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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.
"""Runs a RL loop locally. Mostly for integration testing purposes.
A successful run will bootstrap, selfplay, gather, and start training for
a while. You should see the combined_cost variable drop steadily, and ideally
overfit to a near-zero loss.
"""
import os
import sys
import tempfile
from absl import flags
import preprocessing
import dual_net
import go
import main
from tensorflow import gfile
import subprocess
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')
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...")
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)
if __name__ == '__main__':
remaining_argv = flags.FLAGS(sys.argv, known_only=True)
rl_loop()