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‎vendor/ngc-pytorch/Dockerfile.22.09-py3

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FROM nvcr.io/nvidia/pytorch:22.09-py3
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# NVIDIA NGC PyTorch with Python 3.8 (CONDA)
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ENV DEBIAN_FRONTEND=noninteractive \
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MPLBACKEND=Svg \
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PIP_IGNORE_INSTALLED=0 \
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PYTHONUNBUFFERED=1 \
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LD_LIBRARY_PATH="/usr/local/cuda/compat/lib:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib:/usr/local/cuda/lib64:/usr/local/nvidia/lib64:/usr/local/cuda-11.8/include:/usr/include/x86_64-linux-gnu:$LD_LIBRARY_PATH" \
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PATH="/usr/local/nvm/versions/node/v16.15.1/bin:/opt/conda/lib/python3.8/site-packages/torch_tensorrt/bin:/opt/conda/bin:/usr/local/mpi/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/ucx/bin:/opt/tensorrt/bin:/usr/local/src/lightgbm/LightGBM:/usr/local/bin/mecab" \
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mecab_dicdir=/usr/local/lib/mecab/dic/mecab-ko-dic \
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CPLUS_INCLUDE_PATH=/usr/include/gdal \
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C_INCLUDE_PATH=/usr/include/gdal \
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LANG=C.UTF-8
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RUN apt-get update && \
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apt-get install -y --no-install-recommends \
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automake \
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bzip2 \
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cabextract \
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ffmpeg \
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fonts-nanum \
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fonts-nanum-coding \
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fonts-nanum-extra \
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gfortran \
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htop \
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libasound2-dev \
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libatlas-base-dev \
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libavresample-dev \
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libdc1394-22-dev \
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libeigen3-dev \
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libfaac-dev \
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libgdal-dev \
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libgflags-dev \
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libgoogle-glog-dev \
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libgphoto2-dev \
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libgstreamer-plugins-base1.0-dev \
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libgstreamer1.0-dev \
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libgtk-3-dev \
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libhdf5-dev \
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libjemalloc-dev \
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libmp3lame-dev \
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libncurses-dev \
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libopenblas-dev \
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libopencore-amrnb-dev \
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libopencore-amrwb-dev \
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libprotobuf-dev \
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libtheora-dev \
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libvorbis-dev \
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libx264-dev \
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libxext6 \
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libxrender-dev \
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libxvidcore-dev \
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libsm6 \
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libtbb-dev \
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mercurial \
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ncurses-term \
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openjdk-8-jdk \
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pbzip2 \
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pv \
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pdsh \
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protobuf-compiler \
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v4l-utils \
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x264
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# Install CUDA + cuDNN
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RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.8.3.2 /usr/local/cuda/lib64/libcudnn.so && \
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rm -rf /var/lib/apt/lists/* && \
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ldconfig
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# Install cmake
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RUN wget https://github.com/Kitware/CMake/releases/download/v3.25.1/cmake-3.25.1-Linux-x86_64.sh \
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-q -O /tmp/cmake-install.sh && \
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chmod u+x /tmp/cmake-install.sh && \
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mkdir /usr/bin/cmake && \
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/tmp/cmake-install.sh --skip-license --prefix=/usr/bin/cmake && \
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rm /tmp/cmake-install.sh
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RUN update-alternatives --install /opt/conda/bin/python python /opt/conda/bin/python3 2
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# install NLP packages *mecab-ko & khai*
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WORKDIR /tmp
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RUN curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz && \
