Automated link checking (#117)

* Automated link checking

* Fix links in Jupyter Nbs

.github/workflows/check-links.yml

@@ -1,4 +1,4 @@
-name: Check Markdown Links
+name: Check hyperlinks
 
 on:
   push:
@@ -9,27 +9,22 @@ on:
       - main
 
 jobs:
-  check-links:
+  test:
     runs-on: ubuntu-latest
 
     steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
-      - name: Install Markdown Link Checker
-        run: npm install -g markdown-link-check
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
 
-      - name: Create config for markdown link checker
-        run: |
-          echo '{
-            "projectBaseUrl":"${{ github.workspace }}",
-            "ignorePatterns": [
-              {
-                "pattern": "^#"
-              }
-            ]
-          }' > $GITHUB_WORKSPACE/md_checker_config.json
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest pytest-check-links
 
-      - name: Find Markdown Files and Check Links
-        run: |
-          find . -name \*.md -print0 | xargs -0 -n1 markdown-link-check -c $GITHUB_WORKSPACE/md_checker_config.json
+    - name: Check links
+      run: |
+        pytest --check-links ./

ch02/01_main-chapter-code/ch02.ipynb

@@ -1621,7 +1621,7 @@
    "id": "08218d9f-aa1a-4afb-a105-72ff96a54e73",
    "metadata": {},
    "source": [
-    "- **You may be interested in the bonus content comparing embedding layers with regular linear layers: [../02_bonus_efficient-multihead-attention](../02_bonus_efficient-multihead-attention)**"
+    "- **You may be interested in the bonus content comparing embedding layers with regular linear layers: [../03_bonus_embedding-vs-matmul](../03_bonus_embedding-vs-matmul)**"
    ]
   },
   {
@@ -1874,7 +1874,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

ch05/01_main-chapter-code/ch05.ipynb

@@ -1164,7 +1164,7 @@
    "metadata": {},
    "source": [
     "- In this section, we finally implement the code for training the LLM\n",
-    "- We focus on a simple training function (if you are interested in augmenting this training function with more advanced techniques, such as learning rate warmup, cosine annealing, and gradient clipping, please refer to [Appendix D](../../appendix-D/03_main-chapter-code))\n",
+    "- We focus on a simple training function (if you are interested in augmenting this training function with more advanced techniques, such as learning rate warmup, cosine annealing, and gradient clipping, please refer to [Appendix D](../../appendix-D/01_main-chapter-code))\n",
     "\n",
     "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch05_compressed/train-steps.webp\" width=300px>"
    ]
@@ -2028,7 +2028,7 @@
    "metadata": {},
    "source": [
     "- Previously, we only trained a small GPT-2 model using a very small short-story book for educational purposes\n",
-    "- Interested readers can also find a longer pretraining run on the complete Project Gutenberg book corpus in [../03_bonus_pretraining_on_gutenberg](03_bonus_pretraining_on_gutenberg)\n",
+    "- Interested readers can also find a longer pretraining run on the complete Project Gutenberg book corpus in [../03_bonus_pretraining_on_gutenberg](../03_bonus_pretraining_on_gutenberg)\n",
     "- Fortunately, we don't have to spend tens to hundreds of thousands of dollars to pretrain the model on a large pretraining corpus but can load the pretrained weights provided by OpenAI"
    ]
   },
@@ -2438,7 +2438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,