pytorch设置随机种子 - 保证复现模型所有的训练过程
在使用 PyTorch 时，如果希望通过设置随机数种子，在 GPU 或 CPU 上固定每一次的训练结果，则需要在程序执行的开始处添加以下代码：
def seed_everything():
'''
设置整个开发环境的seed
:param seed:
:param device:
:return:
'''
import os
import random
import numpy as np
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# some cudnn methods can be random even after fixing the seed
# unless you tell it to be deterministic
torch.backends.cudnn.deterministic = Trueimport numpy as np import random import os import torch def seed_torch(seed=2021): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # if you are using multi-GPU. torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.backends.cudnn.enabled = False seed_torch()
第一步 仅导入设置种子和初始化种子值所需的那些库
import tensorflow as tf import os import numpy as np import random SEED = 0
第二步 为所有可能具有随机行为的库初始化种子的函数
def set_seeds(seed=SEED): os.environ['PYTHONHASHSEED'] = str(seed) random.seed(seed) tf.random.set_seed(seed) np.random.seed(seed)
第三步 激活 Tensorflow 确定性功能
def set_global_determinism(seed=SEED): set_seeds(seed=seed) os.environ['TF_DETERMINISTIC_OPS'] = '1' os.environ['TF_CUDNN_DETERMINISTIC'] = '1' tf.config.threading.set_inter_op_parallelism_threads(1) tf.config.threading.set_intra_op_parallelism_threads(1) # Call the above function with seed value set_global_determinism(seed=SEED)
以上为个人经验，希望能给大家一个参考，也希望大家多多支持。
