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‎.github/workflows/docker-gpu-image.yml

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name: GPU Image
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on:
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workflow_dispatch: null
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push:
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paths:
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- Dockerfile.gpu
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- rl-env.yml
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- spatial-env.yml
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- jupyter-ai.yml
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- install.r
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jobs:
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build:
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runs-on: ubuntu-latest
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permissions: write-all
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steps:
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# For biggish images, github actions runs out of disk space.
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# So we cleanup some unwanted things in the disk image, and reclaim that space for our docker use
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# https://github.com/actions/virtual-environments/issues/2606#issuecomment-772683150
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# and https://github.com/easimon/maximize-build-space/blob/b4d02c14493a9653fe7af06cc89ca5298071c66e/action.yml#L104
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# This gives us a total of about 52G of free space, which should be enough for now
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- name: cleanup disk space
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run: |
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sudo rm -rf /usr/local/lib/android /usr/share/dotnet /opt/ghc
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df -h
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- uses: actions/checkout@v3
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- name: Login to GitHub Container Registry
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if: github.repository == 'rocker-org/ml'
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uses: docker/login-action@v1
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with:
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registry: ghcr.io
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username: ${{github.actor}}
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password: ${{secrets.GITHUB_TOKEN}}
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- name: Build the Docker image
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if: github.repository == 'rocker-org/ml'
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run: docker build images/ --no-cache -f images/Dockerfile.cuda --tag ghcr.io/rocker/cuda:latest
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- name: Publish
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if: github.repository == 'rocker-org/ml'
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run: docker push ghcr.io/rocker/cuda:latest
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‎.github/workflows/docker-image.yml

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name: Docker Image CI
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on:
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workflow_dispatch: null
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push:
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paths: ['images/Dockerfile', 'images/install.r', 'images/rl-env.yml', 'images/spatial-env.yml', 'images/jupyter-ai.yml']
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jobs:
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build:
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runs-on: ubuntu-latest
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permissions: write-all
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steps:
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# For biggish images, github actions runs out of disk space.
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# So we cleanup some unwanted things in the disk image, and reclaim that space for our docker use
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# https://github.com/actions/virtual-environments/issues/2606#issuecomment-772683150
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# and https://github.com/easimon/maximize-build-space/blob/b4d02c14493a9653fe7af06cc89ca5298071c66e/action.yml#L104
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# This gives us a total of about 52G of free space, which should be enough for now
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- name: cleanup disk space
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run: |
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sudo rm -rf /usr/local/lib/android /usr/share/dotnet /opt/ghc
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df -h
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- uses: actions/checkout@v3
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- name: Login to GitHub Container Registry
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if: github.repository == 'boettiger-lab/k8s'
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uses: docker/login-action@v1
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with:
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registry: ghcr.io
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username: ${{github.actor}}
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password: ${{secrets.GITHUB_TOKEN}}
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- name: Build the Docker image
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if: github.repository == 'boettiger-lab/k8s'
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run: docker build images/ --tag ghcr.io/boettiger-lab/k8s:latest
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- name: Publish
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if: github.repository == 'boettiger-lab/k8s'
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run: docker push ghcr.io/boettiger-lab/k8s:latest
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‎Dockerfile

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ARG BASE=quay.io/jupyter/minimal-notebook:ubuntu-24.04
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FROM $BASE
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# Make code-server extensions etc persist to container, not hub
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ENV XDG_DATA_HOME=/opt/share
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USER root
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# code-server (VSCode)
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RUN curl -fsSL https://code-server.dev/install.sh | sh && rm -rf .cache
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# apt utilities, code-server setup
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RUN curl -s https://raw.githubusercontent.com/rocker-org/ml/refs/heads/main/install_utilities.sh | bash
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RUN curl -s https://raw.githubusercontent.com/rocker-org/ml/refs/heads/main/install_r.sh | bash
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RUN curl -s https://raw.githubusercontent.com/rocker-org/ml/refs/heads/main/install_rstudio.sh | bash
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## Add rstudio's binaries to path for quarto
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ENV PATH=$PATH:/usr/lib/rstudio-server/bin/quarto/bin
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## switch non-root users from BSPM to r-universe if no sudo
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COPY Rprofile /usr/lib/R/etc/Rprofile.site
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# When run as root, install.r automagically handles any necessary apt-gets
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COPY install.r install.r
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RUN Rscript install.r
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USER ${NB_USER}
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COPY vscode-extensions.txt vscode-extensions.txt
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RUN xargs -n 1 code-server --install-extension < vscode-extensions.txt
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COPY environment.yml environment.yml
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RUN conda update --all --solver=classic -n base -c conda-forge conda && \
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conda env update --file environment.yml
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‎Dockerfile.cuda

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ARG BASE=quay.io/jupyter/pytorch-notebook:cuda12-ubuntu-24.04
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FROM $BASE
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# Make code-server extensions etc persist to container, not hub
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ENV XDG_DATA_HOME=/opt/share
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USER root
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# code-server (VSCode)
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RUN curl -fsSL https://code-server.dev/install.sh | sh && rm -rf .cache
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# apt utilities, code-server setup
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RUN curl -s https://raw.githubusercontent.com/rocker-org/ml/refs/heads/main/install_utilities.sh | bash
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RUN curl -s https://raw.githubusercontent.com/rocker-org/ml/refs/heads/main/install_r.sh | bash
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RUN curl -s https://raw.githubusercontent.com/rocker-org/ml/refs/heads/main/install_rstudio.sh | bash
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## Add rstudio's binaries to path for quarto
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ENV PATH=$PATH:/usr/lib/rstudio-server/bin/quarto/bin
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## switch non-root users from BSPM to r-universe if no sudo
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COPY Rprofile /usr/lib/R/etc/Rprofile.site
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# When run as root, install.r automagically handles any necessary apt-gets
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COPY install.r install.r
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RUN Rscript install.r
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USER ${NB_USER}
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COPY vscode-extensions.txt vscode-extensions.txt
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RUN xargs -n 1 code-server --install-extension < vscode-extensions.txt
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COPY environment.yml environment.yml
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RUN conda update --all --solver=classic -n base -c conda-forge conda && \
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conda env update --file environment.yml
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‎README.md

