Apply 2 suggestion(s) to 2 file(s)

Co-authored-by: Martin Schroschk <martin.schroschk@tu-dresden.de>

doc.zih.tu-dresden.de/docs/access/jupyterhub.md

@@ -60,7 +60,7 @@ We have created three profiles for each cluster, namely:
 | Barnard | 1 core, 1.5 GB, 1 hour             | x86_64 (Intel)  | Python programming        |
 | Barnard | 2 core, 3 GB, 4 hours              | x86_64 (Intel)  | Julia and R programming   |
 | Barnard | 4 core, 8 GB, 8 hours              | x86_64 (Intel)  |                           |
-| Capella | 1 core, 1.5 GB 1 hour                | x86_64 (AMD)    | Python programming        |
+| Capella | 1 core, 1.5 GB, 1 hour                | x86_64 (AMD)    | Python programming        |
 | Capella | 2 core, 3 GB, 4 hours                | x86_64 (AMD)    | R programming             |
 | Capella | 4 core, 8 GB, 8 hours                | x86_64 (AMD)    |                           |
 | Romeo | 1 core, 1.5 GB 1 hour                | x86_64 (AMD)    | Python programming        |

doc.zih.tu-dresden.de/docs/access/jupyterlab.md

@@ -59,8 +59,8 @@ Wait until you receive a message with further instructions on how to connect to
 
     ```console
     marie@local$ ssh -XC marie@login1.capella.hpc.tu-dresden.de
-    marie@login$ ml release/24.04 GCCcore/12.2.0
-    marie@login$ ml Python/3.10.8
+    marie@login$ module load release/24.04 GCCcore/12.2.0
+    marie@login$ module load Python/3.10.8
     marie@login$ source /software/util/JupyterLab/barnard/jupyterlab-4.0.4/bin/activate
     marie@login$ srun --nodes=1 --ntasks=1 --cpus-per-task=4 --mem-per-cpu=8192 --x11 --pty --gres=gpu:1 bash -l
     marie@compute$ jupyter lab -y