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122 lines (100 loc) · 4.06 KB
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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 os
import main
import shipname
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')
def print_flags():
flags = {
'BUCKET_NAME': BUCKET_NAME,
'BASE_DIR': BASE_DIR,
'MODELS_DIR': MODELS_DIR,
'SELFPLAY_DIR': SELFPLAY_DIR,
'SGF_DIR': SGF_DIR,
'TRAINING_CHUNK_DIR': TRAINING_CHUNK_DIR,
}
print("Computed variables are:")
print('\n'.join('--{}={}'.format(flag, value)
for flag, value in flags.items()))
def get_latest_model():
'''Finds the latest model, returning its model number and name
Returns: (17, 000017-modelname)
'''
all_models = gfile.Glob(os.path.join(MODELS_DIR, '*.meta'))
model_filenames = [os.path.basename(m) for m in all_models]
model_numbers_names = [
(shipname.detect_model_num(m), shipname.detect_model_name(m))
for m in model_filenames]
latest_model = sorted(model_numbers_names, reverse=True)[0]
return latest_model
def convert_all():
all_models = gfile.Glob(os.path.join(MODELS_DIR, '*.meta'))
model_filenames = [os.path.basename(m).split('.')[0] for m in all_models]
print(model_filenames)
from tqdm import tqdm
import multiprocessing
pool = multiprocessing.Pool(10)
for m in tqdm(model_filenames[10:]):
p = os.path.join('gs://mugozero-v2/games/', m) + "/**/*.gz"
games = gfile.Glob(p)
pool.map(main.convert, games)
def bootstrap():
bootstrap_name = shipname.generate(0)
bootstrap_model_path = os.path.join(MODELS_DIR, bootstrap_name)
print("Bootstrapping model at {}".format(bootstrap_model_path))
main.bootstrap(bootstrap_model_path)
def selfplay(readouts=1600, verbose=2, resign_thresh
5324
old=0.99):
_, model_name = get_latest_model()
print("Playing a game with model {}".format(model_name))
model_save_file = os.path.join(MODELS_DIR, model_name)
main.selfplay(
load_file=model_save_file,
output_dir=os.path.join(SELFPLAY_DIR, model_name),
holdout_dir=os.path.join(HOLDOUT_DIR, model_name),
output_sgf=SGF_DIR,
readouts=readouts,
verbose=verbose,
)
def gather():
print("Gathering game output...")
main.gather(input_directory=SELFPLAY_DIR,
output_directory=TRAINING_CHUNK_DIR)
def train(logdir=None):
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)
main.train(TRAINING_CHUNK_DIR, save_file=save_file, load_file=load_file,
generation_num=model_num, logdir=logdir)
parser = argparse.ArgumentParser()
argh.add_commands(parser, [train, selfplay, gather, bootstrap, convert_all])
if __name__ == '__main__':
print_flags()
cloud_logging.configure()
argh.dispatch(parser)