Research on dynamic allocation of video memory in Python

itread01 2020-11-17 01:30:18
research dynamic allocation video memory

The following is an experiment to explore Pytorch How video memory is allocated .


Video memory to main memory

I use VSCode Of jupyter To experiment , First, only import pytorch, The code is as follows :

import torch

Open the work manager to view the main memory and video memory . The situation is as follows :

Set up in video memory 1GB Tensor of , Assign to a, The code is as follows :

a = torch.zeros([256,1024,1024],device= 'cpu') 

View main memory and video memory :

You can see that both main memory and video memory are getting bigger , And video memory is not only getting bigger 1G, The extra memory is pytorch Some configuration variables required for execution , We ignore .

Once again, create a... In the video memory 1GB Tensor of , Assign to b, The code is as follows :

b = torch.zeros([256,1024,1024],device= 'cpu') 

View the main video memory :

This time the main memory size has not changed , Video memory is getting higher 1GB, It's reasonable . Then we will b Move to main memory , The code is as follows :

b ='cpu') 

View the main video memory :


Found that the main memory is getting higher 1GB, Video memory is only getting smaller 0.1GB, It's like copying video memory tensor to main memory . Actually ,pytorch It's a copy of the tensor into main memory , But it also records the movement of this tensor in video memory . We then run the following code , Re establishment 1GB The tensor of is assigned to c:

c = torch.zeros([256,1024,1024],device= 'cuda') 

View the main video memory :

Found that only the video memory size increased 0.1GB, This shows that ,Pytorch It does record the movement of tensors in video memory , It's just that there's no immediate release of video memory space , It chooses to override this position the next time a new variable is created . Next , We repeat the above line of code :

c = torch.zeros([256,1024,1024],device= 'cuda') 

The main video memory is as follows :

Clearly we measure the tensor c It's covered , The video memory is getting bigger , Why is that ? Actually ,Pytorch When running this code , Is to find the available video memory location first , Build this 1GB Tensor of , And then assign it to c. But because when this tensor is newly created , The original c Still possess 1GB Video memory ,pytorch You can only retrieve the other one first 1GB Video memory to create this tensor , Then assign this tensor to c. In this way , The original one c The video memory is empty , But as I said before ,pytorch It doesn't immediately release the video memory here , And waiting for the next coverage , So the video memory doesn't decrease .

Let's build 1GB Of d Tensors , We can verify the above conjecture , The code is as follows :

d = torch.zeros([256,1024,1024],device= 'cuda') 

The main video memory is as follows :

Video memory size has not changed , Because pytorch The new tensor is built on the previous step c Empty space , And then assign it to d. in addition , Deleting variables also does not immediately free video memory :

del d

Main video memory condition :

Video memory has not changed , It's also waiting for the next coverage .

Main memory to video memory

And then the experiment above , We build directly in main memory 1GB And assign it to e, The code is as follows :

e = torch.zeros([256,1024,1024],device= 'cpu') 

The main video memory is as follows :

Main memory gets bigger 1GB, be perfectly logical and reasonable . Then the e Move to video memory , The code is as follows :

e ='cuda')

The main video memory is as follows :

The main memory becomes smaller 1GB, The video memory doesn't change because of the tensor above d Deleted, not covered , be perfectly logical and reasonable . The release of main memory is executed immediately .


Through the above experiment , We learned that ,pytorch Memory of failed variables in video memory is not immediately released , It uses the available space in video memory in an overlay way . in addition , If you want to reset a large tensor in video memory , It's better to move it to main memory first , Or delete it directly , Create a new value , Otherwise, it takes twice as much memory to do this , There may be a shortage of video memory . 

The experimental code is summarized as follows :

import torch
a = torch.zeros([256,1024,1024],device= 'cuda')
b = torch.zeros([256,1024,1024],device= 'cuda')
b ='cpu')
c = torch.zeros([256,1024,1024],device= 'cuda')
c = torch.zeros([256,1024,1024],device= 'cuda')
d = torch.zeros([256,1024,1024],device= 'cuda')
del d
e = torch.zeros([256,1024,1024],device= 'cpu')
e ='cuda')

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database