wehub-resource-sync
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
# 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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import os
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import tempfile
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import time
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import unittest
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import paddle
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paddle.enable_static()
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from paddle import base
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from paddle.distributed import fleet
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from paddle.distributed.fleet.base import role_maker
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class TestCommunicator(unittest.TestCase):
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def test_communicator_ps_gpu(self):
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temp_dir = tempfile.TemporaryDirectory()
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path = os.path.join(temp_dir.name, "test_communicator_ps_gpu.txt")
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with open(path, "w") as f:
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data = "1 0.6 1 0.7\n"
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f.write(data)
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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_TRAINER_ENDPOINTS"] = (
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"127.0.0.1:36001,127.0.0.2:36001"
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)
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os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = (
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"127.0.0.1:36002,127.0.0.2:36002"
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)
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os.environ["TRAINING_ROLE"] = "TRAINER"
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os.environ["FLAGS_selected_gpus"] = "0"
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role = role_maker.PaddleCloudRoleMaker()
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fleet.init(role)
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x = paddle.static.data(name='x', shape=[-1, 1], dtype='float32')
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y = paddle.static.data(name='y', shape=[-1, 1], dtype='float32')
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slots_vars = [x, y]
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cost = paddle.nn.functional.square_error_cost(input=x, label=y)
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avg_cost = paddle.mean(cost)
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optimizer = paddle.optimizer.Adam(0.01)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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strategy.a_sync_configs = {
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"launch_barrier": False,
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"use_ps_gpu": 1,
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}
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(avg_cost)
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dataset = paddle.distributed.InMemoryDataset()
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dataset.init(
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batch_size=32, thread_num=1, pipe_command="cat", use_var=slots_vars
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)
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dataset.set_filelist(["test_communicator_ps_gpu.txt"])
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dataset.set_date("20211111")
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dataset.load_into_memory(is_shuffle=True)
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os.environ["TEST_MODE"] = "1"
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exe = base.Executor(base.CPUPlace())
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exe.run(startup_program)
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main_program._fleet_opt = {"stat_var_names": [x.name]}
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fleet.init_worker()
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try:
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exe.train_from_dataset(main_program, dataset)
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except ImportError as e:
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pass
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except Exception as e:
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self.assertTrue(False)
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time.sleep(10)
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fleet.stop_worker()
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temp_dir.cleanup()
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if __name__ == '__main__':
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unittest.main()
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