feat: update notebook server images (kubeflow/kubeflow#7687)
Signed-off-by: Mathew Wicks <5735406+thesuperzapper@users.noreply.github.com>
This commit is contained in:
Mathew Wicks
GitHub
parent
95f002b99f
commit
d4affd429f
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@ -39,8 +39,8 @@ ENV S6_BEHAVIOUR_IF_STAGE2_FAILS 2
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# args - software versions
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# https://kubernetes.io/releases/
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# https://github.com/just-containers/s6-overlay/releases
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ARG KUBECTL_VERSION=v1.29.9
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ARG S6_VERSION=v3.2.0.0
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ARG KUBECTL_VERSION=v1.31.6
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ARG S6_VERSION=v3.2.0.2
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# set shell to bash
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SHELL ["/bin/bash", "-c"]
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@ -10,11 +10,15 @@ ARG TARGETARCH
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USER root
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# args - software versions
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# https://github.com/microsoft/vscode-python/releases
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ARG CODESERVER_PYTHON_VERSION=2024.14.1
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ARG MINIFORGE_VERSION=24.7.1-2
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ARG PIP_VERSION=24.2
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ARG PYTHON_VERSION=3.11.10
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# https://open-vsx.org/extension/ms-python/python
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# https://open-vsx.org/extension/ms-toolsai/jupyter
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# https://github.com/ipython/ipykernel/releases
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ARG CODESERVER_PYTHON_VERSION=2025.0.0
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ARG CODESERVER_JUPYTER_VERSION=2024.11.0
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ARG IPYKERNEL_VERSION=6.29.5
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ARG MINIFORGE_VERSION=24.11.3-0
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ARG PIP_VERSION=24.3.1
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ARG PYTHON_VERSION=3.11.11
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# setup environment for conda
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ENV CONDA_DIR /opt/conda
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@ -52,6 +56,13 @@ RUN case "${TARGETARCH}" in \
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&& conda update -y -q --all \
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&& conda clean -a -f -y
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# install - ipykernel
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# NOTE: we need this for jupyter codeserver extension to work
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RUN echo "ipykernel ==${IPYKERNEL_VERSION}" >> ${CONDA_DIR}/conda-meta/pinned \
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&& conda install -y -q \
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ipykernel==${IPYKERNEL_VERSION} \
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&& conda clean -a -f -y
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# install - requirements.txt
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COPY --chown=${NB_USER}:${NB_GID} requirements.txt /tmp
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RUN python3 -m pip install -r /tmp/requirements.txt --quiet --no-cache-dir \
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@ -59,8 +70,12 @@ RUN python3 -m pip install -r /tmp/requirements.txt --quiet --no-cache-dir \
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# install - codeserver extensions
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RUN code-server --install-extension "ms-python.python@${CODESERVER_PYTHON_VERSION}" --force \
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&& code-server --install-extension "ms-toolsai.jupyter@${CODESERVER_JUPYTER_VERSION}" --force \
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&& code-server --list-extensions --show-versions
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# home - pre-populate home with files for this image
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COPY --chown=${NB_USER}:${NB_GID} home/. ${HOME}/
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# s6 - 01-copy-tmp-home
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# NOTE: the contents of $HOME_TMP are copied to $HOME at runtime
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# this is a workaround because a PVC will be mounted at $HOME
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@ -0,0 +1,13 @@
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# About the Code-Server Python Image
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This file contains notes about the Kubeflow Notebooks _Code-Server Python_ image.
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## Jupyter Extension required HTTPS
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This image comes with the jupyter extension installed, which allows you to run and edit jupyter notebooks in code-server.
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However, because the jupyter extension uses [Service Workers](https://developer.mozilla.org/en-US/docs/Web/API/Service_Worker_API), it requires HTTPS to work.
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That is, if you access this notebook over HTTP, the Jupyter extension will NOT work.
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Additionally, if you are using __Chrome__, the HTTPS certificate must be __valid__ and trusted by your browser.
