Add Dockerfile.gpu for GPU support
Aurélien Geron
parent
abd75d7fd7
commit
d6c2ba1b8f
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@ -2,12 +2,20 @@ FROM continuumio/miniconda3:latest
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RUN apt-get update && apt-get upgrade -y \
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&& apt-get install -y \
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libpq-dev \
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build-essential \
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cmake \
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ffmpeg \
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git \
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libboost-all-dev \
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libjpeg-dev \
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libpq-dev \
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libsdl2-dev swig \
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sudo \
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cmake zlib1g-dev libjpeg-dev xvfb ffmpeg xorg-dev libboost-all-dev libsdl2-dev swig \
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unzip zip \
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unzip \
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xorg-dev \
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xvfb \
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zip \
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zlib1g-dev \
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&& rm -rf /var/lib/apt/lists/*
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COPY environment.yml /tmp/
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@ -0,0 +1,195 @@
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# This Dockerfile includes sections from tensorflow/tensorflow:latest-gpu's Dockerfile:
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# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/gpu.Dockerfile
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# and sections from continuumio/miniconda3:latest's Dockerfile:
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# https://github.com/ContinuumIO/docker-images/blob/master/miniconda3/debian/Dockerfile
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# First we need CUDA and everything else needed to support GPUs
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###############################################
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#### FROM tensorflow/tensorflow:latest-gpu ####
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###############################################
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ARG UBUNTU_VERSION=18.04
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ARG ARCH=
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ARG CUDA=11.0
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FROM nvidia/cuda${ARCH:+-$ARCH}:${CUDA}-base-ubuntu${UBUNTU_VERSION} as base
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# ARCH and CUDA are specified again because the FROM directive resets ARGs
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# (but their default value is retained if set previously)
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ARG ARCH
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ARG CUDA
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ARG CUDNN=8.0.4.30-1
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ARG CUDNN_MAJOR_VERSION=8
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ARG LIB_DIR_PREFIX=x86_64
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ARG LIBNVINFER=7.1.3-1
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ARG LIBNVINFER_MAJOR_VERSION=7
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# Needed for string substitution
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SHELL ["/bin/bash", "-c"]
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# Pick up some TF dependencies
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# [HOML2] Tweaked for handson-ml2: added all the libs before build-essentials
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RUN apt-get update -q && apt-get install -q -y --no-install-recommends \
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bzip2 \
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ca-certificates \
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cmake \
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ffmpeg \
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git \
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libboost-all-dev \
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libglib2.0-0 \
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libjpeg-dev \
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libpq-dev \
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libsdl2-dev \
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libsm6 \
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libxext6 \
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libxrender1 \
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mercurial \
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subversion \
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sudo \
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swig \
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wget \
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xorg-dev \
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xvfb \
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zip \
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zlib1g-dev \
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build-essential \
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cuda-command-line-tools-${CUDA/./-} \
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libcublas-${CUDA/./-} \
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cuda-nvrtc-${CUDA/./-} \
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libcufft-${CUDA/./-} \
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libcurand-${CUDA/./-} \
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libcusolver-${CUDA/./-} \
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libcusparse-${CUDA/./-} \
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curl \
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libcudnn8=${CUDNN}+cuda${CUDA} \
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libfreetype6-dev \
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libhdf5-serial-dev \
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libzmq3-dev \
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pkg-config \
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software-properties-common \
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unzip
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# Install TensorRT if not building for PowerPC
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RUN [[ "${ARCH}" = "ppc64le" ]] || { apt-get update && \
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apt-get install -y --no-install-recommends libnvinfer${LIBNVINFER_MAJOR_VERSION}=${LIBNVINFER}+cuda${CUDA} \
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libnvinfer-plugin${LIBNVINFER_MAJOR_VERSION}=${LIBNVINFER}+cuda${CUDA} \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*; }
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# For CUDA profiling, TensorFlow requires CUPTI.
