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Downloads a model file from a specified URL and saves it to a local file path using HTTP GET request.

Purpose

This function facilitates downloading machine learning models or other files from remote URLs to local storage. It performs a synchronous HTTP GET request, checks the response status, and writes the content to disk if successful. Useful for model deployment pipelines, automated setup scripts, or applications that need to fetch remote resources.

Source Code

def download_model(model_url, save_path):
    response = requests.get(model_url)
    if response.status_code == 200:
        with open(save_path, 'wb') as model_file:
            model_file.write(response.content)
        print(f"Model downloaded and saved to {save_path}")
    else:
        print(f"Failed to download model from {model_url}. Status code: {response.status_code}")

Parameters

Parameter Details

model_url: String containing the complete URL from which to download the model file. Should be a valid HTTP/HTTPS URL pointing to a downloadable resource. Example: 'https://example.com/models/model.pkl'

save_path: String specifying the local file system path where the downloaded model should be saved. Should include the filename and extension. The directory must exist or be writable. Example: './models/downloaded_model.pkl' or '/tmp/model.h5'

Return Value

This function does not return any value (implicitly returns None). It produces side effects by writing to the file system and printing status messages to stdout. Success or failure is communicated through console output rather than return values.

Dependencies

• requests
• os

Required Imports

import requests
import os

Usage Example

import requests
import os

def download_model(model_url, save_path):
    response = requests.get(model_url)
    if response.status_code == 200:
        with open(save_path, 'wb') as model_file:
            model_file.write(response.content)
        print(f"Model downloaded and saved to {save_path}")
    else:
        print(f"Failed to download model from {model_url}. Status code: {response.status_code}")

# Example usage
model_url = "https://example.com/models/my_model.pkl"
save_path = "./models/my_model.pkl"

# Ensure directory exists
os.makedirs(os.path.dirname(save_path), exist_ok=True)

# Download the model
download_model(model_url, save_path)

Best Practices

• Ensure the target directory exists before calling this function, or modify to create directories automatically using os.makedirs()
• Consider adding timeout parameter to requests.get() to prevent hanging on slow connections
• For large files, implement streaming download using response.iter_content() to avoid loading entire file into memory
• Add error handling for network exceptions (requests.exceptions.RequestException) and file I/O errors
• Validate the save_path to prevent path traversal vulnerabilities if user input is involved
• Consider adding checksum verification after download to ensure file integrity
• For production use, implement retry logic with exponential backoff for transient network failures
• Add progress indication for large downloads using libraries like tqdm
• Consider returning a boolean or raising exceptions instead of just printing messages for better error handling in calling code

Tags

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