Python : Efficient CPU usage monitoring with Psutil

python @ Freshers.in

Monitoring CPU usage is essential in performance analysis, system optimization, and ensuring efficient resource management in any computing environment.┬áMonitoring CPU usage in Python is straightforward with the psutil library. Whether you are developing a resource-intensive application, managing a server, or just curious about your system’s performance, this Python-based approach offers a flexible and efficient solution.

Why monitor CPU usage?

  • Performance optimization: Understanding CPU utilization helps in optimizing applications for better performance.
  • Resource management: It aids in efficient allocation and utilization of computing resources.
  • Troubleshooting: Identifying high CPU usage can help in diagnosing and resolving system bottlenecks.

Tools required

  • Python: Ensure you have Python installed on your system.
  • psutil Library: A cross-platform library for retrieving information on system utilization (CPU, memory, disks, network, sensors) in Python.

Installing psutil

First, install the psutil library using pip:

pip install psutil

Writing a Python script to monitor CPU usage

Below is a simple Python script that uses psutil to monitor and print the CPU usage percentage.

Step 1: Import psutil
import psutil
Step 2: Define a Function to Get CPU Usage
def get_cpu_usage():
    # Retrieve and return the CPU usage percentage
    return psutil.cpu_percent(interval=1)

The cpu_percent method provides the system-wide CPU utilization as a percentage. The interval parameter specifies the number of seconds to wait before retrieving the usage. A longer interval provides a more averaged percentage.

Step 3: Main loop to monitor CPU usage continuously
import time
while True:
    cpu_usage = get_cpu_usage()
    print(f"Current CPU Usage: {cpu_usage}%")
    time.sleep(5)  # Sleep for 5 seconds before checking again

This loop continuously checks the CPU usage every 5 seconds and prints the current CPU usage percentage.

Advanced monitoring

For more advanced monitoring, you can:

  • Track usage over tme: Store the CPU usage data in a file or database for historical analysis.
  • Monitor per-core usage: Use psutil.cpu_percent(interval=1, percpu=True) to get usage for each core.
  • Integrate with visualization tools: Use libraries like Matplotlib to visualize CPU usage over time.

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Author: user