在 Python 中获取当前系统状态（当前 CPU、RAM、可用磁盘空间等）的首选方式是什么？unix 和 Windows 平台的奖励积分。
似乎有几种可能的方法可以从我的搜索中提取它：
使用诸如PSI之类的库（目前似乎没有积极开发并且在多个平台上不受支持）或类似pystatgrab 之类的东西（自 2007 年以来似乎没有任何活动，并且不支持 Windows）。
使用特定于平台的代码，例如os.popen("ps")在 *nix 系统中使用 a 或类似代码，在 Windows 平台上使用MEMORYSTATUSin ctypes.windll.kernel32（参见ActiveState 上的这个秘籍）。可以将 Python 类与所有这些代码片段放在一起。
并不是说这些方法不好，而是已经有一种得到良好支持的多平台方法来做同样的事情？
psutil 库为您提供有关各种平台上的 CPU、RAM 等的信息：
psutil 是一个模块，它提供了一个接口，用于通过使用 Python 以可移植的方式检索有关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 的用户可以使用 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
以下是提供更多概念和兴趣概念的其他文档：
使用psutil 库。在 Ubuntu 18.04 上，截至 2019 年 1 月 30 日，pip 安装了 5.5.0（最新版本）。旧版本的行为可能会有所不同。您可以通过在 Python 中执行以下操作来检查您的 psutil 版本：
from __future__ import print_function # for Python2
import psutil
print(psutil.__version__)
要获取一些内存和 CPU 统计信息：
from __future__ import print_function
import psutil
print(psutil.cpu_percent())
print(psutil.virtual_memory()) # physical memory usage
print('memory % used:', psutil.virtual_memory()[2])
（virtual_memory元组）将具有系统范围内使用的内存百分比。在 Ubuntu 18.04 上，这似乎被我高估了几个百分点。
您还可以获取当前 Python 实例使用的内存：
import os
import psutil
pid = os.getpid()
python_process = psutil.Process(pid)
memoryUse = python_process.memory_info()[0]/2.**30 # memory use in GB...I think
print('memory use:', memoryUse)
它给出了 Python 脚本的当前内存使用情况。
在psutil的 pypi 页面上有一些更深入的示例。
仅适用于 Linux：仅依赖于 stdlib 的 RAM 使用单线：
import os
tot_m, used_m, free_m = map(int, os.popen('free -t -m').readlines()[-1].split()[1:])
编辑：指定的解决方案操作系统依赖
下面的代码，没有为我工作的外部库。我在 Python 2.7.9 测试过
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 results
print("CPU Usage = " + CPU_Pct)
和 Ram 使用情况、总计、已使用和免费
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'
要获得程序的逐行内存和时间分析，我建议使用memory_profilerand line_profiler。
安装：
# Time profiler
$ pip install line_profiler
# Memory profiler
$ pip install memory_profiler
# Install the dependency for a faster analysis
$ pip install psutil
共同点是，您可以使用相应的装饰器指定要分析的函数。
示例：我的 Python 文件main.py中有几个要分析的函数。其中之一是linearRegressionfit()。我需要使用装饰器@profile来帮助我分析代码：时间和内存。
对函数定义进行以下更改
@profile
def linearRegressionfit(Xt,Yt,Xts,Yts):
lr=LinearRegression()
model=lr.fit(Xt,Yt)
predict=lr.predict(Xts)
# More Code
对于时间分析，
跑：
$ kernprof -l -v main.py
输出
Total time: 0.181071 s
File: main.py
Function: linearRegressionfit at line 35
Line # Hits Time Per Hit % Time Line Contents
==============================================================
35 @profile
36 def linearRegressionfit(Xt,Yt,Xts,Yts):
37 1 52.0 52.0 0.1 lr=LinearRegression()
38 1 28942.0 28942.0 75.2 model=lr.fit(Xt,Yt)
39 1 1347.0 1347.0 3.5 predict=lr.predict(Xts)
40
41 1 4924.0 4924.0 12.8 print("train Accuracy",lr.score(Xt,Yt))
42 1 3242.0 3242.0 8.4 print("test Accuracy",lr.score(Xts,Yts))
对于内存分析，
跑：
$ python -m memory_profiler main.py
输出
Filename: main.py
Line # Mem usage Increment Line Contents
================================================
35 125.992 MiB 125.992 MiB @profile
36 def linearRegressionfit(Xt,Yt,Xts,Yts):
37 125.992 MiB 0.000 MiB lr=LinearRegression()
38 130.547 MiB 4.555 MiB model=lr.fit(Xt,Yt)
39 130.547 MiB 0.000 MiB predict=lr.predict(Xts)
40
41 130.547 MiB 0.000 MiB print("train Accuracy",lr.score(Xt,Yt))
42 130.547 MiB 0.000 MiB print("test Accuracy",lr.score(Xts,Yts))
此外，还可以使用以下方法绘制内存分析器matplotlib结果
$ mprof run main.py
$ mprof plot
注意：测试于
line_profiler版本 == 3.0.2
memory_profiler版本 == 0.57.0
psutil版本 == 5.7.0
编辑：分析器的结果可以使用TAMPPA包进行解析。使用它，我们可以获得逐行所需的图
tqdm结合和可以得到实时的 CPU 和 RAM 监控psutil。在运行繁重的计算/处理时可能会很方便。
它也适用于 Jupyter，无需任何代码更改：
from tqdm import tqdm
from time import sleep
import psutil
with tqdm(total=100, desc='cpu%', position=1) as cpubar, tqdm(total=100, desc='ram%', position=0) as rambar:
while True:
rambar.n=psutil.virtual_memory().percent
cpubar.n=psutil.cpu_percent()
rambar.refresh()
cpubar.refresh()
sleep(0.5)
此代码片段也可用作 gist。
这是我不久前整理的东西，它只是 Windows，但可以帮助您完成您需要完成的部分工作。
源自：“for sys 可用内存” http://msdn2.microsoft.com/en-us/library/aa455130.aspx
《个别进程信息及python脚本示例》 http://www.microsoft.com/technet/scriptcenter/scripts/default.mspx?mfr=true
注意：WMI 接口/进程也可用于执行类似的任务，我在这里没有使用它，因为当前方法满足了我的需求，但如果有一天需要扩展或改进它，那么可能需要研究 WMI 工具.
用于 python 的 WMI：
http://tgolden.sc.sabren.com/python/wmi.html
编码：
'''
Monitor window processes
derived from:
>for sys available mem
http://msdn2.microsoft.com/en-us/library/aa455130.aspx
> individual process information and python script examples
http://www.microsoft.com/technet/scriptcenter/scripts/default.mspx?mfr=true
NOTE: the WMI interface/process is also available for performing similar tasks
I'm not using it here because the current method covers my needs, but if someday it's needed
to extend or improve this module, then may want to investigate the WMI tools available.
WMI for python:
http://tgolden.sc.sabren.com/python/wmi.html
'''
__revision__ = 3
import win32com.client
from ctypes import *
from ctypes.wintypes import *
import pythoncom
import pywintypes
import datetime
class MEMORYSTATUS(Structure):
_fields_ = [
('dwLength', DWORD),
('dwMemoryLoad', DWORD),
('dwTotalPhys', DWORD),
('dwAvailPhys', DWORD),
('dwTotalPageFile', DWORD),
('dwAvailPageFile', DWORD),
('dwTotalVirtual', DWORD),
('dwAvailVirtual', DWORD),
]
def winmem():
x = MEMORYSTATUS() # create the structure
windll.kernel32.GlobalMemoryStatus(byref(x)) # from cytypes.wintypes
return x
class process_stats:
'''process_stats is able to provide counters of (all?) the items available in perfmon.
Refer to the self.supported_types keys for the currently supported 'Performance Objects'
To add logging support for other data you can derive the necessary data from perfmon:
---------
perfmon can be run from windows 'run' menu by entering 'perfmon' and enter.
Clicking on the '+' will open the 'add counters' menu,
From the 'Add Counters' dialog, the 'Performance object' is the self.support_types key.
