gogo2/GPU_SETUP_SUMMARY.md

GPU Setup Summary - 2025-11-12

Problem

Training was using CPU instead of GPU on AMD Strix Halo system (Radeon 8050S/8060S Graphics).

Root Cause: PyTorch was installed with CPU-only version (2.8.0+cpu), not GPU support.

Solution

Use Docker with pre-configured ROCm instead of installing ROCm directly on the host system.

Why Docker?

1. ✅ Pre-configured ROCm environment
2. ✅ No package conflicts with host system
3. ✅ Easier to update and maintain
4. ✅ Consistent environment across machines
5. ✅ Better isolation

What Was Created

1. Documentation

📄 docs/AMD_STRIX_HALO_DOCKER.md

• Complete Docker setup guide
• ROCm driver installation
• Performance tuning
• Troubleshooting
• Strix Halo-specific optimizations

2. Docker Files

📄 Dockerfile.rocm

• Based on rocm/pytorch:rocm6.2_ubuntu22.04_py3.10_pytorch_release_2.3.0
• Pre-configured with all project dependencies
• Optimized for AMD RDNA 3.5 (Strix Halo)
• Health checks for GPU availability

📄 docker-compose.rocm.yml

• GPU device mapping (/dev/kfd, /dev/dri)
• Memory limits and shared memory (8GB)
• Port mappings for all dashboards
• Environment variables for ROCm optimization
• Includes TensorBoard and Redis services

3. Helper Scripts

📄 scripts/start-docker-rocm.sh

• One-command Docker setup
• Checks Docker installation
• Verifies GPU devices
• Builds and starts containers
• Shows access URLs

4. Requirements Update

📄 requirements.txt

• Removed torchvision and torchaudio (not needed for trading)
• Added note about Docker for AMD GPUs
• CPU PyTorch as default for development

5. README Updates

📄 readme.md

• Added "AMD GPU Docker Setup" section
• Quick start commands
• Performance metrics
• Link to full documentation

Quick Start

For CPU Development (Current Setup)

# Already installed
python ANNOTATE/web/app.py

Training will use CPU (slower but works).

For GPU Training (Docker)

# One-command setup
./scripts/start-docker-rocm.sh

# Enter container
docker exec -it gogo2-rocm-training bash

# Inside container
python ANNOTATE/web/app.py

Access at: http://localhost:8051

Performance Expected

On AMD Strix Halo (Radeon 8050S/8060S):

Task	CPU	GPU (Docker+ROCm)	Speedup
Training	Baseline	2-3x faster	2-3x
Inference	Baseline	5-10x faster	5-10x

Files Modified

Modified:
  - requirements.txt
  - readme.md

Created:
  - docs/AMD_STRIX_HALO_DOCKER.md
  - Dockerfile.rocm
  - docker-compose.rocm.yml
  - scripts/start-docker-rocm.sh
  - GPU_SETUP_SUMMARY.md (this file)

Next Steps

To Use GPU Training:

1. Install Docker (if not already):

   sudo apt install docker.io docker-compose
   sudo usermod -aG docker $USER
   newgrp docker
   

2. Install ROCm Drivers (host system only):

   wget https://repo.radeon.com/amdgpu-install/6.2.4/ubuntu/jammy/amdgpu-install_6.2.60204-1_all.deb
   sudo dpkg -i amdgpu-install_*.deb
   sudo amdgpu-install --usecase=graphics,rocm --no-dkms -y
   sudo reboot
   

3. Build and Run:

   ./scripts/start-docker-rocm.sh
   

4. Verify GPU Works:

   docker exec -it gogo2-rocm-training bash
   rocm-smi
   python3 -c "import torch; print(torch.cuda.is_available())"
   

To Continue with CPU:

No changes needed! Current setup works on CPU.

Important Notes

1. Don't install ROCm PyTorch in venv - Use Docker instead
2. torchvision/torchaudio not needed - Only torch for trading
3. Strix Halo is VERY NEW - ROCm support is experimental but works
4. iGPU shares memory with CPU - Adjust batch sizes accordingly
5. Docker is recommended - Cleaner than host installation

Documentation

• Full guide: docs/AMD_STRIX_HALO_DOCKER.md
• Quick start: readme.md → "AMD GPU Docker Setup"
• Docker compose: docker-compose.rocm.yml
• Start script: scripts/start-docker-rocm.sh

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Status: ✅ Documented and ready to use
Date: 2025-11-12
System: AMD Strix Halo (Radeon 8050S/8060S Graphics, RDNA 3.5)