# 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 os import sys import yaml from fleetrec.trainer.local_engine import Launch from fleetrec.trainer.single_trainer import SingleTrainer from fleetrec.trainer.cluster_trainer import ClusterTrainer from fleetrec.trainer.ctr_trainer import CtrPaddleTrainer from fleetrec.utils import envs class TrainerFactory(object): def __init__(self): pass @staticmethod def _build_trainer(config, yaml_path): print(envs.pretty_print_envs(envs.get_global_envs())) train_mode = envs.get_global_env("train.trainer") if train_mode == "SingleTraining": trainer = SingleTrainer(yaml_path) elif train_mode == "ClusterTraining": trainer = ClusterTrainer(yaml_path) elif train_mode == "CtrTrainer": trainer = CtrPaddleTrainer(config) elif train_mode == "UserDefineTrainer": train_location = envs.get_global_env("train.location") train_dirname = os.path.dirname(train_location) base_name = os.path.splitext(os.path.basename(train_location))[0] sys.path.append(train_dirname) trainer_class = envs.lazy_instance(base_name, "UserDefineTrainer") trainer = trainer_class(yaml_path) else: raise ValueError("trainer only support SingleTraining/ClusterTraining") return trainer @staticmethod def create(config): _config = None if os.path.exists(config) and os.path.isfile(config): with open(config, 'r') as rb: _config = yaml.load(rb.read(), Loader=yaml.FullLoader) else: raise ValueError("fleetrec's config only support yaml") envs.set_global_envs(_config) trainer = TrainerFactory._build_trainer(_config, config) return trainer # server num, worker num if __name__ == "__main__": if len(sys.argv) != 2: raise ValueError("need a yaml file path argv") trainer = TrainerFactory.create(sys.argv[1]) trainer.run()