PyTorch模型训练：设置随机种子，保证每次训练的结果都一样

模型训练：设置随机种子，保证每次训练的结果都一样

def seed_everything(seed=1029):
    '''
    设置整个开发环境的seed
    :param seed:
    :param device:
    :return:
    '''
    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 = True
    torch.backends.cudnn.benchmark = False