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Dockerfile_cuda110_cuml2208_lgbmgpu
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Dockerfile_cuda110_cuml2208_lgbmgpu
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######## build ###########################################################################
FROM nvidia/cuda:11.0.3-base-ubuntu18.04 as builder
ARG VER_CUDA=11.0
ARG VER_RAPIDS=22.08
ARG CONDA_INSTALLER=Miniconda3-latest-Linux-x86_64.sh
ARG CONDA_ROOT=/opt/miniconda3
ARG PYENV_ROOT=/opt/pyenv
ARG PIP_OPTS="--disable-pip-version-check --no-cache-dir"
# ARG PIP_OPTS="--disable-pip-version-check --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple/"
RUN echo building... \
&& cd /tmp/ \
&& mv /etc/apt/sources.list.d /etc/apt/sources.list.d.bak \
&& apt-get update \
&& apt-get install -y wget libxml2 ocl-icd-libopencl1 clinfo \
&& mkdir -p /etc/OpenCL/vendors \
&& echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd \
&& apt-get clean \
&& wget -q https://repo.anaconda.com/miniconda/$CONDA_INSTALLER \
&& sh $CONDA_INSTALLER -b -p $CONDA_ROOT \
&& $CONDA_ROOT/bin/conda init \
&& $CONDA_ROOT/bin/conda install conda-pack \
&& $CONDA_ROOT/bin/conda create -p $PYENV_ROOT -c rapidsai -c nvidia -c conda-forge python=3.8 cudatoolkit=$VER_CUDA cudatoolkit-dev=$VER_CUDA cudf=$VER_RAPIDS cuml=$VER_RAPIDS pynvml xgboost catboost python-geohash boost make cmake gcc gxx glib \
&& $CONDA_ROOT/bin/conda run -p $PYENV_ROOT pip install $PIP_OPTS lightgbm --install-option=--gpu \
&& $CONDA_ROOT/bin/conda run -p $PYENV_ROOT pip install $PIP_OPTS hypergbm[all] \
&& echo "created env: $PYENV_ROOT"
RUN echo packing... \
&& $CONDA_ROOT/bin/conda pack -p $PYENV_ROOT -o $PYENV_ROOT.tar -d $PYENV_ROOT\
&& mkdir /opt/foo \
&& tar xf $PYENV_ROOT.tar -C /opt/foo \
&& mkdir -p /opt/foo/pkgs \
&& cp -r $PYENV_ROOT/pkgs/cuda-toolkit/targets/x86_64-linux /opt/foo/pkgs/cuda-toolkit \
&& cd opt/foo/pkgs/cuda-toolkit \
&& ln -s lib lib64 \
&& echo done
# && echo conda activate hypergbm >> /root/.bashrc \
# && $CONDA_ROOT/bin/conda clean -a -y -q
######## install ###########################################################################
FROM nvidia/cuda:11.0.3-base-ubuntu18.04
ARG PYENV_ROOT=/opt/pyenv
ARG DC_ROOT=/opt/datacanvas
COPY --from=builder /opt/foo $PYENV_ROOT
#COPY --from=builder $PYENV_ROOT/pkgs $PYENV_ROOT/pkgs
RUN echo installing... \
&& mv /etc/apt/sources.list.d /etc/apt/sources.list.d.bak \
&& apt-get update \
&& apt-get install -y ocl-icd-libopencl1 clinfo \
&& mkdir -p /etc/OpenCL/vendors \
&& echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd \
&& apt-get clean \
&& echo source $PYENV_ROOT/bin/activate >> /root/.bashrc \
&& mkdir -p /opt/datacanvas \
&& cp -r $PYENV_ROOT/lib/python3.8/site-packages/hypergbm/examples $DC_ROOT/ \
&& echo "#!/bin/bash\n" > /entrypoint.sh \
&& echo "source $PYENV_ROOT/bin/activate" >> /entrypoint.sh \
&& echo "cd $DC_ROOT" >> /entrypoint.sh \
&& echo "jupyter lab --notebook-dir=$DC_ROOT --ip=0.0.0.0 --port=\$NotebookPort --no-browser --allow-root --ServerApp.token=\$NotebookToken" >> /entrypoint.sh \
&& chmod +x /entrypoint.sh \
&& rm -rf /var/cache \
&& rm -rf /root/.cache \
&& rm -rf /tmp/*
EXPOSE 8888
ENV NotebookToken="" \
NotebookPort=8888
CMD ["/entrypoint.sh"]
# docker run --rm --name hypergbm -p 8830:8888 -e NotebookToken=your-token datacanvas/hypergbm