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As a workaround, if you have a self-signed HTTPS certificate, you could use Firefox, or set the [`unsafely-treat-insecure-origin-as-secure`](chrome://flags/#unsafely-treat-insecure-origin-as-secure) flag in Chrome.
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@ -1,2 +1,2 @@
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# kubeflow packages
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kfp==2.9.0
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kfp==2.11.0
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@ -9,7 +9,7 @@ ARG TARGETARCH
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# args - software versions
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# https://github.com/coder/code-server/releases
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ARG CODESERVER_VERSION=4.93.1
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ARG CODESERVER_VERSION=4.96.4
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USER root
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@ -1,6 +1,13 @@
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#!/command/with-contenv bash
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cd "${HOME}"
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# many tools default to using the home directory for runtime data.
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# as we mount a PVC to the home directory, these files might have the wrong permissions.
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# to avoid this, we set the runtime directory to a path that is NOT a persistent volume.
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export XDG_RUNTIME_DIR="/tmp/runtime-${NB_USER}"
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mkdir -p "${XDG_RUNTIME_DIR}"
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chmod 700 "${XDG_RUNTIME_DIR}"
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cd "${HOME}" || { echo "ERROR: failed to cd to ${HOME}"; exit 1; }
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echo "INFO: starting code-server..."
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exec 2>&1
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exec /usr/bin/code-server \
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@ -8,18 +8,18 @@ FROM $BASE_IMG
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# install - conda packages
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# NOTE: we use mamba to speed things up
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RUN mamba install -y -q \
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bokeh==3.3.4 \
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cloudpickle==2.2.1 \
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dill==0.3.8 \
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ipympl==0.9.4 \
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matplotlib==3.8.4 \
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numpy==1.24.4 \
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pandas==2.1.4 \
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scikit-image==0.22.0 \
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scikit-learn==1.3.2 \
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scipy==1.11.3 \
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bokeh==3.6.3 \
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cloudpickle==3.1.1 \
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dill==0.3.9 \
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ipympl==0.9.6 \
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matplotlib==3.10.0 \
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numpy==1.26.4 \
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pandas==2.2.3 \
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scikit-image==0.25.1 \
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scikit-learn==1.6.1 \
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scipy==1.15.1 \
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seaborn==0.13.2 \
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xgboost==1.7.6 \
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xgboost==2.1.4 \
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&& mamba clean -a -f -y
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# install - requirements.txt
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@ -1,5 +1,5 @@
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# kubeflow packages
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kfp==2.9.0
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kfp==2.11.0
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# jupyterlab extensions
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jupyterlab-git==0.50.1
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jupyterlab-git==0.50.2
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@ -9,18 +9,23 @@ FROM $BASE_IMG
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# https://github.com/pytorch/pytorch/releases
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# https://github.com/pytorch/audio/releases
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# https://github.com/pytorch/vision/releases
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ARG PYTORCH_VERSION=2.3.1
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ARG TORCHAUDIO_VERSION=2.3.1
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ARG TORCHVISION_VERSION=0.18.1
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# NOTE: correct version alignments are found at https://pytorch.org/get-started/previous-versions/
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# they sometimes forget to publish a github release of `torchaudio` or `torchvision` when the cut a
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# patch release of `torch`, but they still publish a patch of `torchaudio` and `torchvision` on pypi
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# TODO: it seems like torch 2.6.0+ with CUDA 12.6+ will have experimental aarch64 support
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# when updating past that version, remember to test building these images on arm
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ARG PYTORCH_VERSION=2.5.1
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ARG TORCHAUDIO_VERSION=2.5.1
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ARG TORCHVISION_VERSION=0.20.1
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# nvidia container toolkit
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# https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html
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ENV NVIDIA_VISIBLE_DEVICES all
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ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
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ENV NVIDIA_REQUIRE_CUDA "cuda>=12.1"
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ENV NVIDIA_REQUIRE_CUDA "cuda>=12.4"
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# install - pytorch (cuda)
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RUN python3 -m pip install --quiet --no-cache-dir --index-url https://download.pytorch.org/whl/cu121 \
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RUN python3 -m pip install --quiet --no-cache-dir --index-url https://download.pytorch.org/whl/cu124 \
