R Econometrics MCP Server

# Multi-Target Dockerfile for RMCP # Supports both development and production environments # # Usage: # Development: docker build --target development -t rmcp:dev . # Production: docker build --target production -t rmcp:prod . # ============================================================================ # STAGE: Base Environment (Pre-built R environment) # ============================================================================ FROM ghcr.io/finite-sample/rmcp/rmcp-base:latest AS base # Base image already contains: # - rocker/r2u:noble with optimized R package installation # - All required system dependencies (Python, build tools, SSL, etc.) # - 50+ R packages for statistical analysis, ML, and visualization # - mkcert for HTTPS development # - Optimized configurations and cleanup # Verify base image and display info RUN echo "📦 Using pre-built RMCP base environment" && \ cat /opt/rmcp-base-info.json && \ echo "🔍 R packages available: $(R -q --slave -e 'cat(length(.packages(all.available=TRUE)))')" && \ echo "✅ Base environment ready" # ============================================================================ # STAGE: Development Environment (Full development stack) # ============================================================================ FROM base AS development # Note: R packages and mkcert are already installed in base image # Install uv for dependency management ARG TARGETPLATFORM RUN --mount=type=cache,target=/root/.cache/uv,id=uv-dev-${TARGETPLATFORM} \ set -eux; \ # Install uv curl -LsSf https://astral.sh/uv/install.sh | sh # Add uv to PATH ENV PATH="/root/.local/bin:$PATH" # Ensure venv tools are first on PATH for subsequent steps/CI ENV PATH="$VENV/bin:$PATH" # Development workspace setup WORKDIR /workspace ENV PYTHONPATH=/workspace # Copy source code for development COPY pyproject.toml ./ # Create a minimal README for build RUN echo "# RMCP Development Environment" > README.md COPY rmcp/ ./rmcp/ # Install RMCP and dependencies using uv sync ENV VIRTUAL_ENV=/workspace/.venv ENV PATH="/workspace/.venv/bin:$PATH" RUN --mount=type=cache,target=/root/.cache/uv,id=uv-dev-sync-${TARGETPLATFORM} \ set -eux; \ export PATH="/root/.local/bin:$PATH"; \ # Install all development dependencies and HTTP extras uv sync --group dev --extra all; \ # Verify installation python -c "import rmcp; print('RMCP installed successfully')" # Default to bash for development work CMD ["bash"] # ============================================================================ # STAGE: Production Builder (Optimized build environment) # ============================================================================ FROM base AS builder # Install uv for dependency management ARG TARGETPLATFORM RUN --mount=type=cache,target=/root/.cache/uv,id=uv-prod-${TARGETPLATFORM} \ set -eux; \ # Install uv curl -LsSf https://astral.sh/uv/install.sh | sh # Add uv to PATH ENV PATH="/root/.local/bin:$PATH" # Copy and build RMCP package WORKDIR /build # Copy files in order of change frequency (metadata first, then code) COPY pyproject.toml ./ # Create minimal README.md for build (excluded by .dockerignore) RUN echo "# RMCP Production Build" > README.md COPY rmcp/ ./rmcp/ # Install production dependencies and build wheel ENV VIRTUAL_ENV=/build/.venv ENV PATH="/build/.venv/bin:$PATH" RUN --mount=type=cache,target=/root/.cache/uv,id=uv-build-${TARGETPLATFORM} \ set -eux; \ export PATH="/root/.local/bin:$PATH"; \ # Build wheel for production installation uv build --wheel --out-dir /build/wheels/ # ============================================================================ # STAGE: Production Runtime (Optimized from base image) # ============================================================================ FROM base AS production-base # Remove development tools to minimize size (aggressive cleanup) RUN set -eux; \ apt-get remove -y \ build-essential \ gcc g++ \ pkg-config \ python3-dev \ git \ curl; \ apt-get autoremove -y; \ apt-get clean; \ # Clean temporary files rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* /root/.cache # ============================================================================ # STAGE: Production Runtime (Final minimal environment) # ============================================================================ FROM production-base AS production ARG TARGETPLATFORM ENV DEBIAN_FRONTEND=noninteractive \ PYTHONDONTWRITEBYTECODE=1 \ PYTHONUNBUFFERED=1 # Note: Runtime dependencies already installed in production-base # (python3, python3-venv, libcurl4, libssl3, libxml2, ca-certificates, # libblas3, liblapack3, r-base-core, littler) # Copy wheel from builder ENV VENV=/opt/venv COPY --from=builder /build/wheels/ /tmp/wheels/ ENV PATH="$VENV/bin:$PATH" # Create fresh virtual environment and install RMCP with HTTP extras RUN python3 -m venv /opt/venv && \ cd /tmp/wheels && \ WHEEL_FILE=$(ls *.whl) && \ /opt/venv/bin/pip install --no-cache-dir "${WHEEL_FILE}[all]" && \ cd / && \ rm -rf /tmp/wheels/ && \ groupadd -r rmcp && \ useradd -r -g rmcp -d /app -s /bin/bash rmcp # Set up application directory WORKDIR /app RUN chown rmcp:rmcp /app # Switch to non-root user USER rmcp # Expose port for HTTP mode EXPOSE 8080 # Health check HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ CMD python -c "import rmcp; print('RMCP OK')" || exit 1 # Default command - for Cloud Run, override with serve-http CMD ["rmcp", "start"] # Metadata LABEL org.opencontainers.image.title="RMCP (R Model Context Protocol)" LABEL org.opencontainers.image.description="Statistical analysis server for AI assistants" LABEL org.opencontainers.image.vendor="RMCP Project" LABEL org.opencontainers.image.source="https://github.com/finite-sample/rmcp"