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- # 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.
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import os
- import random
- import numpy as np
- import paddle
- from paddle.distributed import fleet
- __all__ = ['init_parallel_env', 'set_random_seed', 'init_fleet_env']
- def init_fleet_env(find_unused_parameters=False):
- strategy = fleet.DistributedStrategy()
- strategy.find_unused_parameters = find_unused_parameters
- fleet.init(is_collective=True, strategy=strategy)
- def init_parallel_env():
- env = os.environ
- dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
- if dist:
- trainer_id = int(env['PADDLE_TRAINER_ID'])
- local_seed = (99 + trainer_id)
- random.seed(local_seed)
- np.random.seed(local_seed)
- paddle.distributed.init_parallel_env()
- def set_random_seed(seed):
- paddle.seed(seed)
- random.seed(seed)
- np.random.seed(seed)
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