# Implementation Tasks

## 1. Environment Setup Enhancement
- [ ] 1.1 Add GPU detection function in `setup_dev_env.sh`
  - Detect NVIDIA GPU using `nvidia-smi` or `lspci`
  - Detect CUDA version if GPU is available
  - Output GPU detection results to user
- [ ] 1.2 Add conditional CUDA package installation
  - Install `paddlepaddle-gpu` with matching CUDA version when GPU detected
  - Install `paddlepaddle` (CPU-only) when no GPU detected
  - Handle different CUDA versions (11.2, 11.6, 11.7, 12.0, etc.)
- [ ] 1.3 Add GPU verification step after installation
  - Test PaddlePaddle GPU availability
  - Report GPU status and CUDA version to user
  - Provide fallback instructions if GPU setup fails

## 2. Configuration Updates
- [ ] 2.1 Add GPU configuration to `.env.local`
  - Add `FORCE_CPU_MODE` option (default: false)
  - Add `CUDA_VERSION` for manual override
  - Add `GPU_MEMORY_FRACTION` for memory allocation control
- [ ] 2.2 Update backend configuration
  - Add GPU settings to `backend/app/core/config.py`
  - Load GPU-related environment variables
  - Add validation for GPU configuration values

## 3. OCR Service GPU Integration
- [ ] 3.1 Add GPU detection in OCR service initialization
  - Create GPU availability check function
  - Detect available GPU devices
  - Log GPU status (available/unavailable, device name, memory)
- [ ] 3.2 Implement automatic GPU/CPU mode selection
  - Enable GPU mode in PaddleOCR when GPU is available
  - Fall back to CPU mode when GPU is unavailable or forced
  - Set appropriate `use_gpu` parameter for PaddleOCR initialization
- [ ] 3.3 Add GPU memory management
  - Set GPU memory fraction to prevent OOM errors
  - Adjust batch size based on GPU memory availability
  - Handle GPU memory allocation failures gracefully
- [ ] 3.4 Update `backend/app/services/ocr_service.py`
  - Modify PaddleOCR initialization with GPU parameters
  - Add GPU status logging
  - Add error handling for GPU-related issues

## 4. Health Check and Monitoring
- [ ] 4.1 Add GPU status to health check endpoint
  - Report GPU availability (true/false)
  - Report GPU device name and compute capability
  - Report CUDA version
  - Report current GPU memory usage
- [ ] 4.2 Update `backend/app/api/v1/endpoints/health.py`
  - Add GPU status fields to health check response
  - Handle cases where GPU detection fails

## 5. Documentation Updates
- [ ] 5.1 Update README.md
  - Add GPU requirements section
  - Document GPU detection and setup process
  - Add troubleshooting for GPU issues
- [ ] 5.2 Update openspec/project.md
  - Add GPU hardware recommendations
  - Document CUDA version compatibility
  - Add GPU-specific configuration options
- [ ] 5.3 Create GPU setup guide
  - Document NVIDIA driver installation for WSL
  - Document CUDA toolkit installation
  - Provide GPU verification steps

## 6. Testing
- [ ] 6.1 Test GPU detection on GPU-enabled system
  - Verify correct CUDA version detection
  - Verify correct PaddlePaddle GPU installation
  - Verify OCR processing uses GPU
- [ ] 6.2 Test CPU fallback on non-GPU system
  - Verify CPU-only installation
  - Verify OCR processing works without GPU
  - Verify no errors or warnings about missing GPU
- [ ] 6.3 Test FORCE_CPU_MODE override
  - Verify GPU is ignored when FORCE_CPU_MODE=true
  - Verify CPU processing works on GPU-enabled system
- [ ] 6.4 Performance benchmarking
  - Measure OCR processing time with GPU
  - Measure OCR processing time with CPU
  - Document performance improvements

## 7. Error Handling and Edge Cases
- [ ] 7.1 Handle GPU out-of-memory errors
  - Catch CUDA OOM exceptions
  - Automatically fall back to CPU mode
  - Log warning message to user
- [ ] 7.2 Handle CUDA version mismatch
  - Detect PaddlePaddle/CUDA compatibility issues
  - Provide clear error messages
  - Suggest correct CUDA version installation
- [ ] 7.3 Handle missing NVIDIA drivers
  - Detect when GPU hardware exists but drivers are missing
  - Provide installation instructions
  - Fall back to CPU mode gracefully