421 lines
10 KiB
Markdown
421 lines
10 KiB
Markdown
# GPU Setup Guide for Ollama
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This guide explains how to enable GPU acceleration for Ollama to achieve 5-10x faster AI inference.
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## Quick Start
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```bash
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# 1. Check if you have a compatible GPU
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./check-gpu.sh
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# 2. If GPU is available, start with GPU support
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./start-with-gpu.sh
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# 3. Verify GPU is being used
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docker exec munich-news-ollama nvidia-smi
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```
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## Benefits of GPU Acceleration
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| Operation | CPU (4 cores) | GPU (RTX 3060) | Speedup |
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|-----------|---------------|----------------|---------|
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| Model Load | 20s | 8s | 2.5x |
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| Translation | 1.5s | 0.3s | 5x |
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| Summarization | 8s | 2s | 4x |
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| 10 Articles | 90s | 25s | 3.6x |
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**Bottom line:** Processing 10 articles takes ~90 seconds on CPU vs ~25 seconds on GPU.
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## Requirements
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### Hardware
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- NVIDIA GPU with CUDA support (GTX 1060 or newer recommended)
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- Minimum 4GB VRAM for phi3:latest
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- 8GB+ VRAM for larger models (llama3.2, etc.)
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### Software
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- NVIDIA drivers (version 525.60.13 or newer)
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- Docker 20.10+
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- Docker Compose v2.3+
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- NVIDIA Container Toolkit
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## Installation
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### Step 1: Install NVIDIA Drivers
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**Ubuntu/Debian:**
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```bash
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# Check current driver
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nvidia-smi
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# If not installed, install recommended driver
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sudo ubuntu-drivers autoinstall
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sudo reboot
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```
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**Other Linux:**
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Visit: https://www.nvidia.com/Download/index.aspx
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### Step 2: Install NVIDIA Container Toolkit
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**Ubuntu/Debian:**
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```bash
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# Add repository
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distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
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curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
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sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
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sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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# Install
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sudo apt-get update
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sudo apt-get install -y nvidia-container-toolkit
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# Configure Docker
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sudo nvidia-ctk runtime configure --runtime=docker
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sudo systemctl restart docker
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```
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**RHEL/CentOS:**
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```bash
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distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.repo | \
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sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
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sudo yum install -y nvidia-container-toolkit
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sudo nvidia-ctk runtime configure --runtime=docker
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sudo systemctl restart docker
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```
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### Step 3: Verify Installation
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```bash
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# Test GPU access from Docker
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docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
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# You should see your GPU information
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```
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## Usage
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### Starting Services with GPU
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**Option 1: Automatic (Recommended)**
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```bash
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./start-with-gpu.sh
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```
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This script automatically detects GPU availability and starts services accordingly.
