📊 Training Monitor Dashboard

Training Monitoring Best Practices

Key Metrics to Watch

• Training Loss: Should decrease steadily. If stuck, try higher LR.
• Validation Loss: Should track training. If diverges → overfitting.
• Accuracy Gap: Large gap = overfitting. Add regularization.
• Learning Rate: Monitor schedule, adjust if needed.
• Gradient Norms: Too large = exploding, too small = vanishing.
• GPU Utilization: Should be 80-100%. Low = bottleneck elsewhere.

Warning Signs

• Loss = NaN: Exploding gradients. Lower LR, check data.
• No Improvement: Check data loading, verify labels.
• Slow Progress: Increase LR or batch size.
• Oscillating Loss: Reduce learning rate.
• Val Loss Increasing: Stop training, overfitting started.

Tools for Monitoring

• TensorBoard: TensorFlow/PyTorch visualization
• Weights & Biases: Cloud-based experiment tracking
• MLflow: Experiment management platform
• Neptune.ai: Metadata store for ML