# Dockerfile.gpu - CUDA-enabled PaddleOCR REST API # Supports: x86_64 with NVIDIA GPU (CUDA 12.x) # For DGX Spark (ARM64 + CUDA): build natively on the device FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04 LABEL maintainer="Sergio Jimenez" LABEL description="PaddleOCR Tuning REST API - GPU/CUDA version" WORKDIR /app # Set environment variables ENV DEBIAN_FRONTEND=noninteractive ENV PYTHONUNBUFFERED=1 ENV CUDA_VISIBLE_DEVICES=0 # Install Python 3.11 and system dependencies RUN apt-get update && apt-get install -y --no-install-recommends \ python3.11 \ python3.11-venv \ python3-pip \ libgl1 \ libglib2.0-0 \ libsm6 \ libxext6 \ libxrender1 \ libgomp1 \ && rm -rf /var/lib/apt/lists/* \ && ln -sf /usr/bin/python3.11 /usr/bin/python # Install Python dependencies from requirements file COPY requirements-gpu.txt . RUN pip install --no-cache-dir -r requirements-gpu.txt # Copy application code COPY paddle_ocr_tuning_rest.py . COPY dataset_manager.py . # Build arguments for models to bake into image ARG DET_MODEL=PP-OCRv5_server_det ARG REC_MODEL=PP-OCRv5_server_rec # Set as environment variables (can be overridden at runtime) ENV PADDLE_DET_MODEL=${DET_MODEL} ENV PADDLE_REC_MODEL=${REC_MODEL} # Download models during build (not at runtime) RUN python -c "\ import os; \ from paddleocr import PaddleOCR; \ det = os.environ.get('PADDLE_DET_MODEL', 'PP-OCRv5_server_det'); \ rec = os.environ.get('PADDLE_REC_MODEL', 'PP-OCRv5_server_rec'); \ print(f'Downloading models: det={det}, rec={rec}'); \ ocr = PaddleOCR(text_detection_model_name=det, text_recognition_model_name=rec); \ print('Models downloaded successfully!')" # Volume for dataset and optional additional model cache VOLUME ["/app/dataset", "/root/.paddlex"] # Expose API port EXPOSE 8000 # Health check HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \ CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1 # Run the API server CMD ["uvicorn", "paddle_ocr_tuning_rest:app", "--host", "0.0.0.0", "--port", "8000"]