minigo/main.py at master · ZedZero/minigo

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# 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.

import argparse

import argh

import os.path

import collections

10BC8

import random

import re

import socket

import sys

import time

import cloud_logging

from tqdm import tqdm

import gzip

import numpy as np

import tensorflow as tf

from tensorflow import gfile

import go

import dual_net

from gtp_wrapper import make_gtp_instance, MCTSPlayer

import preprocessing

import selfplay_mcts

from utils import logged_timer as timer

import evaluation

import sgf_wrapper

import utils

# How many positions we should aggregate per 'chunk'.

EXAMPLES_PER_RECORD = 10000

# How many positions to draw from for our training window.

# AGZ used the most recent 500k games, which, assuming 250 moves/game = 125M

WINDOW_SIZE = 125000000

def _ensure_dir_exists(directory):

if directory.startswith('gs://'):

return

os.makedirs(directory, exist_ok=True)

def gtp(load_file: "The path to the network model files"=None,

readouts: 'How many simulations to run per move'=100,

cgos_mode: 'Whether to use CGOS time constraints'=False,

verbose=1):

engine = make_gtp_instance(load_file,

readouts_per_move=readouts,

verbosity=verbose,

cgos_mode=cgos_mode)

sys.stderr.write("GTP engine ready\n")

sys.stderr.flush()

while not engine.disconnect:

inpt = input()

# handle either single lines at a time

# or multiple commands separated by '\n'

try:

cmd_list = inpt.split("\n")

except:

cmd_list = [inpt]

for cmd in cmd_list:

engine_reply = engine.send(cmd)

sys.stdout.write(engine_reply)

sys.stdout.flush()

def bootstrap(save_file):

dual_net.DualNetworkTrainer(save_file).bootstrap()

def train(chunk_dir, save_file, load_file=None, generation_num=0,

logdir=None, num_steps=None, verbosity=1):

tf_records = sorted(gfile.Glob(os.path.join(chunk_dir, '*.tfrecord.zz')))

tf_records = tf_records[-1 * (WINDOW_SIZE // EXAMPLES_PER_RECORD):]

print("Training from:", tf_records[0], "to", tf_records[-1])

n = dual_net.DualNetworkTrainer(save_file, logdir=logdir)

with timer("Training"):

n.train(tf_records, init_from=load_file,

num_steps=num_steps, verbosity=verbosity)

def validate(*tf_record_dirs, load_file=None, logdir=None, num_steps=100):

"""Computes the error terms for a set of holdout data specified by

`holdout_dir`, using the model specified at `load_file` and logging TB

metrics to the dir in `logdir`, using `num_steps` batches of examples

"""

n = dual_net.DualNetworkTrainer(logdir=logdir)

with timer("Building lists of holdout files"):

tf_records = [item for sublist in map(lambda path: gfile.Glob(

os.path.join(path, '*.zz')), tf_record_dirs) for item in sublist]

with timer("Validating from {} to {}".format(os.path.basename(tf_records[0]),

os.path.basename(tf_records[-1]))):

n.validate(tf_records, batch_size=dual_net.TRAIN_BATCH_SIZE,

init_from=load_file, num_steps=num_steps)

def evaluate(

black_model: 'The path to the model to play black',

white_model: 'The path to the model to play white',

output_dir: 'Where to write the evaluation results'='data/evaluate/sgf',

readouts: 'How many readouts to make per move.'=400,

games: 'the number of games to play'=16,

verbose: 'How verbose the players should be (see selfplay)' = 1):

black_model = os.path.abspath(black_model)

white_model = os.path.abspath(white_model)

with timer("Loading weights"):

black_net = dual_net.DualNetwork(black_model)

white_net = dual_net.DualNetwork(white_model)

with timer("%d games" % games):

players = evaluation.play_match(

black_net, white_net, games, readouts, verbose)

for idx, p in enumerate(players):

fname = "{:s}-vs-{:s}-{:d}".format(black_net.name, white_net.name, idx)

with open(os.path.join(output_dir, fname + '.sgf'), 'w') as f:

