# Copyright (c) 2020 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. import os import unittest import paddle import paddle.distributed.fleet as fleet paddle.enable_static() class TestDistributedStrategyAuto(unittest.TestCase): def setUp(self): os.environ["POD_IP"] = "127.0.0.1" os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001" os.environ["PADDLE_TRAINERS_NUM"] = "2" os.environ[ "PADDLE_PSERVERS_IP_PORT_LIST" ] = "127.0.0.1:36001,127.0.0.2:36001" def test_distributed_strategy_auto(self): fleet.init(is_collective=True) input_x = paddle.fluid.layers.data( name="x", shape=[32], dtype='float32' ) input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64') fc_1 = paddle.static.nn.fc(x=input_x, size=64, activation='tanh') fc_2 = paddle.static.nn.fc(x=fc_1, size=64, activation='tanh') prediction = paddle.static.nn.fc(x=[fc_2], size=2, activation='softmax') cost = paddle.nn.functional.cross_entropy( input=prediction, label=input_y, reduction='none', use_softmax=False ) avg_cost = paddle.mean(x=cost) strategy = paddle.distributed.fleet.DistributedStrategy() strategy.auto = True optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.01) optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy) optimizer.minimize(avg_cost) applied_meta_list = fleet._get_applied_meta_list() print("applied_meta_list: {}".format(applied_meta_list)) if __name__ == "__main__": unittest.main()