Use Turing Actions for Documenter site and Navbar and upgrade package to use latest compats

.github/workflows/CI.yml

@@ -16,7 +16,6 @@ jobs:
       matrix:
         version:
           - '1'
-          - '1.6'
           - 'nightly'
         os:
           - ubuntu-latest
@@ -28,12 +27,12 @@ jobs:
             arch: x64
             coverage: true
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v6
       - uses: julia-actions/setup-julia@v1
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v1
+      - uses: actions/cache@v5
         env:
           cache-name: cache-artifacts
         with:

.github/workflows/DocNav.yml

@@ -1,49 +1,40 @@
-name: Add Navbar
+name: Rebuild docs with newest navbar
 
 on:
-  page_build:  # Triggers the workflow on push events to gh-pages branch
-  workflow_dispatch:  # Allows manual triggering
+  # 3:25 AM UTC every Sunday -- choose an uncommon time to avoid
+  # periods of heavy GitHub Actions usage
   schedule:
-    - cron: '0 0 * * 0'  # Runs every week on Sunday at midnight (UTC)
+    - cron: '25 3 * * 0'
+  # Whenever needed
+  workflow_dispatch:
+
+permissions:
+  contents: write
 
 jobs:
-  add-navbar:
+  update-navbar:
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
+
     steps:
-      - name: Checkout gh-pages
-        uses: actions/checkout@v4
+      - name: Checkout gh-pages branch
+        uses: actions/checkout@v6
         with:
           ref: gh-pages
-          fetch-depth: 0
 
-      - name: Download insert_navbar.sh
-        run: |
-          curl -O https://raw.githubusercontent.com/TuringLang/turinglang.github.io/main/assets/scripts/insert_navbar.sh
-          chmod +x insert_navbar.sh
+      - name: Insert navbar
+        uses: TuringLang/actions/DocsNav@main
+        with:
+          doc-path: '.'
+          navbar-url: https://raw.githubusercontent.com/JuliaGaussianProcesses/.github/refs/heads/main/DocumenterNavbar/JuliaGPNavbar.html
 
-      - name: Update Navbar
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Commit and push changes
         run: |
-          git config user.name github-actions[bot]
-          git config user.email github-actions[bot]@users.noreply.github.com
-
-          # Define the URL of the navbar to be used
-          NAVBAR_URL="https://raw.githubusercontent.com/TuringLang/turinglang.github.io/main/assets/scripts/JuliaGPNavbar.html"
-
-          # Update all HTML files in the current directory (gh-pages root)
-          ./insert_navbar.sh . $NAVBAR_URL
-
-          # Remove the insert_navbar.sh file
-          rm insert_navbar.sh
-
-          # Check if there are any changes
           if [[ -n $(git status -s) ]]; then
-            git add .
-            git commit -m "Added navbar and removed insert_navbar.sh"
-            git push "https://${GITHUB_ACTOR}:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git" gh-pages
+            git config user.name github-actions[bot]
+            git config user.email github-actions[bot]@users.noreply.github.com
+            git add -A
+            git commit -m "Update navbar (automated)"
+            git push
           else
             echo "No changes to commit"
           fi

.github/workflows/Docs.yml

@@ -6,21 +6,27 @@ on:
       - master
     tags: '*'
   pull_request:
+  workflow_dispatch:
+
+concurrency:
+  # Skip intermediate builds: always.
+  # Cancel intermediate builds: only if it is a pull request build.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+
+permissions:
+  contents: write
+  pull-requests: write
 
 jobs:
-  build:
+  docs:
     runs-on: ubuntu-latest
+
     steps:
-      - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@latest
-        with:
-          version: '1.6'
-      - name: Install dependencies
-        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
-      - name: Build and deploy
+      - name: Build and deploy Documenter.jl docs
         env:
           GKSwstype: nul # turn off GR's interactive plotting for notebooks
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
-          JULIA_DEBUG: Documenter # Print `@debug` statements (https://github.com/JuliaDocs/Documenter.jl/issues/955)
-        run: julia --project=docs/ docs/make.jl
+        uses: TuringLang/actions/DocsDocumenter@main
+        with:
+          julia-version: '1'
+          navbar-url: https://raw.githubusercontent.com/JuliaGaussianProcesses/.github/refs/heads/main/DocumenterNavbar/JuliaGPNavbar.html

.github/workflows/DocsPreviewCleanup.yml

@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout gh-pages branch
-        uses: actions/checkout@v2
+        uses: actions/checkout@v6
         with:
           ref: gh-pages
       - name: Delete preview and history + push changes

.github/workflows/Format.yml

@@ -7,7 +7,7 @@ jobs:
   format:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v6
       - uses: julia-actions/setup-julia@latest
         with:
           version: 1

