# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import time import os import paddle import paddle.fluid as fluid import paddle.fluid.incubate.fleet.base.role_maker as role_maker import paddle.fleet as fleet class TestCommunicator(unittest.TestCase): def net(self): x = fluid.layers.data(name='x', shape=[13], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) y = fluid.layers.data(name='y', shape=[1], dtype='float32') cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_cost = fluid.layers.mean(cost) return avg_cost def test_communicator_sync(self): os.environ["TRAINING_ROLE"] = "TRAINER" os.environ["PADDLE_PSERVER_NUMS"] = "2" os.environ["PADDLE_TRAINERS_NUM"] = "2" os.environ["POD_IP"] = "127.0.0.1" os.environ["PADDLE_PORT"] = "36001" os.environ["PADDLE_TRAINER_ID"] = "0" os.environ["PADDLE_TRAINERS_NUM"] = "2" os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \ "127.0.0.1:36001,127.0.0.2:36001" fleet.init(role_maker.PaddleCloudRoleMaker()) avg_cost = self.net() optimizer = fluid.optimizer.SGD(0.01) strategy = paddle.fleet.DistributedStrategy() strategy.a_sync = False optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(avg_cost) fleet.init_worker() time.sleep(10) fleet.stop_worker() if __name__ == '__main__': unittest.main()