62be895b9e
Two workers × ~5 GB Surya model load = ~10 GB required, exceeding the 8 GB memory cap and causing OOM on the first /train call. Two OS processes also cause model-state divergence after training, contradicting the single-node constraint documented in ADR-001. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
27 lines
773 B
Docker
27 lines
773 B
Docker
FROM python:3.11-slim
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WORKDIR /app
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# curl for healthcheck; libgomp1 for PyTorch CPU threading; libvips for kraken PDF support
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RUN apt-get update && apt-get install -y --no-install-recommends \
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curl \
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libgomp1 \
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libvips42 \
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&& rm -rf /var/lib/apt/lists/*
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# PyTorch CPU-only — separate layer; the whl/cpu index strips all CUDA variants (~2 GB saved)
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# torchvision must also come from the CPU index to match torch's operator registrations
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RUN pip install --no-cache-dir \
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torch==2.7.1 \
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torchvision==0.22.1 \
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--index-url https://download.pytorch.org/whl/cpu
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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EXPOSE 8000
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1"]
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