GPL tutorial - add GPU header and open in colab button

.github/workflows/tutorials.yml

@@ -71,4 +71,4 @@ jobs:
         token: ${{ secrets.GITHUB_TOKEN }}
 
     - name: Run tutorials
-      run: ./.github/utils/tutorials.sh ${{ env.pythonLocation }} "${{ steps.diff.outputs.added_modified }}" "Tutorial2_ Tutorial9_ Tutorial13_"
+      run: ./.github/utils/tutorials.sh ${{ env.pythonLocation }} "${{ steps.diff.outputs.added_modified }}" "Tutorial2_ Tutorial9_ Tutorial13_ Tutorial18_"

docs/_src/tutorials/tutorials/18.md

@@ -8,6 +8,8 @@ id: "tutorial18md"
 --->
 
 # Generative Pseudo Labeling for Domain Adaptation of Dense Retrievals
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial18_GPL.ipynb)
+
 #### Note: Adapted to Haystack from Nils Riemers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)
 
 The NLP models we use every day were trained on a corpus of data that reflects the world from the past. In the meantime, we've experienced world-changing events, like the COVID pandemics, and we'd like our models to know about them. Training a model from scratch is tedious work but what if we could just update the models with new data? Generative Pseudo Labeling comes to the rescue.
@@ -37,6 +39,15 @@ If we search again with the updated model, we get the search results we would ex
 - 95.14	Polio is transmitted via contaminated water or food
 - 94.13	HIV is transmitted via sex or sharing needles
 
+### Prepare the Environment
+
+#### Colab: Enable the GPU runtime
+Make sure you enable the GPU runtime to experience decent speed in this tutorial.
+**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
+
+<img src="https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/colab_gpu_runtime.jpg">
+
+
 
 ```python
 !nvidia-smi

tutorials/Tutorial18_GPL.ipynb

@@ -4,6 +4,8 @@
    "cell_type": "markdown",
    "source": [
     "# Generative Pseudo Labeling for Domain Adaptation of Dense Retrievals\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial18_GPL.ipynb)\n",
+    "\n",
     "#### Note: Adapted to Haystack from Nils Riemers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)\n",
     "\n",
     "The NLP models we use every day were trained on a corpus of data that reflects the world from the past. In the meantime, we've experienced world-changing events, like the COVID pandemics, and we'd like our models to know about them. Training a model from scratch is tedious work but what if we could just update the models with new data? Generative Pseudo Labeling comes to the rescue.\n",
@@ -31,7 +33,15 @@
     "- 97.70\tCorona is transmitted via the air\n",
     "- 96.71\tEbola is transmitted via direct contact with blood\n",
     "- 95.14\tPolio is transmitted via contaminated water or food\n",
-    "- 94.13\tHIV is transmitted via sex or sharing needles"
+    "- 94.13\tHIV is transmitted via sex or sharing needles\n",
+    "\n",
+    "### Prepare the Environment\n",
+    "\n",
+    "#### Colab: Enable the GPU runtime\n",
+    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
+    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/colab_gpu_runtime.jpg\">\n"
    ],
    "metadata": {
     "collapsed": false,