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# Rocker stack for Machine Learning in R
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This repository contains images for machine learning and GPU-based computation in R. **EDIT** Dockerfiles are now built in modular build system at https://github.com/rocker-org/rocker-versioned2 . This repo remains for documentation around the ML part of the stack.
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The dependency stack looks like so:
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```
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-| rocker/cuda
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-| rocker/ml
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-| rocker/ml-verse
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```
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All three are CUDA compatible and will optionally take R version tags (`rocker/ml:4.0.5`) with the option of additional trailing CUDA version tag (e.g. `rocker/ml:4.0.5-cuda10.1`).
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## Quick start
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**Note: GPU use requires [nvidia-docker](https://github.com/NVIDIA/nvidia-docker/)** runtime to run!
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Run a bash shell or R command line:
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```
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# CPU-only
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docker run --rm -ti rocker/ml R
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# Machines with nvidia-docker and GPU support
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docker run --gpus all --rm -ti rocker/ml R
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```
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Or run in RStudio instance:
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```
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docker run --gpus all -e PASSWORD=mu -p 8787:8787 rocker/ml
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```
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## Tags
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See [current `ml` tags](https://hub.docker.com/r/rocker/ml/tags?page=1&ordering=last_updated)
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See [current `ml-verse` tags](https://hub.docker.com/r/rocker/ml-verse/tags?page=1&ordering=last_updated)
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## Python versions and virtualenvs
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The ML images configure a default python virtualenv using the Ubuntu system python (3.8.5 for current Ubuntu 20.04 LTS), see [install_python.sh](https://github.com/rocker-org/rocker-versioned2/blob/master/scripts/install_python.sh). This virtualenv is user-writable and the default detected by `reticulate` (using `WORKON_HOME` and `PYTHON_VENV_PATH` variables).
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Images also configure [pipenv](https://github.com/pypa/pipenv) with [pyenv](https://github.com/pyenv/pyenv) by default. This makes it very easy to manage projects that require specific versions of Python as well as specific python modules. For instance, a project using the popular `[greta](https://greta-stats.org/)` package for GPU-accelerated Bayesian inference needs Tensorflow 1.x, which requires Python <= 3.7, might do:
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```bash
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pipenv --python 3.7
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```
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In the bash terminal to set up a pipenv-managed virtualenv in the working directory using Python 3.7. Then in R we can activate this virtualenv
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```r
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venv <- system("pipenv --venv", inter = TRUE)
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reticulate::use_virtualenv(venv, required = TRUE)
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```
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We can now install tensorflow version needed, e.g.
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```r
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install.packages("tensorflow")
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tensorflow::install_tensorflow(version="1.14.0-gpu", extra_packages="tensorflow-probability==0.7.0")
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```
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## Notes
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All images are based on the current Ubuntu LTS (ubuntu 20.04) and based on the official [NVIDIA CUDA docker build recipes](https://gitlab.com/nvidia/container-images/cuda/)
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**PLEASE NOTE**: older images, `rocker/ml-gpu`, `rocker/tensorflow` and `rocker/tensorflow-gpu`, built with cuda 9.0, are deprecated and no longer supported.
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This repository contains images for machine learning and GPU-based computation in R.

‎Rprofile

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# Based on Jeroen's config:
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# https://github.com/r-universe-org/base-image/blob/f20ec9fc6f51ef8a89aad489206a43790bd9bf77/Rprofile
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local({
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has_sudo <- function() {
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system("sudo -n true", intern = TRUE) == 0
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}
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# Only use Runiverse if we don't have sudo
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if (!has_sudo()) {
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rver <- getRversion()
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distro <- system2('lsb_release', '-sc', stdout = TRUE)
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options(HTTPUserAgent = sprintf("R/%s R (%s)", rver, paste(rver, R.version$platform, R.version$arch, R.version$os)))
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options(repos = c(CRAN = sprintf("https://packagemanager.rstudio.com/all/__linux__/%s/latest", distro)))
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# Enable BioConductor repos
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utils::setRepositories(ind = 1:4, addURLs = c(fallback = "https://cloud.r-project.org"))
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# Enable universe repo(s)
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my_universe <- Sys.getenv("MY_UNIVERSE", "https://cran.r-universe.dev")
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if(nchar(my_universe)){
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my_repos <- trimws(strsplit(my_universe, ';')[[1]])
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binaries <- sprintf('%s/bin/linux/%s/%s', my_repos[1], distro, substr(rver, 1, 3))
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options(repos = c(binaries = binaries, universe = my_repos, getOption("repos")))
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}
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}
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# Other settings
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options(crayon.enabled = TRUE)
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Sys.unsetenv(c("CI", "GITHUB_ACTIONS"))
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# Dummy token for API limits
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if(is.na(Sys.getenv("GITHUB_PAT", NA))){
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dummy <- c('ghp_SXg', 'LNM', 'Tu4cnal', 'tdqkZtBojc3s563G', 'iqv')
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Sys.setenv(GITHUB_PAT = paste(dummy, collapse = 'e'))
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}
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})
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‎deprecated/Makefile

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