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Copy pathlocal_rl_loop.py
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126 lines (112 loc) · 4.67 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.
"""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
import selfplay
import example_buffer as eb
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.
flags.FLAGS.conv_width = 8
flags.FLAGS.fc_width = 16
flags.FLAGS.trunk_layers = 1
flags.FLAGS.train_batch_size = 16
flags.FLAGS.shuffle_buffer_size = 1000
dual_net.EXAMPLES_PER_GENERATION = 64
flags.FLAGS.num_readouts = 10
with tempfile.TemporaryDirectory() as base_dir:
flags.FLAGS.base_dir = base_dir
working_dir = os.path.join(base_dir, 'models_in_training')
flags.FLAGS.model_dir = working_dir
model_save_path = os.path.join(base_dir, 'models', '000000-bootstrap')
local_eb_dir = os.path.join(base_dir, 'scratch')
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
selfplay.run_game(
load_file=model_save_path,
holdout_dir=holdout_dir,
selfplay_dir=model_selfplay_dir,
sgf_dir=sgf_dir,
holdout_pct=0
5E45
)
selfplay.run_game(
load_file=model_save_path,
holdout_dir=holdout_dir,
selfplay_dir=model_selfplay_dir,
sgf_dir=sgf_dir,
holdout_pct=0)
# Do one holdout run to test validation
selfplay.run_game(
load_file=model_save_path,
holdout_dir=holdout_dir,
selfplay_dir=model_selfplay_dir,
sgf_dir=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...")
eb.make_chunk_for(output_dir=gather_dir,
local_dir=local_eb_dir,
game_dir=selfplay_dir,
model_num=1,
positions=dual_net.EXAMPLES_PER_GENERATION,
threads=8,
samples_per_game=200)
print("Training on gathered game data...")
main.train_dir(gather_dir,
next_model_save_file)
print("Trying validate on 'holdout' game...")
main.validate(holdout_dir)
print("Verifying that new checkpoint is playable...")
selfplay.run_game(
load_file=next_model_save_file,
holdout_dir=holdout_dir,
selfplay_dir=model_selfplay_dir,
sgf_dir=sgf_dir,
holdout_pct=0)
if __name__ == '__main__':
# horrible horrible hack to pass flag validation.
# Problems come from local_rl_loop calling into main() as library calls
# rather than subprocess calls. Subprocessing calls will allow us to pass
# flags and have them parsed as normal.
remaining_argv = flags.FLAGS(['', '--base_dir=foobar'], known_only=True)
rl_loop()