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ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
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# Link the libcuda stub to the location where tensorflow is searching for it and reconfigure
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# dynamic linker run-time bindings
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RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 \
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&& echo "/usr/local/cuda/lib64/stubs" > /etc/ld.so.conf.d/z-cuda-stubs.conf \
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&& ldconfig
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# [HOML2] Tweaked for handson-ml2: removed Python3 & TensorFlow installation using pip
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#################################################
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#### End of tensorflow/tensorflow:latest-gpu ####
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#################################################
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ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
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ENV PATH /opt/conda/bin:/opt/conda/envs/tf2/bin:$PATH
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# Next we need to install miniconda
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############################################
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#### FROM continuumio/miniconda3:latest ####
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############################################
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# [HOML2] Tweaked for handson-ml2: removed the beginning of the Dockerfile
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CMD [ "/bin/bash" ]
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# Leave these args here to better use the Docker build cache
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ARG CONDA_VERSION=py38_4.9.2
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ARG CONDA_MD5=122c8c9beb51e124ab32a0fa6426c656
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RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-${CONDA_VERSION}-Linux-x86_64.sh -O miniconda.sh && \
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echo "${CONDA_MD5} miniconda.sh" > miniconda.md5 && \
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if ! md5sum --status -c miniconda.md5; then exit 1; fi && \
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mkdir -p /opt && \
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sh miniconda.sh -b -p /opt/conda && \
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rm miniconda.sh miniconda.md5 && \
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ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
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echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
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echo "conda activate base" >> ~/.bashrc && \
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find /opt/conda/ -follow -type f -name '*.a' -delete && \
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find /opt/conda/ -follow -type f -name '*.js.map' -delete && \
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/opt/conda/bin/conda clean -afy
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##############################################
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#### End of continuumio/miniconda3:latest ####
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##############################################
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# Now we're ready to create our conda environment
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COPY environment.yml /tmp/
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RUN conda update -y -n base conda \
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&& echo ' - pyvirtualdisplay' >> /tmp/environment.yml \
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&& conda env create -f /tmp/environment.yml \
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&& conda clean -y -t \
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&& rm /tmp/environment.yml
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ARG username
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ARG userid
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ARG home=/home/${username}
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ARG workdir=${home}/handson-ml2
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RUN adduser ${username} --uid ${userid} --gecos '' --disabled-password \
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&& echo "${username} ALL=(root) NOPASSWD:ALL" > /etc/sudoers.d/${username} \
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&& chmod 0440 /etc/sudoers.d/${username}
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WORKDIR ${workdir}
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RUN chown ${username}:${username} ${workdir}
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USER ${username}
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WORKDIR ${workdir}
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# The config below enables diffing notebooks with nbdiff (and nbdiff support
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# in git diff command) after connecting to the container by "make exec" (or
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# "docker-compose exec handson-ml2 bash")
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# You may also try running:
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# nbdiff NOTEBOOK_NAME.ipynb
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# to get nbdiff between checkpointed version and current version of the
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# given notebook.
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RUN git-nbdiffdriver config --enable --global
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# INFO: Optionally uncomment any (one) of the following RUN commands below to ignore either
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# metadata or details in nbdiff within git diff
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#RUN git config --global diff.jupyternotebook.command 'git-nbdiffdriver diff --ignore-metadata'
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RUN git config --global diff.jupyternotebook.command 'git-nbdiffdriver diff --ignore-details'
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COPY docker/bashrc.bash /tmp/
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RUN cat /tmp/bashrc.bash >> ${home}/.bashrc \
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&& echo "export PATH=\"${workdir}/docker/bin:$PATH\"" >> ${home}/.bashrc \
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&& sudo rm /tmp/bashrc.bash
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# INFO: Uncomment lines below to enable automatic save of python-only and html-only
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# exports alongside the notebook
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#COPY docker/jupyter_notebook_config.py /tmp/
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#RUN cat /tmp/jupyter_notebook_config.py >> ${home}/.jupyter/jupyter_notebook_config.py
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#RUN sudo rm /tmp/jupyter_notebook_config.py
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# INFO: Uncomment the RUN command below to disable git diff paging
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#RUN git config --global core.pager ''
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# INFO: Uncomment the RUN command below for easy and constant notebook URL (just localhost:8888)
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# That will switch Jupyter to using empty password instead of a token.
