Switch to DemoCards.jl (#206)

* Use DemoCards.jl

* Add cache files to reduce load on CI

* Fix notebooks

* Add compat

* Discussed changes

* Remove the pre-rendered files

Author: Dsantra92

docs/Project.toml

@@ -1,13 +1,16 @@
 [deps]
+DemoCards = "311a05b2-6137-4a5a-b473-18580a3d38b5"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MarkdownLiteral = "736d6165-7244-6769-4267-6b50796e6954"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
-PlutoStaticHTML = "359b1769-a58e-495b-9770-312e911026ad"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+
+
+[compat]
+DemoCards = "^0.4.10"

docs/make.jl

@@ -1,31 +1,10 @@
 using Flux, NNlib, GraphNeuralNetworks, Graphs, SparseArrays
-using Documenter
+using Documenter, DemoCards
 
-# See this for a template of Pluto+Documenter 
-# https://github.com/rikhuijzer/PlutoStaticHTML.jl/blob/main/docs/make.jl
-using Pluto, PlutoStaticHTML
-using Documenter: MathJax3
+tutorials, tutorials_cb, tutorial_assets = makedemos("tutorials")
 
-pluto_src_folder = joinpath(@__DIR__, "src", "tutorials")
-
-"""
-    build()
-
-Run all Pluto notebooks (".jl" files) in `NOTEBOOK_DIR`.
-"""
-function build()
-    println("Building notebooks")
-    hopts = HTMLOptions(; append_build_context=false)
-    output_format = documenter_output
-    bopts = BuildOptions(pluto_src_folder; output_format)
-    build_notebooks(bopts, hopts)
-    return nothing
-end
-
-# Build the notebooks; defaults to true.
-if get(ENV, "BUILD_DOCS_NOTEBOOKS", "true") == "true"
-    build()
-end
+assets = []
+isnothing(tutorial_assets) || push!(assets, tutorial_assets)
 
 DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup, 
     :(using GraphNeuralNetworks, Graphs, SparseArrays, NNlib, Flux); 
@@ -38,19 +17,14 @@ makedocs(;
     modules = [GraphNeuralNetworks, NNlib, Flux, Graphs, SparseArrays],
     doctest = false, 
     clean = true,    
-    format= Documenter.HTML(; mathengine, prettyurls),
+    format= Documenter.HTML(; mathengine, prettyurls, assets=assets),
     sitename = "GraphNeuralNetworks.jl",
     pages = ["Home" => "index.md",
              "Graphs" => "gnngraph.md",
              "Message Passing" => "messagepassing.md",
              "Model Building" => "models.md",
              "Datasets" => "datasets.md",
-             "Tutorials" => 
-                [
-                    "Intro to Graph Neural Networks" => "tutorials/gnn_intro_pluto.md",
-                    "Graph Classification" => "tutorials/graph_classification_pluto.md",
-                    "Node Classification" => "tutorials/node_classification_pluto.md",
-                ],
+             "Tutorials" => tutorials,
              "API Reference" =>
                [
                 "GNNGraph" => "api/gnngraph.md",
@@ -65,4 +39,6 @@ makedocs(;
             ],
 )
 
+tutorials_cb()
+
 deploydocs(repo="github.com/CarloLucibello/GraphNeuralNetworks.jl.git")

docs/src/tutorials/.gitignore

@@ -0,0 +1,3 @@
+{
+    "theme": "grid"
+}

docs/tutorials/config.json

@@ -0,0 +1,19 @@
+# [Tutorials](@id tutorials)
+
+---
+
+{{{democards}}}
+
+---
+
+## Contributions
+
+If you have a suggestion on adding new tutorials, feel free to create a new issue
+[here](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/issues/new).
+Users are invited to contribute demonstrations of their own.
+If you want to contribute new tutorials and looking for inspiration,
+checkout these tutorials from
+[PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).
+You are expected to use [Pluto.jl](https://github.com/fonsp/Pluto.jl) notebooks
+with [DemoCards.jl](https://github.com/JuliaDocs/DemoCards.jl).
+Please check out exsisting tutorials for more details.

docs/tutorials/index.md

@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.5
+# v0.19.11
 
 using Markdown
 using InteractiveUtils
@@ -10,13 +10,13 @@ begin
     using Pkg
     Pkg.activate(; temp=true)
     packages = [
-        PackageSpec(; path=joinpath(@__DIR__,"..","..","..")),
+        PackageSpec(; name="GraphNeuralNetworks", version="0.4"),
         PackageSpec(; name="Flux", version="0.13"),
-		PackageSpec(; name="MLDatasets", version="0.7"),
-		PackageSpec(; name="GraphMakie"),
-		PackageSpec(; name="Graphs"),
-		PackageSpec(; name="CairoMakie"),
-		PackageSpec(; name="PlutoUI"),
+        PackageSpec(; name="MLDatasets", version="0.7"),
+        PackageSpec(; name="GraphMakie"),
+        PackageSpec(; name="Graphs"),
+        PackageSpec(; name="CairoMakie"),
+        PackageSpec(; name="PlutoUI"),
     ]
     Pkg.add(packages)
 end
@@ -36,10 +36,19 @@ begin
 	Random.seed!(17) # for reproducibility
 end;
 