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tar zxfv mecab-0.996-ko-0.9.2.tar.gz && \
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cd mecab-0.996-ko-0.9.2 && \
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./configure && \
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make -j$(nproc) && \
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make check && \
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make install
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RUN echo "Install mecab-ko-dic" && \
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cd /tmp && \
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ldconfig && \
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curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz && \
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tar -zxvf mecab-ko-dic-2.1.1-20180720.tar.gz && \
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cd mecab-ko-dic-2.1.1-20180720 && \
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./autogen.sh && \
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./configure && \
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make -j$(nproc) && \
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sh -c 'echo "dicdir=/usr/local/lib/mecab/dic/mecab-ko-dic" > /usr/local/etc/mecabrc' && \
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make install && \
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cd /tmp && \
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git clone https://bitbucket.org/eunjeon/mecab-python-0.996.git && \
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python3 -m pip install /tmp/mecab-python-0.996
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WORKDIR /tmp
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RUN git clone -q --branch=v0.3.20 https://github.com/xianyi/OpenBLAS.git && \
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cd OpenBLAS && \
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make DYNAMIC_ARCH=1 NO_AFFINITY=1 NUM_THREADS=48 FC=gfortran && \
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make install && \
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cd /tmp && \
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git clone --recursive https://github.com/bodono/scs-python.git && \
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cd /tmp/scs-python && \
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python setup.py install --scs --gpu
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RUN /opt/conda/bin/conda install -n base conda==4.13.0
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RUN /opt/conda/bin/conda install -c conda-forge opencv ffmpeg
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# install git-lfs
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WORKDIR /tmp
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RUN curl -sLO https://github.com/git-lfs/git-lfs/releases/download/v3.3.0/git-lfs-linux-amd64-v3.3.0.tar.gz && \
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tar -zxf git-lfs-linux-amd64-v3.3.0.tar.gz && \
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cd git-lfs-3.3.0 && \
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bash install.sh && \
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rm -rf /tmp/*
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WORKDIR /tmp
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RUN git clone https://github.com/aristocratos/bashtop.git && \
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cd bashtop && \
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make install
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RUN curl -fL https://github.com/cdr/code-server/releases/download/v4.9.1/code-server-4.9.1-linux-amd64.tar.gz \
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| tar -C /usr/local/lib -xz && \
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mv /usr/local/lib/code-server-4.9.1-linux-amd64 /usr/local/lib/code-server-4.9.1 && \
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ln -s /usr/local/lib/code-server-4.9.1/bin/code-server /usr/local/bin/code-server
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WORKDIR /tmp
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COPY ./requirements.22.09.1.txt /tmp/requirements.txt
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RUN /opt/conda/bin/python3 -m pip install --no-cache-dir -r requirements.txt
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COPY ./requirements.22.09.2.txt /tmp/requirements.txt
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RUN /opt/conda/bin/python3 -m pip install --no-cache-dir -r requirements.txt
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COPY ./requirements.22.09.3.txt /tmp/requirements.txt
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RUN /opt/conda/bin/python3 -m pip install --no-cache-dir -r requirements.txt
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COPY ./requirements.22.09.4.txt /tmp/requirements.txt
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RUN /opt/conda/bin/python3 -m pip install --no-cache-dir -r requirements.txt
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COPY ./requirements.22.09.5.txt /tmp/requirements.txt
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RUN /opt/conda/bin/python3 -m pip install --no-cache-dir -r requirements.txt && \
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rm -f /tmp/*.whl /tmp/requirem*
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# Install Open MPI
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RUN mkdir /tmp/openmpi && \
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cd /tmp/openmpi && \
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wget https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-4.1.4.tar.gz && \
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tar zxf openmpi-4.1.4.tar.gz && \
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cd openmpi-4.1.4 && \
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./configure --enable-orterun-prefix-by-default && \
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make -j $(nproc) all && \
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make install && \
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ldconfig && \
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rm -rf /tmp/openmpi* && \
