mirror of
https://github.com/jupyter/docker-stacks.git
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37 lines
1.4 KiB
Docker
37 lines
1.4 KiB
Docker
# Copyright (c) Jupyter Development Team.
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# Distributed under the terms of the Modified BSD License.
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ARG REGISTRY=quay.io
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ARG OWNER=jupyter
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ARG BASE_CONTAINER=$REGISTRY/$OWNER/scipy-notebook
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FROM $BASE_CONTAINER
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LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
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# Fix: https://github.com/hadolint/hadolint/wiki/DL4006
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# Fix: https://github.com/koalaman/shellcheck/wiki/SC3014
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SHELL ["/bin/bash", "-o", "pipefail", "-c"]
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# Install TensorFlow, CUDA and cuDNN with pip
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RUN pip install --no-cache-dir \
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"jupyter-server-proxy" \
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"tensorflow[and-cuda]" && \
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fix-permissions "${CONDA_DIR}" && \
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fix-permissions "/home/${NB_USER}"
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COPY --chown="${NB_UID}:${NB_GID}" 20tensorboard-proxy-env.sh /usr/local/bin/before-notebook.d/
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# workaround for https://github.com/tensorflow/tensorflow/issues/63362
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RUN mkdir -p "${CONDA_DIR}/etc/conda/activate.d/" && \
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fix-permissions "${CONDA_DIR}"
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COPY --chown="${NB_UID}:${NB_GID}" nvidia-lib-dirs.sh "${CONDA_DIR}/etc/conda/activate.d/"
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# https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html#dockerfiles
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ENV NVIDIA_VISIBLE_DEVICES="all" \
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NVIDIA_DRIVER_CAPABILITIES="compute,utility"
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# Puts the nvidia-smi binary (system management interface) on path
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# with associated library files to execute it
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ENV PATH="${PATH}:/usr/local/nvidia/bin" \
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LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/nvidia/lib64"
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