torch.manual_seed(123456) - torch.cuda.manual_seed_all(123456)

电玩女神 2023-02-15 13:53 5阅读 0赞

# **torch.manual\_seed(123456) - torch.cuda.manual\_seed\_all(123456)** #

**PYTORCH DOCUMENTATION**  
[https://pytorch.org/docs/master/index.html][https_pytorch.org_docs_master_index.html]

import torch
    
    torch.manual_seed(123456)
    torch.cuda.manual_seed_all(123456)

## 1. `torch.manual_seed(seed)` ##

`torch.manual_seed` (Python function, in `torch`)  
[https://pytorch.org/docs/master/generated/torch.manual\_seed.html][https_pytorch.org_docs_master_generated_torch.manual_seed.html]

Sets the seed for generating random numbers. Returns a `torch.Generator` object.  
为 CPU 设置种子用于生成随机数，以使得结果是确定的。

**Parameters**  
seed \[int\] - The desired seed.

## 2. `torch.cuda.manual_seed(seed)` ##

`torch.cuda.manual_seed` (Python function, in `torch.cuda`)  
Sets the seed for generating random numbers for the current GPU. It’s safe to call this function if CUDA is not available; in that case, it is silently ignored.  
为当前 GPU 设置种子用于生成随机数，以使得结果是确定的。

**Parameters**  
seed \[int\] - The desired seed.

If you are working with a multi-GPU model, this function is insufficient to get determinism. To seed all GPUs, use `manual_seed_all()`.

insufficient /ˌɪnsəˈfɪʃnt/：adj. 不足的，不能胜任的，缺乏能力的

## 3. `torch.cuda.manual_seed_all(seed)` ##

`torch.cuda.manual_seed_all` (Python function, in `torch.cuda`)  
Sets the seed for generating random numbers on all GPUs. It’s safe to call this function if CUDA is not available; in that case, it is silently ignored.  
为所有的 GPU 设置种子用于生成随机数，以使得结果是确定的。

**Parameters**  
seed \[int\] - The desired seed.

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # yongqiang cheng
    
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import torch
    
    torch.manual_seed(123456)
    torch.cuda.manual_seed_all(123456)
    
    print(torch.rand([1, 5]))
    print(torch.rand([1, 5]))
    print(torch.rand([1, 5]))
    
    print("9" * 16)
    
    torch.manual_seed(123456)
    torch.cuda.manual_seed_all(123456)
    
    print(torch.rand([1, 5]))
    print(torch.rand([1, 5]))
    print(torch.rand([1, 5]))

/home/yongqiang/miniconda3/envs/pt-1.4_py-3.6/bin/python /home/yongqiang/pycharm_work/yongqiang.py
    tensor([[0.5043, 0.8178, 0.4798, 0.9201, 0.6819]])
    tensor([[0.6900, 0.6925, 0.3804, 0.4479, 0.4954]])
    tensor([[0.0728, 0.9644, 0.5524, 0.0060, 0.1053]])
    9999999999999999
    tensor([[0.5043, 0.8178, 0.4798, 0.9201, 0.6819]])
    tensor([[0.6900, 0.6925, 0.3804, 0.4479, 0.4954]])
    tensor([[0.0728, 0.9644, 0.5524, 0.0060, 0.1053]])

[https_pytorch.org_docs_master_index.html]: https://pytorch.org/docs/master/index.html
[https_pytorch.org_docs_master_generated_torch.manual_seed.html]: https://pytorch.org/docs/master/generated/torch.manual_seed.html