gogo2/QUICK_START.md

Quick Start Guide

🚀 Fastest Way to Start

First Time Setup

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!

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)

# 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

# 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)

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

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

2. Main Dashboard (Live Trading)

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

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

3. Training Runner

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

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

• Order book analysis
• Market microstructure
• Liquidity monitoring

Troubleshooting

Port Already in Use

# Kill stale processes
python kill_dashboard.py

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

GPU Not Working

# 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

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

Documentation

• 📖 Full Setup: readme.md
• 🐳 Docker Guide: docs/AMD_STRIX_HALO_DOCKER.md
• 🔌 Container Usage: docs/USING_EXISTING_ROCM_CONTAINER.md
• 🎓 Training Guide: ANNOTATE/TRAINING_GUIDE.md
• 🔧 Kill Processes: kill_dashboard.py

Common Commands

# 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

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