如何在Python中获取当前系统状态(当前CPU、RAM、空闲磁盘空间等)?理想情况下,它可以同时适用于Unix和Windows平台。

从我的搜索中似乎有一些可能的方法:

使用像PSI这样的库(目前似乎没有积极开发,在多个平台上也不支持)或像pystatgrab这样的库(从2007年开始似乎没有活动,也不支持Windows)。 使用平台特定的代码,例如使用os.popen("ps")或*nix系统的类似代码,以及ctypes.windll中的MEMORYSTATUS。Windows平台的kernel32(请参阅ActiveState上的配方)。可以将所有这些代码片段放在一个Python类中。

这并不是说这些方法不好,而是是否已经有一种支持良好的多平台方式来做同样的事情?


当前回答

基于cpu使用代码@Hrabal,这是我使用的:

from subprocess import Popen, PIPE

def get_cpu_usage():
    ''' Get CPU usage on Linux by reading /proc/stat '''

    sub = Popen(('grep', 'cpu', '/proc/stat'), stdout=PIPE, stderr=PIPE)
    top_vals = [int(val) for val in sub.communicate()[0].split('\n')[0].split[1:5]]

    return (top_vals[0] + top_vals[2]) * 100. /(top_vals[0] + top_vals[2] + top_vals[3])

其他回答

psutil库提供了各种平台上关于CPU、RAM等的信息:

psutil是一个模块,提供了一个接口,通过使用Python以可移植的方式检索正在运行的进程和系统利用率(CPU,内存)的信息,实现了ps、top和Windows任务管理器等工具提供的许多功能。 它目前支持Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD和NetBSD, 32位和64位架构,Python版本从2.6到3.5 (Python 2.4和2.5的用户可能使用2.1.3版本)。


一些例子:

#!/usr/bin/env python
import psutil
# gives a single float value
psutil.cpu_percent()
# gives an object with many fields
psutil.virtual_memory()
# you can convert that object to a dictionary 
dict(psutil.virtual_memory()._asdict())
# you can have the percentage of used RAM
psutil.virtual_memory().percent
79.2
# you can calculate percentage of available memory
psutil.virtual_memory().available * 100 / psutil.virtual_memory().total
20.8

以下是其他文档,提供了更多的概念和感兴趣的概念:

https://psutil.readthedocs.io/en/latest/

下面的代码,没有外部库为我工作。我在Python 2.7.9测试

CPU使用率

import os
    
CPU_Pct=str(round(float(os.popen('''grep 'cpu ' /proc/stat | awk '{usage=($2+$4)*100/($2+$4+$5)} END {print usage }' ''').readline()),2))
print("CPU Usage = " + CPU_Pct)  # print results

和Ram使用,总,使用和免费

import os
mem=str(os.popen('free -t -m').readlines())
"""
Get a whole line of memory output, it will be something like below
['             total       used       free     shared    buffers     cached\n', 
'Mem:           925        591        334         14         30        355\n', 
'-/+ buffers/cache:        205        719\n', 
'Swap:           99          0         99\n', 
'Total:        1025        591        434\n']
 So, we need total memory, usage and free memory.
 We should find the index of capital T which is unique at this string
"""
T_ind=mem.index('T')
"""
Than, we can recreate the string with this information. After T we have,
"Total:        " which has 14 characters, so we can start from index of T +14
and last 4 characters are also not necessary.
We can create a new sub-string using this information
"""
mem_G=mem[T_ind+14:-4]
"""
The result will be like
1025        603        422
we need to find first index of the first space, and we can start our substring
from from 0 to this index number, this will give us the string of total memory
"""
S1_ind=mem_G.index(' ')
mem_T=mem_G[0:S1_ind]
"""
Similarly we will create a new sub-string, which will start at the second value. 
The resulting string will be like
603        422
Again, we should find the index of first space and than the 
take the Used Memory and Free memory.
"""
mem_G1=mem_G[S1_ind+8:]
S2_ind=mem_G1.index(' ')
mem_U=mem_G1[0:S2_ind]

mem_F=mem_G1[S2_ind+8:]
print 'Summary = ' + mem_G
print 'Total Memory = ' + mem_T +' MB'
print 'Used Memory = ' + mem_U +' MB'
print 'Free Memory = ' + mem_F +' MB'

我不相信有一个支持良好的多平台库可用。请记住,Python本身是用C编写的,因此任何库都会像上面建议的那样,对运行哪个特定于操作系统的代码段做出明智的决定。

使用crontab运行不会打印pid

设置:*/1 * * * * sh dog.sh这一行在crontab -e

import os
import re

CUT_OFF = 90

def get_cpu_load():
    cmd = "ps -Ao user,uid,comm,pid,pcpu --sort=-pcpu | head -n 2 | tail -1"
    response = os.popen(cmd, 'r').read()
    arr = re.findall(r'\S+', response)
    print(arr)
    needKill = float(arr[-1]) > CUT_OFF
    if needKill:
        r = os.popen(f"kill -9 {arr[-2]}")
        print('kill:', r)

if __name__ == '__main__':
    # Test CPU with 
    # $ stress --cpu 1
    # crontab -e
    # Every 1 min
    # */1 * * * * sh dog.sh
    # ctlr o, ctlr x
    # crontab -l
    print(get_cpu_load())

为此,我们选择使用常用的信息源,因为我们可以发现空闲内存的瞬时波动,并且认为查询meminfo数据源是有帮助的。这也帮助我们获得了一些预先解析的相关参数。

Code

import os

linux_filepath = "/proc/meminfo"
meminfo = dict(
    (i.split()[0].rstrip(":"), int(i.split()[1]))
    for i in open(linux_filepath).readlines()
)
meminfo["memory_total_gb"] = meminfo["MemTotal"] / (2 ** 20)
meminfo["memory_free_gb"] = meminfo["MemFree"] / (2 ** 20)
meminfo["memory_available_gb"] = meminfo["MemAvailable"] / (2 ** 20)

输出参考(为了进一步分析,我们去掉了所有换行符)

MemTotal: 1014500 kB MemFree: 562680 kB MemAvailable: 646364 kB Buffers: 15144 kB Cached: 210720 kB SwapCached: 0 kB Active: 261476 kB Inactive: 128888 kB Active(anon): 167092 kB Inactive(anon): 20888 kB Active(file): 94384 kB Inactive(file): 108000 kB Unevictable: 3652 kB Mlocked: 3652 kB SwapTotal: 0 kB SwapFree: 0 kB Dirty: 0 kB Writeback: 0 kB AnonPages: 168160 kB Mapped: 81352 kB Shmem: 21060 kB Slab: 34492 kB SReclaimable: 18044 kB SUnreclaim: 16448 kB KernelStack: 2672 kB PageTables: 8180 kB NFS_Unstable: 0 kB Bounce: 0 kB WritebackTmp: 0 kB CommitLimit: 507248 kB Committed_AS: 1038756 kB VmallocTotal: 34359738367 kB VmallocUsed: 0 kB VmallocChunk: 0 kB HardwareCorrupted: 0 kB AnonHugePages: 88064 kB CmaTotal: 0 kB CmaFree: 0 kB HugePages_Total: 0 HugePages_Free: 0 HugePages_Rsvd: 0 HugePages_Surp: 0 Hugepagesize: 2048 kB DirectMap4k: 43008 kB DirectMap2M: 1005568 kB