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torch==${PYTORCH_VERSION} \
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torchaudio==${TORCHAUDIO_VERSION} \
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torchvision==${TORCHVISION_VERSION}
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@ -8,18 +8,18 @@ FROM $BASE_IMG
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# install - conda packages
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# NOTE: we use mamba to speed things up
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RUN mamba install -y -q \
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bokeh==3.3.4 \
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cloudpickle==2.2.1 \
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dill==0.3.8 \
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ipympl==0.9.4 \
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matplotlib==3.8.4 \
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numpy==1.24.4 \
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pandas==2.1.4 \
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scikit-image==0.22.0 \
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scikit-learn==1.3.2 \
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scipy==1.11.3 \
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bokeh==3.6.3 \
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cloudpickle==3.1.1 \
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dill==0.3.9 \
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ipympl==0.9.6 \
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matplotlib==3.10.0 \
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numpy==1.26.4 \
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pandas==2.2.3 \
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scikit-image==0.25.1 \
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scikit-learn==1.6.1 \
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scipy==1.15.1 \
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seaborn==0.13.2 \
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xgboost==1.7.6 \
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xgboost==2.1.4 \
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&& mamba clean -a -f -y
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# install - requirements.txt
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@ -1,5 +1,5 @@
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# kubeflow packages
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kfp==2.9.0
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kfp==2.11.0
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# jupyterlab extensions
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jupyterlab-git==0.50.1
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jupyterlab-git==0.50.2
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@ -8,18 +8,18 @@ FROM $BASE_IMG
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# install - conda packages
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# NOTE: we use mamba to speed things up
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RUN mamba install -y -q \
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bokeh==3.3.4 \
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cloudpickle==2.2.1 \
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dill==0.3.8 \
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ipympl==0.9.4 \
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matplotlib==3.8.4 \
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numpy==1.24.4 \
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pandas==2.1.4 \
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scikit-image==0.22.0 \
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scikit-learn==1.3.2 \
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scipy==1.11.3 \
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bokeh==3.6.3 \
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cloudpickle==3.1.1 \
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dill==0.3.9 \
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ipympl==0.9.6 \
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matplotlib==3.10.0 \
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numpy==1.26.4 \
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pandas==2.2.3 \
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scikit-image==0.25.1 \
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scikit-learn==1.6.1 \
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scipy==1.15.1 \
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seaborn==0.13.2 \
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xgboost==1.7.6 \
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xgboost==2.1.4 \
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&& mamba clean -a -f -y
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# install - requirements.txt
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@ -1,5 +1,5 @@
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# kubeflow packages
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kfp==2.9.0
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kfp==2.11.0
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# jupyterlab extensions
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jupyterlab-git==0.50.1
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jupyterlab-git==0.50.2
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@ -164,8 +164,8 @@ COPY --chown=${NB_USER}:users requirements.txt /tmp
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RUN python3 -m pip install -r /tmp/requirements.txt --quiet --no-cache-dir \
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&& rm -f /tmp/requirements.txt
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# install - note about Gaudi workloads and hugepages
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COPY README.md "${HOME}"
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# home - pre-populate home with files for this image
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COPY --chown=${NB_USER}:${NB_GID} home/. ${HOME}/
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# s6 - 01-copy-tmp-home
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# NOTE: the contents of $HOME_TMP are copied to $HOME at runtime
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@ -1,9 +0,0 @@
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# A note about Gaudi workloads
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Some Gaudi workloads require hugepages to run correctly. One can find a list of workloads listed [here](https://console.cloud.intel.com/docs/guides/k8s_guide.html#hugepages-settings-by-model). As of now, Kubeflow Notebooks lack the capability to request hugepages resources for individual notebooks.
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To add hugepages resources for the Notebook Pod, there are a couple of options:
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* Use [Limit Ranges](https://kubernetes.io/docs/concepts/policy/limit-range/) to set default hugepages amount for all Pods in the namespace.
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* Edit the Kubeflow's notebook object after it has been created and add the hugepages resources to the pod template.