-# ╔═╡ 03a9e023-e682-4ea3-a10b-14c4d101b291
+# ╔═╡ cc051aa1-b929-4bca-b261-7f797a644a2b
 md"""
-# Introduction: Hands-on Graph Neural Networks
+---
+title: Hands-on introduction to Graph Neural Networks
+cover: assets/intro_1.png
+author: "[Carlo Lucibello](https://github.com/CarloLucibello)"
+date: 2022-05-24
+description: A beginner level introduction to graph machine learning using GraphNeuralNetworks.jl.
+---
+"""
 
+# ╔═╡ 03a9e023-e682-4ea3-a10b-14c4d101b291
+md"""
 *This Pluto notebook is a julia adaptation of the Pytorch Geometric tutorials that can be found [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).*
 
 Recently, deep learning on graphs has emerged to one of the hottest research fields in the deep learning community.
@@ -330,6 +339,7 @@ Furthermore, we did this all with a few lines of code, thanks to the GraphNeural
 """
 
 # ╔═╡ Cell order:
+# ╟─cc051aa1-b929-4bca-b261-7f797a644a2b
 # ╟─03a9e023-e682-4ea3-a10b-14c4d101b291
 # ╟─6f20e59c-b002-4d22-9ee0-b62596574776
 # ╠═361e0948-d91a-11ec-2d95-2db77435a0c1

docs/tutorials/introductory_tutorials/assets/graph_classification.gif

@@ -1,9 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.6
-
-#> [frontmatter]
-#> title = "Graph Classification with Graph Neural Networks"
-#> date = "2022-05-23"
+# v0.19.11
 
 using Markdown
 using InteractiveUtils
@@ -14,10 +10,10 @@ begin
     using Pkg
     Pkg.activate(; temp=true)
     Pkg.add([
-        PackageSpec(; path=joinpath(@__DIR__,"..","..","..")),
+        PackageSpec(; name="GraphNeuralNetworks", version="0.4"),
         PackageSpec(; name="Flux", version="0.13"),
-		PackageSpec(; name="MLDatasets", version="0.7"),
-		PackageSpec(; name="MLUtils"),
+        PackageSpec(; name="MLDatasets", version="0.7"),
+        PackageSpec(; name="MLUtils"),
 	])
 	Pkg.develop("GraphNeuralNetworks")
 end
@@ -36,10 +32,19 @@ begin
 	Random.seed!(17) # for reproducibility
 end;
 
-# ╔═╡ 15136fd8-f9b2-4841-9a95-9de7b8969687
+# ╔═╡ c07e1be9-adb6-4454-8128-bc8917406c58
 md"""
-# Graph Classification with Graph Neural Networks
+---
+title: Graph Classification with Graph Neural Networks
+cover: assets/graph_classification.gif
+author: "[Carlo Lucibello](https://github.com/CarloLucibello)"
+date: 2022-05-23
+description: Tutorial for Graph Classification using GraphNeuralNetworks.jl
+---
+"""
 
+# ╔═╡ 15136fd8-f9b2-4841-9a95-9de7b8969687
+md"""
 *This Pluto notebook is a julia adaptation of the Pytorch Geometric tutorials that can be found [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).*
 
 In this tutorial session we will have a closer look at how to apply **Graph Neural Networks (GNNs) to the task of graph classification**.
@@ -271,9 +276,10 @@ You have learned how graphs can be batched together for better GPU utilization,
 """
 
 # ╔═╡ Cell order:
-# ╠═c97a0002-2253-45b6-9266-017189dbb6fe
+# ╟─c07e1be9-adb6-4454-8128-bc8917406c58
+# ╟─cc97a0002-2253-45b6-9266-017189dbb6fe
 # ╠═361e0948-d91a-11ec-2d95-2db77435a0c1
-# ╟─15136fd8-f9b2-4841-9a95-9de7b8969687
+# ╠═15136fd8-f9b2-4841-9a95-9de7b8969687
 # ╠═f6e86958-e96f-4c77-91fc-c72d8967575c
 # ╠═24f76360-8599-46c8-a49f-4c31f02eb7d8
 # ╠═5d5e5152-c860-4158-8bc7-67ee1022f9f8