Switch to pluto frontmatter (#214)

Author: Dsantra92

docs/Project.toml

@@ -13,4 +13,4 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 
 [compat]
-DemoCards = "^0.4.10"
+DemoCards = "^0.4.11"

docs/pluto_output/gnn_intro_pluto.md

@@ -1,5 +1,12 @@
 ### A Pluto.jl notebook ###
-# v0.19.11
+# v0.19.13
+
+#> [frontmatter]
+#> author = "[Carlo Lucibello](https://github.com/CarloLucibello)"
+#> title = "Hands-on introduction to Graph Neural Networks"
+#> date = "2022-05-22"
+#> description = "A beginner level introduction to graph machine learning using GraphNeuralNetworks.jl"
+#> cover = "assets/intro_1.png"
 
 using Markdown
 using InteractiveUtils
@@ -36,17 +43,6 @@ begin
 	Random.seed!(17) # for reproducibility
 end;
 
-# ╔═╡ cc051aa1-b929-4bca-b261-7f797a644a2b
-md"""
----
-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).*
@@ -339,7 +335,6 @@ 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/pluto_output/graph_classification_pluto.md

@@ -1,5 +1,12 @@
 ### A Pluto.jl notebook ###
-# v0.19.11
+# v0.19.13
+
+#> [frontmatter]
+#> author = "[Carlo Lucibello](https://github.com/CarloLucibello)"
+#> title = "Graph Classification with Graph Neural Networks"
+#> date = "2022-05-23"
+#> description = "Tutorial for Graph Classification using GraphNeuralNetworks.jl"
+#> cover = "assets/graph_classification.gif"
 
 using Markdown
 using InteractiveUtils
@@ -32,17 +39,6 @@ begin
 	Random.seed!(17) # for reproducibility
 end;
 
-# ╔═╡ c07e1be9-adb6-4454-8128-bc8917406c58
-md"""
----
-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).*
@@ -276,17 +272,16 @@ You have learned how graphs can be batched together for better GPU utilization,
 """
 
 # ╔═╡ Cell order:
-# ╟─c07e1be9-adb6-4454-8128-bc8917406c58
-# ╟─cc97a0002-2253-45b6-9266-017189dbb6fe
-# ╠═361e0948-d91a-11ec-2d95-2db77435a0c1
-# ╠═15136fd8-f9b2-4841-9a95-9de7b8969687
+# ╟─c97a0002-2253-45b6-9266-017189dbb6fe
+# ╟─361e0948-d91a-11ec-2d95-2db77435a0c1
+# ╟─15136fd8-f9b2-4841-9a95-9de7b8969687
 # ╠═f6e86958-e96f-4c77-91fc-c72d8967575c
 # ╠═24f76360-8599-46c8-a49f-4c31f02eb7d8
 # ╠═5d5e5152-c860-4158-8bc7-67ee1022f9f8
 # ╠═33163dd2-cb35-45c7-ae5b-d4854d141773
 # ╠═a8d6a133-a828-4d51-83c4-fb44f9d5ede1
-# ╠═3b3e0a79-264b-47d7-8bda-2a6db7290828
-# ╠═7f7750ff-b7fa-4fe2-a5a8-6c9c26c479bb
+# ╟─3b3e0a79-264b-47d7-8bda-2a6db7290828
+# ╟─7f7750ff-b7fa-4fe2-a5a8-6c9c26c479bb
 # ╠═936c09f6-ee62-4bc2-a0c6-749a66080fd2
 # ╟─2c6ccfdd-cf11-415b-b398-95e5b0b2bbd4
 # ╠═519477b2-8323-4ece-a7eb-141e9841117c

docs/pluto_output/node_classification_pluto.md

@@ -1,5 +1,12 @@
 ### A Pluto.jl notebook ###
-# v0.19.11
+# v0.19.13
+
+#> [frontmatter]
+#> author = "[Deeptendu Santra](https://github.com/Dsantra92)"
+#> title = "Node Classification with Graph Neural Networks"
+#> date = "2022-09-25"
+#> description = "Tutorial for Node classification using GraphNeuralNetworks.jl"
+#> cover = "assets/node_classsification.gif"
 
 using Markdown
 using InteractiveUtils
@@ -36,17 +43,6 @@ begin
     Random.seed!(17) # for reproducibility
 end
 
-# ╔═╡ 8db76e69-01ee-42d6-8721-19a3848693ae
-md"""
----
-title: Node Classification with Graph Neural Networks
-cover: assets/node_classsification.gif
-author: "[Deeptendu Santra](https://github.com/Dsantra92)"
-date: 2022-09-25
-description: Tutorial for Node classification using GraphNeuralNetworks.jl
----
-"""
-
 # ╔═╡ ca2f0293-7eac-4d9a-9a2f-fda47fd95a99
 md"""
 Following our previous tutorial in GNNs, we covered how to create graph neural networks.
@@ -399,7 +395,6 @@ In this tutorial, we have seen how to apply GNNs to real-world problems, and, in
 """
 
 # ╔═╡ Cell order:
-# ╟─8db76e69-01ee-42d6-8721-19a3848693ae
 # ╟─2c710e0f-4275-4440-a3a9-27eabf61823a
 # ╟─ca2f0293-7eac-4d9a-9a2f-fda47fd95a99
 # ╟─4455f18c-2bd9-42ed-bce3-cfe6561eab23