wehub-resource-sync
56 lines
1.8 KiB
Python
56 lines
1.8 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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from nets import mlp
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from utils import gen_data
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import paddle
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from paddle import base
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from paddle.incubate.distributed.fleet import role_maker
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from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler import (
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fleet,
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)
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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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input_y = paddle.cast(input_y, dtype="float32")
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with base.device_guard("gpu"):
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input_y = paddle.cast(input_y, dtype="int64")
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cost = mlp(input_x, input_y)
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optimizer = paddle.optimizer.Adagrad(learning_rate=0.01)
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role = role_maker.PaddleCloudRoleMaker()
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fleet.init(role)
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optimizer = fleet.distributed_optimizer(optimizer)
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optimizer.minimize(cost)
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if fleet.is_server():
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fleet.init_server()
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fleet.run_server()
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elif fleet.is_worker():
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place = base.CPUPlace()
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exe = base.Executor(place)
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exe.run(fleet.startup_program)
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step = 1001
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for i in range(step):
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cost_val = exe.run(
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program=fleet.main_program, feed=gen_data(), fetch_list=[cost.name]
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)
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print(
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f"worker_index: {fleet.worker_index()}, step{i} cost = {cost_val[0]:f}"
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)
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