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Use Turing Actions for Documenter site and Navbar and upgrade package to use latest compats (#151)
* Use Turing Actions for Documenter site and Navbar * only 1 process at a time to fix docs * update CI * debump formatter checkout * more debump formatter checkout * Ohh, shame on me -- I was changing version without v * support latest Julia versions * fix tests and update few more things * remove Julia 1.6 from CI * fix docs * format * ignore size thresold in docs * address all copilot suggestions
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‎.github/workflows/CI.yml‎

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matrix:
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version:
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- '1'
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- '1.6'
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- 'nightly'
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os:
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- ubuntu-latest
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arch: x64
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coverage: true
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steps:
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- uses: actions/checkout@v2
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- uses: actions/checkout@v6
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- uses: julia-actions/setup-julia@v1
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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- uses: actions/cache@v1
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- uses: actions/cache@v5
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env:
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cache-name: cache-artifacts
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with:

‎.github/workflows/DocNav.yml‎

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name: Add Navbar
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name: Rebuild docs with newest navbar
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on:
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page_build: # Triggers the workflow on push events to gh-pages branch
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workflow_dispatch: # Allows manual triggering
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# 3:25 AM UTC every Sunday -- choose an uncommon time to avoid
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# periods of heavy GitHub Actions usage
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schedule:
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- cron: '0 0 * * 0' # Runs every week on Sunday at midnight (UTC)
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- cron: '25 3 * * 0'
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# Whenever needed
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workflow_dispatch:
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permissions:
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contents: write
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jobs:
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add-navbar:
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update-navbar:
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runs-on: ubuntu-latest
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permissions:
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contents: write
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steps:
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- name: Checkout gh-pages
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uses: actions/checkout@v4
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- name: Checkout gh-pages branch
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uses: actions/checkout@v6
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with:
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ref: gh-pages
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fetch-depth: 0
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- name: Download insert_navbar.sh
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run: |
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curl -O https://raw.githubusercontent.com/TuringLang/turinglang.github.io/main/assets/scripts/insert_navbar.sh
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chmod +x insert_navbar.sh
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- name: Insert navbar
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uses: TuringLang/actions/DocsNav@main
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with:
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doc-path: '.'
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navbar-url: https://raw.githubusercontent.com/JuliaGaussianProcesses/.github/refs/heads/main/DocumenterNavbar/JuliaGPNavbar.html
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- name: Update Navbar
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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- name: Commit and push changes
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run: |
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git config user.name github-actions[bot]
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git config user.email github-actions[bot]@users.noreply.github.com
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# Define the URL of the navbar to be used
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NAVBAR_URL="https://raw.githubusercontent.com/TuringLang/turinglang.github.io/main/assets/scripts/JuliaGPNavbar.html"
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# Update all HTML files in the current directory (gh-pages root)
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./insert_navbar.sh . $NAVBAR_URL
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# Remove the insert_navbar.sh file
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rm insert_navbar.sh
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# Check if there are any changes
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if [[ -n $(git status -s) ]]; then
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git add .
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git commit -m "Added navbar and removed insert_navbar.sh"
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git push "https://${GITHUB_ACTOR}:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git" gh-pages
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git config user.name github-actions[bot]
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git config user.email github-actions[bot]@users.noreply.github.com
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git add -A
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git commit -m "Update navbar (automated)"
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git push
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else
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echo "No changes to commit"
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fi

‎.github/workflows/Docs.yml‎

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- master
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tags: '*'
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pull_request:
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workflow_dispatch:
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concurrency:
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# Skip intermediate builds: always.
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# Cancel intermediate builds: only if it is a pull request build.
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
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permissions:
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contents: write
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pull-requests: write
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jobs:
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build:
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docs:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@latest
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with:
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version: '1.6'
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- name: Install dependencies
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run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
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- name: Build and deploy
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- name: Build and deploy Documenter.jl docs
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env:
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GKSwstype: nul # turn off GR's interactive plotting for notebooks
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
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JULIA_DEBUG: Documenter # Print `@debug` statements (https://github.com/JuliaDocs/Documenter.jl/issues/955)
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run: julia --project=docs/ docs/make.jl
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uses: TuringLang/actions/DocsDocumenter@main
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with:
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julia-version: '1'
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navbar-url: https://raw.githubusercontent.com/JuliaGaussianProcesses/.github/refs/heads/main/DocumenterNavbar/JuliaGPNavbar.html

‎.github/workflows/DocsPreviewCleanup.yml‎

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runs-on: ubuntu-latest
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steps:
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- name: Checkout gh-pages branch
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uses: actions/checkout@v2
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uses: actions/checkout@v6
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with:
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ref: gh-pages
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- name: Delete preview and history + push changes

‎.github/workflows/Format.yml‎

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format:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- uses: actions/checkout@v6
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- uses: julia-actions/setup-julia@latest
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with:
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version: 1

‎Project.toml‎

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Reexport = "1"
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SpecialFunctions = "1, 2"
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StatsBase = "0.33, 0.34"
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julia = "1.6"
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julia = "1"

‎docs/Project.toml‎

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[compat]
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ApproximateGPs = "0.3,0.4"
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Documenter = "0.27"
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Documenter = "1"

‎docs/make.jl‎

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using ApproximateGPs
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JuliaGPsDocs.generate_examples(ApproximateGPs)
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JuliaGPsDocs.generate_examples(ApproximateGPs; ntasks=1)
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### Build documentation
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using Documenter
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makedocs(;
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sitename="ApproximateGPs.jl",
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format=Documenter.HTML(),
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format=Documenter.HTML(;
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size_threshold_ignore=[
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"examples/a-regression/index.md",
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"examples/b-classification/index.md",
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"examples/c-comparisons/index.md",
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],
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),
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modules=[ApproximateGPs],
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pages=[
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"Home" => "index.md",
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"userguide.md",
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"API" => joinpath.(Ref("api"), ["index.md", "sparsevariational.md", "laplace.md"]),
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"Examples" => JuliaGPsDocs.find_generated_examples(ApproximateGPs),
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],
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strict=true,
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checkdocs=:exports,
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doctestfilters=JuliaGPsDocs.DOCTEST_FILTERS,
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)
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deploydocs(;
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repo="github.com/JuliaGaussianProcesses/ApproximateGPs.jl.git", push_preview=true
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)

‎examples/a-regression/Project.toml‎

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Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
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[compat]
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Flux = "0.14, 0.15, 0.16"

‎examples/a-regression/script.jl‎

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A # square-root of variational covariance
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end
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Flux.@functor SVGPModel (k, z, m, A);
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Flux.@layer SVGPModel trainable=(k, z, m, A)
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# Set the observation noise for our model, along with a `jitter` term
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# to help with numerical stability.
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#
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# Training the model now simply proceeds with the usual `Flux.jl` training loop.
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opt = ADAM(0.001) # Define the optimiser
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params = Flux.params(model); # Extract the model parameters
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opt = Flux.Adam(0.001)
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# One of the major advantages of the SVGP model is that it allows stochastic
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# estimation of the ELBO by using minibatching of the training data. This is
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# very straightforward to achieve with `Flux.jl`'s utilities:
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b = 100 # minibatch size
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data_loader = Flux.Data.DataLoader((x, y); batchsize=b)
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data_loader = Flux.DataLoader((x, y); batchsize=b)
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# The loss (negative ELBO) before training
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using IterTools: ncycle
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params = Flux.params(model)
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Flux.train!(
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(x, y) -> loss(model, x, y; num_data=N),
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params,
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ncycle(data_loader, 300), # Train for 300 epochs
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opt,
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);
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)
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# Negative ELBO after training
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