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170 lines (141 loc) · 5.87 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.
"""Bookkeeping around other entry points to automate the rl pipeline."""
import argparse
import logging
import os
import sys
import time
from absl import flags
import argh
from tensorflow import gfile
import itertools
import random
import cloud_logging
import dual_net
import fsdb
import main
import selfplay as selfplay_lib
import shipname
# How many games before the selfplay workers will stop trying to play more.
MAX_GAMES_PER_GENERATION = 20000
# How many games minimum, until the trainer will train
MIN_GAMES_PER_GENERATION = 10000
# What percent of games to holdout from training per generation
HOLDOUT_PCT = 0.05
def bootstrap(working_dir):
bootstrap_name = shipname.generate(0)
bootstrap_model_path = os.path.join(fsdb.models_dir(), bootstrap_name)
print("Bootstrapping with working dir {}\n Model 0 exported to {}".format(
working_dir, bootstrap_model_path))
main.bootstrap(working_dir, bootstrap_model_path)
def selfplay(verbose=2):
_, model_name = fsdb.get_latest_model()
games = gfile.Glob(os.path.join(fsdb.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(fsdb.models_dir(), model_name)
selfplay_dir = os.path.join(fsdb.selfplay_dir(), model_name)
game_holdout_dir = os.path.join(fsdb.holdout_dir(), model_name)
sgf_dir = os.path.join(fsdb.sgf_dir(), model_name)
selfplay_lib.run_game(
load_file=model_save_path,
selfplay_dir=selfplay_dir,
holdout_dir=game_holdout_dir,
sgf_dir=sgf_dir,
holdout_pct=HOLDOUT_PCT,
verbose=verbose,
)
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 validate(working_dir, 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, _ = fsdb.get_latest_model()
else:
model_num = int(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), fsdb.get_models()))
# Run on the most recent 30 generations,
# TODO(brianklee): make this hyperparameter dependency explicit/not hardcoded
holdout_dirs = [os.path.join(fsdb.holdout_dir(), pair[1])
for pair in models[-30:]]
main.validate(working_dir, *holdout_dirs,
validate_name=validate_name)
def validate_hourly(working_dir, validate_name=None):
""" compiles a list of games based on the new hourly directory format. Then
calls validate on it """
holdout_files = (os.path.join(fsdb.holdout_dir(), d, f)
for d in reversed(gfile.ListDirectory(fsdb.holdout_dir()))
for f in gfile.ListDirectory(os.path.join(fsdb.holdout_dir(), d))
if gfile.IsDirectory(os.path.join(fsdb.holdout_dir(), d)))
holdout_files = list(itertools.islice(holdout_files, 20000))
random.shuffle(holdout_files)
dual_net.validate(holdout_files)
def backfill():
models = [m[1] for m in fsdb.get_models()]
import dual_net
import tensorflow as tf
from tqdm import tqdm
features, labels = dual_net.get_inference_input()
dual_net.model_fn(features, labels, tf.estimator.ModeKeys.PREDICT,
dual_net.get_default_hyperparams())
for model_name in tqdm(models):
if model_name.endswith('-upgrade'):
continue
try:
load_file = os.path.join(fsdb.models_dir(), model_name)
dest_file = os.path.join(fsdb.models_dir(), model_name)
main.convert(load_file, dest_file)
except:
print('failed on', model_name)
continue
parser = argparse.ArgumentParser()
argh.add_commands(parser, [train, selfplay, backfill,
bootstrap, fsdb.game_counts, validate,
validate_hourly])
if __name__ == '__main__':
cloud_logging.configure()
remaining_argv = flags.FLAGS(sys.argv, known_only=True)
argh.dispatch(parser, argv=remaining_argv[1:])