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The upcoming Notebooks 2.0 release will improve the situation with its `podTemplate` feature.
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@ -0,0 +1,15 @@
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# About the PyTorch Gaudi Image
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This file contains notes about the Kubeflow Notebooks _PyTorch Gaudi_ image.
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## Hugepages Support
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Some Gaudi workloads require hugepages to run correctly.
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One can find a list of workloads listed [here](https://console.cloud.intel.com/docs/guides/k8s_guide.html#hugepages-settings-by-model).
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As of now, Kubeflow Notebooks lacks the capability to request hugepages resources for individual notebooks.
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To add hugepages resources for the Notebook Pod, there are a couple of options:
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* Use [Limit Ranges](https://kubernetes.io/docs/concepts/policy/limit-range/) to set a default hugepages amount for all Pods in the namespace.
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* Edit the Notebook object after it has been created and add the hugepages resources to the pod template.
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The upcoming Notebooks 2.0 release will improve the situation with its `podTemplate` feature.
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@ -9,9 +9,12 @@ FROM $BASE_IMG
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# https://github.com/pytorch/pytorch/releases
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# https://github.com/pytorch/audio/releases
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# https://github.com/pytorch/vision/releases
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ARG PYTORCH_VERSION=2.3.1
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ARG TORCHAUDIO_VERSION=2.3.1
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ARG TORCHVISION_VERSION=0.18.1
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# NOTE: correct version alignments are found at https://pytorch.org/get-started/previous-versions/
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# they sometimes forget to publish a github release of `torchaudio` or `torchvision` when the cut a
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# patch release of `torch`, but they still publish a patch of `torchaudio` and `torchvision` on pypi
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ARG PYTORCH_VERSION=2.5.1
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ARG TORCHAUDIO_VERSION=2.5.1
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ARG TORCHVISION_VERSION=0.20.1
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# install - pytorch (cpu)
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RUN python3 -m pip install --quiet --no-cache-dir --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple \
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@ -22,32 +22,34 @@ USER $NB_UID
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# NOTE: we use mamba to speed things up
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RUN mamba install -y -q \
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altair \
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beautifulsoup4==4.12.3 \
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bokeh==3.3.4 \
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beautifulsoup4==4.13.3 \
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bokeh==3.6.3 \
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bottleneck \
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brotli \
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cloudpickle \
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dask==2024.5.1 \
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dask==2024.12.1 \
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dill \
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h5py \
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ipympl \
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matplotlib==3.8.4 \
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matplotlib==3.10.0 \
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numba \
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numexpr \
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openblas==0.3.25 \
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numpy==1.26.4 \
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openblas==0.3.28 \
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openpyxl \
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pandas==2.1.4 \
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pandas==2.2.3 \
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patsy \
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protobuf \
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pytables==3.9.2 \
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scikit-image==0.22.0 \
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scikit-learn==1.3.2 \
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scipy==1.11.3 \
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pytables==3.10.2 \
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scikit-image==0.25.1 \
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scikit-learn==1.6.1 \
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scipy==1.15.1 \
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seaborn==0.13.2 \
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sqlalchemy==2.0.30 \
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sqlalchemy==2.0.38 \
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statsmodels \
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sympy \
