test_communicator_sync.py 2.1 KB
Newer Older
T
tangwei12 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
#   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

20
import os
T
tangwei12 已提交
21 22 23 24
import paddle
import paddle.fluid as fluid

import paddle.fluid.incubate.fleet.base.role_maker as role_maker
25
import paddle.fleet as fleet
T
tangwei12 已提交
26 27 28 29 30 31 32 33 34 35 36 37 38


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):
39 40 41 42 43 44 45 46 47
        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"
T
tangwei12 已提交
48

49
        fleet.init(role_maker.PaddleCloudRoleMaker())
T
tangwei12 已提交
50 51 52 53
        avg_cost = self.net()

        optimizer = fluid.optimizer.SGD(0.01)

54 55 56
        strategy = paddle.fleet.DistributedStrategy()
        strategy.a_sync = False

T
tangwei12 已提交
57 58 59 60 61 62 63 64 65 66
        optimizer = fleet.distributed_optimizer(optimizer, strategy)
        optimizer.minimize(avg_cost)

        fleet.init_worker()
        time.sleep(10)
        fleet.stop_worker()


if __name__ == '__main__':
    unittest.main()