--> Where spaces are removed and symbols are entered as text (Ex. # == Number, % == Percent)
For the items you wish to log add the proper attribute name in the list in the self.supported_types dictionary,
keyed by the 'Performance Object' name as mentioned above.
---------
NOTE: The 'NETFramework_NETCLRMemory' key does not seem to log dotnet 2.0 properly.
Initially the python implementation was derived from:
http://www.microsoft.com/technet/scriptcenter/scripts/default.mspx?mfr=true
'''
def __init__(self,process_name_list=[],perf_object_list=[],filter_list=[]):
'''process_names_list == the list of all processes to log (if empty log all)
perf_object_list == list of process counters to log
filter_list == list of text to filter
print_results == boolean, output to stdout
'''
pythoncom.CoInitialize() # Needed when run by the same process in a thread
self.process_name_list = process_name_list
self.perf_object_list = perf_object_list
self.filter_list = filter_list
self.win32_perf_base = 'Win32_PerfFormattedData_'
# Define new datatypes here!
self.supported_types = {
'NETFramework_NETCLRMemory': [
'Name',
'NumberTotalCommittedBytes',
'NumberTotalReservedBytes',
'NumberInducedGC',
'NumberGen0Collections',
'NumberGen1Collections',
'NumberGen2Collections',
'PromotedMemoryFromGen0',
'PromotedMemoryFromGen1',
'PercentTimeInGC',
'LargeObjectHeapSize'
],
'PerfProc_Process': [
'Name',
'PrivateBytes',
'ElapsedTime',
'IDProcess',# pid
'Caption',
'CreatingProcessID',
'Description',
'IODataBytesPersec',
'IODataOperationsPersec',
'IOOtherBytesPersec',
'IOOtherOperationsPersec',
'IOReadBytesPersec',
'IOReadOperationsPersec',
'IOWriteBytesPersec',
'IOWriteOperationsPersec'
]
}
def get_pid_stats(self, pid):
this_proc_dict = {}
pythoncom.CoInitialize() # Needed when run by the same process in a thread
if not self.perf_object_list:
perf_object_list = self.supported_types.keys()
for counter_type in perf_object_list:
strComputer = "."
objWMIService = win32com.client.Dispatch("WbemScripting.SWbemLocator")
objSWbemServices = objWMIService.ConnectServer(strComputer,"root\cimv2")
query_str = '''Select * from %s%s''' % (self.win32_perf_base,counter_type)
colItems = objSWbemServices.ExecQuery(query_str) # "Select * from Win32_PerfFormattedData_PerfProc_Process")# changed from Win32_Thread
if len(colItems) > 0:
for objItem in colItems:
if hasattr(objItem, 'IDProcess') and pid == objItem.IDProcess:
for attribute in self.supported_types[counter_type]:
eval_str = 'objItem.%s' % (attribute)
this_proc_dict[attribute] = eval(eval_str)
this_proc_dict['TimeStamp'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.') + str(datetime.datetime.now().microsecond)[:3]
break
return this_proc_dict
def get_stats(self):
'''
Show process stats for all processes in given list, if none given return all processes
If filter list is defined return only the items that match or contained in the list
Returns a list of result dictionaries
'''
pythoncom.CoInitialize() # Needed when run by the same process in a thread
proc_results_list = []
if not self.perf_object_list:
perf_object_list = self.supported_types.keys()
for counter_type in perf_object_list:
strComputer = "."