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vincent \
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xgboost==2.1.4 \
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xlrd \
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&& mamba clean -a -f -y
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@ -1,5 +1,5 @@
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# kubeflow packages
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kfp==2.9.0
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kfp==2.11.0
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# jupyterlab extensions
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jupyterlab-git==0.50.1
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jupyterlab-git==0.50.2
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@ -8,18 +8,18 @@ FROM $BASE_IMG
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# install - conda packages
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# NOTE: we use mamba to speed things up
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RUN mamba install -y -q \
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bokeh==3.3.4 \
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cloudpickle==2.2.1 \
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dill==0.3.8 \
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ipympl==0.9.4 \
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matplotlib==3.8.4 \
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numpy==1.24.4 \
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pandas==2.1.4 \
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scikit-image==0.22.0 \
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scikit-learn==1.3.2 \
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scipy==1.11.3 \
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bokeh==3.6.3 \
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cloudpickle==3.1.1 \
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dill==0.3.9 \
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ipympl==0.9.6 \
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matplotlib==3.10.0 \
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numpy==1.26.4 \
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pandas==2.2.3 \
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scikit-image==0.25.1 \
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scikit-learn==1.6.1 \
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scipy==1.15.1 \
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seaborn==0.13.2 \
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xgboost==1.7.6 \
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xgboost==2.1.4 \
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&& mamba clean -a -f -y
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# install - requirements.txt
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|
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@ -1,5 +1,5 @@
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# kubeflow packages
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kfp==2.9.0
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kfp==2.11.0
|
||||
|
||||
# jupyterlab extensions
|
||||
jupyterlab-git==0.50.1
|
||||
jupyterlab-git==0.50.2
|
||||
|
|
@ -10,10 +10,10 @@ ARG TARGETARCH
|
|||
# args - software versions
|
||||
# - TensorFlow CUDA version matrix: https://www.tensorflow.org/install/source#gpu
|
||||
# - Extra PyPi from NVIDIA (for TensorRT): https://pypi.nvidia.com/
|
||||
# - TODO: it seems like TensorRT will be removed from TensorFlow in 2.18.0
|
||||
# when updating past that version, remember to remove all TensorRT
|
||||
# related packages and configs in this Dockerfile
|
||||
ARG TENSORFLOW_VERSION=2.15.1
|
||||
# - TODO: TensorRT will be removed from TensorFlow in 2.18.0
|
||||
# when updating past that version, either remove all TensorRT related packages,
|
||||
# or investigate if there is a way to keep TensorRT support
|
||||
ARG TENSORFLOW_VERSION=2.17.1
|
||||
ARG TENSORRT_VERSION=8.6.1.post1
|
||||
ARG TENSORRT_LIBS_VERSION=8.6.1
|
||||
ARG TENSORRT_BINDINGS_VERSION=8.6.1
|
||||
|
|
@ -22,10 +22,12 @@ ARG TENSORRT_BINDINGS_VERSION=8.6.1
|
|||
# https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html
|
||||
ENV NVIDIA_VISIBLE_DEVICES all
|
||||
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
|
||||
ENV NVIDIA_REQUIRE_CUDA "cuda>=12.2"
|
||||
ENV NVIDIA_REQUIRE_CUDA "cuda>=12.3"
|
||||
|
||||
# install - tensorflow
|
||||
# - About '[and-cuda]' option: https://github.com/tensorflow/tensorflow/blob/v2.15.1/tensorflow/tools/pip_package/setup.py#L166-L177
|
||||
# - About '[and-cuda]' option: https://github.com/tensorflow/tensorflow/blob/v2.17.1/tensorflow/tools/pip_package/setup.py#L153-L164
|
||||
# - TODO: when updating TensorRT, you might need to change `tensorrt`, `tensorrt-libs`, and `tensorrt-bindings`
|
||||
# to `tensorrt-cu12`, `tensorrt-cu12-libs`, and `tensorrt-cu12-bindings` respectively
|
||||
RUN python3 -m pip install --quiet --no-cache-dir --extra-index-url https://pypi.nvidia.com \
|
||||
tensorflow[and-cuda]==${TENSORFLOW_VERSION} \
|
||||
tensorrt==${TENSORRT_VERSION} \
|
||||
|
|
@ -34,8 +36,9 @@ RUN python3 -m pip install --quiet --no-cache-dir --extra-index-url https://pypi
|
|||
|
||||
# create symlinks for TensorRT libs
|
||||
# - https://github.com/tensorflow/tensorflow/issues/61986#issuecomment-1880489731
|
||||
# - libnvinfer.so.8.6.1 -> libnvinfer.so.8
|
||||
# - libnvinfer_plugin.so.8.6.1 -> libnvinfer_plugin.so.8
|
||||
# - We are creating symlinks for the following libs, as this is where TF looks for them:
|
||||
# - libnvinfer.so.8.6.1 -> libnvinfer.so.8
|
||||
# - libnvinfer_plugin.so.8.6.1 -> libnvinfer_plugin.so.8
|
||||
ENV PYTHON_SITE_PACKAGES /opt/conda/lib/python3.11/site-packages
|
||||
ENV TENSORRT_LIBS ${PYTHON_SITE_PACKAGES}/tensorrt_libs
|
||||
RUN ln -s ${TENSORRT_LIBS}/libnvinfer.so.${TENSORRT_LIBS_VERSION%%.*} ${TENSORRT_LIBS}/libnvinfer.so.${TENSORRT_LIBS_VERSION} \
