ca05c91799
Model Loader: - Update model_loader.py with complete dots.ocr loading code - Proper device detection (CUDA/CPU/MPS) with fallback - Memory optimization (low_cpu_mem_usage) - Better error handling and logging - Support for local model paths and HF Hub Docker: - Multi-stage Dockerfile (CPU/CUDA builds) - docker-compose.yml for parser-service - .dockerignore for clean builds - Model cache volume for persistence Configuration: - Support DOTS_OCR_MODEL_ID and DEVICE env vars (backward compatible) - Better defaults and environment variable handling Deployment: - Add DEPLOYMENT.md with detailed instructions - Local deployment (venv) - Docker Compose deployment - Ollama runtime setup - Troubleshooting guide Integration: - Add parser-service to main docker-compose.yml - Configure volumes and networks - Health checks and dependencies
65 lines
1.5 KiB
Docker
65 lines
1.5 KiB
Docker
# Multi-stage build for PARSER Service
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# Stage 1: Base with system dependencies
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FROM python:3.11-slim as base
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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poppler-utils \
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libgl1-mesa-glx \
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libglib2.0-0 \
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git \
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&& rm -rf /var/lib/apt/lists/*
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# Stage 2: CPU-only build
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FROM base as cpu
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# Copy requirements and install CPU-only dependencies
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COPY requirements.txt .
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RUN pip install --no-cache-dir \
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torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu && \
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pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY . .
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# Create temp directory and model cache
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RUN mkdir -p /tmp/parser /root/.cache/huggingface
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# Expose port
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EXPOSE 9400
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# Run application
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CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "9400"]
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# Stage 3: CUDA build (optional, use --target=cuda)
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FROM base as cuda
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# Install CUDA dependencies
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RUN apt-get update && apt-get install -y \
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nvidia-cuda-toolkit \
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&& rm -rf /var/lib/apt/lists/*
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# Copy requirements and install CUDA dependencies
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COPY requirements.txt .
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RUN pip install --no-cache-dir \
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torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 && \
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pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY . .
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# Create temp directory and model cache
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RUN mkdir -p /tmp/parser /root/.cache/huggingface
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# Expose port
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EXPOSE 9400
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# Run application
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CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "9400"]
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# Default to CPU build
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FROM cpu
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