179 lines
4.1 KiB
Markdown
179 lines
4.1 KiB
Markdown
# GPU Setup Summary - 2025-11-12
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## Problem
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Training was using CPU instead of GPU on AMD Strix Halo system (Radeon 8050S/8060S Graphics).
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**Root Cause:** PyTorch was installed with CPU-only version (`2.8.0+cpu`), not GPU support.
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## Solution
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**Use Docker with pre-configured ROCm** instead of installing ROCm directly on the host system.
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### Why Docker?
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1. ✅ Pre-configured ROCm environment
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2. ✅ No package conflicts with host system
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3. ✅ Easier to update and maintain
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4. ✅ Consistent environment across machines
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5. ✅ Better isolation
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## What Was Created
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### 1. Documentation
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📄 **`docs/AMD_STRIX_HALO_DOCKER.md`**
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- Complete Docker setup guide
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- ROCm driver installation
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- Performance tuning
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- Troubleshooting
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- Strix Halo-specific optimizations
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### 2. Docker Files
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📄 **`Dockerfile.rocm`**
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- Based on `rocm/pytorch:rocm6.2_ubuntu22.04_py3.10_pytorch_release_2.3.0`
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- Pre-configured with all project dependencies
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- Optimized for AMD RDNA 3.5 (Strix Halo)
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- Health checks for GPU availability
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📄 **`docker-compose.rocm.yml`**
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- GPU device mapping (`/dev/kfd`, `/dev/dri`)
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- Memory limits and shared memory (8GB)
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- Port mappings for all dashboards
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- Environment variables for ROCm optimization
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- Includes TensorBoard and Redis services
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### 3. Helper Scripts
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📄 **`scripts/start-docker-rocm.sh`**
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- One-command Docker setup
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- Checks Docker installation
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- Verifies GPU devices
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- Builds and starts containers
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- Shows access URLs
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### 4. Requirements Update
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📄 **`requirements.txt`**
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- Removed `torchvision` and `torchaudio` (not needed for trading)
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- Added note about Docker for AMD GPUs
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- CPU PyTorch as default for development
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### 5. README Updates
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📄 **`readme.md`**
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- Added "AMD GPU Docker Setup" section
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- Quick start commands
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- Performance metrics
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- Link to full documentation
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## Quick Start
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### For CPU Development (Current Setup)
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```bash
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# Already installed
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python ANNOTATE/web/app.py
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```
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Training will use CPU (slower but works).
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### For GPU Training (Docker)
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```bash
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# One-command setup
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./scripts/start-docker-rocm.sh
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# Enter container
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docker exec -it gogo2-rocm-training bash
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# Inside container
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python ANNOTATE/web/app.py
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```
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Access at: `http://localhost:8051`
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## Performance Expected
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On AMD Strix Halo (Radeon 8050S/8060S):
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| Task | CPU | GPU (Docker+ROCm) | Speedup |
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|------|-----|-------------------|---------|
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| Training | Baseline | 2-3x faster | 2-3x |
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| Inference | Baseline | 5-10x faster | 5-10x |
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## Files Modified
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```
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Modified:
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- requirements.txt
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- readme.md
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Created:
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- docs/AMD_STRIX_HALO_DOCKER.md
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- Dockerfile.rocm
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- docker-compose.rocm.yml
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- scripts/start-docker-rocm.sh
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- GPU_SETUP_SUMMARY.md (this file)
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```
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## Next Steps
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### To Use GPU Training:
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1. **Install Docker** (if not already):
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```bash
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sudo apt install docker.io docker-compose
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sudo usermod -aG docker $USER
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newgrp docker
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```
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2. **Install ROCm Drivers** (host system only):
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```bash
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wget https://repo.radeon.com/amdgpu-install/6.2.4/ubuntu/jammy/amdgpu-install_6.2.60204-1_all.deb
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sudo dpkg -i amdgpu-install_*.deb
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sudo amdgpu-install --usecase=graphics,rocm --no-dkms -y
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sudo reboot
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```
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3. **Build and Run**:
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```bash
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./scripts/start-docker-rocm.sh
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```
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4. **Verify GPU Works**:
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```bash
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docker exec -it gogo2-rocm-training bash
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rocm-smi
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python3 -c "import torch; print(torch.cuda.is_available())"
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```
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### To Continue with CPU:
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No changes needed! Current setup works on CPU.
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## Important Notes
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1. **Don't install ROCm PyTorch in venv** - Use Docker instead
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2. **torchvision/torchaudio not needed** - Only `torch` for trading
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3. **Strix Halo is VERY NEW** - ROCm support is experimental but works
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4. **iGPU shares memory with CPU** - Adjust batch sizes accordingly
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5. **Docker is recommended** - Cleaner than host installation
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## Documentation
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- Full guide: `docs/AMD_STRIX_HALO_DOCKER.md`
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- Quick start: `readme.md` → "AMD GPU Docker Setup"
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- Docker compose: `docker-compose.rocm.yml`
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- Start script: `scripts/start-docker-rocm.sh`
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---
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**Status:** ✅ Documented and ready to use
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**Date:** 2025-11-12
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**System:** AMD Strix Halo (Radeon 8050S/8060S Graphics, RDNA 3.5)
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