Updating 2022 dates and small Lisa update

Author: phlippe

docs/index.rst

@@ -7,7 +7,7 @@ Welcome to the UvA Deep Learning Tutorials!
 ===========================================
 
 | *Course website*: https://uvadlc.github.io/
-| *Course edition*: DL1 - Fall 2021, DL2 - Spring 2022, Being kept up to date
+| *Course edition*: DL1 - Fall 2022, DL2 - Spring 2022, Being kept up to date
 | *Repository*: https://github.com/phlippe/uvadlc_notebooks
 | *Recordings*: `YouTube Playlist <https://www.youtube.com/playlist?list=PLdlPlO1QhMiAkedeu0aJixfkknLRxk1nA>`_
 | *Author*: Phillip Lippe
@@ -31,26 +31,25 @@ Further, the content presented will be relevant for the graded assignment and ex
 The tutorials have been integrated as official tutorials of PyTorch Lightning.
 Thus, you can also view them in `their documentation <https://pytorch-lightning.readthedocs.io/en/latest/>`_.
 
-Schedule (Deep Learning 1)
---------------------------
+Schedule (Deep Learning 1, edition 2022)
+----------------------------------------
 
 +------------------------------------------+---------------------------------------------------+
 | **Date**                                 | **Notebook**                                      |
 +------------------------------------------+---------------------------------------------------+
-| Thursday, 4. November 2021, 13.00-15.00  | Tutorial 2: Introduction to PyTorch               |
-|                                          | Guide 1: Working with the Lisa cluster            |
+| Tuesday, 1. November 2022, 17.00-18.00   | Tutorial 2: Introduction to PyTorch               |
 +------------------------------------------+---------------------------------------------------+
-| Thursday, 11. November 2021, 13.00-14.00 | Tutorial 3: Activation functions                  |
+| Tuesday, 8. November 2022, 17.00-18.00   | Tutorial 3: Activation functions                  |
 +------------------------------------------+---------------------------------------------------+
-| Thursday, 18. November 2021, 13.00-14.00 | Tutorial 4: Optimization and Initialization       |
+| Tuesday, 15. November 2022, 17.00-18.00  | Tutorial 4: Optimization and Initialization       |
 +------------------------------------------+---------------------------------------------------+
-| Thursday, 25. November 2021, 13.00-14.00 | Tutorial 5: Inception, ResNet and DenseNet        |
+| Tuesday, 22. November 2022, 17.00-18.00  | Tutorial 5: Inception, ResNet and DenseNet        |
 +------------------------------------------+---------------------------------------------------+
-| Thursday, 2. December 2021, 13.00-14.00  | Tutorial 7: Graph Neural Networks                 |
+| Tuesday, 29. November 2022, 17.00-18.00  | Tutorial 6: Transformers and Multi-Head Attention |
 +------------------------------------------+---------------------------------------------------+
-| Thursday, 9. December 2021, 13.00-14.00  | Tutorial 6: Transformers and Multi-Head Attention |
+| Tuesday, 6. December 2022, 17.00-18.00   | Tutorial 7: Graph Neural Networks                 |
 +------------------------------------------+---------------------------------------------------+
-| Thursday, 16. December 2021, 13.00-14.00 | Tutorial 9: Deep Autoencoders                     |
+| Tuesday, 13. December 2022, 17.00-18.00  | TBA                                               |
 +------------------------------------------+---------------------------------------------------+
 
 How to run the notebooks

docs/tutorial_notebooks/tutorial1/Lisa_Cluster.ipynb

@@ -376,11 +376,26 @@
     "--seed 45 --learning_rate 1e-2\n",
     "```"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Additional partitions\n",
+    "\n",
+    "During the Master's program at the University of Amsterdam, you will likely gain accounts for Lisa during other courses as well, as well as during your Master thesis (if performed at university). In this case, you can get access to partitions besides `gpu_shared_course` allowing you to run more computationally heavy experiments. A full list of partitions can be found [here](https://servicedesk.surf.nl/wiki/display/WIKI/Lisa+usage+and+accounting). Besides the nodes with the GeForce 1080Ti GPUs, the most commonly used partition for DL experiments is called `gpu_titanrtx` which has nodes with the following config: \n",
+    "\n",
+    "| Processor             | CPU Cores   |   RAM Memory | GPUs                            |\n",
+    "|:---------------------:|:-----------:|:------------:|:-------------------------------:|\n",
+    "| Gold 5118 (2.3GHz)    | 24          | 192GB        | 4x Titan RTX, 24 GB GDDR6       |\n",
+    "\n",
+    "The Titan RTX are faster and provide more GPU memory than the 1080Ti's. However, these nodes cost more than twice as many credits (91.2 vs 42.1 for full node per hour, see [accounting](https://servicedesk.surf.nl/wiki/display/WIKI/Lisa+usage+and+accounting)), so use them only if needed. Furthermore, in many cases, you do not need a full node and might only need a single GPU. Similar to the course partition, there exist `gpu_titanrtx_shared` and `gpu_shared` which allow you access to partial node uses, like 1 or 2 GPUs. "
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -394,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,