def test(self):
role = self.build_role()
fleet.init(role)
strategy = self.build_strategy()
inputs = self.build_input()
avg_cost = self.build_net(inputs)
self.build_optimizer(avg_cost, strategy)
def setUp(self):
os.environ[
"PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:4001,127.0.0.1:4002"
os.environ["PADDLE_TRAINERS_NUM"] = str(2)
os.environ["TRAINING_ROLE"] = "PSERVER"
os.environ["PADDLE_PORT"] = "4001"
os.environ["POD_IP"] = "127.0.0.1"
role = role_maker.PaddleCloudRoleMaker()
fleet.init(role)
self.strategy = paddle.distributed.fleet.DistributedStrategy()
self.strategy.a_sync = True
def runtime_main(test_class):
parser = argparse.ArgumentParser(description='Run Fleet test.')
parser.add_argument('--role',
type=str,
required=True,
choices=['pserver', 'trainer', 'heter_trainer'])
parser.add_argument('--endpoints', type=str, required=False, default="")
parser.add_argument('--trainer_endpoints',
type=str,
required=False,
default="")
parser.add_argument('--heter_trainer_endpoints',
type=str,
required=False,
default="")
parser.add_argument('--heter_trainer_device',
type=str,
required=False,
default="gpu")
parser.add_argument('--gloo_path', type=str, required=False, default="")
parser.add_argument('--current_id', type=int, required=False, default=0)
parser.add_argument('--trainers', type=int, required=False, default=1)
parser.add_argument('--mode', type=str, required=False, default='async')
parser.add_argument('--geo_sgd_need_push_nums',
type=int,
required=False,
default=2)
parser.add_argument('--reader',
type=str,
required=False,
default='dataset')
args = parser.parse_args()
model = test_class()
role = model.build_role(args)
fleet.init(role)
strategy = model.build_strategy(args)
avg_cost = model.net(args)
model.build_optimizer(avg_cost, strategy)
fleet_util._set_strategy(strategy)
fleet_util._set_role_maker(role)
if args.role == "pserver" or args.role == "heter_trainer":
model.run_pserver(args)
else:
if args.reader == "dataset":
model.run_dataset_trainer(args)
else:
model.run_pyreader_trainer(args)
def test_2ps_0_load(self):
# init No.0 server env
env = {}
env["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:4001,127.0.0.1:4002"
env["PADDLE_TRAINERS_NUM"] = str(2)
env["TRAINING_ROLE"] = "PSERVER"
env["PADDLE_PORT"] = "4001"
env["POD_IP"] = "127.0.0.1"
for k, v in env.items():
os.environ[k] = str(v)
"""
array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
[0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],
[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
[0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6],
[0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7],
[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]])
"""
emb_array = np.arange(0, 1, 0.1).repeat(10).reshape(10, 10)
fc_array = np.arange(0, 1, 0.1).repeat(10).reshape(10, 10)
model_path = self.save_origin_model(emb_array, fc_array)
role = role_maker.PaddleCloudRoleMaker()
fleet.init(role)
loss = self.net(emb_array, fc_array)
strategy = paddle.distributed.fleet.DistributedStrategy()
strategy.a_sync = True
optimizer = fluid.optimizer.Adam(1e-3)
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(loss)
fleet.init_server(model_path)
fc_w = np.array(fluid.global_scope().find_var("fc").get_tensor())
emb = np.array(
fluid.global_scope().find_var("embedding.block0").get_tensor())
assert fc_w.all() == fc_array.all()
assert emb.all() == emb_array[::2].all()
shutil.rmtree(model_path)