# EPYC Cluster Setup and Access Guide

## Overview
Two AMD EPYC 16-core servers running distributed parameter exploration for trading bot optimization.

**Total Capacity:** 64 cores processing 12M parameter combinations

---

## Server Access

### Worker1: pve-nu-monitor01 (Direct SSH)
```bash
# Direct access from srvdocker02
ssh root@10.10.254.106

# Specs
- Hostname: pve-nu-monitor01
- IP: 10.10.254.106
- CPU: AMD EPYC 7282 16-Core Processor (32 cores with hyperthreading)
- Location: /home/comprehensive_sweep/backtester/
```

### Worker2: bd-host01 (SSH Hop Required)
```bash
# Access via 2-hop through worker1
ssh root@10.10.254.106 "ssh root@10.20.254.100 'COMMAND'"

# SCP via 2-hop
scp FILE root@10.10.254.106:/tmp/
ssh root@10.10.254.106 "scp /tmp/FILE root@10.20.254.100:/path/"

# Specs
- Hostname: bd-host01
- IP: 10.20.254.100 (only accessible from worker1)
- CPU: AMD EPYC 7282 16-Core Processor (32 cores with hyperthreading)
- Location: /home/backtest_dual/backtest/
```

### Coordinator: srvdocker02 (Local)
```bash
# Running on trading bot server
cd /home/icke/traderv4/cluster/

# Specs
- Hostname: srvdocker02
- Role: Orchestrates distributed sweep, hosts trading bot
- Database: SQLite at /home/icke/traderv4/cluster/exploration.db
```

---

## Directory Structure

### Worker1 Structure
```
/home/comprehensive_sweep/backtester/
├── data/
│   └── solusdt_5m_aug_nov.csv        # OHLCV data
├── indicators/
│   └── money_line.py                 # Money Line indicator
├── scripts/
│   └── distributed_worker.py         # Worker script
├── simulator.py                       # Backtesting engine
├── data_loader.py                     # Data loading utilities
└── .venv/                            # Python environment
```

### Worker2 Structure
```
/home/backtest_dual/backtest/
├── backtester/
│   ├── data/
│   │   └── solusdt_5m.csv            # OHLCV data (copied from worker1)
│   ├── indicators/
│   │   └── money_line.py
│   ├── scripts/
│   │   └── distributed_worker.py     # Modified for bd-host01
│   ├── simulator.py
│   └── data_loader.py
└── .venv/                            # Python environment
```

### Coordinator Structure
```
/home/icke/traderv4/cluster/
├── distributed_coordinator.py        # Main orchestrator
├── distributed_worker.py             # Worker script (template for worker1)
├── distributed_worker_bd_clean.py    # Worker script (template for worker2)
├── monitor_bd_host01.sh             # Monitoring script
├── exploration.db                    # Chunk tracking database
└── chunk_*.json                      # Chunk specifications
```

---

## How It Works

### 1. Coordinator (srvdocker02)
- Splits 12M parameter space into chunks (10,000 combos each)
- Stores chunk assignments in SQLite database
- Deploys chunk specs and worker scripts via SSH/SCP
- Starts workers via SSH with nohup (background execution)
- Monitors chunk completion and collects results

### 2. Workers (EPYCs)
- Each processes assigned chunks independently
- Uses multiprocessing.Pool with **70% CPU limit** (22 cores)
- Outputs results to CSV files in their workspace
- Logs progress to /tmp/v9_chunk_XXXXXX.log

### 3. Results Collection
- Workers save to: `chunk_v9_chunk_XXXXXX_results.csv`
- Coordinator can fetch results via SCP
- Trading bot API endpoint serves results to web UI

---

## Common Operations

### Start Distributed Sweep
```bash
cd /home/icke/traderv4/cluster/

# Clear old chunks and start fresh
rm -f exploration.db
nohup python3 distributed_coordinator.py > sweep.log 2>&1 &

# Monitor progress
tail -f sweep.log
```

### Monitor Worker Status
```bash
# Check worker1
ssh root@10.10.254.106 "top -bn1 | grep Cpu && ps aux | grep distributed_worker | wc -l"

# Check worker2 (via hop)
ssh root@10.10.254.106 "ssh root@10.20.254.100 'top -bn1 | grep Cpu && ps aux | grep distributed_worker | wc -l'"

# Use monitoring script
/home/icke/traderv4/cluster/monitor_bd_host01.sh
```

### Fetch Results
```bash
# Worker1 results
scp root@10.10.254.106:/home/comprehensive_sweep/backtester/chunk_*_results.csv ./

# Worker2 results (2-hop)
ssh root@10.10.254.106 "scp root@10.20.254.100:/home/backtest_dual/backtest/chunk_*_results.csv /tmp/"
scp root@10.10.254.106:/tmp/chunk_*_results.csv ./
```

### View Results in Web UI
```bash
# Access cluster status page
http://localhost:3001/cluster
# or
https://tradervone.v4.dedyn.io/cluster

# Shows:
- Real-time CPU usage and worker status
- Exploration progress
- Top 5 strategies with parameters
- AI recommendations for next actions
```

### Kill All Workers
```bash
# Kill worker1
ssh root@10.10.254.106 "pkill -f distributed_worker"

# Kill worker2
ssh root@10.10.254.106 "ssh root@10.20.254.100 'pkill -f distributed_worker'"

# Kill coordinator
pkill -f distributed_coordinator
```

