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69 lines
2.2 KiB
69 lines
2.2 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import time
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import os
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import paddle
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paddle.enable_static()
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import paddle.fluid as fluid
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import paddle.distributed.fleet.base.role_maker as role_maker
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import paddle.distributed.fleet as fleet
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class TestCommunicator(unittest.TestCase):
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def net(self):
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x = fluid.layers.data(name='x', shape=[1], dtype='float32')
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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cost = fluid.layers.square_error_cost(input=x, label=y)
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avg_cost = fluid.layers.mean(cost)
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return avg_cost
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def test_communicator_sync(self):
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os.environ["TRAINING_ROLE"] = "TRAINER"
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os.environ["PADDLE_PSERVER_NUMS"] = "2"
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os.environ["PADDLE_TRAINERS_NUM"] = "2"
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os.environ["POD_IP"] = "127.0.0.1"
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os.environ["PADDLE_PORT"] = "36001"
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os.environ["PADDLE_TRAINER_ID"] = "0"
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os.environ["PADDLE_TRAINERS_NUM"] = "2"
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os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
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"127.0.0.1:36001,127.0.0.2:36001"
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fleet.init(role_maker.PaddleCloudRoleMaker())
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avg_cost = self.net()
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optimizer = fluid.optimizer.SGD(0.01)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = False
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strategy.a_sync_configs = {"launch_barrier": False}
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(avg_cost)
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os.environ["TEST_MODE"] = "1"
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fleet.init_worker()
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time.sleep(10)
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fleet.stop_worker()
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if __name__ == '__main__':
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unittest.main()
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