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# To avoid making a security hole you SHOULD in fact not only uncomment but
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# regenerate the hash for your own non-empty password and replace the hash below.
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# You can compute a password hash in any notebook, just run the code:
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# from notebook.auth import passwd
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# passwd()
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# and take the hash from the output
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#RUN mkdir -p ${home}/.jupyter && \
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# echo 'c.NotebookApp.password = u"sha1:c6bbcba2d04b:f969e403db876dcfbe26f47affe41909bd53392e"' \
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# >> ${home}/.jupyter/jupyter_notebook_config.py
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@ -71,3 +71,56 @@ You can see changes you made relative to the version in git using `git diff` whi
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You may also try `nbd NOTEBOOK_NAME.ipynb` command (custom, see bashrc file) to compare one of your notebooks with its `checkpointed` version.<br/>
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To be precise, the output will tell you *what modifications should be re-played on the **manually saved** version of the notebook (located in `.ipynb_checkpoints` subdirectory) to update it to the **current** i.e. **auto-saved** version (given as command's argument - located in working directory)*.
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## GPU Support on Linux (experimental)
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If you're using Linux, and you have a TensorFlow-compatible GPU card (NVidia card with Compute Capability ≥ 3.5) that you would like TensorFlow to use inside the docker container, then you should download and install the latest driver for your card from [nvidia.com](https://www.nvidia.com/Download/index.aspx?lang=en-us). You will also need to install [NVidia Docker support](https://github.com/NVIDIA/nvidia-docker): if you are using Docker 19.03 or above, you must install the `nvidia-container-toolkit` package, and for earlier versions, you must install `nvidia-docker2`.
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To build the image, edit `docker-compose.yml`, replace the line `dockerfile: ./docker/Dockerfile` with `dockerfile: ./docker/Dockerfile.gpu`, and then run:
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```bash
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$ cd /path/to/project/handson-ml2/docker
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$ docker-compose build
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```
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To run the image, it's depends. If you have `docker-compose` version 1.28 or above, that's great! You can simply uncomment the `deploy` section in `docker-compose.yml`, and then run:
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```bash
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$ cd /path/to/project/handson-ml2/docker
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$ docker-compose up
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[...]
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or http://127.0.0.1:8888/?token=[...]
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```
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However, if you have an earlier version of `docker-compose`, it's simpler to use `docker run` directly. If you are using Docker 19.03 or above, you can run:
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```bash
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$ cd /path/to/project/handson-ml2
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$ docker run --name handson-ml2 --gpus all -p 8888:8888 -p 6006:6006 --log-opt mode=non-blocking --log-opt max-buffer-size=50m -d -v `pwd`:/home/devel/handson-ml2 handson-ml2 /opt/conda/envs/tf2/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
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```
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If you are using an older version of Docker, then replace `--gpus all` with `--runtime=nvidia`.
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Then, display the container's logs and point your browser to the URL printed on the screen:
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```bash
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$ docker logs handson-ml2
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[I 09:07:10.805 NotebookApp] Writing notebook server cookie secret to /home/devel/.local/share/jupyter/runtime/notebook_cookie_secret
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[...]
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or http://127.0.0.1:8888/?token=[...]
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```
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If everything goes well, Jupyter should appear, and if you open a notebook and execute the following code, it should show a GPU device in the list:
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```python
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import tensorflow as tf
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tf.config.list_physical_devices()
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```
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Lastly, to stop and destroy the container (but not the image), run:
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```bash
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$ docker stop handson-ml2
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$ docker rm handson-ml2
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```
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@ -3,7 +3,7 @@ services:
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handson-ml2:
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build:
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context: ../
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dockerfile: ./docker/Dockerfile
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dockerfile: ./docker/Dockerfile #Dockerfile.gpu
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args:
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- username=devel
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- userid=1000
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@ -20,3 +20,8 @@ services:
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volumes:
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- ../:/home/devel/handson-ml2
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command: /opt/conda/envs/tf2/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
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#deploy:
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# resources:
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# reservations:
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# devices:
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# - capabilities: [gpu]
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