# This Dockerfile includes sections from tensorflow/tensorflow:latest-gpu's Dockerfile: # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/gpu.Dockerfile # and sections from continuumio/miniconda3:latest's Dockerfile: # https://github.com/ContinuumIO/docker-images/blob/master/miniconda3/debian/Dockerfile # First we need CUDA and everything else needed to support GPUs ############################################### #### FROM tensorflow/tensorflow:latest-gpu #### ############################################### ARG UBUNTU_VERSION=18.04 ARG ARCH= ARG CUDA=11.0 FROM nvidia/cuda${ARCH:+-$ARCH}:${CUDA}-base-ubuntu${UBUNTU_VERSION} as base # ARCH and CUDA are specified again because the FROM directive resets ARGs # (but their default value is retained if set previously) ARG ARCH ARG CUDA ARG CUDNN=8.0.4.30-1 ARG CUDNN_MAJOR_VERSION=8 ARG LIB_DIR_PREFIX=x86_64 ARG LIBNVINFER=7.1.3-1 ARG LIBNVINFER_MAJOR_VERSION=7 # Needed for string substitution SHELL ["/bin/bash", "-c"] # Pick up some TF dependencies # [HOML2] Tweaked for handson-ml2: added all the libs before build-essentials RUN apt-get update -q && apt-get install -q -y --no-install-recommends \ bzip2 \ ca-certificates \ cmake \ ffmpeg \ git \ libboost-all-dev \ libglib2.0-0 \ libjpeg-dev \ libpq-dev \ libsdl2-dev \ libsm6 \ libxext6 \ libxrender1 \ mercurial \ subversion \ sudo \ swig \ wget \ xorg-dev \ xvfb \ zip \ zlib1g-dev \ build-essential \ cuda-command-line-tools-${CUDA/./-} \ libcublas-${CUDA/./-} \ cuda-nvrtc-${CUDA/./-} \ libcufft-${CUDA/./-} \ libcurand-${CUDA/./-} \ libcusolver-${CUDA/./-} \ libcusparse-${CUDA/./-} \ curl \ libcudnn8=${CUDNN}+cuda${CUDA} \ libfreetype6-dev \ libhdf5-serial-dev \ libzmq3-dev \ pkg-config \ software-properties-common \ unzip # Install TensorRT if not building for PowerPC RUN [[ "${ARCH}" = "ppc64le" ]] || { apt-get update && \ apt-get install -y --no-install-recommends libnvinfer${LIBNVINFER_MAJOR_VERSION}=${LIBNVINFER}+cuda${CUDA} \ libnvinfer-plugin${LIBNVINFER_MAJOR_VERSION}=${LIBNVINFER}+cuda${CUDA} \ && apt-get clean \ && rm -rf /var/lib/apt/lists/*; } # For CUDA profiling, TensorFlow requires CUPTI. ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH # Link the libcuda stub to the location where tensorflow is searching for it and reconfigure # dynamic linker run-time bindings RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 \ && echo "/usr/local/cuda/lib64/stubs" > /etc/ld.so.conf.d/z-cuda-stubs.conf \ && ldconfig # [HOML2] Tweaked for handson-ml2: removed Python3 & TensorFlow installation using pip ################################################# #### End of tensorflow/tensorflow:latest-gpu #### ################################################# ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 ENV PATH /opt/conda/bin:/opt/conda/envs/tf2/bin:$PATH # Next we need to install miniconda ############################################ #### FROM continuumio/miniconda3:latest #### ############################################ # [HOML2] Tweaked for handson-ml2: removed the beginning of the Dockerfile CMD [ "/bin/bash" ] # Leave these args here to better use the Docker build cache ARG CONDA_VERSION=py38_4.9.2 ARG CONDA_MD5=122c8c9beb51e124ab32a0fa6426c656 RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-${CONDA_VERSION}-Linux-x86_64.sh -O miniconda.sh && \ echo "${CONDA_MD5} miniconda.sh" > miniconda.md5 && \ if ! md5sum --status -c miniconda.md5; then exit 1; fi && \ mkdir -p /opt && \ sh miniconda.sh -b -p /opt/conda && \ rm miniconda.sh miniconda.md5 && \ ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ echo "conda activate base" >> ~/.bashrc && \ find /opt/conda/ -follow -type f -name '*.a' -delete && \ find /opt/conda/ -follow -type f -name '*.js.map' -delete && \ /opt/conda/bin/conda clean -afy ############################################## #### End of continuumio/miniconda3:latest #### ############################################## # Now we're ready to create our conda environment COPY environment.yml /tmp/ RUN conda update -y -n base conda \ && echo ' - pyvirtualdisplay' >> /tmp/environment.yml \ && conda env create -f /tmp/environment.yml \ && conda clean -y -t \ && rm /tmp/environment.yml ARG username ARG userid ARG home=/home/${username} ARG workdir=${home}/handson-ml2 RUN adduser ${username} --uid ${userid} --gecos '' --disabled-password \ && echo "${username} ALL=(root) NOPASSWD:ALL" > /etc/sudoers.d/${username} \ && chmod 0440 /etc/sudoers.d/${username} WORKDIR ${workdir} RUN chown ${username}:${username} ${workdir} USER ${username} WORKDIR ${workdir} # The config below enables diffing notebooks with nbdiff (and nbdiff support # in git diff command) after connecting to the container by "make exec" (or # "docker-compose exec handson-ml2 bash") # You may also try running: # nbdiff NOTEBOOK_NAME.ipynb # to get nbdiff between checkpointed version and current version of the # given notebook. RUN git-nbdiffdriver config --enable --global # INFO: Optionally uncomment any (one) of the following RUN commands below to ignore either # metadata or details in nbdiff within git diff #RUN git config --global diff.jupyternotebook.command 'git-nbdiffdriver diff --ignore-metadata' RUN git config --global diff.jupyternotebook.command 'git-nbdiffdriver diff --ignore-details' COPY docker/bashrc.bash /tmp/ RUN cat /tmp/bashrc.bash >> ${home}/.bashrc \ && echo "export PATH=\"${workdir}/docker/bin:$PATH\"" >> ${home}/.bashrc \ && sudo rm /tmp/bashrc.bash # INFO: Uncomment lines below to enable automatic save of python-only and html-only # exports alongside the notebook #COPY docker/jupyter_notebook_config.py /tmp/ #RUN cat /tmp/jupyter_notebook_config.py >> ${home}/.jupyter/jupyter_notebook_config.py #RUN sudo rm /tmp/jupyter_notebook_config.py # INFO: Uncomment the RUN command below to disable git diff paging #RUN git config --global core.pager '' # INFO: Uncomment the RUN command below for easy and constant notebook URL (just localhost:8888) # That will switch Jupyter to using empty password instead of a token. # To avoid making a security hole you SHOULD in fact not only uncomment but # regenerate the hash for your own non-empty password and replace the hash below. # You can compute a password hash in any notebook, just run the code: # from notebook.auth import passwd # passwd() # and take the hash from the output #RUN mkdir -p ${home}/.jupyter && \ # echo 'c.NotebookApp.password = u"sha1:c6bbcba2d04b:f969e403db876dcfbe26f47affe41909bd53392e"' \ # >> ${home}/.jupyter/jupyter_notebook_config.py