import requests import os def download_image(url): try: # Create a unique filename from the URL filename = os.path.join("downloads", url.split("/")[-1]) # Download image content response = requests.get(url, timeout=10) if response.status_code == 200: with open(filename, 'wb') as f: f.write(response.content) return f"Success: {url}" except Exception as e: return f"Error downloading {url}: {e}" Use code with caution. 2. Implement the Multiprocessing Pool
While multithreading is often recommended for I/O-bound tasks like downloads, is superior if you need to perform heavy image processing (like resizing or filtering) immediately after the download, as it provides true parallelism. How to Use multiprocessing to Download Images python multiprocessing download images
Use the Pool.map() method to apply the download function to your list of URLs. This automatically splits the workload across your available CPU cores. import requests import os def download_image(url): try: #
Efficiently Downloading Images with Python Multiprocessing Downloading thousands of images one by one is a slow, sequential process where your CPU sits idle most of the time while waiting for network responses. By using the multiprocessing module, you can bypass the Global Interpreter Lock (GIL) and leverage multiple CPU cores to download images in parallel, significantly cutting down execution time. How to Use multiprocessing to Download Images Use the Pool
First, define a standalone function that handles a single image download using the requests library.