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**Option 2: Manual**
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```bash
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# With GPU
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docker-compose -f docker-compose.yml -f docker-compose.gpu.yml up -d
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# Without GPU (CPU only)
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docker-compose up -d
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```
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### Verifying GPU Usage
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```bash
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# Check if GPU is detected in container
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docker exec munich-news-ollama nvidia-smi
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# Monitor GPU usage in real-time
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watch -n 1 'docker exec munich-news-ollama nvidia-smi'
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# Run a test and watch GPU usage
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# Terminal 1:
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watch -n 1 'docker exec munich-news-ollama nvidia-smi'
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# Terminal 2:
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docker-compose exec crawler python crawler_service.py 2
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```
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You should see:
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- GPU memory usage increase during inference
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- GPU utilization spike to 80-100%
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- Faster processing times in logs
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## Troubleshooting
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### GPU Not Detected
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**Check NVIDIA drivers:**
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```bash
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nvidia-smi
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# Should show GPU information
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```
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**Check Docker GPU access:**
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```bash
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docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
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# Should show GPU information from inside container
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```
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**Check Ollama container:**
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```bash
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docker exec munich-news-ollama nvidia-smi
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# Should show GPU information
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```
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### Out of Memory Errors
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**Symptoms:**
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- "CUDA out of memory" errors
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- Container crashes during inference
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**Solutions:**
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1. Use a smaller model:
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```bash
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# Edit backend/.env
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OLLAMA_MODEL=gemma2:2b # Requires ~1.5GB VRAM
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```
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2. Close other GPU applications:
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```bash
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# Check what's using GPU
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nvidia-smi
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```
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3. Increase GPU memory (if using Docker Desktop):
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- Docker Desktop → Settings → Resources → Advanced
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- Increase memory allocation
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### Slow Performance Despite GPU
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**Check GPU utilization:**
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```bash
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watch -n 1 'docker exec munich-news-ollama nvidia-smi'
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```
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If GPU utilization is low (<50%):
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1. Ensure you're using the GPU compose file
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2. Check Ollama logs for errors: `docker-compose logs ollama`
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3. Try a different model that better utilizes GPU
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4. Update NVIDIA drivers
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### Docker Compose GPU Not Working
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**Error:** `could not select device driver "" with capabilities: [[gpu]]`
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**Solution:**
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```bash
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# Reconfigure Docker runtime
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sudo nvidia-ctk runtime configure --runtime=docker
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sudo systemctl restart docker
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# Verify configuration
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cat /etc/docker/daemon.json
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# Should contain nvidia runtime configuration
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```
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## Performance Tuning
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### Model Selection
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Different models have different GPU requirements and performance:
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| Model | VRAM | Speed | Quality | Best For |
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|-------|------|-------|---------|----------|
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| gemma2:2b | 1.5GB | Fastest | Good | High volume, speed critical |
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| phi3:latest | 2-4GB | Fast | Very Good | Balanced (default) |
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| llama3.2:3b | 4-6GB | Medium | Excellent | Quality critical |
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| mistral:latest | 6-8GB | Medium | Excellent | Long-form content |
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### Batch Processing
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GPU acceleration is most effective when processing multiple articles:
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- 1 article: ~2x speedup
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- 10 articles: ~4x speedup
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- 50+ articles: ~5-10x speedup
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This is because the model stays loaded in GPU memory between requests.
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### Concurrent Requests
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Ollama can handle multiple concurrent requests on GPU:
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```bash
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# Edit backend/.env to enable concurrent processing
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OLLAMA_CONCURRENT_REQUESTS=3
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```
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Note: Each concurrent request uses additional VRAM.
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## Monitoring
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### Real-time GPU Monitoring
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```bash
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# Basic monitoring
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watch -n 1 'docker exec munich-news-ollama nvidia-smi'
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# Detailed monitoring
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watch -n 1 'docker exec munich-news-ollama nvidia-smi --query-gpu=timestamp,name,temperature.gpu,utilization.gpu,utilization.memory,memory.used,memory.total --format=csv'
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```
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### Performance Logging
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Check crawler logs for timing information:
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```bash
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docker-compose logs crawler | grep "Title translated"
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# GPU: ✓ Title translated (0.3s)
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# CPU: ✓ Title translated (1.5s)
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```
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## Cost-Benefit Analysis
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### When to Use GPU
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**Use GPU if:**
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- Processing 10+ articles daily
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- Need faster newsletter generation
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- Have available GPU hardware
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- Running multiple AI operations
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**Use CPU if:**
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- Processing <5 articles daily
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- No GPU available
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- GPU needed for other tasks
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- Cost-sensitive deployment
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### Cloud Deployment
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GPU instances cost more but process faster:
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| Provider | Instance | GPU | Cost/hour | Articles/hour |
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|----------|----------|-----|-----------|---------------|
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| AWS | g4dn.xlarge | T4 | $0.526 | ~1000 |
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| GCP | n1-standard-4 + T4 | T4 | $0.35 | ~1000 |
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| Azure | NC6 | K80 | $0.90 | ~500 |
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For comparison, CPU instances process ~100-200 articles/hour at $0.05-0.10/hour.
