def Parameter_Server(Synchronizer, cluster, log_path, model_path, procs):
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
)
config.gpu_options.allow_growth = True
server = tf.train.Server(cluster,
job_name="ps",
task_index=0,
config=config)
#config.gpu_options.per_process_gpu_memory_fraction = 0.2
sess = tf.Session(target=server.target, config=config)
summary_writer = tf.summary.FileWriter(log_path)
mini_net = MiniNetwork(sess,
index=0,
summary_writer=summary_writer,
rl_training=True,
cluster=cluster,
ppo_load_path=FLAGS.restore_model_path,
ppo_save_path=model_path)
agent = MiniAgent(agent_id=-1,
global_buffer=Buffer(),
net=mini_net,
restore_model=FLAGS.restore_model)
print("Parameter server: waiting for cluster connection...")
sess.run(tf.report_uninitialized_variables())
print("Parameter server: cluster ready!")
print("Parameter server: initializing variables...")
agent.init_network()
print("Parameter server: variables initialized")
last_win_rate = 0.
update_counter = 0
while update_counter <= TRAIN_ITERS:
agent.reset_old_network()
# wait for update
Synchronizer.wait()
logging("Update Network!")
# TODO count the time , compare cpu and gpu
time.sleep(1)
# update finish
Synchronizer.wait()
logging("Update Network finished!")
steps, win_rate = agent.update_summary(update_counter)
logging("Steps: %d, win rate: %f" % (steps, win_rate))
update_counter += 1
if win_rate >= last_win_rate:
agent.save_model()
last_win_rate = win_rate
for p in procs:
print('Process terminate')
p.terminate()
def Worker(index, update_game_num, Synchronizer, cluster, model_path):
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
)
config.gpu_options.allow_growth = True
worker = tf.train.Server(cluster,
job_name="worker",
task_index=index,
config=config)
#config.gpu_options.per_process_gpu_memory_fraction = 0.2
sess = tf.Session(target=worker.target, config=config)
mini_net = MiniNetwork(sess,
index=index,
summary_writer=None,
rl_training=True,
cluster=cluster,
ppo_load_path=FLAGS.restore_model_path,
ppo_save_path=model_path)
global_buffer = Buffer()
agents = []
for i in range(THREAD_NUM):
agent = MiniAgent(agent_id=i,
global_buffer=global_buffer,
net=mini_net,
restore_model=FLAGS.restore_model)
agents.append(agent)
print("Worker %d: waiting for cluster connection..." % index)
sess.run(tf.report_uninitialized_variables())
print("Worker %d: cluster ready!" % index)
while len(sess.run(tf.report_uninitialized_variables())):
print("Worker %d: waiting for variable initialization..." % index)
time.sleep(1)
print("Worker %d: variables initialized" % index)
game_num = np.ceil(update_game_num // THREAD_NUM)
UPDATE_EVENT.clear()
ROLLING_EVENT.set()
difficulty = INITIAL_DIFF
# Run threads
threads = []
for i in range(THREAD_NUM - 1):
t = threading.Thread(target=run_thread,
args=(agents[i], game_num, Synchronizer,
difficulty))
threads.append(t)
t.daemon = True
t.start()
time.sleep(3)
run_thread(agents[-1], game_num, Synchronizer, difficulty)
for t in threads:
t.join()
def Parameter_Server(Synchronizer, cluster, log_path, model_path, procs):
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
)
config.gpu_options.allow_growth = True
server = tf.train.Server(cluster,
job_name="ps",
task_index=0,
config=config)
sess = tf.Session(target=server.target, config=config)
summary_writer = tf.summary.FileWriter(log_path)
Net = MiniNetwork(sess=sess,
summary_writer=summary_writer,
rl_training=FLAGS.training,
cluster=cluster,
index=0,
device=DEVICE[0 % len(DEVICE)],
ppo_load_path=FLAGS.restore_model_path,
ppo_save_path=model_path)
Sec_Net = SecondNetwork(sess=sess,
rl_training=False,
reuse=True,
cluster=None,
index=0,
load_model=True,
net_path_name=net_path_name)
agent = mini_source_agent.MiniSourceAgent(
index=-1,
net=Net,
sec_net=Sec_Net,
restore_model=FLAGS.restore_model,
rl_training=FLAGS.training)
print("Parameter server: waiting for cluster connection...")
sess.run(tf.report_uninitialized_variables())
print("Parameter server: cluster ready!")
print("Parameter server: initializing variables...")
agent.init_network()
print("Parameter server: variables initialized")
update_counter = 0
max_win_rate = 0.
while update_counter < TRAIN_ITERS:
agent.reset_old_network()
# wait for update
Synchronizer.wait()
logging("Update Network!")
# TODO count the time , compare cpu and gpu
time.sleep(1)
# update finish
Synchronizer.wait()
logging("Update Network finished!")
steps, win_rate = agent.update_summary(update_counter)
logging("Steps: %d, win rate: %f" % (steps, win_rate))
update_counter += 1
if win_rate >= max_win_rate:
agent.save_model()
max_win_rate = win_rate
return max_win_rate
def Worker(index, update_game_num, Synchronizer, cluster, model_path):
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
)
config.gpu_options.allow_growth = True
worker = tf.train.Server(cluster,
job_name="worker",
task_index=index,
config=config)
sess = tf.Session(target=worker.target, config=config)
Net = MiniNetwork(sess=sess,
summary_writer=None,
rl_training=FLAGS.training,
cluster=cluster,
index=index,
device=DEVICE[index % len(DEVICE)],
ppo_load_path=FLAGS.restore_model_path,
ppo_save_path=model_path)
Sec_Net = SecondNetwork(sess=sess,
rl_training=False,
reuse=True,
cluster=None,
index=index,
load_model=True,
net_path_name=net_path_name)
global_buffer = Buffer()
agents = []
for i in range(THREAD_NUM):
agent = mini_source_agent.MiniSourceAgent(
index=i,
global_buffer=global_buffer,
net=Net,
sec_net=Sec_Net,
restore_model=FLAGS.restore_model,
rl_training=FLAGS.training,
strategy_agent=None)
agents.append(agent)
print("Worker %d: waiting for cluster connection..." % index)
sess.run(tf.report_uninitialized_variables())
print("Worker %d: cluster ready!" % index)
while len(sess.run(tf.report_uninitialized_variables())):
print("Worker %d: waiting for variable initialization..." % index)
time.sleep(1)
print("Worker %d: variables initialized" % index)
game_num = np.ceil(update_game_num // THREAD_NUM)
UPDATE_EVENT.clear()
ROLLING_EVENT.set()
# Run threads
threads = []
for i in range(THREAD_NUM - 1):
t = threading.Thread(target=run_thread,
args=(agents[i], game_num, Synchronizer,
FLAGS.difficulty))
threads.append(t)
t.daemon = True
t.start()
time.sleep(3)
run_thread(agents[-1], game_num, Synchronizer, FLAGS.difficulty)
for t in threads:
t.join()