add save/load for parameter server (#26235)
* add save/load for parameter serverrevert-24895-update_cub
123malin
5 years ago
committed by
GitHub
parent
0cc63cc343
commit
57d434df5d
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,102 @@
|
||||
# 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 unittest
|
||||
import paddle
|
||||
import os
|
||||
import paddle.fluid as fluid
|
||||
|
||||
|
||||
class TestFleetBase(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_ps_minimize(self):
|
||||
import paddle
|
||||
import paddle.distributed.fleet as fleet
|
||||
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
|
||||
|
||||
os.environ["TRAINING_ROLE"] = "PSERVER"
|
||||
os.environ["POD_IP"] = "127.0.0.1"
|
||||
os.environ["PADDLE_PORT"] = "36001"
|
||||
|
||||
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.fluid.layers.fc(input=input_x, size=64, act='tanh')
|
||||
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
|
||||
prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
|
||||
cost = paddle.fluid.layers.cross_entropy(
|
||||
input=prediction, label=input_y)
|
||||
avg_cost = paddle.fluid.layers.mean(x=cost)
|
||||
|
||||
role = role_maker.PaddleCloudRoleMaker(is_collective=False)
|
||||
fleet.init(role)
|
||||
strategy = paddle.distributed.fleet.DistributedStrategy()
|
||||
strategy.a_sync = False
|
||||
optimizer = paddle.optimizer.SGD(learning_rate=0.001)
|
||||
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
place = fluid.CPUPlace()
|
||||
exe = fluid.Executor(place)
|
||||
pe = fluid.ParallelExecutor(use_cuda=False, loss_name=avg_cost.name)
|
||||
compiled_prog = fluid.compiler.CompiledProgram(
|
||||
fluid.default_main_program())
|
||||
self.assertRaises(
|
||||
Exception,
|
||||
fleet.save_inference_model,
|
||||
dirname='/tmp/',
|
||||
feeded_var_names=['x', 'y'],
|
||||
target_vars=[avg_cost],
|
||||
executor=pe)
|
||||
|
||||
self.assertRaises(
|
||||
Exception,
|
||||
fleet.save_inference_model,
|
||||
dirname='/tmp/',
|
||||
feeded_var_names=['x', 'y'],
|
||||
target_vars=[avg_cost],
|
||||
executor="exe")
|
||||
|
||||
self.assertRaises(
|
||||
Exception,
|
||||
fleet.save_inference_model,
|
||||
dirname='/tmp/',
|
||||
feeded_var_names=['x', 'y'],
|
||||
target_vars=[avg_cost],
|
||||
executor=exe,
|
||||
main_program=compiled_prog)
|
||||
|
||||
self.assertRaises(
|
||||
Exception, fleet.save_persistables, executor=pe, dirname='/tmp/')
|
||||
|
||||
self.assertRaises(
|
||||
Exception, fleet.save_persistables, executor="exe", dirname='/tmp/')
|
||||
|
||||
self.assertRaises(
|
||||
Exception,
|
||||
fleet.save_persistables,
|
||||
executor=exe,
|
||||
dirname='/tmp/',
|
||||
main_program=compiled_prog)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@ -0,0 +1,52 @@
|
||||
# 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 unittest
|
||||
import os
|
||||
import paddle
|
||||
import paddle.distributed.fleet as fleet
|
||||
import paddle.distributed.fleet.base.role_maker as role_maker
|
||||
import paddle.fluid as fluid
|
||||
|
||||
|
||||
class TestFleetBase(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_collective_minimize(self):
|
||||
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.fluid.layers.fc(input=input_x, size=64, act='tanh')
|
||||
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
|
||||
prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
|
||||
cost = paddle.fluid.layers.cross_entropy(
|
||||
input=prediction, label=input_y)
|
||||
avg_cost = paddle.fluid.layers.mean(x=cost)
|
||||
|
||||
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
|
||||
fleet.init(role)
|
||||
strategy = fleet.DistributedStrategy()
|
||||
optimizer = paddle.optimizer.SGD(learning_rate=0.001)
|
||||
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue