在 Python 中获取硬件和系统信息

作为 Python 开发人员,使用第三方库来完成您真正想要的工作是很方便的,而不是每次都重新发明*。在本教程中,您将熟悉psutil,它是Python 中用于进程和系统监控的跨平台库,以及用于在 Python 中提取系统和硬件信息的内置平台模块。

最后,我将向您展示如何打印 GPU 信息(当然,如果您有的话)。

这是本教程的目录:

  1. 系统信息
  2. CPU信息
  3. 内存使用情况
  4. 磁盘使用情况
  5. 网络信息
  6. 图形处理器信息

相关: 如何使用 ipaddress 模块在 Python 中操作 IP 地址

 

 

在我们深入研究之前,您需要安装 psutil:

pip3 install psutil
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打开一个新的 python 文件,让我们开始,导入必要的模块:

import psutil
import platform
from datetime import datetime
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让我们创建一个函数,将大量字节转换为缩放格式(例如,以千、兆、千兆等为单位):

def get_size(bytes, suffix="B"):
    """
    Scale bytes to its proper format
    e.g:
        1253656 => '1.20MB'
        1253656678 => '1.17GB'
    """
    factor = 1024
    for unit in ["", "K", "M", "G", "T", "P"]:
        if bytes < factor:
            return f"{bytes:.2f}{unit}{suffix}"
        bytes /= factor
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系统信息

我们在这里需要平台模块:

 

 

print("="*40, "System Information", "="*40)
uname = platform.uname()
print(f"System: {uname.system}")
print(f"Node Name: {uname.node}")
print(f"Release: {uname.release}")
print(f"Version: {uname.version}")
print(f"Machine: {uname.machine}")
print(f"Processor: {uname.processor}")
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获取计算机启动的日期和时间:

# Boot Time
print("="*40, "Boot Time", "="*40)
boot_time_timestamp = psutil.boot_time()
bt = datetime.fromtimestamp(boot_time_timestamp)
print(f"Boot Time: {bt.year}/{bt.month}/{bt.day} {bt.hour}:{bt.minute}:{bt.second}")
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CPU信息

让我们获取一些 CPU 信息,例如总内核数、使用情况等:

# let's print CPU information
print("="*40, "CPU Info", "="*40)
# number of cores
print("Physical cores:", psutil.cpu_count(logical=False))
print("Total cores:", psutil.cpu_count(logical=True))
# CPU frequencies
cpufreq = psutil.cpu_freq()
print(f"Max Frequency: {cpufreq.max:.2f}Mhz")
print(f"Min Frequency: {cpufreq.min:.2f}Mhz")
print(f"Current Frequency: {cpufreq.current:.2f}Mhz")
# CPU usage
print("CPU Usage Per Core:")
for i, percentage in enumerate(psutil.cpu_percent(percpu=True, interval=1)):
    print(f"Core {i}: {percentage}%")
print(f"Total CPU Usage: {psutil.cpu_percent()}%")
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psutil的cpu_count()函数返回内核数,而cpu_freq()函数返回 CPU 频率,namedtuple包括以 Mhz 表示的当前、最小和最大频率,您可以设置percpu=True为获取每个 CPU 频率。

cpu_percent()方法返回一个浮点数,表示当前 CPU 利用率的百分比,设置interval为 1(秒)将比较一秒前后经过的系统 CPU 时间,我们设置percpuTrue以获取每个内核的 CPU 使用率。

内存使用情况

# Memory Information
print("="*40, "Memory Information", "="*40)
# get the memory details
svmem = psutil.virtual_memory()
print(f"Total: {get_size(svmem.total)}")
print(f"Available: {get_size(svmem.available)}")
print(f"Used: {get_size(svmem.used)}")
print(f"Percentage: {svmem.percent}%")
print("="*20, "SWAP", "="*20)
# get the swap memory details (if exists)
swap = psutil.swap_memory()
print(f"Total: {get_size(swap.total)}")
print(f"Free: {get_size(swap.free)}")
print(f"Used: {get_size(swap.used)}")
print(f"Percentage: {swap.percent}%")
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virtual_memory()方法返回有关系统内存使用情况的统计信息namedtuple,包括(可用total物理内存总量)、available(可用内存,即未使用)usedpercent(即百分比)等字段。swap_memory()是相同的,但用于交换内存。

