Fix commands and update expected output in changed code

doc.zih.tu-dresden.de/docs/software/distributed_training.md

@@ -259,7 +259,7 @@ Or if you want to use Horovod on the cluster `alpha`, you can load it with the d
 
 ```console
 marie@alpha$ module spider Horovod                         #Check available modules
-marie@alpha$ module load release/23.04  GCC/10.2.0  CUDA/11.1.1  OpenMPI/4.0.5 Horovod/0.21.1-TensorFlow-2.4.1
+marie@alpha$ module load release/23.04  release/23.04  GCC/11.3.0  OpenMPI/4.1.4  Horovod/0.28.1-CUDA-11.7.0-TensorFlow-2.11.0
 ```
 
 #### Horovod Installation

doc.zih.tu-dresden.de/docs/software/hyperparameter_optimization.md

@@ -202,7 +202,7 @@ There are the following script preparation steps for OmniOpt:
         [workspace](../data_lifecycle/workspaces.md).
 
         ```console
-        marie@login$ module load release/23.04  GCC/10.2.0  CUDA/11.1.1 OpenMPI/4.0.5 PyTorch/1.9.0
+        marie@login$ module load release/23.04  GCC/11.3.0  OpenMPI/4.1.4 PyTorch/1.12.1
         marie@login$ mkdir </path/to/workspace/python-environments>    #create folder
         marie@login$ virtualenv --system-site-packages </path/to/workspace/python-environments/torchvision_env>
         marie@login$ source </path/to/workspace/python-environments/torchvision_env>/bin/activate #activate virtual environment
@@ -212,11 +212,10 @@ There are the following script preparation steps for OmniOpt:
     ```console
     # Job submission on alpha nodes with 1 GPU on 1 node with 800 MB per CPU
     marie@login$ srun --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=800 bash
-    marie@alpha$ module load release/23.04 GCC/10.2.0  CUDA/11.1.1 OpenMPI/4.0.5 PyTorch/1.9.0
+    marie@alpha$ module load release/23.04 GCC/11.3.0  OpenMPI/4.1.4 PyTorch/1.12.1
     # Activate virtual environment
     marie@alpha$ source </path/to/workspace/python-environments/torchvision_env>/bin/activate
 
-    Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies loaded.
     marie@alpha$ python </path/to/your/script/mnistFashion.py> --out-layer1=200 --batchsize=10 --epochs=3
     [...]
     Epoch 3

doc.zih.tu-dresden.de/docs/software/modules.md

@@ -257,13 +257,18 @@ In some cases a desired software is available as an extension of a module.
     Finaly, you can load the dependencies and `tensorboard/2.4.1` and check the version.
 
     ```console
-    marie@login$ module load release/23.04 GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5
+    marie@login$ module load release/23.04  GCC/11.3.0  OpenMPI/4.1.4
+
+    Modules GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5 and 15 dependencies loaded.
+    marie@login$ module load TensorFlow/2.11.0-CUDA-11.7.0
+
+    Aktiviere Module:
+      1) CUDA/11.7.0     2) GDRCopy/2.3
+
+    Module TensorFlow/2.11.0-CUDA-11.7.0 and 39 dependencies loaded.
 
-    Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5 and 15 dependencies loaded.
-    marie@login$ module load TensorFlow/2.4.1
-    Module TensorFlow/2.4.1 and 34 dependencies loaded.
     marie@login$ tensorboard --version
-    2.4.1
+    2.11.1
     ```
 
 ## Toolchains

doc.zih.tu-dresden.de/docs/software/pytorch.md

@@ -65,8 +65,8 @@ marie@login.power$ module spider pytorch
 we know that we can load PyTorch (including torchvision) with
 
 ```console
-marie@power$ module load torchvision/0.7.0-fossCUDA-2019b-Python-3.7.4-PyTorch-1.6.0
-Module torchvision/0.7.0-fossCUDA-2019b-Python-3.7.4-PyTorch-1.6.0 and 55 dependencies loaded.
+marie@power$ module load release/23.04  GCC/11.3.0  OpenMPI/4.1.4 torchvision/0.13.1
+Modules GCC/11.3.0, OpenMPI/4.1.4, torchvision/0.13.1 and 62 dependencies loaded.
 ```
 
 Now, we check that we can access PyTorch: