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What is Docker?
Docker is a containerization platform that packages applications and their dependencies into lightweight, portable containers that run consistently across different environments.
Key Concepts:
• Container: Lightweight, standalone package
• Image: Blueprint for creating containers
• Dockerfile: Instructions to build images
• Registry: Storage for Docker images
Why Use Docker for ML?
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Environment Consistency
Same behavior across development, testing, and production
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Dependency Isolation
Avoid conflicts between different ML frameworks
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Portability
Deploy anywhere - cloud, on-premise, or edge
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Easy Scaling
Horizontal scaling with container orchestration
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Version Control
Track and manage different model versions
Practical Docker Examples
Dockerfile
Docker Commands
Docker Compose
Kubernetes
# Dockerfile for ML model deployment
FROM python:3.9-slim
# Set working directory
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y \
gcc \
g++ \
&& rm -rf /var/lib/apt/lists/*
# Copy requirements first for better caching
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy model and application code
COPY model.pkl .
COPY app.py .
COPY utils/ ./utils/
# Create non-root user for security
RUN useradd -m -u 1000 mluser && chown -R mluser:mluser /app
USER mluser
# Expose port
EXPOSE 8080
# Health check
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
CMD curl -f http://localhost:8080/health || exit 1
# Run application
CMD ["gunicorn", "--bind", "0.0.0.0:8080", "--workers", "4", "--timeout", "120", "app:app"]
Click on the tabs above to explore different Docker configurations and commands!