jupyterlab-datascience-gpu/Dockerfile

ARG NVIDIA_IMAGE=nvcr.io/nvidia/tensorflow:22.12-tf2-py3

FROM ${NVIDIA_IMAGE}

#### copied from https://github.com/jupyter/docker-stacks/tree/main/docker-stacks-foundation
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.

# Ubuntu 22.04 (jammy)
# https://hub.docker.com/_/ubuntu/tags?page=1&name=jammy
ARG ROOT_CONTAINER=ubuntu:22.04


LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
ARG NB_USER="jovyan"
ARG NB_UID="1000"
ARG NB_GID="100"

# Fix: https://github.com/hadolint/hadolint/wiki/DL4006
# Fix: https://github.com/koalaman/shellcheck/wiki/SC3014
SHELL ["/bin/bash", "-o", "pipefail", "-c"]

USER root

# Install all OS dependencies for notebook server that starts but lacks all
# features (e.g., download as all possible file formats)
ENV DEBIAN_FRONTEND noninteractive
RUN apt-get update --yes && \
    # - apt-get upgrade is run to patch known vulnerabilities in apt-get packages as
    #   the ubuntu base image is rebuilt too seldom sometimes (less than once a month)
    apt-get upgrade --yes && \
    apt-get install --yes --no-install-recommends \
    # - bzip2 is necessary to extract the micromamba executable.
    bzip2 \
    ca-certificates \
    locales \
    sudo \
    # - tini is installed as a helpful container entrypoint that reaps zombie
    #   processes and such of the actual executable we want to start, see
    #   https://github.com/krallin/tini#why-tini for details.
    tini \
    wget && \
    apt-get clean && rm -rf /var/lib/apt/lists/* && \
    echo "en_US.UTF-8 UTF-8" > /etc/locale.gen && \
    locale-gen

# Configure environment
ENV CONDA_DIR=/opt/conda \
    SHELL=/bin/bash \
    NB_USER="${NB_USER}" \
    NB_UID=${NB_UID} \
    NB_GID=${NB_GID} \
    LC_ALL=en_US.UTF-8 \
    LANG=en_US.UTF-8 \
    LANGUAGE=en_US.UTF-8
ENV PATH="${CONDA_DIR}/bin:${PATH}" \
    HOME="/home/${NB_USER}"

# Copy a script that we will use to correct permissions after running certain commands
COPY fix-permissions /usr/local/bin/fix-permissions
RUN chmod a+rx /usr/local/bin/fix-permissions

# Enable prompt color in the skeleton .bashrc before creating the default NB_USER
# hadolint ignore=SC2016
RUN sed -i 's/^#force_color_prompt=yes/force_color_prompt=yes/' /etc/skel/.bashrc && \
   # Add call to conda init script see https://stackoverflow.com/a/58081608/4413446
   echo 'eval "$(command conda shell.bash hook 2> /dev/null)"' >> /etc/skel/.bashrc

# Create NB_USER with name jovyan user with UID=1000 and in the 'users' group
# and make sure these dirs are writable by the `users` group.
RUN echo "auth requisite pam_deny.so" >> /etc/pam.d/su && \
    sed -i.bak -e 's/^%admin/#%admin/' /etc/sudoers && \
    sed -i.bak -e 's/^%sudo/#%sudo/' /etc/sudoers && \
    useradd -l -m -s /bin/bash -N -u "${NB_UID}" "${NB_USER}" && \
    mkdir -p "${CONDA_DIR}" && \
    chown "${NB_USER}:${NB_GID}" "${CONDA_DIR}" && \
    chmod g+w /etc/passwd && \
    fix-permissions "${HOME}" && \
    fix-permissions "${CONDA_DIR}"

USER ${NB_UID}

# Pin python version here, or set it to "default"
ARG PYTHON_VERSION=3.10

# Setup work directory for backward-compatibility
RUN mkdir "/home/${NB_USER}/work" && \
    fix-permissions "/home/${NB_USER}"

