In Python machine learning, how do I retry transient errors?

In Python machine learning, transient errors can be encountered during data processing, model training, or evaluation. These errors typically occur due to temporary issues such as network timeouts, throttling, or resource limits. To handle such errors, you can implement a retry mechanism using error handling techniques. Below is an example of how to implement retries using the `retrying` library.

import random import time import requests from retrying import retry @retry(stop_max_attempt_number=3, wait_fixed=2000) def fetch_data(url): response = requests.get(url) if response.status_code != 200: raise Exception("Transient error occurred.") return response.json() def main(): url = "https://api.example.com/data" try: data = fetch_data(url) print("Data fetched successfully:", data) except Exception as e: print("Failed to fetch data after retries:", e) if __name__ == "__main__": main()

Python Machine Learning Transient Errors Retry Mechanism Error Handling

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