Building Python images using `uv`

Dockerfile

FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim

WORKDIR /app

# Compile Python bytecode during installation for faster cold starts
# Trade-off: longer build time for better runtime performance
ENV UV_COMPILE_BYTECODE=1

# Use copy mode to avoid hard link warnings across Docker layers
ENV UV_LINK_MODE=copy

# Install dependencies in separate layer for Docker layer caching optimization
# Dependencies change less frequently than source code, so this improves rebuild performance
RUN --mount=type=cache,target=/root/.cache/uv \
    --mount=type=bind,source=uv.lock,target=uv.lock \
    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
    uv sync --locked --no-install-project

# Copy application source code after dependencies to maximize cache hits
COPY . /app

# Install the project itself (now that source code is available)
RUN --mount=type=cache,target=/root/.cache/uv \
    uv sync --locked

# Activate the virtual environment by prepending its bin directory to PATH
# This allows direct execution of Python and installed packages
ENV PATH="/app/.venv/bin:$PATH"

# Default to running the main application module
ENTRYPOINT ["python", "main.py"]