# DQN Runpod Monitoring Quick Reference

**Purpose**: Quick commands for monitoring DQN training on Runpod

---

## Real-Time Monitoring

### View Training Logs (Automatic with --monitor)
The deployment script automatically streams logs when using `--monitor` flag.

**Manual Log Monitoring**:
```bash
# If you need to reconnect to logs
export PYTHONPATH=/home/jgrusewski/Work/foxhunt:$PYTHONPATH
source .venv/bin/activate

python3 scripts/monitor_logs.py <pod_id>
```

---

## Key Metrics to Watch

### 1. Reward Variance (Every Epoch)
```
Look for: "Reward std=0.152"
✅ GOOD: std > 0.1 (healthy variance)
⚠️  BAD:  std < 0.01 (constant rewards - training bug)
```

### 2. Action Distribution (Every 10 Epochs)
```
Action Distribution [Epoch 10]:
  BUY=28.5% (1423) | SELL=31.2% (1556) | HOLD=40.3% (2011)

✅ GOOD: Each action 20-40%
⚠️  BAD:  One action < 10% or > 80%
```

### 3. Q-Value Balance (Every 10 Epochs)
```
Average Q-values [Epoch 10]:
  BUY=0.1234 | SELL=0.1189 | HOLD=0.1201

✅ GOOD: All within 50% of each other
⚠️  BAD:  One action > 10x another
```

### 4. Exploration Decay
```
Look for: "Final epsilon: 0.XXXX"

Epoch 1:   epsilon ~0.300
Epoch 10:  epsilon ~0.270
Epoch 50:  epsilon ~0.150
Epoch 100: epsilon ~0.050
```

### 5. Training Progress
```
Look for checkpoint saves:
💾 Checkpoint saved: dqn_epoch_10.safetensors (12345678 bytes)
💾 Checkpoint saved: dqn_epoch_20.safetensors (12345678 bytes)
...
```

---

## S3 Checkpoint Verification

### List All Checkpoints
```bash
aws s3 ls s3://se3zdnb5o4/ml_training/dqn_fixed_reward/checkpoints/ \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io \
  --recursive
```

**Expected Output**:
```
2025-11-01 10:15:32   12345678 checkpoints/dqn_epoch_10.safetensors
2025-11-01 10:25:45   12345678 checkpoints/dqn_epoch_20.safetensors
2025-11-01 10:35:58   12345678 checkpoints/dqn_epoch_30.safetensors
...
```

### Download Latest Checkpoint
```bash
# List checkpoints sorted by time
aws s3 ls s3://se3zdnb5o4/ml_training/dqn_fixed_reward/ \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io \
  --recursive | sort -k1,2

# Download latest
aws s3 cp s3://se3zdnb5o4/ml_training/dqn_fixed_reward/dqn_final_epoch100.safetensors \
  ml/trained_models/ \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io
```

---

## Pod Management

### Check Pod Status
```bash
curl -H "Authorization: Bearer $RUNPOD_API_KEY" \
  https://rest.runpod.io/v1/pods
```

**Response**:
```json
{
  "pods": [
    {
      "id": "abc123",
      "name": "foxhunt-training",
      "status": "RUNNING",
      "gpuType": "RTX A4000",
      "runtime": 3600  // seconds
    }
  ]
}
```

### Stop Pod (Graceful)
```bash
POD_ID="<your_pod_id>"

curl -X POST \
  -H "Authorization: Bearer $RUNPOD_API_KEY" \
  https://rest.runpod.io/v1/pods/$POD_ID/stop
```

### Terminate Pod (Force)
```bash
POD_ID="<your_pod_id>"

curl -X POST \
  -H "Authorization: Bearer $RUNPOD_API_KEY" \
  https://rest.runpod.io/v1/pods/$POD_ID/terminate
```

---

## Training Health Checks

### Check 1: Training Started
**What to look for**: "🏋️  Starting training..."
**When**: Within 1-2 minutes of deployment
**If missing**: Check pod logs for errors

### Check 2: Replay Buffer Filling
**What to look for**: "Building replay buffer: X/104346"
**When**: First 5-10 minutes
**If stuck**: Data loading issue or OOM

### Check 3: First Checkpoint
**What to look for**: "💾 Checkpoint saved: dqn_epoch_10.safetensors"
**When**: ~10-15 minutes
**If missing**: Check S3 credentials or disk space

### Check 4: Reward Variance
**What to look for**: "Reward std > 0.1"
**When**: Every epoch after 10
**If failing**: Reward function bug (constant rewards)

### Check 5: Action Diversity
**What to look for**: "Action Distribution [Epoch X]"
**When**: Every 10 epochs
**If imbalanced**: Epsilon too low or Q-value bug

