# 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 threading import numpy import paddle import paddle.fluid as fluid from paddle.fluid.communicator import Communicator import paddle.fluid.incubate.fleet.base.role_maker as role_maker from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory paddle.enable_static() class TestCommunicator(unittest.TestCase): def net(self): x = fluid.layers.data(name='x', shape=[1], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32') cost = fluid.layers.square_error_cost(input=x, label=y) avg_cost = fluid.layers.mean(cost) return avg_cost def test_communicator_async(self): role = role_maker.UserDefinedRoleMaker( current_id=0, role=role_maker.Role.WORKER, worker_num=2, server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"]) fleet.init(role) avg_cost = self.net() optimizer = fluid.optimizer.SGD(0.01) strategy = StrategyFactory.create_async_strategy() optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(avg_cost) fleet.init_worker() time.sleep(10) fleet.stop_worker() if __name__ == '__main__': unittest.main()