# Quick Start Guide

## 🚀 Fastest Way to Start

### First Time Setup

```bash
cd /mnt/shared/DEV/repos/d-popov.com/gogo2
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Auto-detect and install correct PyTorch (NVIDIA/AMD/CPU)
./scripts/setup-pytorch.sh
```

### Daily Use (After Setup)

Your system is **ready to go** with GPU support!

```bash
cd /mnt/shared/DEV/repos/d-popov.com/gogo2
source venv/bin/activate
python kill_dashboard.py  # Kill any stale processes
python ANNOTATE/web/app.py
```

**Access:** http://localhost:8051

**GPU Status:**
- ✅ AMD Radeon Graphics (Strix Halo 8050S/8060S)
- ✅ ROCm 6.2 PyTorch installed
- ✅ 47GB shared memory
- ✅ 2-3x faster training vs CPU

## Alternative: Use Existing Docker Container

You have `amd-strix-halo-llama-rocm` container already running with ROCm support:

### Setup Container (One-Time)

```bash
# 1. Install Python in container (Fedora-based)
docker exec amd-strix-halo-llama-rocm dnf install -y python3.12 python3-pip python3-devel git

# 2. Create symlinks
docker exec amd-strix-halo-llama-rocm bash -c "ln -sf /usr/bin/python3.12 /usr/bin/python3 && ln -sf /usr/bin/python3.12 /usr/bin/python"

# 3. Copy project to container
docker exec amd-strix-halo-llama-rocm mkdir -p /workspace
docker cp /mnt/shared/DEV/repos/d-popov.com/gogo2 amd-strix-halo-llama-rocm:/workspace/

# 4. Install dependencies
docker exec amd-strix-halo-llama-rocm bash -c "cd /workspace/gogo2 && pip3 install -r requirements.txt && pip3 install torch --index-url https://download.pytorch.org/whl/rocm6.2"
```

### Start ANNOTATE in Container

```bash
# Enter container
docker exec -it amd-strix-halo-llama-rocm bash

# Inside container:
cd /workspace/gogo2
python3 ANNOTATE/web/app.py --port 8051
```

**Access:** http://localhost:8051 (if port is exposed)

**Helper script:** `./scripts/attach-to-rocm-container.sh` (guides you through setup)

## Development Workflows

### 1. ANNOTATE Dashboard (Manual Trading)

```bash
source venv/bin/activate
python ANNOTATE/web/app.py
```

- Create trade annotations
- Train models on annotations
- Test inference

### 2. Main Dashboard (Live Trading)

```bash
source venv/bin/activate
python main_dashboard.py --port 8050
```

- Real-time market data
- Live predictions
- Performance monitoring

### 3. Training Runner

```bash
source venv/bin/activate

# Real-time training (4 hours)
python training_runner.py --mode realtime --duration 4 --symbol ETH/USDT

# Backtest training
python training_runner.py --mode backtest --start-date 2024-01-01 --end-date 2024-12-31
```

### 4. COB Dashboard

```bash
source venv/bin/activate
python web/cob_realtime_dashboard.py
```

- Order book analysis
- Market microstructure
- Liquidity monitoring

## Troubleshooting

### Port Already in Use

```bash
# Kill stale processes
python kill_dashboard.py

# Or manually
lsof -i :8051
kill -9 <PID>
```

### GPU Not Working

```bash
# Check GPU
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}'); print(f'Device: {torch.cuda.get_device_name(0)}')"

# Should show:
# CUDA: True
# Device: AMD Radeon Graphics
```

### Missing Dependencies

```bash
# Reinstall
pip install -r requirements.txt
pip install torch --index-url https://download.pytorch.org/whl/rocm6.2
```

## Documentation

- **📖 Full Setup:** [readme.md](readme.md)
- **🐳 Docker Guide:** [docs/AMD_STRIX_HALO_DOCKER.md](docs/AMD_STRIX_HALO_DOCKER.md)
- **🔌 Container Usage:** [docs/USING_EXISTING_ROCM_CONTAINER.md](docs/USING_EXISTING_ROCM_CONTAINER.md)
- **🎓 Training Guide:** [ANNOTATE/TRAINING_GUIDE.md](ANNOTATE/TRAINING_GUIDE.md)
- **🔧 Kill Processes:** [kill_dashboard.py](kill_dashboard.py)

## Common Commands

```bash
# Activate environment
source venv/bin/activate

# Check Python/GPU
python --version
python -c "import torch; print(torch.cuda.is_available())"

# Kill stale processes
python kill_dashboard.py

# List Docker containers
docker ps -a

# Attach to container
docker exec -it amd-strix-halo-llama-rocm bash

# View logs
tail -f logs/*.log
```

## Next Steps

1. ✅ **Start ANNOTATE** - Create trading annotations
2. 📊 **Train Models** - Use your annotations to train
3. 🔴 **Live Inference** - Test predictions in real-time
4. 📈 **Monitor Performance** - Track accuracy and profits

---

**System:** AMD Strix Halo (Radeon 8050S/8060S)  
**Status:** ✅ Ready for GPU-accelerated training  
**Last Updated:** 2025-11-12