most recent n_back models"""
all_models = gfile.Glob(os.path.join(MODELS_DIR, '*.meta'))
model_filenames = sorted([os.path.basename(m).split('.')[0]
for m in all_models], reverse=True)
for m in model_filenames[:n_back]:
games = gfile.Glob(os.path.join(SELFPLAY_DIR, m, '*.zz'))
print(m, len(games))
def bootstrap():
bootstrap_name = shipname.generate(0)
bootstrap_model_path = os.path.join(MODELS_DIR, bootstrap_name)
print("Bootstrapping with working dir {}\n Model 0 exported to {}".format(
ESTIMATOR_WORKING_DIR, bootstrap_model_path))
main.bootstrap(ESTIMATOR_WORKING_DIR, bootstrap_model_path)
def selfplay(readouts=1600, verbose=2, resign_threshold=0.99):
_, model_name = get_latest_model()
games = gfile.Glob(os.path.join(SELFPLAY_DIR, model_name, '*.zz'))
if len(games) > MAX_GAMES_PER_GENERATION:
print("{} has enough games ({})".format(model_name, len(games)))
time.sleep(10*60)
sys.exit(1)
print("Playing a game with model {}".format(model_name))
model_save_path = os.path.join(MODELS_DIR, model_name)
game_output_dir = os.path.join(SELFPLAY_DIR, model_name)
game_holdout_dir = os.path.join(HOLDOUT_DIR, model_name)
sgf_dir = os.path.join(SGF_DIR, model_name)
main.selfplay(
load_file=model_save_path,
output_dir=game_output_dir,
holdout_dir=game_holdout_dir,
output_sgf=sgf_dir,
readouts=readouts,
holdout_pct=HOLDOUT_PCT,
resign_threshold=resign_threshold,
verbose=verbose,
)
def gather():
print("Gathering game output...")
main.gather(input_directory=SELFPLAY_DIR,
output_directory=TRAINING_CHUNK_DIR)
def train():
model_num, model_name = get_latest_model()
print("Training on gathered game data, initializing from {}".format(model_name))
new_model_name = shipname.generate(model_num + 1)
print("New model will be {}".format(new_model_name))
load_file = os.path.join(MODELS_DIR, model_name)
save_file = os.path.join(MODELS_DIR, new_model_name)
try:
main.train(ESTIMATOR_WORKING_DIR, TRAINING_CHUNK_DIR, save_file,
generation_num=model_num + 1)
except:
print("Got an error training, muddling on...")
logging.exception("Train error")
def validate(model_num=None, validate_name=None):
""" Runs validate on the directories up to the most recent model, or up to
(but not including) the model specified by `model_num`
"""
if model_num is None:
model_num, model_name = get_latest_model()
else:
model_num = int(model_num)
model_name = get_model(model_num)
# Model N was trained on games up through model N-2, so the validation set
# should only be for models through N-2 as well, thus the (model_num - 1)
# term.
models = list(
filter(lambda num_name: num_name[0] < (model_num - 1), get_models()))
# Run on the most recent 50 generations,
# TODO(brianklee): make this hyperparameter dependency explicit/not hardcoded
holdout_dirs = [os.path.join(HOLDOUT_DIR, pair[1])
for pair in models[-50:]]
main.validate(ESTIMATOR_WORKING_DIR, *holdout_dirs,
checkpoint_name=os.path.join(MODELS_DIR, model_name),
validate_name=validate_name)
parser = argparse.ArgumentParser()
argh.add_commands(parser, [train, selfplay, gather,
bootstrap, game_counts, validate])
if __name__ == '__main__':
print_flags()
cloud_logging.configure()
argh.dispatch(parser)