|
||||
|
|
|
|||
|
|
@ -8,18 +8,18 @@ FROM $BASE_IMG
|
|||
# install - conda packages
|
||||
# NOTE: we use mamba to speed things up
|
||||
RUN mamba install -y -q \
|
||||
bokeh==3.3.4 \
|
||||
cloudpickle==2.2.1 \
|
||||
dill==0.3.8 \
|
||||
ipympl==0.9.4 \
|
||||
matplotlib==3.8.4 \
|
||||
numpy==1.24.4 \
|
||||
pandas==2.1.4 \
|
||||
scikit-image==0.22.0 \
|
||||
scikit-learn==1.3.2 \
|
||||
scipy==1.11.3 \
|
||||
bokeh==3.6.3 \
|
||||
cloudpickle==3.1.1 \
|
||||
dill==0.3.9 \
|
||||
ipympl==0.9.6 \
|
||||
matplotlib==3.10.0 \
|
||||
numpy==1.26.4 \
|
||||
pandas==2.2.3 \
|
||||
scikit-image==0.25.1 \
|
||||
scikit-learn==1.6.1 \
|
||||
scipy==1.15.1 \
|
||||
seaborn==0.13.2 \
|
||||
xgboost==1.7.6 \
|
||||
xgboost==2.1.4 \
|
||||
&& mamba clean -a -f -y
|
||||
|
||||
# install - requirements.txt
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
# kubeflow packages
|
||||
kfp==2.9.0
|
||||
kfp==2.11.0
|
||||
|
||||
# jupyterlab extensions
|
||||
jupyterlab-git==0.50.1
|
||||
jupyterlab-git==0.50.2
|
||||
|
|
@ -7,14 +7,7 @@ FROM $BASE_IMG
|
|||
|
||||
# args - software versions
|
||||
# https://github.com/tensorflow/tensorflow/releases
|
||||
ARG TENSORFLOW_VERSION=2.15.1
|
||||
|
||||
# install - h5py
|
||||
# TODO: remove when upstream ARM wheels for `h5py` are available again:
|
||||
# https://github.com/h5py/h5py/issues/2408
|
||||
RUN mamba install -y -q \
|
||||
h5py==3.11.0 \
|
||||
&& mamba clean -a -f -y
|
||||
ARG TENSORFLOW_VERSION=2.17.1
|
||||
|
||||
# install - tensorflow
|
||||
RUN python3 -m pip install --quiet --no-cache-dir \
|
||||
|
|
|
|||
|
|
@ -12,12 +12,12 @@ USER root
|
|||
# args - software versions
|
||||
# https://github.com/jupyterlab/jupyterlab/releases
|
||||
# https://github.com/jupyter/notebook/releases
|
||||
ARG JUPYTERLAB_VERSION=4.2.5
|
||||
ARG JUPYTER_VERSION=7.2.2
|
||||
ARG MINIFORGE_VERSION=24.7.1-2
|
||||
ARG JUPYTERLAB_VERSION=4.3.5
|
||||
ARG JUPYTER_VERSION=7.3.2
|
||||
ARG MINIFORGE_VERSION=24.11.3-0
|
||||
ARG NODE_MAJOR_VERSION=20
|
||||
ARG PIP_VERSION=24.2
|
||||
ARG PYTHON_VERSION=3.11.10
|
||||
ARG PIP_VERSION=24.3.1
|
||||
ARG PYTHON_VERSION=3.11.11
|
||||
|
||||
# install -- node.js
|
||||
RUN export DEBIAN_FRONTEND=noninteractive \
|
||||
|
|
|
|||
|
|
@ -1,10 +1,18 @@
|
|||
#!/command/with-contenv bash
|
||||
|
||||
# the runtime directory must be a path that is NOT a persistent volume
|
||||
# many tools default to using the home directory for runtime data.
|
||||
# as we mount a PVC to the home directory, these files might have the wrong permissions.
|
