Prepare notebooks for central examples

CHANGELOG.md

@@ -20,6 +20,7 @@ The rules for this file:
 ### Authors
 <!-- GitHub usernames of contributors to this release -->
 - @lilyminium
+- @yoshanuikabundi
 
 ### Reviewers
 - 
@@ -34,6 +35,7 @@ The rules for this file:
 ### Changed
 <!-- Changes in existing functionality -->
 - Migrate away from pkg_resources to importlib_resources (PR #35)
+- Update examples for central examples page (PR #40)
 
 ## v0.2.1
 

examples/prepare-dataset/prepare-dataset.ipynb

@@ -1,26 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "39925afd-2d45-4e2d-b36d-269df8f6c7f0",
+   "metadata": {},
+   "source": [
+    "# Prepare a NAGL dataset for training"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ab93793c",
    "metadata": {
     "tags": []
    },
    "source": [
-    "# Prepare a dataset for training\n",
-    "\n",
-    "<br />\n",
-    "<details style=\"border: 1px solid #cfcfcf; border-radius: 2px; background: #f7f7f7;padding:2px;\">\n",
-    "<summary><small>▼ Dependency installation instructions</small></summary>\n",
-    "Install example dependencies into a new Conda environment using the provided environment.yaml:\n",
-    "\n",
-    "```shell\n",
-    "mamba env create --file ../../devtools/conda-envs/examples_env.yaml --name openff-nagl-examples\n",
-    "mamba activate openff-nagl-examples\n",
-    "jupyter notebook prepare-dataset.ipynb\n",
-    "```\n",
-    "</details>\n",
-    "\n",
     "Training a GCN requires a collection of examples that the GCN should reproduce and interpolate between. This notebook describes how to prepare such a dataset for predicting partial charges."
    ]
   },
@@ -214,7 +208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

examples/train-gnn-notebook/train-gnn-notebook.ipynb

@@ -1,27 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "480cf57f-a1ec-4c18-84a6-40af4268511e",
+   "metadata": {},
+   "source": [
+    "# Training a Graph Neural Network with NAGL"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "70ed15cf",
    "metadata": {
     "tags": []
    },
    "source": [
-    "# Training a Graph Neural Network with NAGL\n",
-    "\n",
-    "<br />\n",
-    "<details style=\"border: 1px solid #cfcfcf; border-radius: 2px; background: #f7f7f7;padding:2px;\">\n",
-    "<summary><small>Dependency installation instructions</small></summary>\n",
-    "Install example dependencies into a new Conda environment using the provided environment.yaml:\n",
-    "\n",
-    "```shell\n",
-    "mamba env create --file ../../devtools/conda-envs/examples_env.yaml --name openff-nagl-examples\n",
-    "mamba activate openff-nagl-examples\n",
-    "jupyter notebook train-gnn-notebook.ipynb\n",
-    "```\n",
-    "</details>\n",
-    "\n",
-    "\n",
     "This notebook will go through the process of training a new Graph Neural Network (GNN) on a small dataset of alkanes, and demonstrate inference with the resulting model. On the way, we'll put together a tiny test dataset, and talk a bit about the architecture of the GNN we're training."
    ]
   },
@@ -706,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,