feat(#369): Implement Comprehensive Email Error Reporting System for Training Process

[PStarH]

Email Error Reporting System Implementation

Overview

This PR implements a comprehensive email error reporting system for the training process. The system is designed to monitor various stages of model training and send detailed error notifications when issues occur, enabling quick problem detection and resolution.

System Architecture

Email Service Implementation

• Created a new EmailService class to handle all email notifications
• Implemented HTML email template system for formatted error messages
• Added support for log file attachments and system resource information
• Configured SMTP settings for reliable email delivery

Major Features

1. Model Download Stage

• Added error notifications in the _download_model method:
  • When the model path doesn't exist after download
  • When exceptions occur during the download process
  • Including model name, error message, and specific step context

2. Weight Merging Stage

• Added error notifications in the merge_weights method:
  • When the base model path doesn't exist
  • When the personal training model output path doesn't exist
  • When merged model files are not found
  • Including model name, error message, process step context, and log file (if available)

3. Model Conversion Stage

• Added error notifications in the convert_model method:
  • When merged model output doesn't exist
  • When model conversion script execution fails
  • When GGUF model file is not found
  • Including model name, error message, process step context, and log file (if available)

Error Notification Features

Content Structure

Each error notification email includes:

• Model name and version information
• Detailed error message with stack trace
• Specific step and context where the error occurred
• System resource information (e.g., memory usage, disk space)
• Related log files (when available)

Smart Log Handling

• Automatic extraction of relevant log sections
• Intelligent truncation of large log files
• Highlighting of error-related log entries
• Support for multiple log file attachments

Testing

Automated Tests

Implemented unit tests for:

• Email service configuration
• Template rendering
• Log file handling
• Error message formatting

Scenario Testing

The following scenarios have been tested:

1. Model download failure
2. Base model or training output missing
3. Weight merging failure
4. Model conversion failure
5. GGUF file generation failure

All scenarios correctly send error notification emails with complete information.

Impact

This new email notification system will:

• Provide immediate alerts for training process issues
• Deliver comprehensive error context for faster debugging
• Reduce system monitoring overhead
• Enable proactive problem resolution
• Improve overall training process reliability and maintainability

Future Enhancements

Potential future improvements:

• Customizable notification templates
• Error categorization and priority levels
• Dashboard integration for error tracking
• Advanced log analysis features