Capítulo 3: Contenerización con Docker

Dockerfile para ML

# Multi-stage build for smaller images
FROM python:3.10-slim as builder

WORKDIR /app
COPY requirements.txt .
RUN pip wheel --no-cache-dir --wheel-dir /wheels -r requirements.txt

# Production image
FROM python:3.10-slim

WORKDIR /app

# Copy wheels and install
COPY --from=builder /wheels /wheels
RUN pip install --no-cache-dir /wheels/*

# Copy application
COPY src/ ./src/
COPY models/ ./models/

# Create non-root user
RUN useradd -m appuser && chown -R appuser:appuser /app
USER appuser

EXPOSE 8080

HEALTHCHECK --interval=30s --timeout=5s \
  CMD curl -f http://localhost:8080/health || exit 1

CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8080"]

Soporte de GPU con NVIDIA Docker

FROM nvidia/cuda:12.1-runtime-ubuntu22.04

# Install Python and dependencies
RUN apt-get update && apt-get install -y python3 python3-pip
COPY requirements.txt .
RUN pip3 install -r requirements.txt

COPY . /app
WORKDIR /app

CMD ["python3", "serve.py"]

# Run with GPU
docker run --gpus all -p 8080:8080 ml-model:latest

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