[英]How to get current CPU and RAM usage in Python?
How can I get the current system status (current CPU, RAM, free disk space, etc.) in Python?如何在 Python 中获取当前系统状态(当前 CPU、RAM、可用磁盘空间等)? Ideally, it would work for both Unix and Windows platforms.
理想情况下,它适用于 Unix 和 Windows 平台。
There seems to be a few possible ways of extracting that from my search:似乎有几种可能的方法可以从我的搜索中提取它:
Using a library such as PSI (that currently seems not actively developed and not supported on multiple platforms) or something like pystatgrab (again no activity since 2007 it seems and no support for Windows).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)。
Using platform specific code such as using a os.popen("ps")
or similar for the *nix systems and MEMORYSTATUS
in ctypes.windll.kernel32
(see this recipe on ActiveState ) for the Windows platform.使用特定于平台的代码,例如对 *nix 系统使用
os.popen("ps")
或类似的代码,对 Windows 平台使用ctypes.windll.kernel32
中的MEMORYSTATUS
(参见ActiveState 上的这个秘籍)。 One could put a Python class together with all those code snippets.可以将 Python 类与所有这些代码片段放在一起。
It's not that those methods are bad but is there already a well-supported, multi-platform way of doing the same thing?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?
The psutil library gives you information about CPU, RAM, etc., on a variety of platforms: psutil 库为您提供有关各种平台上的 CPU、RAM 等的信息:
psutil is a module providing an interface for retrieving information on running processes and system utilization (CPU, memory) in a portable way by using Python, implementing many functionalities offered by tools like ps, top and Windows task manager.
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。
It currently supports Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD and NetBSD, both 32-bit and 64-bit architectures, with Python versions from 2.6 to 3.5 (users of Python 2.4 and 2.5 may use 2.1.3 version).
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)。
Some examples: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
Here's other documentation that provides more concepts and interest concepts:以下是提供更多概念和兴趣概念的其他文档:
Use the psutil library .使用psutil 库。 On Ubuntu 18.04, pip installed 5.5.0 (latest version) as of 1-30-2019.
在 Ubuntu 18.04 上,截至 2019 年 1 月 30 日,pip 安装了 5.5.0(最新版本)。 Older versions may behave somewhat differently.
旧版本的行为可能会有所不同。 You can check your version of psutil by doing this in Python:
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__)
To get some memory and CPU stats: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])
The virtual_memory
(tuple) will have the percent memory used system-wide. virtual_memory
(元组)将具有系统范围内使用的内存百分比。 This seemed to be overestimated by a few percent for me on Ubuntu 18.04.在 Ubuntu 18.04 上,这似乎被我高估了几个百分点。
You can also get the memory used by the current Python instance: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)
which gives the current memory use of your Python script.它给出了 Python 脚本的当前内存使用情况。
There are some more in-depth examples on the pypi page for psutil .在psutil 的 pypi 页面上有一些更深入的示例。
Only for Linux: One-liner for the RAM usage with only stdlib dependency: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:])
One can get real time CPU and RAM monitoring by combining tqdm
and psutil
.通过结合
tqdm
和psutil
可以得到实时的 CPU 和 RAM 监控。 It may be handy when running heavy computations / processing.在运行繁重的计算/处理时可能会很方便。
It also works in Jupyter without any code changes: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)
It's convenient to put those progress bars in separate process using multiprocessing library.使用多处理库将这些进度条放在单独的进程中很方便。
This code snippet is also available as a gist .此代码片段也可用作 gist 。
Below codes, without external libraries worked for me.下面的代码,没有为我工作的外部库。 I tested at Python 2.7.9
我在 Python 2.7.9 测试过
CPU Usage 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
And Ram Usage, Total, Used and Free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'
To get a line-by-line memory and time analysis of your program, I suggest using memory_profiler
and line_profiler
.要获得程序的逐行内存和时间分析,我建议使用
memory_profiler
和line_profiler
。
Installation:安装:
# Time profiler
$ pip install line_profiler
# Memory profiler
$ pip install memory_profiler
# Install the dependency for a faster analysis
$ pip install psutil
The common part is, you specify which function you want to analyse by using the respective decorators.共同点是,您可以使用相应的装饰器指定要分析的函数。
Example: I have several functions in my Python file main.py
that I want to analyse.示例:我的 Python 文件
main.py
中有几个要分析的函数。 One of them is linearRegressionfit()
.其中之一是
linearRegressionfit()
。 I need to use the decorator @profile
that helps me profile the code with respect to both: Time & Memory.我需要使用装饰器
@profile
来帮助我分析代码:时间和内存。
Make the following changes to the function definition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
For Time Profiling ,对于时间分析,
Run:跑:
$ kernprof -l -v main.py
Output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))
For Memory Profiling ,对于内存分析,
Run:跑:
$ python -m memory_profiler main.py
Output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))
Also, the memory profiler results can also be plotted using matplotlib
using此外,还可以使用
matplotlib
绘制内存分析器结果
$ mprof run main.py
$ mprof plot
line_profiler
version == 3.0.2 line_profiler
版本 == 3.0.2
memory_profiler
version == 0.57.0 memory_profiler
版本 == 0.57.0
psutil
version == 5.7.0 psutil
版本 == 5.7.0
EDIT: The results from the profilers can be parsed using the TAMPPA package.编辑:分析器的结果可以使用TAMPPA包进行解析。 Using it, we can get line-by-line desired plots as
使用它,我们可以获得逐行所需的图
Here's something I put together a while ago, it's windows only but may help you get part of what you need done.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。
Derived from: "for sys available mem" http://msdn2.microsoft.com/en-us/library/aa455130.aspx源自:“for sys 可用内存” 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 《个别进程信息及python脚本示例》 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, then may want to investigate the WMI tools a vailable.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,因为当前的方法可以满足我的需求,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.