 

 

我们使用先前定义的get_size()函数以缩放方式打印值,因为这些统计信息以字节表示。

 

磁盘使用情况

# Disk Information
print("="*40, "Disk Information", "="*40)
print("Partitions and Usage:")
# get all disk partitions
partitions = psutil.disk_partitions()
for partition in partitions:
    print(f"=== Device: {partition.device} ===")
    print(f"  Mountpoint: {partition.mountpoint}")
    print(f"  File system type: {partition.fstype}")
    try:
        partition_usage = psutil.disk_usage(partition.mountpoint)
    except PermissionError:
        # this can be catched due to the disk that
        # isn't ready
        continue
    print(f"  Total Size: {get_size(partition_usage.total)}")
    print(f"  Used: {get_size(partition_usage.used)}")
    print(f"  Free: {get_size(partition_usage.free)}")
    print(f"  Percentage: {partition_usage.percent}%")
# get IO statistics since boot
disk_io = psutil.disk_io_counters()
print(f"Total read: {get_size(disk_io.read_bytes)}")
print(f"Total write: {get_size(disk_io.write_bytes)}")
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正如预期的那样,disk_usage()函数将磁盘使用统计信息返回为namedtuple,包括totalused以及free以字节表示的空间。

网络信息

# Network information
print("="*40, "Network Information", "="*40)
# get all network interfaces (virtual and physical)
if_addrs = psutil.net_if_addrs()
for interface_name, interface_addresses in if_addrs.items():
    for address in interface_addresses:
        print(f"=== Interface: {interface_name} ===")
        if str(address.family) == 'AddressFamily.AF_INET':
            print(f"  IP Address: {address.address}")
            print(f"  Netmask: {address.netmask}")
            print(f"  Broadcast IP: {address.broadcast}")
        elif str(address.family) == 'AddressFamily.AF_PACKET':
            print(f"  MAC Address: {address.address}")
            print(f"  Netmask: {address.netmask}")
            print(f"  Broadcast MAC: {address.broadcast}")
# get IO statistics since boot
net_io = psutil.net_io_counters()
print(f"Total Bytes Sent: {get_size(net_io.bytes_sent)}")
print(f"Total Bytes Received: {get_size(net_io.bytes_recv)}")
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net_if_addrs()函数返回与系统上安装的每个网络接口卡相关联的地址。

好的,这是我个人 linux 机器的结果输出:

