wehub-resource-sync
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
# Copyright (c) 2020 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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import os
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import unittest
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import paddle
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from paddle.distributed import fleet
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paddle.enable_static()
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class TestDistributedStrategyAuto(unittest.TestCase):
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def setUp(self):
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os.environ["POD_IP"] = "127.0.0.1"
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os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
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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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)
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def test_distributed_strategy_auto(self):
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fleet.init(is_collective=True)
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input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32')
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input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
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fc_1 = paddle.static.nn.fc(x=input_x, size=64, activation='tanh')
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fc_2 = paddle.static.nn.fc(x=fc_1, size=64, activation='tanh')
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prediction = paddle.static.nn.fc(x=[fc_2], size=2, activation='softmax')
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cost = paddle.nn.functional.cross_entropy(
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input=prediction, label=input_y, reduction='none', use_softmax=False
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)
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avg_cost = paddle.mean(x=cost)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.auto = True
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optimizer = paddle.optimizer.SGD(learning_rate=0.01)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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optimizer.minimize(avg_cost)
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applied_meta_list = fleet._get_applied_meta_list()
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print(f"applied_meta_list: {applied_meta_list}")
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if __name__ == "__main__":
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unittest.main()
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