def main(unused_argv):
base_config = config_util.get_config(urdf_root=FLAGS.urdf_data_path)
config = config_util.generate_config(
base_config, current_time_string=FLAGS.current_time_string)
servers = []
server_creds = loas2.loas2_server_credentials()
port = FLAGS.port
if not config.run_on_borg:
port = 20000 + FLAGS.server_id
server = grpc.server(futures.ThreadPoolExecutor(max_workers=100),
ports=(port, ))
blackbox_object = config.blackbox_object_fn()
np.random.seed(FLAGS.server_id)
if config.algorithm == "zero_order":
if FLAGS.server_id < config.test_workers:
worker_mode = "Test"
task_ids = range(config.train_set_size,
config.train_set_size + config.test_set_size)
else:
worker_mode = "Train"
task_ids = range(config.train_set_size)
servicer = blackbox_maml_objects.GeneralMAMLBlackboxWorker(
worker_id=FLAGS.server_id,
blackbox_object=blackbox_object,
task_ids=task_ids,
task_batch_size=config.task_batch_size,
worker_mode=worker_mode)
zero_order_pb2_grpc.add_EvaluationServicer_to_server(servicer, server)
elif config.algorithm == "first_order":
tasks = [
config.make_task_fn(s)
for s in range(config.train_set_size + config.test_set_size)
]
servicer = blackbox_maml_objects.GradientMAMLWorker(
FLAGS.server_id, blackbox_object=blackbox_object, tasks=tasks)
first_order_pb2_grpc.add_EvaluationServicer_to_server(servicer, server)
server.add_secure_port("[::]:{}".format(port), server_creds)
servers.append(server)
server.start()
print("Start server {}".format(FLAGS.server_id))
# prevent the main thread from exiting
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
for server in servers:
server.stop(0)
def main(unused_argv):
base_config = config_util.get_config()
config = config_util.generate_config(
base_config, current_time_string=FLAGS.current_time_string)
blackbox_object = config.blackbox_object_fn()
init_current_input = blackbox_object.get_initial()
init_best_input = []
init_best_core_hyperparameters = []
init_best_value = -float("inf")
init_iteration = 0
np.random.seed(0)
# ------------------ OPTIMIZERS ----------------------------------------------
num_servers = config.num_servers
logging.info("Number of Servers: %d", num_servers)
if not config.run_locally:
servers = [
"{}.{}".format(i, FLAGS.server_address) for i in range(num_servers)
]
else:
servers = [
"127.0.0.1:{}".format(20000 + i) for i in range(num_servers)
]
logging.info("Running servers:")
logging.info(servers)
stubs = []
for server in servers:
channel = grpc.insecure_channel(server)
grpc.channel_ready_future(channel).result()
if config.algorithm == "zero_order":
stubs.append(zero_order_pb2_grpc.EvaluationStub(channel))
elif config.algorithm == "first_order":
stubs.append(first_order_pb2_grpc.EvaluationStub(channel))
tf.gfile.MakeDirs(config.global_logfoldername)
logging.info("LOGGING FOLDER: %s", config.global_logfoldername)
tf.gfile.MakeDirs(config.test_mamlpt_parallel_vals_folder)
if config.log_states:
tf.gfile.MakeDirs(config.states_folder)
if config.recording:
tf.gfile.MakeDirs(config.video_folder)
with tf.gfile.Open(config.hparams_file, "w") as hparams_file:
json.dump(config.json_hparams, hparams_file)
# Runs main client's procedure responsible for optimization.
if config.algorithm == "zero_order":
es_blackbox_optimizer = config.es_blackbox_optimizer_fn(
blackbox_object.get_metaparams())
zero_order_maml_learner_grpc.run_blackbox(
config,
es_blackbox_optimizer,
init_current_input,
init_best_input,
init_best_core_hyperparameters,
init_best_value,
init_iteration,
stubs=stubs,
log_bool=True)
elif config.algorithm == "first_order":
train_tasks = {
"object": blackbox_object,
"tasks":
[config.make_task_fn(t) for t in range(config.train_set_size)],
"ids": range(config.train_set_size)
}
test_tasks = {
"object":
blackbox_object,
"tasks": [
config.make_task_fn(t)
for t in range(config.train_set_size, config.train_set_size +
config.test_set_size)
],
"ids":
range(config.train_set_size,
config.train_set_size + config.test_set_size)
}
first_order_maml_learner_grpc.run_blackbox(config, train_tasks,
test_tasks,
init_current_input, stubs)