WMI for python:用于 python 的 WMI:
http://tgolden.sc.sabren.com/python/wmi.html http://tgolden.sc.sabren.com/python/wmi.html
The code: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
We chose to use usual information source for this because we could find instantaneous fluctuations in free memory and felt querying the meminfo data source was helpful.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。 This also helped us get a few more related parameters that were pre-parsed.
这也帮助我们获得了更多预先解析的相关参数。
Code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)
Output for reference (we stripped all newlines for further analysis)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 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
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 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
I feel like these answers were written for Python 2, and in any case nobody's made mention of the standard resource
package that's available for Python 3. It provides commands for obtaining the resource limits of a given process (the calling Python process by default).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可用于 Python 3 的标准
resource
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。 This isn't the same as getting the current usage of resources by the system as a whole, but it could solve some of the same problems like eg "I want to make sure I only use X much RAM with this script."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”。
This aggregate all the goodies: psutil
+ os
to get Unix & Windows compatibility: That allows us to get:这汇总了所有好东西:
psutil
+ os
以获得 Unix 和 Windows 兼容性:这使我们能够获得:
code: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
"... current system status (current CPU, RAM, free disk space, etc.)" And "*nix and Windows platforms" can be a difficult combination to achieve. “...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。
The operating systems are fundamentally different in the way they manage these resources.操作系统在管理这些资源的方式上有着根本的不同。 Indeed, they differ in core concepts like defining what counts as system and what counts as application time.
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。
"Free disk space"? “可用磁盘空间”? What counts as "disk space?"
什么算作“磁盘空间”? All partitions of all devices?
所有设备的所有分区? What about foreign partitions in a multi-boot environment?
多引导环境中的外部分区呢?
I don't think there's a clear enough consensus between Windows and *nix that makes this possible.我认为 Windows 和 *nix 之间没有足够明确的共识来使这成为可能。 Indeed, there may not even be any consensus between the various operating systems called Windows.
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。 Is there a single Windows API that works for both XP and Vista?
是否有一个适用于 XP 和 Vista 的 Windows API?
Taken feedback from first response and done small changes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
This script for CPU usage: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()
For CPU details use psutil library有关 CPU 详细信息,请使用psutil库
https://psutil.readthedocs.io/en/latest/#cpu
https://psutil.readthedocs.io/en/latest/#cpu
For RAM Frequency (in MHz) use the built in Linux library dmidecode and manipulate the output a bit ;).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;)。 this command needs root permission hence supply your password too.
此命令需要 root 权限,因此也提供您的密码。 just copy the following commend replacing mypass with your password
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")
------------------- Output ---------------------------
- - - - - - - - - - 输出 - - - - - - - - - - - - - -
1600 MT/s1600 公吨/秒
Unknown未知
1600 MT/s1600 公吨/秒
Unknown 0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()]
-------------------------- output -------------------------
- - - - - - - - - - - - - 输出 - - - - - - - - - - - - --
['1600', '1600']['1600', '1600']
you can read /proc/meminfo to get used memory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)
You can use psutil or psmem with subprocess example code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()
Reference参考
https://github.com/Leo-g/python-flask-cmd https://github.com/Leo-g/python-flask-cmd
Based on the cpu usage code by @Hrabal, this is what I use: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])
Run with crontab won't print pid使用 crontab 运行不会打印 pid
Setup: */1 * * * * sh dog.sh
this line in crontab -e
设置:
*/1 * * * * sh dog.sh
crontab -e
中的这一行
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())
Shell-out not needed for @CodeGench 's solution , so assuming Linux and Python's standard libraries: @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))
I don't believe that there is a well-supported multi-platform library available.我不相信有一个支持良好的多平台库可用。 Remember that Python itself is written in C so any library is simply going to make a smart decision about which OS-specific code snippet to run, as you suggested above.
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。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.