Dockerfile

# Use Ubuntu as base image with Python 3.10
FROM ubuntu:22.04

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV RUST_VERSION=1.81.0
ENV PYTHONPATH=/app/python

# Install system dependencies
RUN apt-get update && apt-get install -y \
    build-essential \
    cmake \
    curl \
    git \
    libbz2-dev \
    libffi-dev \
    libgdbm-dev \
    liblzma-dev \
    libncurses5-dev \
    libreadline-dev \
    libsqlite3-dev \
    libssl-dev \
    pkg-config \
    python3.10 \
    python3.10-dev \
    python3.10-venv \
    python3-pip \
    wget \
    zlib1g-dev \
    && rm -rf /var/lib/apt/lists/*

# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain ${RUST_VERSION}
ENV PATH="/root/.cargo/bin:${PATH}"

# Set working directory
WORKDIR /app

# Copy project files
COPY . .

# Build Rust project
RUN cargo build --release

# Install Python dependencies
RUN python3.10 -m pip install --upgrade pip setuptools wheel
RUN python3.10 -m pip install \
    dgl==1.1.3 \
    pandas \
    "numpy>=1.22.0,<1.24.0" \
    "scipy>=1.8.0,<1.11.0" \
    aigverse \
    pyyaml \
    scikit-learn \
    pydantic \
    chardet \
    matplotlib \
    seaborn \
    pathlib2
# Install PyTorch and related packages
RUN pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
# Install other Python dependencies
RUN pip install torch_geometric
RUN pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.6.0+cu124.html
RUN pip install optuna
# Install the Python package
# RUN cd python && python3.10 -m pip install -e .

# Create directories for data and models
RUN mkdir -p /app/data/circuit_features /app/data/graph_cache /app/models /app/benchmarks /app/output

# Set default command
CMD ["/bin/bash"]

# Usage examples:
# Build: docker build -t solver-tuner .
# 
# Run interactive session:
# docker run -it --rm -v $(pwd)/data:/app/data -v $(pwd)/benchmarks:/app/benchmarks -v $(pwd)/output:/app/output -v $(pwd)/models:/app/models solver-tuner
#
# Run training:
# docker run -it --rm -v $(pwd)/data:/app/data -v $(pwd)/benchmarks:/app/benchmarks -v $(pwd)/output:/app/output -v $(pwd)/models:/app/models solver-tuner \
#   bash -c "trainer data/test_experiment.csv data/test_circuit --model-out=models/model.pt --features-dir=data/circuit_features/ --epochs=20 --learning-rate=0.01"
#
# Run prediction:
# docker run -it --rm -v $(pwd)/data:/app/data -v $(pwd)/benchmarks:/app/benchmarks -v $(pwd)/output:/app/output -v $(pwd)/models:/app/models solver-tuner \
#   bash -c "predictor --aig-file benchmarks/6s8.aig --model models/model.pt --find-best"