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# WandB Configuration for Medical VQA Training Monitoring
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## QUICK START:
### 1. Create WandB Account
Go to: https://wandb.ai/
Sign up with GitHub or Email
### 2. Get API Key
Go to: https://wandb.ai/settings/profile
Copy your API key
### 3. Set Environment Variable
export WANDB_API_KEY="your_api_key_here"
# Or in Jupyter:
import os
os.environ['WANDB_API_KEY'] = 'your_api_key_here'
### 4. Run Training
python train_medical.py --variant A1
# Automatically logs to WandB!
## WHAT GETS LOGGED:
✅ Training Metrics (per epoch):
- train_loss
- train_accuracy
- train_bleu
- train_rouge
- train_bertscore
✅ Validation Metrics (per epoch):
- val_loss
- val_accuracy
- val_bleu
- val_rouge
- val_bertscore
✅ Model Info:
- Number of parameters
- Model architecture
- Config settings
✅ Hardware:
- GPU usage
- Memory
- Training time
✅ Learning Rate:
- Current LR per epoch
- Warmup schedule
## MONITORING DASHBOARD:
View live at: https://wandb.ai/QuangVoAI/MedicalVQA-Vietnam
Features:
- Real-time loss graphs
- Metric comparison across variants
- Training progress
- System resource monitoring
- Hyperparameter tracking
- Model checkpoints
## ADVANCED:
Save Checkpoints to WandB:
wandb.save('checkpoint.pt')
Log Custom Metrics:
wandb.log({'custom_metric': value, 'epoch': epoch})
Compare Models:
Visit: https://wandb.ai/QuangVoAI/MedicalVQA-Vietnam/reports
## OFFLINE MODE:
If you don't have internet:
export WANDB_MODE=offline
python train_medical.py --variant A1
# Saves locally, can sync later
## TIPS:
1. Set descriptive run names:
wandb.init(..., name="A2_50epochs_final")
2. Add tags for easy filtering:
wandb.init(..., tags=["production", "50-epochs"])
3. Create reports with charts:
Use WandB UI to create custom reports
4. Compare multiple runs:
Group runs by config/variant
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