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78 lines
2.9 KiB
78 lines
2.9 KiB
# 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 unittest
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import paddle.fluid as fluid
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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from paddle.fluid.incubate.fleet.collective import CollectiveOptimizer, fleet, TrainStatus
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import os
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from paddle.distributed.fs_wrapper import LocalFS, BDFS
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class FleetTest(unittest.TestCase):
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def _test_check_point(self, fs, dir_path):
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file_name = "persistables"
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os.environ["TRAINING_ROLE"] = "TRAINER"
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os.environ["PADDLE_TRAINER_ID"] = "0"
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os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:6070"
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32')
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label = fluid.data(name='label', shape=[None, 1], dtype='int64')
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feeder = fluid.DataFeeder(
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feed_list=[image, label], place=fluid.CPUPlace())
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predict = fluid.layers.fc(input=image, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=predict, label=label)
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avg_loss = fluid.layers.mean(loss)
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optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
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dist_optimizer = fleet.distributed_optimizer(optimizer)
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dist_optimizer.minimize(avg_loss)
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exe = fluid.Executor(fluid.CPUPlace())
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exe.run(fluid.default_startup_program())
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status = TrainStatus(2)
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fleet.save_check_point(exe, dir_path, train_status=status, fs=fs)
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n1 = fleet._get_last_checkpoint_no(dir_path, fs=fs)
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status2 = fleet.load_check_point(exe, dir_path, trainer_id=0, fs=fs)
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self.assertEqual(status2, status)
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fleet.save_check_point(exe, dir_path, train_status=status, fs=fs)
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n2 = fleet._get_last_checkpoint_no(dir_path, fs=fs)
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self.assertEqual(n2, n1 + 1)
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fleet.clean_redundant_check_points(dir_path, fs=fs)
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def test_hdfs_check_point(self):
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try:
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fs = BDFS("xxxx", "xxxx", 1 * 1000, 1 * 1000)
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dir_path = "/user/Paddle_Data/gongweibao/edl_test/my_paddle_model"
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self._test_check_point(fs, dir_path)
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except Exception as e:
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print(e)
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def test_local_check_point(self):
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fs = LocalFS()
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dir_path = "./my_paddle_model"
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self._test_check_point(fs, dir_path)
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if __name__ == '__main__':
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unittest.main()
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