Version 2

.github/workflows/deploy-jupyter-book-preview.yml

@@ -0,0 +1,47 @@
+name: deploy-book
+
+# Only run this on PRs
+on:
+  pull_request:
+    branches: [ main ]
+
+  workflow_dispatch:
+
+# This job installs dependencies, build the book, and pushes it to `gh-pages`
+jobs:
+
+  deploy-book:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - uses: s-weigand/setup-conda@v1
+      with:
+        python-version: 3.8
+
+    - name: Install dependencies
+      run: |
+        conda install -c conda-forge pygraphviz
+        cd package && python -m pip --use-deprecated=legacy-resolver --no-cache-dir install .
+        pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cpu.html
+    - name: Build dmol-book package
+      run: |
+        pip install build
+        cd package && python -m build --sdist --wheel --outdir dist/ .
+    - name: Publish distribution 📦 to PyPI
+      uses: pypa/gh-action-pypi-publish@master
+      continue-on-error: true
+      with:
+        password: ${{ secrets.PYPI_API_TOKEN }}
+        packages_dir: package/dist/
+    - name: Build the book
+      run: env TF_CPP_MIN_LOG_LEVEL=3 jupyter-book build .
+    - name: Deploy Jupyter book to GitHub pages
+      uses: peaceiris/[email protected]
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        publish_dir: _build/html
+        #force_orphan: true
+        keep_files: true
+        destination_dir: ${{ steps.myref.outputs.branch }}
+        cname: dmol.pub

.github/workflows/deploy-jupyter-book.yml

@@ -43,4 +43,6 @@ jobs:
         github_token: ${{ secrets.GITHUB_TOKEN }}
         publish_dir: _build/html
         force_orphan: true
+        #keep_files: true
         cname: dmol.pub
+        destination_dir: latest

dl/gnn.ipynb

@@ -488,14 +488,14 @@
     "name: gcnanim\n",
     "alt: animation of the graph convolution layer operation\n",
     "----\n",
-    "Animation of the graph convolution layer operation. The left is input, right is output node features. Note that two layers are shown (see title change). As the animation plays out, you can see how the information about the atoms propagates through the molecule via the averaging over neigbhors. So the oxygen goes from being just an oxygen, to an oxygen bonded to C and H, to an oxygen bonded to an H and CH3. The colors just reflect the same information in the numerical values.\n",
+    "Animation of the graph convolution layer operation. The left is input, right is output node features. Note that two layers are shown (see title change). As the animation plays out, you can see how the information about the atoms propagates through the molecule via the averaging over neighbors. So the oxygen goes from being just an oxygen, to an oxygen bonded to C and H, to an oxygen bonded to an H and CH3. The colors just reflect the same information in the numerical values.\n",
     "```\n",
     "\n",
     "\n",
     "### GCN Implementation\n",
     "\n",
     "Let's now create a tensor implementation of the GCN. We'll skip the activation and trainable weights for now.\n",
-    "We must first compute our rank 2 adjacency matrix. The `smiles2graph` code above computes an adjacency tensor with feature vectors. We can fix that with a simple reduction and add the identity at the same time\n"
+    "We must first compute our rank 2 adjacency matrix. The `smiles2graph` code above computes an adjacency tensor with feature vectors. We can fix that with a simple reduction and add the identity at the same time"
    ]
   },
   {
@@ -1819,7 +1819,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3.7.8 64-bit",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1833,7 +1833,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.8.12"
   },
   "vscode": {
    "interpreter": {