Monitoring the training process is a crucial aspect of developing robust machine learning models. Effective monitoring helps in understanding the model’s behavior, diagnosing issues, and making informed decisions. Here are 100 tips and tricks for monitoring training:
1. General Monitoring Principles
- Establish a clear understanding of the training objectives and goals.
- Define key performance metrics that align with the problem and objectives.
- Monitor metrics that provide insights into both training and validation performance.
- Implement logging mechanisms to record relevant information during training.
- Set up a robust system for tracking and visualizing training progress.
- Regularly check for convergence by analyzing training and validation curves.
- Monitor training time and resource utilization for efficiency.
- Use visualization tools for real-time monitoring of key metrics.
- Consider the use of external monitoring tools for large-scale or distributed training.
- Share insights on monitoring strategies with the broader machine learning community.