# Download and install Micromamba, and initialize Conda prefix.
#   <https://github.com/mamba-org/mamba#micromamba>
#   Similar projects using Micromamba:
#     - Micromamba-Docker: <https://github.com/mamba-org/micromamba-docker>
#     - repo2docker: <https://github.com/jupyterhub/repo2docker>
# Install Python, Mamba and jupyter_core
# Cleanup temporary files and remove Micromamba
# Correct permissions
# Do all this in a single RUN command to avoid duplicating all of the
# files across image layers when the permissions change
RUN sleep 120
COPY --chown="${NB_UID}:${NB_GID}" ./jupyterlab-datascience-gpu/initial-condarc "${CONDA_DIR}/.condarc"
WORKDIR /tmp
RUN set -x && \
    arch=$(uname -m) && \
    if [ "${arch}" = "x86_64" ]; then \
        # Should be simpler, see <https://github.com/mamba-org/mamba/issues/1437>
        arch="64"; \
    fi && \
    wget -qO /tmp/micromamba.tar.bz2 \
        "https://micromamba.snakepit.net/api/micromamba/linux-${arch}/latest" && \
    tar -xvjf /tmp/micromamba.tar.bz2 --strip-components=1 bin/micromamba && \
    rm /tmp/micromamba.tar.bz2 && \
    PYTHON_SPECIFIER="python=${PYTHON_VERSION}" && \
    if [[ "${PYTHON_VERSION}" == "default" ]]; then PYTHON_SPECIFIER="python"; fi && \
    # Install the packages
    ./micromamba install \
        --root-prefix="${CONDA_DIR}" \
        --prefix="${CONDA_DIR}" \
        --yes \
        "${PYTHON_SPECIFIER}" \
        'mamba' \
        'jupyter_core' && \
    rm micromamba && \
    # Pin major.minor version of python
    mamba list python | grep '^python ' | tr -s ' ' | cut -d ' ' -f 1,2 >> "${CONDA_DIR}/conda-meta/pinned" && \
    mamba clean --all -f -y && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

# Configure container startup
ENTRYPOINT ["tini", "-g", "--"]
CMD ["start.sh"]

# Copy local files as late as possible to avoid cache busting
COPY start.sh /usr/local/bin/

# Switch back to jovyan to avoid accidental container runs as root
USER ${NB_UID}

WORKDIR "${HOME}"

################ copied from https://github.com/iot-salzburg/gpu-jupyter/tree/master/src

# LABEL authors="Christoph Schranz <christoph.schranz@salzburgresearch.at>, Mathematical Michael <consistentbayes@gmail.com>"



# Install Tensorflow, check compatibility here:
# https://www.tensorflow.org/install/source#gpu
# installation via conda leads to errors in version 4.8.2
RUN pip install --upgrade pip && \
    pip install --no-cache-dir "tensorflow==2.10.1"
RUN pip install --upgrade pip && \
    pip install --no-cache-dir keras==2.11.0

# Install PyTorch with dependencies
RUN conda install --quiet --yes \
    pyyaml mkl mkl-include setuptools cmake cffi typing && \
    conda clean --all -f -y && \
    fix-permissions $CONDA_DIR && \
    fix-permissions /home/$NB_USER

# Check compatibility here:
# https://pytorch.org/get-started/locally/
# Installation via conda leads to errors installing cudatoolkit=11.1
RUN pip install --no-cache-dir torch torchvision torchaudio torchviz --extra-index-url https://download.pytorch.org/whl/cu116

ENV CUDA_PATH=/opt/conda/

USER root

# Install nvtop to monitor the gpu tasks
RUN apt-get update && \
    apt-get install -y cmake libncurses5-dev libncursesw5-dev git && \
    rm -rf /var/lib/apt/lists/*

RUN git clone https://github.com/Syllo/nvtop.git /run/nvtop && \
    mkdir -p /run/nvtop/build && cd /run/nvtop/build && \
    (cmake .. -DNVML_RETRIEVE_HEADER_ONLINE=True 2> /dev/null || echo "cmake was not successful") && \
    (make 2> /dev/null || echo "make was not successful") && \
    (make install 2> /dev/null || echo "make install was not successful") && \
    cd /tmp && rm -rf /tmp/nvtop

RUN fix-permissions /home/$NB_USER

USER $NB_UID