f.write(sgf_wrapper.make_sgf(p[0].position.recent,

p[0].make_result_string(

p[0].position),

black_name=os.path.basename(

black_model),

white_name=os.path.basename(white_model)))

def selfplay(

load_file: "The path to the network model files",

output_dir: "Where to write the games"="data/selfplay",

holdout_dir: "Where to write the games"="data/holdout",

output_sgf: "Where to write the sgfs"="sgf/",

readouts: 'How many simulations to run per move'=100,

verbose: '>=2 will print debug info, >=3 will print boards' = 1,

resign_threshold: 'absolute value of threshold to resign at' = 0.95,

holdout_pct: 'how many games to hold out for validation' = 0.05):

clean_sgf = os.path.join(output_sgf, 'clean')

full_sgf = os.path.join(output_sgf, 'full')

_ensure_dir_exists(clean_sgf)

_ensure_dir_exists(full_sgf)

_ensure_dir_exists(output_dir)

_ensure_dir_exists(holdout_dir)

with timer("Loading weights from %s ... " % load_file):

network = dual_net.DualNetwork(load_file)

network.name = os.path.basename(load_file)

with timer("Playing game"):

player = selfplay_mcts.play(

network, readouts, resign_threshold, verbose)

output_name = '{}-{}'.format(int(time.time()), socket.gethostname())

game_data = player.extract_data()

with gfile.GFile(os.path.join(clean_sgf, '{}.sgf'.format(output_name)), 'w') as f:

f.write(player.to_sgf(use_comments=False))

with gfile.GFile(os.path.join(full_sgf, '{}.sgf'.format(output_name)), 'w') as f:

f.write(player.to_sgf())

tf_examples = preprocessing.make_dataset_from_selfplay(game_data)

# Hold out 5% of games for evaluation.

if random.random() < holdout_pct:

fname = os.path.join(holdout_dir, "{}.tfrecord.zz".format(output_name))

else:

fname = os.path.join(output_dir, "{}.tfrecord.zz".format(output_name))

preprocessing.write_tf_examples(fname, tf_examples)

def gather(

input_directory: 'where to look for games'='data/selfplay/',

output_directory: 'where to put collected games'='data/training_chunks/',

examples_per_record: 'how many tf.examples to gather in each chunk'=EXAMPLES_PER_RECORD):

_ensure_dir_exists(output_directory)

models = [model_dir.strip('/')

for model_dir in sorted(gfile.ListDirectory(input_directory))[-50:]]

with timer("Finding existing tfrecords..."):

model_gamedata = {

model: gfile.Glob(

os.path.join(input_directory, model, '*.tfrecord.zz'))

for model in models

}

print("Found %d models" % len(models))

for model_name, record_files in sorted(model_gamedata.items()):

print(" %s: %s files" % (model_name, len(record_files)))

meta_file = os.path.join(output_directory, 'meta.txt')

try:

with gfile.GFile(meta_file, 'r') as f:

already_processed = set(f.read().split())

except tf.errors.NotFoundError:

already_processed = set()

num_already_processed = len(already_processed)

for model_name, record_files in sorted(model_gamedata.items()):

if set(record_files) <= already_processed:

continue

print("Gathering files for %s:" % model_name)

for i, example_batch in enumerate(

tqdm(preprocessing.shuffle_tf_examples(examples_per_record, record_files))):

output_record = os.path.join(output_directory,

'{}-{}.tfrecord.zz'.format(model_name, str(i)))

preprocessing.write_tf_examples(

output_record, example_batch, serialize=False)

already_processed.update(record_files)

print("Processed %s new files" %

(len(already_processed) - num_already_processed))

with gfile.GFile(meta_file, 'w') as f:

f.write('\n'.join(sorted(already_processed)))

parser = argparse.ArgumentParser()

argh.add_commands(parser, [gtp, bootstrap, train,

selfplay, gather, evaluate, validate])

if __name__ == '__main__':

cloud_logging.configure()

argh.dispatch(parser)

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