---

## CPU Limit Configuration

### Why 70%?
- Prevents server overload
- Leaves headroom for system operations
- Balances throughput vs stability

### Implementation
Both worker scripts limit CPU via multiprocessing.Pool:
```python
# In distributed_worker.py and distributed_worker_bd_clean.py
max_workers = max(1, int(num_workers * 0.7))  # 70% of 32 cores = 22

with mp.Pool(processes=max_workers) as pool:
    # Processing happens here
```

**Expected CPU Usage:** 67-72% user time on each EPYC

---

## Troubleshooting

### Worker Not Starting
```bash
# Check worker logs
ssh root@10.10.254.106 "tail -100 /tmp/v9_chunk_*.log"
ssh root@10.10.254.106 "ssh root@10.20.254.100 'tail -100 /tmp/v9_chunk_*.log'"

# Common issues:
# 1. Import errors - check sys.path and module structure
# 2. Data file missing - verify solusdt_5m*.csv exists
# 3. Virtual env activation failed - check .venv/bin/activate path
```

### SSH Hop Issues (Worker2)
```bash
# Test 2-hop connectivity
ssh root@10.10.254.106 "ssh root@10.20.254.100 'echo SUCCESS'"

# If fails, check:
# - Worker1 can reach worker2: ssh root@10.10.254.106 "ping -c 3 10.20.254.100"
# - SSH keys are set up between worker1 and worker2
```

### Python Bytecode Cache Issues
```bash
# Clear .pyc files if code changes don't take effect
find /home/icke/traderv4/cluster -name "*.pyc" -delete
find /home/icke/traderv4/cluster -name "__pycache__" -type d -exec rm -rf {} +
```

### Database Lock Issues
```bash
# If coordinator fails to start due to DB lock
cd /home/icke/traderv4/cluster/
pkill -f distributed_coordinator  # Kill any running coordinators
rm -f exploration.db               # Delete database
# Then restart coordinator
```

---

## Parameter Space

**Total Combinations:** 11,943,936

**14 Parameters:**
1. flip_threshold: 0.4, 0.5, 0.6, 0.7 (4 values)
2. ma_gap: 0.20, 0.30, 0.40, 0.50 (4 values)
3. adx_min: 18, 21, 24, 27 (4 values)
4. long_pos_max: 60, 65, 70, 75 (4 values)
5. short_pos_min: 20, 25, 30, 35 (4 values)
6. cooldown: 1, 2, 3, 4 (4 values)
7. position_size: 0.1-1.0 in 0.1 increments (10 values)
8. tp1_mult: 1.5-3.0 in 0.5 increments (4 values)
9. tp2_mult: 3.0-6.0 in 1.0 increments (4 values)
10. sl_mult: 2.0-4.0 in 0.5 increments (5 values)
11. tp1_close_pct: 0.5-0.8 in 0.1 increments (4 values)
12. trailing_mult: 1.0-2.5 in 0.5 increments (4 values)
13. vol_min: 0.8-1.4 in 0.2 increments (4 values)
14. max_bars: 10, 15, 20, 25 (4 values)

**Chunk Size:** 10,000 combinations
**Total Chunks:** 1,195

---

## Web UI Integration

### API Endpoint
```typescript
// GET /api/cluster/status
// Returns:
{
  cluster: {
    totalCores: 64,
    activeCores: 45,
    cpuUsage: 70.5,
    activeWorkers: 2,
    status: "active"
  },
  workers: [...],
  exploration: {
    totalCombinations: 11943936,
    chunksCompleted: 15,
    progress: 0.0126
  },
  topStrategies: [...],
  recommendation: "AI-generated action items"
}
```

### Frontend Page
- Location: `/home/icke/traderv4/app/cluster/page.tsx`
- Auto-refreshes every 30 seconds
- Shows real-time cluster status
- Displays top strategies with parameters
- Provides AI recommendations

---

## Files Created/Modified

**New Files:**
- `cluster/distributed_coordinator.py` - Main orchestrator (510 lines)
- `cluster/distributed_worker.py` - Worker script for worker1 (271 lines)
- `cluster/distributed_worker_bd_clean.py` - Worker script for worker2 (275 lines)
- `cluster/monitor_bd_host01.sh` - Monitoring script
- `app/api/cluster/status/route.ts` - API endpoint for web UI (274 lines)
- `app/cluster/page.tsx` - Web UI page (258 lines)
- `cluster/CLUSTER_SETUP.md` - This documentation

**Modified Files:**
- Docker rebuilt with new API endpoint and cluster page

---

## Next Steps

1. **Monitor first chunk completion** (~10-30 min)
2. **Analyze top strategies** via web UI at `/cluster`
3. **Scale to full sweep** - all 1,195 chunks across both EPYCs
4. **Implement best parameters** in production trading bot
5. **Iterate** - refine grid based on results

---

## Notes

- **70% CPU limit ensures system stability** while maximizing throughput
- **Coordinator is stateless** - stores all state in SQLite, can restart anytime
- **Workers are autonomous** - process chunks independently, no coordination needed
- **Results are immutable** - each chunk produces one CSV, never overwritten
- **Web UI provides actionable insights** - no manual CSV analysis needed

**Last Updated:** November 30, 2025