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worker.py
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worker.py
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#!/usr/bin/env python
import cv2
import go_vncdriver
import tensorflow as tf
import argparse
import logging
import sys, signal
import time
import os
from a3c import A3C
from envs import create_env
import distutils.version
use_tf12_api = distutils.version.LooseVersion(tf.VERSION) >= distutils.version.LooseVersion('0.12.0')
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# Disables write_meta_graph argument, which freezes entire process and is mostly useless.
class FastSaver(tf.train.Saver):
def save(self, sess, save_path, global_step=None, latest_filename=None,
meta_graph_suffix="meta", write_meta_graph=True):
super(FastSaver, self).save(sess, save_path, global_step, latest_filename,
meta_graph_suffix, False)
def run(args, server):
env = create_env(args.env_id, client_id=str(args.task), remotes=args.remotes)
trainer = A3C(env, args.task, args.visualise)
# Variable names that start with "local" are not saved in checkpoints.
if use_tf12_api:
variables_to_save = [v for v in tf.global_variables() if not v.name.startswith("local")]
init_op = tf.variables_initializer(variables_to_save)
init_all_op = tf.global_variables_initializer()
else:
variables_to_save = [v for v in tf.all_variables() if not v.name.startswith("local")]
init_op = tf.initialize_variables(variables_to_save)
init_all_op = tf.initialize_all_variables()
saver = FastSaver(variables_to_save)
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)
logger.info('Trainable vars:')
for v in var_list:
logger.info(' %s %s', v.name, v.get_shape())
def init_fn(ses):
logger.info("Initializing all parameters.")
ses.run(init_all_op)
config = tf.ConfigProto(device_filters=["/job:ps", "/job:worker/task:{}/cpu:0".format(args.task)])
logdir = os.path.join(args.log_dir, 'train')
if use_tf12_api:
summary_writer = tf.summary.FileWriter(logdir + "_%d" % args.task)
else:
summary_writer = tf.train.SummaryWriter(logdir + "_%d" % args.task)
logger.info("Events directory: %s_%s", logdir, args.task)
sv = tf.train.Supervisor(is_chief=(args.task == 0),
logdir=logdir,
saver=saver,
summary_op=None,
init_op=init_op,
init_fn=init_fn,
summary_writer=summary_writer,
ready_op=tf.report_uninitialized_variables(variables_to_save),
global_step=trainer.global_step,
save_model_secs=30,
save_summaries_secs=30)
num_global_steps = 100000000
logger.info(
"Starting session. If this hangs, we're mostly likely waiting to connect to the parameter server. " +
"One common cause is that the parameter server DNS name isn't resolving yet, or is misspecified.")
with sv.managed_session(server.target, config=config) as sess, sess.as_default():
sess.run(trainer.sync)
trainer.start(sess, summary_writer)
global_step = sess.run(trainer.global_step)
logger.info("Starting training at step=%d", global_step)
while not sv.should_stop() and (not num_global_steps or global_step < num_global_steps):
trainer.process(sess)
global_step = sess.run(trainer.global_step)
# Ask for all the services to stop.
sv.stop()
logger.info('reached %s steps. worker stopped.', global_step)
def cluster_spec(num_workers, num_ps):
"""
More tensorflow setup for data parallelism
"""
cluster = {}
port = 12222
all_ps = []
host = '127.0.0.1'
for _ in range(num_ps):
all_ps.append('{}:{}'.format(host, port))
port += 1
cluster['ps'] = all_ps
all_workers = []
for _ in range(num_workers):
all_workers.append('{}:{}'.format(host, port))
port += 1
cluster['worker'] = all_workers
return cluster
def main(_):
"""
Setting up Tensorflow for data parallel work
"""
parser = argparse.ArgumentParser(description=None)
parser.add_argument('-v', '--verbose', action='count', dest='verbosity', default=0, help='Set verbosity.')
parser.add_argument('--task', default=0, type=int, help='Task index')
parser.add_argument('--job-name', default="worker", help='worker or ps')
parser.add_argument('--num-workers', default=1, type=int, help='Number of workers')
parser.add_argument('--log-dir', default="/tmp/pong", help='Log directory path')
parser.add_argument('--env-id', default="PongDeterministic-v3", help='Environment id')
parser.add_argument('-r', '--remotes', default=None,
help='References to environments to create (e.g. -r 20), '
'or the address of pre-existing VNC servers and '
'rewarders to use (e.g. -r vnc://localhost:5900+15900,vnc://localhost:5901+15901)')
# Add visualisation argument
parser.add_argument('--visualise', action='store_true',
help="Visualise the gym environment by running env.render() between each timestep")
args = parser.parse_args()
spec = cluster_spec(args.num_workers, 1)
cluster = tf.train.ClusterSpec(spec).as_cluster_def()
def shutdown(signal, frame):
logger.warn('Received signal %s: exiting', signal)
sys.exit(128+signal)
signal.signal(signal.SIGHUP, shutdown)
signal.signal(signal.SIGINT, shutdown)
signal.signal(signal.SIGTERM, shutdown)
if args.job_name == "worker":
server = tf.train.Server(cluster, job_name="worker", task_index=args.task,
config=tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=2))
run(args, server)
else:
server = tf.train.Server(cluster, job_name="ps", task_index=args.task,
config=tf.ConfigProto(device_filters=["/job:ps"]))
while True:
time.sleep(1000)
if __name__ == "__main__":
tf.app.run()