for model_file in model_files: models_by_name[model_file.name].append(model_file)
Tested on a realistic Skin ML dataset: (100k skin lesion images, 50 GB)
This article dives deep into creating a robust solution—a custom, automated script designed to protect your machine learning assets, skin texture libraries, and model checkpoints. file backup script skin ml
: This is a simplified version of the full skin backup system that focuses exclusively on color settings. According to the script documentation on GitHub , it allows users to: View a list of skin-provided color themes with screenshots. Save custom color settings into a new theme.
def send_notification(status, details=""): """Send a simple desktop notification (cross-platform)""" try: if os.name == 'nt': # Windows from plyer import notification notification.notify( title=f"Skin ML Backup - status", message=details[:100], timeout=5 ) else: # Linux/macOS subprocess.run(["notify-send", f"Skin ML Backup", f"status: details[:50]"]) except: pass # Silent fail if no notification system for model_file in model_files: models_by_name[model_file
Open your crontab:
# Step 5: Update latest pointer create_symlink_latest(backup_dest) Save custom color settings into a new theme
Here’s what it looked like (in Python, for Windows):
Also run after every training epoch completion (triggered from your training script):
