描述

def set_seed(seed):
    random.seed(seed)  # Python random module.
    np.random.seed(seed)  # Numpy module.
    set_seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    torch.random.manual_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 = cudnn_benchmark
    torch.backends.cudnn.deterministic = cudnn_deterministic

贡献者指南

技术栈

pythonpytorch

领域

machine learning

议题类型

research

难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。

3

预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。

under 1 hour

活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。

needs maintainer response

清晰度议题是否清楚说明期望改动、验收标准和下一步。

needs investigation

前置要求

Basic understanding of PyTorchKnowledge of reproducibility concepts

新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。

40

研究方向

Investigate the xformers repository for existing utilities or documentation related to random seed setting and reproducibility. Look at the issue history for similar questions or answers. Consider contributing by adding a clear guide or a utility function to the documentation. Refer to the PyTorch reproducibility documentation for best practices.