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---
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title: CI/CD
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---
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Continuous Integration / continous delivery (CI/CD) is a basic tool in todays Data Science toolstack to automate processes and execute repetite tasks on schedule.
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Running tasks in CI/CD should be fast and reliable, no matter the language, architecture or computing environment at hand.
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As CI/CD requires installing many packages over and over again, having binaries and optimized distributions helps to reduce runtime.
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This page presents snippets for well and lesser known CI/CD engines to help getting started with the R package binaries of this project.
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::: {.callout-note}
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The presented snippets rely on external projects and libraries which get updated regurarly.
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There is no guarantee to work out-of-the box at all times.
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If you found an error, please open a pull request in the linked repo.
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:::
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## GitHub Actions
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### VM
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GitHub Actions can be run directly on a VM or in a containered context.
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The available VM images can be found [here](https://github.com/actions/runner-images#available-images).
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The only Linux distribution available is Ubuntu.
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For VM workflows, [r-lib/actions](https://github.com/r-lib/actions/blob/v2/examples) provides many examples for different use cases.
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To use package binaries from this project, use the following config for `r-lib/actions/setup-r@v2`:
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```yaml
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- uses: r-lib/actions/setup-r@v2
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with:
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r-version: ${{ matrix.config.r }}
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http-user-agent: ${{ matrix.config.http-user-agent }}
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Ncpus: 2
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cran: 'https://cran.devxy.io/amd64/noble/latest'
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```
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(`Ncpus: 2` has been set to allow for parallel installations.)
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### Container
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An alternative is to run Actions in a containerized context.
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This spins up a container of the selected image in the VM and allows running on other distributions than Ubuntu, e.g. on Alpine.
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To avoid having to install R first and configure a custom repository every time, [specialized images](https://hub.docker.com/r/devxygmbh/r-alpine) are provided which already have everything in place:
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```yaml
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jobs:
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container:
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runs-on: ubuntu-latest
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container: devxygmbh:r-alpine
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steps:
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- run: |
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R -q -e 'install.packages("pak", repos = sprintf("https://r-lib.github.io/p/pak/devel/%s/%s/%s", .Platform$pkgType, R.Version()$os, R.Version()$arch))'
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R -q -e 'getOption("repos")'
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R -q -e 'options(Ncpus = 2); pak::local_install_dev_deps()'
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```
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::: {.callout-note}
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When running in a containerized context, the predefined actions from `r-lib/actions` cannot be used.
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Shell/R commands must be used directly.
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:::
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::: {.callout-note}
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Running containerized in Alpine should be substantially faster than running in a VM context.
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:::
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::: {.callout-tip}
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(91 packages)
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- Containerized: **1m 21s**
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- VM: 2m 11s
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Both runs are based on the assumption that caching is not used. With caching enabled, the performance of both approaches should be roughly comparable.
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Source: [pat-s/workflow-compare](https://github.com/pat-s/workflow-compare)
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:::
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## GitLab Runner
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WIP |