def configure_logger(log_dir):
logger.configure(log_dir, format_strs=['log'])
global tb
tb = logger.Logger(log_dir, [
logger.make_output_format('tensorboard', log_dir),
logger.make_output_format('csv', log_dir),
logger.make_output_format('stdout', log_dir)
])
global log
log = logger.log
def configure_logger(log_dir, add_tb=1, add_wb=1, args=None):
logger.configure(log_dir, format_strs=['log'])
global tb
log_types = [
logger.make_output_format('log', log_dir),
logger.make_output_format('json', log_dir),
logger.make_output_format('stdout', log_dir)
]
if add_tb: log_types += [logger.make_output_format('tensorboard', log_dir)]
if add_wb:
log_types += [logger.make_output_format('wandb', log_dir, args=args)]
tb = logger.Logger(log_dir, log_types)
global log
log = logger.log
def __init__(self, log_interval=503):
super(DiagnosticsInfoI, self).__init__()
self.log = lp.make_output_format('csv', log_dir, 'bonus' + str(tasks))
self._episode_time = time.time()
self._last_time = time.time()
self._local_t = 0
self._log_interval = log_interval
self._episode_reward = 0
self._episode_length = 0
self._all_rewards = []
self._num_vnc_updates = 0
self._last_episode_id = -1
self.once = True
self.log_dict = dict()
def run(args, server):
log = lp.make_output_format('csv', args.log_dir, 'bonus' + str(args.task))
env = create_env(args.log_dir,
args.task,
args.env_id,
client_id=str(args.task),
remotes=args.remotes)
trainer = A3C(env, args.task, args.visualise, log)
# 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 = 256000000
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)