||||
# to avoid this, we set the runtime directory to a path that is NOT a persistent volume.
|
||||
export XDG_RUNTIME_DIR="/tmp/runtime-${NB_USER}"
|
||||
mkdir -p "${XDG_RUNTIME_DIR}"
|
||||
chmod 700 "${XDG_RUNTIME_DIR}"
|
||||
|
||||
# the jupyter runtime directory must be a path that is NOT a persistent volume
|
||||
# as volumes often cause permission issues https://github.com/jupyter/notebook/issues/5058
|
||||
# note, jupyter ignores XDG_RUNTIME_DIR: https://github.com/jupyter/jupyter_core/pull/143
|
||||
export JUPYTER_RUNTIME_DIR="/tmp/jupyter_runtime"
|
||||
|
||||
cd "${HOME}"
|
||||
cd "${HOME}" || { echo "ERROR: failed to cd to ${HOME}"; exit 1; }
|
||||
echo "INFO: starting jupyter..."
|
||||
exec 2>&1
|
||||
exec /opt/conda/bin/jupyter lab \
|
||||
|
|
|
|||
|
|
@ -8,9 +8,9 @@ FROM $BASE_IMG
|
|||
ARG TARGETARCH
|
||||
|
||||
# args - software versions (python)
|
||||
ARG MINIFORGE_VERSION=24.7.1-2
|
||||
ARG PIP_VERSION=24.2
|
||||
ARG PYTHON_VERSION=3.11.10
|
||||
ARG MINIFORGE_VERSION=24.11.3-0
|
||||
ARG PIP_VERSION=24.3.1
|
||||
ARG PYTHON_VERSION=3.11.11
|
||||
|
||||
# args - software versions (R)
|
||||
# - rstudio: https://dailies.rstudio.com/release/
|
||||
|
|
@ -18,11 +18,11 @@ ARG PYTHON_VERSION=3.11.10
|
|||
# - r-png: https://www.rforge.net/png/news.html
|
||||
# - r-reticulate: https://github.com/rstudio/reticulate/releases
|
||||
# - r-shiny: https://github.com/rstudio/shiny/releases
|
||||
ARG RSTUDIO_VERSION=2024.09.0-375
|
||||
ARG R_BASE_VERSION=4.4.1
|
||||
ARG RSTUDIO_VERSION=2024.12.1-563
|
||||
ARG R_BASE_VERSION=4.4.2
|
||||
ARG R_PNG_VERSION=0.1_8
|
||||
ARG R_RETICULATE_VERSION=1.39.0
|
||||
ARG R_SHINY_VERSION=1.9.1
|
||||
ARG R_RETICULATE_VERSION=1.40.0
|
||||
ARG R_SHINY_VERSION=1.10.0
|
||||
|
||||
USER root
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,12 @@
|
|||
#!/command/with-contenv bash
|
||||
|
||||
# many tools default to using the home directory for runtime data.
|
||||
# as we mount a PVC to the home directory, these files might have the wrong permissions.
|
||||
# to avoid this, we set the runtime directory to a path that is NOT a persistent volume.
|
||||
export XDG_RUNTIME_DIR="/tmp/runtime-${NB_USER}"
|
||||
mkdir -p "${XDG_RUNTIME_DIR}"
|
||||
chmod 700 "${XDG_RUNTIME_DIR}"
|
||||
|
||||
# the runtime state directory must be a path that is NOT a persistent volume
|
||||
# note, this env-var is not read by rstudio, but is used later in this script
|
||||
export RSTUDIO_RUNTIME_DIR="/tmp/rstudio_runtime"
|
||||
|
|
|
|||
Loading…
Reference in New Issue