Dockerfile

# Reproduce the measurement environment elsewhere.
#
#   docker build -t commprof .
#   docker run --gpus all -it commprof commprof check
#
# The base image matches the CUDA version of the wheel index below. That pairing is
# the point of this file: a mismatch between the CUDA runtime and the kernels in the
# PyTorch build is the failure this whole project gates on at startup.

FROM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu24.04

ENV DEBIAN_FRONTEND=noninteractive \
    PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=1 \
    COMMPROF_NO_PROGRESS=1

RUN apt-get update && apt-get install -y --no-install-recommends \
        python3 \
        python3-pip \
        python3-venv \
        git \
        make \
        latexmk \
        texlive-latex-recommended \
        texlive-latex-extra \
        texlive-science \
        texlive-fonts-recommended \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /workspace

# Dependencies first, so a source change does not re-download three gigabytes of wheels.
COPY requirements.txt requirements-dev.txt ./
RUN python3 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
RUN pip install --upgrade pip \
    && pip install -r requirements-dev.txt

COPY . .
RUN pip install -e . --no-deps

# Fails the build if the image cannot see a GPU at runtime, which is the intended
# way to find out that --gpus all was forgotten.
HEALTHCHECK --interval=1m --timeout=30s --retries=1 \
    CMD python -c "import torch; raise SystemExit(0 if torch.cuda.is_available() else 1)"

CMD ["commprof", "check"]