<span style="color:#212529"><span style="background-color:#ffffff"><span style="background-color:#f5f2f0"><span style="color:#000000"><code class="language-markup">======================================== System Information ========================================
System: Linux
Node Name: rockikz
Release: 4.17.0-kali1-amd64
Version: #1 SMP Debian 4.17.8-1kali1 (2018-07-24)
Machine: x86_64
Processor:
======================================== Boot Time ========================================
Boot Time: 2019/8/21 9:37:26
======================================== CPU Info ========================================
Physical cores: 4
Total cores: 4
Max Frequency: 3500.00Mhz
Min Frequency: 1600.00Mhz
Current Frequency: 1661.76Mhz
CPU Usage Per Core:
Core 0: 0.0%
Core 1: 0.0%
Core 2: 11.1%
Core 3: 0.0%
Total CPU Usage: 3.0%
======================================== Memory Information ========================================
Total: 3.82GB
Available: 2.98GB
Used: 564.29MB
Percentage: 21.9%
==================== SWAP ====================
Total: 0.00B
Free: 0.00B
Used: 0.00B
Percentage: 0%
======================================== Disk Information ========================================
Partitions and Usage:
=== Device: /dev/sda1 ===
  Mountpoint: /
  File system type: ext4
  Total Size: 451.57GB
  Used: 384.29GB
  Free: 44.28GB
  Percentage: 89.7%
Total read: 2.38GB
Total write: 2.45GB
======================================== Network Information ========================================
=== Interface: lo ===
  IP Address: 127.0.0.1
  Netmask: 255.0.0.0
  Broadcast IP: None
=== Interface: lo ===
=== Interface: lo ===
  MAC Address: 00:00:00:00:00:00
  Netmask: None
  Broadcast MAC: None
=== Interface: wlan0 ===
  IP Address: 192.168.1.101
  Netmask: 255.255.255.0
  Broadcast IP: 192.168.1.255
=== Interface: wlan0 ===
=== Interface: wlan0 ===
  MAC Address: 64:70:02:07:40:50
  Netmask: None
  Broadcast MAC: ff:ff:ff:ff:ff:ff
=== Interface: eth0 ===
  MAC Address: d0:27:88:c6:06:47
  Netmask: None
  Broadcast MAC: ff:ff:ff:ff:ff:ff
Total Bytes Sent: 123.68MB
Total Bytes Received: 577.94MB</code></span></span></span></span>
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如果您使用的是笔记本电脑,则可以使用 psutil.sensors_battery() 获取电池信息。

 

另外,如果你是一个Linux用户,你可以使用 psutil.sensors_fan() 来获得风扇的RPM(每分钟转数) ,也 psutil.sensors_temperatures() 来获得各种设备的温度。

图形处理器信息

psutil不向我们提供 GPU 信息。因此,我们需要安装GPUtil

pip3 install gputil
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GPUtil是一个 Python 模块,仅用于获取 NVIDIA GPU 的 GPU 状态,它定位计算机上的所有 GPU,确定它们的可用性并返回可用 GPU 的有序列表。它需要安装最新的 NVIDIA 驱动程序。

此外,我们需要安装tabulate 模块,这将允许我们以表格方式打印 GPU 信息:

pip3 install tabulate
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以下代码行打印您机器中的所有 GPU 及其详细信息:

 

# GPU information
import GPUtil
from tabulate import tabulate
print("="*40, "GPU Details", "="*40)
gpus = GPUtil.getGPUs()
list_gpus = []
for gpu in gpus:
    # get the GPU id
    gpu_id = gpu.id
    # name of GPU
    gpu_name = gpu.name
    # get % percentage of GPU usage of that GPU
    gpu_load = f"{gpu.load*100}%"
    # get free memory in MB format
    gpu_free_memory = f"{gpu.memoryFree}MB"
    # get used memory
    gpu_used_memory = f"{gpu.memoryUsed}MB"
    # get total memory
    gpu_total_memory = f"{gpu.memoryTotal}MB"
    # get GPU temperature in Celsius
    gpu_temperature = f"{gpu.temperature} °C"
    gpu_uuid = gpu.uuid
    list_gpus.append((
        gpu_id, gpu_name, gpu_load, gpu_free_memory, gpu_used_memory,
        gpu_total_memory, gpu_temperature, gpu_uuid
    ))

print(tabulate(list_gpus, headers=("id", "name", "load", "free memory", "used memory", "total memory",
                                   "temperature", "uuid")))
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这是我机器中的输出:

======================================== GPU Details ========================================
  id  name              load    free memory    used memory    total memory    temperature    uuid
----  ----------------  ------  -------------  -------------  --------------  -------------  ----------------------------------------
   0  GeForce GTX 1050  2.0%    3976.0MB       120.0MB        4096.0MB        52.0 °C        GPU-c9b08d82-f1e2-40b6-fd20-543a4186d6ce
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太好了,现在您可以将这些信息集成到您的 Python 监视器应用程序和实用程序中!

检查我们在本教程中使用的库的文档:

 

 

您还可以使用 psutil 来 监控操作系统进程,例如每个进程的 CPU 和内存使用情况等。

 

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