# Copyright (c) 2019 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. from __future__ import print_function import os import unittest import paddle.fluid as fluid import paddle.fluid.incubate.fleet.base.role_maker as role_maker from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory from test_dist_fleet_base import TestFleetBase from dist_fleet_simnet_bow import train_network import paddle paddle.enable_static() class TestDistGeoCtr_2x2(TestFleetBase): def _setup_config(self): self._mode = "geo" self._reader = "pyreader" self._geo_sgd_need_push_nums = 5 def check_with_place(self, model_file, delta=1e-3, check_error_log=False, need_envs={}): required_envs = { "PATH": os.getenv("PATH", ""), "PYTHONPATH": os.getenv("PYTHONPATH", ""), "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""), "FLAGS_rpc_deadline": "5000", # 5sec to fail fast "http_proxy": "" } required_envs.update(need_envs) if check_error_log: required_envs["GLOG_v"] = "4" required_envs["GLOG_logtostderr"] = "1" tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs) def test_dist_train(self): self.check_with_place( "dist_fleet_ctr.py", delta=1e-5, check_error_log=True) class TestGeoSgdTranspiler(unittest.TestCase): def test_pserver(self): role = role_maker.UserDefinedRoleMaker( current_id=0, role=role_maker.Role.SERVER, worker_num=2, server_endpoints=["127.0.0.1:36011", "127.0.0.1:36012"]) fleet.init(role) batch_size = 128 is_sparse = True is_distribute = False strategy = StrategyFactory.create_geo_strategy(5) avg_cost, _, _, _ = train_network(batch_size, is_distribute, is_sparse) optimizer = fluid.optimizer.SGD(0.1) optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(avg_cost) pserver_startup_program = fleet.startup_program pserver_mian_program = fleet.main_program if __name__ == "__main__": unittest.main()