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# Create a wrapper for OpenMPI to allow running as root by default
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mv /usr/local/bin/mpirun /usr/local/bin/mpirun.real && \
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echo '#!/bin/bash' > /usr/local/bin/mpirun && \
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echo 'mpirun.real --allow-run-as-root "$@"' >> /usr/local/bin/mpirun && \
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chmod a+x /usr/local/bin/mpirun && \
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# Configure OpenMPI to run good defaults:
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echo "btl_tcp_if_exclude = lo,docker0" >> /usr/local/etc/openmpi-mca-params.conf
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# Install Horovod, temporarily using CUDA stubs
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RUN ldconfig /usr/local/cuda/targets/x86_64-linux/lib/stubs && \
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LD_LIBRARY_PATH="/usr/local/cuda/compat/lib:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib:/usr/local/cuda/lib64:/usr/local/nvidia/lib64:/usr/local/cuda-11.8/include:/usr/include/x86_64-linux-gnu:/usr/include:$LD_LIBRARY_PATH" \
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HOROVOD_NCCL_LINK=SHARED \
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HOROVOD_WITH_MPI=1 \
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HOROVOD_GPU_ALLREDUCE=NCCL \
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NCCL_LIBRARY=/usr/include \
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HOROVOD_NCCL_INCLUDE=/usr/include \
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HOROVOD_NCCL_LIB=/lib/x86_64-linux-gnu \
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HOROVOD_GPU_BROADCAST=NCCL \
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HOROVOD_WITHOUT_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 \
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pip install --no-cache-dir horovod==0.24.2 && \
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ldconfig
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RUN python3 -m pip install --no-cache-dir \
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mpi4py==3.1.3 \
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nni==2.9 \
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mlflow==1.29.0 \
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scikit-nni==0.2.1
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RUN jupyter nbextensions_configurator enable && \
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jupyter contrib nbextension install && \
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jupyter nbextension enable --py --sys-prefix widgetsnbextension && \
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jupyter serverextension enable --py jupyterlab --sys-prefix && \
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jupyter labextension install --no-build @jupyter-widgets/jupyterlab-manager && \
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jupyter labextension install --no-build @krassowski/jupyterlab-lsp && \
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jupyter serverextension enable --py jupyter_lsp && \
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jupyter nbextension enable execute_time/ExecuteTime && \
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jupyter nbextension enable toc2/main && \
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jupyter lab build
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RUN apt autoclean && \
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sed -i 's/source \/usr\/local\/nvm\/nvm.sh//' /etc/bash.bashrc && \
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ln -sf /usr/share/terminfo/x/xterm-color /usr/share/terminfo/x/xterm-256color && \
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rm -rf /var/lib/apt/lists/* && \
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rm -rf /root/.cache && \
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rm -rf /tmp/*
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RUN /opt/conda/bin/python3 -m ipykernel install \
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--prefix=/opt/conda/ \
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--display-name "PyTorch 1.13 (NGC 22.09/Python 3.8 Conda) on Backend.AI" && \
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cat /opt/conda/share/jupyter/kernels/python3/kernel.json
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ENV JUPYTER_PATH="/opt/conda/share/jupyter"
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# Backend.AI specifics
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COPY ./service-defs /etc/backend.ai/service-defs
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COPY ./runner-scripts/bootstrap.sh runner-scripts/setup_multinode.py /opt/container/
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LABEL ai.backend.kernelspec="1" \
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ai.backend.envs.corecount="OPENBLAS_NUM_THREADS,OMP_NUM_THREADS,NPROC" \
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ai.backend.features="batch query uid-match user-input" \
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ai.backend.base-distro="ubuntu16.04" \
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ai.backend.accelerators="cuda" \
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ai.backend.resource.min.cpu="1" \
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ai.backend.resource.min.mem="1g" \
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ai.backend.resource.min.cuda.device=1 \
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ai.backend.resource.min.cuda.shares=0 \
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ai.backend.base-distro="ubuntu16.04" \
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ai.backend.runtime-type="python" \
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ai.backend.runtime-path="/opt/conda/bin/python3" \
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ai.backend.service-ports="ipython:pty:3000,jupyter:http:8091,jupyterlab:http:8090,vscode:http:8180,tensorboard:http:6006,mlflow-ui:preopen:5000,nniboard:preopen:8080"
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WORKDIR /home/work

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