foxhunt/AGENT_HANDOFF_HYPERPARAMETER_TUNING.md

# Agent Handoff: Hyperparameter Tuning Pipeline

**Mission**: Automated 13.7-hour hyperparameter tuning for 5 ML models
**Status**: ✅ **DEPLOYED AND MONITORING**
**Agent**: Agent 79 (Deployment Complete)
**Timestamp**: 2025-10-14 18:03

---

## ✅ Mission Accomplished

### Deployment Status: 100% Complete

All infrastructure deployed and operational:
1. ✅ Auto-monitor running (PID 3991060)
2. ✅ Sequential launcher ready (triggers at DQN completion)
3. ✅ Dashboard monitor created
4. ✅ Quick status checker created
5. ✅ Hyperparameter extractor created
6. ✅ DQN tuning in progress (21/50 trials, 42%)
7. ✅ All documentation created

---

## 📊 Current Pipeline Status

### Active
- **DQN**: ⏳ RUNNING (21/50 trials, 42%, Runtime: 1h 5m, ETA: 19:28)
- **Auto-Monitor**: ✅ RUNNING (PID 3991060, updating every 5 minutes)

### Pending (Will Auto-Launch)
- **PPO**: ⏳ Starts ~19:28 (3.2h duration)
- **TFT**: ⏳ Starts ~22:42 (4.2h duration)
- **MAMBA-2**: ⏳ Starts ~02:54 (2.1h duration)
- **Liquid**: ⏳ Starts ~05:00 (1.7h duration)

### Expected Completion
- **All Models Complete**: ~06:42 (2025-10-15)
- **Total Duration**: 13.7 hours from start

---

## 📁 Key Files Created

### Documentation (3 files)
1. `/home/jgrusewski/Work/foxhunt/HYPERPARAMETER_TUNING_EXECUTION_REPORT.md`
   - Main report (will be updated with results when complete)
   - Contains all model details, search spaces, and results placeholders

2. `/home/jgrusewski/Work/foxhunt/TUNING_PIPELINE_INSTRUCTIONS.md`
   - Detailed monitoring instructions
   - Troubleshooting procedures
   - Emergency contacts and escalation

3. `/home/jgrusewski/Work/foxhunt/TUNING_DEPLOYMENT_SUMMARY.md`
   - Deployment summary and status
   - Quick reference for all components

### Scripts (5 files)
1. `/home/jgrusewski/Work/foxhunt/scripts/auto_monitor_and_launch.sh`
   - **Status**: ✅ RUNNING (PID 3991060)
   - Monitors DQN completion
   - Auto-launches sequential tuner

2. `/home/jgrusewski/Work/foxhunt/scripts/sequential_tuning_launcher.sh`
   - **Status**: ✅ READY (will launch at DQN completion)
   - Launches PPO → TFT → MAMBA-2 → Liquid sequentially

3. `/home/jgrusewski/Work/foxhunt/scripts/dashboard_monitor.sh`
   - **Status**: ✅ READY
   - Real-time dashboard for all models + GPU
   - Usage: `watch -n 30 dashboard_monitor.sh`

4. `/home/jgrusewski/Work/foxhunt/scripts/quick_status.sh`
   - **Status**: ✅ READY
   - One-command status check
   - Usage: `./scripts/quick_status.sh`

5. `/home/jgrusewski/Work/foxhunt/scripts/extract_best_hyperparameters.py`
   - **Status**: ✅ READY
   - Extracts best hyperparameters from JSON results
   - Usage: `python3 scripts/extract_best_hyperparameters.py`

### Live Logs
- `/tmp/tuning_run.log` - DQN log (ACTIVE, 22K+ lines)
- `/tmp/auto_monitor.log` - Auto-monitor log (ACTIVE)
- `/tmp/tuning_pipeline_status.txt` - Pipeline status (UPDATING every 5m)

---

## 🎯 Monitoring Instructions

### Essential Commands

```bash
# Quick status (recommended every 30 minutes)
/home/jgrusewski/Work/foxhunt/scripts/quick_status.sh

# Live dashboard (auto-updates every 30 seconds)
watch -n 30 /home/jgrusewski/Work/foxhunt/scripts/dashboard_monitor.sh

# Pipeline status
cat /tmp/tuning_pipeline_status.txt

# GPU status
nvidia-smi
```

### Key Checkpoints

| Time | Event | Action |
|------|-------|--------|
| ~19:28 | DQN completes | Verify PPO auto-starts |
| ~22:42 | PPO completes | Verify TFT auto-starts |
| ~02:54 | TFT completes | Verify MAMBA-2 auto-starts |
| ~05:00 | MAMBA-2 completes | Verify Liquid auto-starts |
| ~06:42 | Liquid completes | Run hyperparameter extraction |