objWMIService = win32com.client.Dispatch("WbemScripting.SWbemLocator")
objSWbemServices = objWMIService.ConnectServer(strComputer,"root\cimv2")
query_str = '''Select * from %s%s''' % (self.win32_perf_base,counter_type)
colItems = objSWbemServices.ExecQuery(query_str) # "Select * from Win32_PerfFormattedData_PerfProc_Process")# changed from Win32_Thread
try:
if len(colItems) > 0:
for objItem in colItems:
found_flag = False
this_proc_dict = {}
if not self.process_name_list:
found_flag = True
else:
# Check if process name is in the process name list, allow print if it is
for proc_name in self.process_name_list:
obj_name = objItem.Name
if proc_name.lower() in obj_name.lower(): # will log if contains name
found_flag = True
break
if found_flag:
for attribute in self.supported_types[counter_type]:
eval_str = 'objItem.%s' % (attribute)
this_proc_dict[attribute] = eval(eval_str)
this_proc_dict['TimeStamp'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.') + str(datetime.datetime.now().microsecond)[:3]
proc_results_list.append(this_proc_dict)
except pywintypes.com_error, err_msg:
# Ignore and continue (proc_mem_logger calls this function once per second)
continue
return proc_results_list
def get_sys_stats():
''' Returns a dictionary of the system stats'''
pythoncom.CoInitialize() # Needed when run by the same process in a thread
x = winmem()
sys_dict = {
'dwAvailPhys': x.dwAvailPhys,
'dwAvailVirtual':x.dwAvailVirtual
}
return sys_dict
if __name__ == '__main__':
# This area used for testing only
sys_dict = get_sys_stats()
stats_processor = process_stats(process_name_list=['process2watch'],perf_object_list=[],filter_list=[])
proc_results = stats_processor.get_stats()
for result_dict in proc_results:
print result_dict
import os
this_pid = os.getpid()
this_proc_results = stats_processor.get_pid_stats(this_pid)
print 'this proc results:'
print this_proc_results
为此，我们选择使用通常的信息源，因为我们可以发现可用内存的瞬时波动，并且觉得查询meminfo数据源很有帮助。这也帮助我们获得了更多预先解析的相关参数。
代码
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)
输出供参考（我们删除了所有换行符以进行进一步分析）
MemTotal：1014500 kB MemFree：562680 kB MemAvailable：646364 kB 缓冲区：15144 kB 缓存：210720 kB SwapCached：0 kB 活动：261476 kB 非活动：128888 kB 活动（匿名）：167092 kB 非活动（匿名）：208 : 94384 kB 非活动(文件): 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 SReclaimable：18044 KB Sunreclable：16448 KB攻击：8180 KB NFS_UNSTABLE：0 KB弹跳：0 KB WERCHBACKTMP：0 KB ComputLimit：507248 KB Commited_AS：34359738367 KB vmallocused：0 kB vmallocchunk：0 kb hardwarecurrupted：0 kb hardwarecurrupted： 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
我觉得这些答案是为 Python 2 编写的，并且无论如何没有人提到resource可用于 Python 3 的标准包。它提供了用于获取给定进程（默认调用 Python 进程）的资源限制的命令。这与获取整个系统的当前资源使用情况不同，但它可以解决一些相同的问题，例如“我想确保我在这个脚本中只使用 X 多 RAM”。
这汇集了所有的好处：
psutil+os获得 Unix 和 Windows 兼容性： 这使我们能够获得：
代码：
import os
import psutil # need: pip install psutil
In [32]: psutil.virtual_memory()
Out[32]: svmem(total=6247907328, available=2502328320, percent=59.9, used=3327135744, free=167067648, active=3671199744, inactive=1662668800, buffers=844783616, cached=1908920320, shared=123912192, slab=613048320)
In [33]: psutil.virtual_memory().percent
Out[33]: 60.0
In [34]: psutil.cpu_percent()
Out[34]: 5.5
In [35]: os.sep
Out[35]: '/'
In [36]: psutil.disk_usage(os.sep)
Out[36]: sdiskusage(total=50190790656, used=41343860736, free=6467502080, percent=86.5)
In [37]: psutil.disk_usage(os.sep).percent
Out[37]: 86.5
“...当前系统状态（当前 CPU、RAM、可用磁盘空间等）”和“*nix 和 Windows 平台”可能是难以实现的组合。