---

## Cost Monitoring

### Calculate Current Cost
```bash
# Get pod runtime in seconds
POD_RUNTIME_HOURS=$(echo "scale=2; $RUNTIME_SECONDS / 3600" | bc)

# RTX A4000 = $0.25/hr
COST=$(echo "scale=2; $POD_RUNTIME_HOURS * 0.25" | bc)

echo "Current cost: \$$COST"
```

### Set Cost Alarm (Manual)
```bash
# Check every 10 minutes
while true; do
  RUNTIME=$(curl -s -H "Authorization: Bearer $RUNPOD_API_KEY" \
    https://rest.runpod.io/v1/pods/$POD_ID | jq -r '.runtime')

  HOURS=$(echo "scale=2; $RUNTIME / 3600" | bc)
  COST=$(echo "scale=2; $HOURS * 0.25" | bc)

  echo "[$(date)] Runtime: ${HOURS}h | Cost: \$${COST}"

  # Alert if cost > $1.00 (4 hours)
  if (( $(echo "$COST > 1.00" | bc -l) )); then
    echo "⚠️  WARNING: Cost exceeded \$1.00! Consider terminating pod."
  fi

  sleep 600  # 10 minutes
done
```

---

## Troubleshooting Commands

### Issue: No logs appearing
```bash
# Check if S3 credentials are set
grep -E "RUNPOD_S3" .env.runpod

# Try manual S3 list
aws s3 ls s3://se3zdnb5o4/ \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io
```

### Issue: Pod stuck
```bash
# Check pod status
curl -H "Authorization: Bearer $RUNPOD_API_KEY" \
  https://rest.runpod.io/v1/pods/$POD_ID

# Force restart
curl -X POST \
  -H "Authorization: Bearer $RUNPOD_API_KEY" \
  https://rest.runpod.io/v1/pods/$POD_ID/restart
```

### Issue: OOM errors
```bash
# Check pod logs for memory errors
python3 scripts/monitor_logs.py $POD_ID | grep -i "out of memory\|OOM\|killed"

# If OOM, reduce batch_size or buffer_size and redeploy
```

---

## Expected Timeline

| Time | Milestone | What to Check |
|------|-----------|---------------|
| 0-2 min | Pod deployed | Logs start streaming |
| 2-5 min | Training starts | "🏋️  Starting training..." |
| 5-10 min | Replay buffer fills | "Building replay buffer: 500/104346" |
| 10-15 min | Epoch 10 complete | First checkpoint saved |
| 15-30 min | Action diversity logs | "Action Distribution [Epoch 10]" |
| 30-60 min | Epoch 50 complete | Mid-training checkpoint |
| 60-90 min | Epoch 100 complete | Final checkpoint saved |
| 90-120 min | Training complete | "🎉 DQN training complete!" |

---

## Quick Checks (Copy-Paste)

```bash
# 1. Check if pod is running
curl -H "Authorization: Bearer $RUNPOD_API_KEY" \
  https://rest.runpod.io/v1/pods | jq -r '.pods[] | select(.name=="foxhunt-training") | .status'

# 2. Count checkpoints saved
aws s3 ls s3://se3zdnb5o4/ml_training/dqn_fixed_reward/checkpoints/ \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io \
  --recursive | wc -l

# 3. Get latest checkpoint timestamp
aws s3 ls s3://se3zdnb5o4/ml_training/dqn_fixed_reward/ \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io \
  --recursive | sort -k1,2 | tail -1

# 4. Estimate current cost
POD_RUNTIME=$(curl -s -H "Authorization: Bearer $RUNPOD_API_KEY" \
  https://rest.runpod.io/v1/pods | jq -r '.pods[] | select(.name=="foxhunt-training") | .runtime')
echo "Runtime: $(echo "scale=2; $POD_RUNTIME / 3600" | bc)h | Cost: \$$(echo "scale=2; $POD_RUNTIME / 3600 * 0.25" | bc)"
```

---

## When to Terminate Pod

**Terminate if**:
- ✅ Training completes successfully (100 epochs)
- ❌ Constant reward warnings persist after epoch 20
- ❌ Action diversity < 10% for any action after epoch 30
- ❌ Q-value divergence > 1000 after epoch 50
- ❌ Cost exceeds $1.00 (4 hours) without progress
- ❌ OOM errors or repeated crashes

**Keep running if**:
- Training is progressing normally
- Checkpoints saving every 10 epochs
- Reward variance > 0.1
- Action diversity 20-40% each
- Cost < $0.50

---

## References

- **Deployment Script**: `deploy_dqn_retrain.sh`
- **Validation Checklist**: `DQN_RETRAIN_VALIDATION_CHECKLIST.md`
- **Runpod Quick Ref**: `RUNPOD_DEPLOY_QUICK_REF.md`