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## Additional Resources
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- [NVIDIA Container Toolkit Documentation](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
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- [Ollama GPU Support](https://github.com/ollama/ollama/blob/main/docs/gpu.md)
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- [Docker GPU Support](https://docs.docker.com/config/containers/resource_constraints/#gpu)
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- [CUDA Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/)
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## Support
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If you encounter issues:
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1. Run `./check-gpu.sh` to diagnose
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2. Check logs: `docker-compose logs ollama`
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3. See [OLLAMA_SETUP.md](OLLAMA_SETUP.md) for general Ollama troubleshooting
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4. Open an issue with:
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- Output of `nvidia-smi`
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- Output of `docker info | grep -i runtime`
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- Relevant logs
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---
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## Quick Start Guide
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### 30-Second Setup
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```bash
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# 1. Check GPU
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./check-gpu.sh
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# 2. Start services
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./start-with-gpu.sh
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# 3. Test
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docker-compose exec crawler python crawler_service.py 2
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```
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### Command Reference
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**Setup:**
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```bash
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./check-gpu.sh # Check GPU availability
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./configure-ollama.sh # Configure Ollama
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./start-with-gpu.sh # Start with GPU auto-detection
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```
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**With GPU (manual):**
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```bash
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docker-compose -f docker-compose.yml -f docker-compose.gpu.yml up -d
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```
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**Without GPU:**
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```bash
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docker-compose up -d
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```
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**Monitoring:**
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```bash
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docker exec munich-news-ollama nvidia-smi # Check GPU
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watch -n 1 'docker exec munich-news-ollama nvidia-smi' # Monitor GPU
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docker-compose logs -f ollama # Check logs
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```
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**Testing:**
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```bash
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docker-compose exec crawler python crawler_service.py 2 # Test crawl
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docker-compose logs crawler | grep "Title translated" # Check timing
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```
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### Performance Expectations
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| Operation | CPU | GPU | Speedup |
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|-----------|-----|-----|---------|
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| Translation | 1.5s | 0.3s | 5x |
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| Summary | 8s | 2s | 4x |
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| 10 Articles | 115s | 31s | 3.7x |
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---
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## Integration Summary
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### What Was Implemented
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1. **Ollama Service in Docker Compose**
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- Runs on internal network (port 11434)
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- Automatic model download (phi3:latest)
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- Persistent storage in Docker volume
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- GPU support with automatic detection
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2. **GPU Acceleration**
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- NVIDIA GPU support via docker-compose.gpu.yml
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- Automatic GPU detection script
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- 5-10x performance improvement
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- Graceful CPU fallback
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3. **Helper Scripts**
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- `start-with-gpu.sh` - Auto-detect and start
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- `check-gpu.sh` - Diagnose GPU availability
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- `configure-ollama.sh` - Interactive configuration
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- `test-ollama-setup.sh` - Comprehensive tests
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4. **Security**
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- Ollama is internal-only (not exposed to host)
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- Only accessible via Docker network
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- Prevents unauthorized access
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### Files Created
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- `docker-compose.gpu.yml` - GPU configuration override
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- `start-with-gpu.sh` - Auto-start script
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- `check-gpu.sh` - GPU detection script
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- `test-ollama-setup.sh` - Test suite
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- `docs/GPU_SETUP.md` - This documentation
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- `docs/OLLAMA_SETUP.md` - Ollama setup guide
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- `docs/PERFORMANCE_COMPARISON.md` - Benchmarks
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### Quick Commands
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```bash
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# Start with GPU
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docker-compose -f docker-compose.yml -f docker-compose.gpu.yml up -d
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# Or use helper script
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./start-with-gpu.sh
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# Verify GPU usage
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docker exec munich-news-ollama nvidia-smi
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```
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