Project.toml

@@ -38,4 +38,4 @@ PDMats = "0.11"
 Reexport = "1"
 SpecialFunctions = "1, 2"
 StatsBase = "0.33, 0.34"
-julia = "1.6"
+julia = "1"

docs/Project.toml

@@ -4,4 +4,4 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 
 [compat]
 ApproximateGPs = "0.3,0.4"
-Documenter = "0.27"
+Documenter = "1"

docs/make.jl

@@ -6,7 +6,7 @@ using JuliaGPsDocs
 
 using ApproximateGPs
 
-JuliaGPsDocs.generate_examples(ApproximateGPs)
+JuliaGPsDocs.generate_examples(ApproximateGPs; ntasks=1)
 
 ### Build documentation
 using Documenter
@@ -23,19 +23,20 @@ DocMeta.setdocmeta!(
 
 makedocs(;
     sitename="ApproximateGPs.jl",
-    format=Documenter.HTML(),
+    format=Documenter.HTML(;
+        size_threshold_ignore=[
+            "examples/a-regression/index.md",
+            "examples/b-classification/index.md",
+            "examples/c-comparisons/index.md",
+        ],
+    ),
     modules=[ApproximateGPs],
     pages=[
         "Home" => "index.md",
         "userguide.md",
         "API" => joinpath.(Ref("api"), ["index.md", "sparsevariational.md", "laplace.md"]),
         "Examples" => JuliaGPsDocs.find_generated_examples(ApproximateGPs),
     ],
-    strict=true,
     checkdocs=:exports,
     doctestfilters=JuliaGPsDocs.DOCTEST_FILTERS,
 )
-
-deploydocs(;
-    repo="github.com/JuliaGaussianProcesses/ApproximateGPs.jl.git", push_preview=true
-)

examples/a-regression/Project.toml

@@ -10,3 +10,6 @@ PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
+
+[compat]
+Flux = "0.14, 0.15, 0.16"

examples/a-regression/script.jl

@@ -81,7 +81,7 @@ struct SVGPModel
     A  # square-root of variational covariance
 end
 
-Flux.@functor SVGPModel (k, z, m, A);
+Flux.@layer SVGPModel trainable=(k, z, m, A)
 
 # Set the observation noise for our model, along with a `jitter` term
 # to help with numerical stability.
@@ -167,15 +167,14 @@ plot!(-1:0.001:1, init_post; label="Initial Posterior", color=4)
 #
 # Training the model now simply proceeds with the usual `Flux.jl` training loop.
 
-opt = ADAM(0.001)  # Define the optimiser
-params = Flux.params(model);  # Extract the model parameters
+opt = Flux.Adam(0.001)
 
 # One of the major advantages of the SVGP model is that it allows stochastic
 # estimation of the ELBO by using minibatching of the training data. This is
 # very straightforward to achieve with `Flux.jl`'s utilities:
 
 b = 100 # minibatch size
-data_loader = Flux.Data.DataLoader((x, y); batchsize=b)
+data_loader = Flux.DataLoader((x, y); batchsize=b)
 
 # The loss (negative ELBO) before training
 
@@ -186,12 +185,13 @@ loss(model, x, y)
 
 using IterTools: ncycle
 
+params = Flux.params(model)
 Flux.train!(
     (x, y) -> loss(model, x, y; num_data=N),
     params,
     ncycle(data_loader, 300), # Train for 300 epochs
     opt,
-);
+)
 
 # Negative ELBO after training
 

src/LaplaceApproximationModule.jl

@@ -183,18 +183,19 @@ struct LaplaceCache{
     Tv1<:AbstractVector,
     Tv2<:AbstractVector,
     Tv3<:AbstractVector,
-    Td<:Diagonal,
+    Tv4<:AbstractVector,
+    Tv5<:AbstractVector,
     Tf<:Real,
     Tc<:Cholesky,
 }
     K::Tm  # kernel matrix
     f::Tv1  # mode of posterior p(f | y)
-    W::Td  # diagonal matrix of ∂²/∂fᵢ² loglik
-    Wsqrt::Td  # sqrt(W)
+    W::Tv2  # diagonal of -∂²/∂fᵢ² loglik
+    Wsqrt::Tv3  # sqrt.(W)
     loglik::Tf  # ∑ᵢlog p(yᵢ|fᵢ)
-    d_loglik::Tv2  # ∂/∂fᵢloglik
-    B_ch::Tc  # cholesky(I + Wsqrt * K * Wsqrt)
-    a::Tv3  # K⁻¹ f
+    d_loglik::Tv4  # ∂/∂fᵢloglik
+    B_ch::Tc  # cholesky(I + Diagonal(Wsqrt) * K * Diagonal(Wsqrt))
+    a::Tv5  # K⁻¹ f
 end
 
 function _laplace_train_intermediates(dist_y_given_f, ys, K, f)
@@ -209,12 +210,12 @@ function _laplace_train_intermediates(dist_y_given_f, ys, K, f)
     ll, d_ll, d2_ll = loglik_and_derivs(dist_y_given_f, ys, f)
 
