Dockerfile

# Stage 1: Base image with Poetry and dependencies
FROM pytorch/pytorch:2.4.0-cuda12.4-cudnn9-runtime as base

LABEL maintainer="Soutrik soutrik1991@gmail.com" \
      description="Base Docker image for running a Python app with Poetry and GPU support."

# Install Poetry and necessary system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends curl && \
    curl -sSL https://install.python-poetry.org | python3 - && \
    apt-get clean && rm -rf /var/lib/apt/lists/*

# Add Poetry to the PATH explicitly
ENV PATH="/root/.local/bin:$PATH"

# Configure Poetry environment
ENV POETRY_NO_INTERACTION=1 \
    POETRY_VIRTUALENVS_IN_PROJECT=1 \
    POETRY_CACHE_DIR=/tmp/poetry_cache

# Set the working directory to /app
WORKDIR /app

# Copy pyproject.toml and poetry.lock to install dependencies
COPY pyproject.toml poetry.lock /app/

# Install dependencies without installing the package itself
RUN --mount=type=cache,target=/tmp/poetry_cache poetry install --only main --no-root

# Stage 2: Build stage for the application
FROM base as builder

# Copy application source code and necessary files
COPY src /app/src
COPY configs /app/configs
COPY .project-root /app/.project-root
COPY main.py /app/main.py

# Stage 3: Final runtime stage
FROM base as runner

# Copy application source code and necessary files from builder stage
COPY --from=builder /app/src /app/src
COPY --from=builder /app/configs /app/configs
COPY --from=builder /app/.project-root /app/.project-root
COPY --from=builder /app/main.py /app/main.py

# Copy virtual environment from the builder stage
COPY --from=builder /app/.venv /app/.venv

# Copy client files
COPY run_client.sh /app/run_client.sh

# Set the working directory to /app
WORKDIR /app

# Set the environment path to use the virtual environment
ENV PATH="/app/.venv/bin:$PATH"

# Default command
CMD ["python", "-m", "main"]