操作系统在管理这些资源的方式上有着根本的不同。实际上，它们在核心概念上有所不同，例如定义什么是系统以及什么是应用程序时间。
“可用磁盘空间”？什么算作“磁盘空间”？所有设备的所有分区？多引导环境中的外部分区呢？
我认为 Windows 和 *nix 之间没有足够明确的共识来使这成为可能。事实上，在称为 Windows 的各种操作系统之间甚至可能没有任何共识。是否有一个适用于 XP 和 Vista 的 Windows API？
从第一反应中获取反馈并进行小改动
#!/usr/bin/env python
#Execute commond on windows machine to install psutil>>>>python -m pip install psutil
import psutil
print (' ')
print ('----------------------CPU Information summary----------------------')
print (' ')
# gives a single float value
vcc=psutil.cpu_count()
print ('Total number of CPUs :',vcc)
vcpu=psutil.cpu_percent()
print ('Total CPUs utilized percentage :',vcpu,'%')
print (' ')
print ('----------------------RAM Information summary----------------------')
print (' ')
# you can convert that object to a dictionary
#print(dict(psutil.virtual_memory()._asdict()))
# gives an object with many fields
vvm=psutil.virtual_memory()
x=dict(psutil.virtual_memory()._asdict())
def forloop():
for i in x:
print (i,"--",x[i]/1024/1024/1024)#Output will be printed in GBs
forloop()
print (' ')
print ('----------------------RAM Utilization summary----------------------')
print (' ')
# you can have the percentage of used RAM
print('Percentage of used RAM :',psutil.virtual_memory().percent,'%')
#79.2
# you can calculate percentage of available memory
print('Percentage of available RAM :',psutil.virtual_memory().available * 100 / psutil.virtual_memory().total,'%')
#20.8
此脚本用于 CPU 使用：
import os
def get_cpu_load():
""" Returns a list CPU Loads"""
result = []
cmd = "WMIC CPU GET LoadPercentage "
response = os.popen(cmd + ' 2>&1','r').read().strip().split("\r\n")
for load in response[1:]:
result.append(int(load))
return result
if __name__ == '__main__':
print get_cpu_load()
有关 CPU 详细信息，请使用psutil库
对于 RAM 频率（以 MHz 为单位），请使用内置的 Linux 库dmidecode并稍微操作输出；）。此命令需要 root 权限，因此也提供您的密码。只需复制以下建议，将mypass替换为您的密码
import os
os.system("echo mypass | sudo -S dmidecode -t memory | grep 'Clock Speed' | cut -d ':' -f2")------------------- 产量 ---------------
1600 MT/ s
未知
1600 MT/s
未知 0
[i for i in os.popen("echo mypass | sudo -S dmidecode -t memory | grep 'Clock Speed' | cut -d ':' -f2").read().split(' ') if i.isdigit()]- - - - - - - - - - - - - 输出 - - - - - - - - - - - - --
['1600', '1600']
您可以阅读 /proc/meminfo 以获取已用内存
file1 = open('/proc/meminfo', 'r')
for line in file1:
if 'MemTotal' in line:
x = line.split()
memTotal = int(x[1])
if 'Buffers' in line:
x = line.split()
buffers = int(x[1])
if 'Cached' in line and 'SwapCached' not in line:
x = line.split()
cached = int(x[1])
if 'MemFree' in line:
x = line.split()
memFree = int(x[1])
file1.close()
percentage_used = int ( ( memTotal - (buffers + cached + memFree) ) / memTotal * 100 )
print(percentage_used)
您可以将 psutil 或 psmem 与子流程示例代码一起使用
import subprocess
cmd = subprocess.Popen(['sudo','./ps_mem'],stdout=subprocess.PIPE,stderr=subprocess.PIPE)
out,error = cmd.communicate()
memory = out.splitlines()
参考
根据@Hrabal 的 cpu 使用代码，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])
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())
@CodeGench的解决方案不需要外壳，因此假设 Linux 和 Python 的标准库：
def cpu_load():
with open("/proc/stat", "r") as stat:
(key, user, nice, system, idle, _) = (stat.readline().split(None, 5))
assert key == "cpu", "'cpu ...' should be the first line in /proc/stat"
busy = int(user) + int(nice) + int(system)
return 100 * busy / (busy + int(idle))
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 语言编写的，因此任何库都会像您上面建议的那样，对运行哪个特定于操作系统的代码片段做出明智的决定。