     # inner loop iteration of RW Algorithm 3.1, lines 4-7
-    W = -Diagonal(d2_ll)
-    Wsqrt = sqrt(W)
-    B = I + Wsqrt * K * Wsqrt
+    W = -d2_ll
+    Wsqrt = sqrt.(W)
+    B = I + (Wsqrt .* K) .* Wsqrt'
     B_ch = cholesky(Symmetric(B))
-    b = W * f + d_ll
-    a = b - Wsqrt * (B_ch \ (Wsqrt * K * b))
+    b = W .* f .+ d_ll
+    a = b - Wsqrt .* (B_ch \ (Wsqrt .* (K * b)))
 
     #return (; K, f, W, Wsqrt, loglik=ll, d_loglik=d_ll, B_ch, a)
     return LaplaceCache(K, f, W, Wsqrt, ll, d_ll, B_ch, a)
@@ -321,7 +322,7 @@ function ChainRulesCore.frule(
     # fdot = (I - K grad2_log_p_y_given_f(f))⁻¹ Kdot grad_log_p_y_given_f(f)
     # (I - K grad2_log_p_y_given_f(f)) = (I + K W) = (√W)⁻¹ (I + √W K √W) √W = (√W)⁻¹ B √W
     # fdot = (√W)⁻¹ B⁻¹ √W Kdot grad_log_p_y_given_f(f)
-    ∂f_opt = cache.Wsqrt \ (cache.B_ch \ (cache.Wsqrt * (ΔK * cache.d_loglik)))
+    ∂f_opt = (cache.B_ch \ (cache.Wsqrt .* (ΔK * cache.d_loglik))) ./ cache.Wsqrt
 
     return f_opt, ∂f_opt
 end
@@ -357,7 +358,9 @@ function ChainRulesCore.rrule(::typeof(newton_inner_loop), dist_y_given_f, ys, K
         )
 
         # ∂K = df/dK Δf
-        ∂K = @thunk(cache.Wsqrt * (cache.B_ch \ (cache.Wsqrt \ Δf_opt)) * cache.d_loglik')
+        ∂K = @thunk(
+            (cache.Wsqrt .* (cache.B_ch \ (Δf_opt ./ cache.Wsqrt))) * cache.d_loglik'
+        )
 
         return (∂self, ∂dist_y_given_f, ∂ys, ∂K)
     end
@@ -379,7 +382,7 @@ function laplace_f_cov(cache)
     # = (√W⁻¹) (I - (I + √W K √W)⁻¹) (√W⁻¹)
     # = (√W⁻¹) (I - B⁻¹) (√W⁻¹)
     B_ch = cache.B_ch
-    Wsqrt_inv = inv(cache.Wsqrt)
+    Wsqrt_inv = Diagonal(inv.(cache.Wsqrt))
     return Wsqrt_inv * (I - inv(B_ch)) * Wsqrt_inv
 end
 
@@ -423,7 +426,7 @@ function _laplace_predict_intermediates(cache, prior_at_x, xnew)
     k_x_xnew = cov(prior_at_x.f, prior_at_x.x, xnew)
     f_mean = mean(prior_at_x.f, xnew) + k_x_xnew' * cache.d_loglik  # RW (3.21)
     L = cache.B_ch.L
-    v = L \ (cache.Wsqrt * k_x_xnew)  # RW (3.29)
+    v = L \ (cache.Wsqrt .* k_x_xnew)  # RW (3.29)
     return f_mean, v
 end
 
@@ -454,8 +457,8 @@ end
 
 function Statistics.cov(f::LaplacePosteriorGP, x::AbstractVector, y::AbstractVector)
     L = f.data.B_ch.L
-    vx = L \ (f.data.Wsqrt * cov(f.prior.f, f.prior.x, x))
-    vy = L \ (f.data.Wsqrt * cov(f.prior.f, f.prior.x, y))
+    vx = L \ (f.data.Wsqrt .* cov(f.prior.f, f.prior.x, x))
+    vy = L \ (f.data.Wsqrt .* cov(f.prior.f, f.prior.x, y))
     return cov(f.prior.f, x, y) - vx' * vy
 end
 

src/TestUtils.jl

@@ -12,7 +12,8 @@ using ApproximateGPs
 
 function generate_data()
     X = range(0, 23.5; length=48)
-    # The random number generator changed in 1.6->1.7. The following vector was generated in Julia 1.6.
+    # The random number generator changed across older Julia releases. The following vector
+    # was generated before that change to keep this test stable across supported versions.
     # The generating code below is only kept for illustrative purposes.
     #! format: off
     Y = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]