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# Copyright (c) 2022 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 platform
import random
import multiprocessing as mp
import numpy as np
import paddle
def get_environ_info():
"""
Collect environment information.
"""
env_info = dict()
# TODO is_compiled_with_cuda() has not been moved
compiled_with_cuda = paddle.is_compiled_with_cuda()
if compiled_with_cuda:
if 'gpu' in paddle.get_device():
gpu_nums = paddle.distributed.get_world_size()
else:
gpu_nums = 0
if gpu_nums == 0:
os.environ['CUDA_VISIBLE_DEVICES'] = ''
place = 'gpu' if compiled_with_cuda and gpu_nums else 'cpu'
env_info['place'] = place
env_info['num'] = int(os.environ.get('CPU_NUM', 1))
if place == 'gpu':
env_info['num'] = gpu_nums
return env_info
def get_num_workers(num_workers):
if not platform.system() == 'Linux':
# Dataloader with multi-process model is not supported
# on MacOS and Windows currently.
return 0
if num_workers == 'auto':
num_workers = mp.cpu_count() // 2 if mp.cpu_count() // 2 < 2 else 2
return num_workers
def init_parallel_env():
env = os.environ
if 'FLAGS_allocator_strategy' not in os.environ:
os.environ['FLAGS_allocator_strategy'] = 'auto_growth'
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)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()