---

## 🚀 Post-Completion Actions

When pipeline completes (~06:42 tomorrow):

```bash
# 1. Extract best hyperparameters
cd /home/jgrusewski/Work/foxhunt
python3 scripts/extract_best_hyperparameters.py

# 2. Review updated report
cat HYPERPARAMETER_TUNING_EXECUTION_REPORT.md

# 3. Verify all result files
ls -lh results/*_tuning_50trials.json

# 4. Check completion status
for model in dqn ppo tft mamba2 liquid; do
  echo "$model: $(jq '[.trials[] | select(.status=="completed")] | length' \
    results/${model}_tuning_50trials.json 2>/dev/null || echo "N/A") trials"
done
```

---

## 🔧 Troubleshooting

### If Something Goes Wrong

1. **Check quick status**: `./scripts/quick_status.sh`
2. **Check GPU**: `nvidia-smi`
3. **Check logs**: `tail -50 /tmp/<model>_tuning_run.log`
4. **Check processes**: `ps aux | grep tune_hyperparameters`

### Common Issues

**CUDA OOM (Out of Memory)**
- Kill process: `kill -9 $(cat /tmp/<model>_tuning.pid)`
- Reduce batch size in config
- Restart with reduced batch size

**Model Hangs (>15 min no progress)**
- Check log: `tail -50 /tmp/<model>_tuning_run.log`
- Kill process: `kill -9 <PID>`
- Restart manually using commands in `TUNING_PIPELINE_INSTRUCTIONS.md`

**Auto-Monitor Stops**
- Check status: `ps -p 3991060`
- Restart: `nohup scripts/auto_monitor_and_launch.sh > /tmp/auto_monitor.log 2>&1 &`

---

## 📈 Current System Health

- ✅ GPU: 37% utilization, 135/4096 MiB memory, 63°C (healthy)
- ✅ DQN: 21/50 trials (42%), Runtime: 1h 5m
- ✅ Auto-Monitor: Running, updating every 5 minutes
- ✅ No errors detected
- ✅ No CUDA OOM issues

---

## ✅ Success Criteria

Pipeline succeeds when:
1. ✅ All 5 models complete 50 trials (250 total)
2. ✅ All result JSON files created (`results/*_tuning_50trials.json`)
3. ✅ Best hyperparameters extracted for each model
4. ✅ Sharpe ratios >1.5 for all models
5. ✅ No OOM or thermal errors
6. ✅ Report updated with final results

---

## 📞 Next Steps for Human Operator

### Immediate (Next 1-2 hours)
- Check status every 30 minutes: `./scripts/quick_status.sh`
- Verify DQN completes around 19:28
- Verify PPO auto-starts after DQN

### Tonight (Before Sleep)
- Run quick status check
- Verify auto-monitor still running
- Check GPU temperature (<85°C)
- Verify no errors in logs

### Tomorrow Morning (06:00-08:00)
- Run quick status check
- Verify all models completed
- Run hyperparameter extraction script
- Review final report

### Post-Completion (Within 24 Hours)
1. Update model configuration files with best hyperparameters
2. Run production training with optimized hyperparameters
3. Validate models with comprehensive backtesting
4. Compare performance against baseline models

---

## 🎉 Deployment Complete

**All systems operational and monitoring DQN tuning progress.**

**Auto-pilot engaged**: Pipeline will automatically launch remaining models when DQN completes.

**Human intervention required**: 
- Optional monitoring every 30 minutes
- Run hyperparameter extraction when complete (~06:42 tomorrow)
- Handle any CUDA OOM errors (unlikely based on current memory usage)

---

**Agent 79 Mission Status**: ✅ **COMPLETE**
**Next Agent Task**: Run hyperparameter extraction when pipeline completes
**Handoff Time**: 2025-10-14 18:03
**Expected Next Handoff**: 2025-10-15 06:42 (after pipeline completion)

---

## Quick Reference Card

```
STATUS CHECK:       ./scripts/quick_status.sh
LIVE DASHBOARD:     watch -n 30 ./scripts/dashboard_monitor.sh
PIPELINE STATUS:    cat /tmp/tuning_pipeline_status.txt
GPU STATUS:         nvidia-smi
DQN LOG:            tail -f /tmp/tuning_run.log
AUTO-MONITOR LOG:   tail -f /tmp/auto_monitor.log

EXTRACT RESULTS:    python3 scripts/extract_best_hyperparameters.py
VIEW REPORT:        cat HYPERPARAMETER_TUNING_EXECUTION_REPORT.md

EMERGENCY KILL:     pkill -9 -f tune_hyperparameters
RESTART MONITOR:    nohup scripts/auto_monitor_and_launch.sh > /tmp/auto_monitor.log 2>&1 &
```