diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md
index 40534bd3bcd13a6bb1e01e6d8dd90b5a9cfdbb4e..5324f550e30e66b6ec6830cf7fddbb921b0dbdbf 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md
@@ -21,7 +21,6 @@ It has 34 nodes, each with:
The easiest way is using the [module system](../software/modules.md).
The software for the `alpha` partition is available in `modenv/hiera` module environment.
-
To check the available modules for `modenv/hiera`, use the command
```bash
diff --git a/doc.zih.tu-dresden.de/docs/software/distributed_training.md b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
index c43e2890880d3376f88f29c6cb882df4ec37dc91..165dad639c6ab2a90f99724aa43c05c0fea20d17 100644
--- a/doc.zih.tu-dresden.de/docs/software/distributed_training.md
+++ b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
@@ -5,10 +5,10 @@
### Distributed TensorFlow
TODO
-
+
### Distributed Pytorch
-**hint: just copied some old content as starting point**
+just copied some old content as starting point
#### Using Multiple GPUs with PyTorch
@@ -180,4 +180,4 @@ install command after loading the NCCL module:
```Bash
module load NCCL/2.3.7-fosscuda-2018b
HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL HOROVOD_WITHOUT_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 pip install --no-cache-dir horovod
-```
\ No newline at end of file
+```
diff --git a/doc.zih.tu-dresden.de/docs/software/hyperparameter_optimization.md b/doc.zih.tu-dresden.de/docs/software/hyperparameter_optimization.md
index b75d6031e9774bf6d6c275c83cd8914a750ce50b..07ec3f6cee2cb9c6a45d69cd49e2c6e68affaebc 100644
--- a/doc.zih.tu-dresden.de/docs/software/hyperparameter_optimization.md
+++ b/doc.zih.tu-dresden.de/docs/software/hyperparameter_optimization.md
@@ -196,11 +196,13 @@ There are three script preparation steps for OmniOpt:
### Configure and Run OmniOpt
-As a starting point, configuring OmniOpt is done via a GUI at [https://imageseg.scads.ai/omnioptgui/](https://imageseg.scads.ai/omnioptgui/).
-This GUI guides through the configuration process and as result the config file is created automatically according to the GUI input.
-If you are more familiar with using OmniOpt later on, this config file can be modified directly without using the GUI.
+As a starting point, configuring OmniOpt is done via a GUI at
+[https://imageseg.scads.ai/omnioptgui/](https://imageseg.scads.ai/omnioptgui/).
+This GUI guides through the configuration process and as result the config file is created
+automatically according to the GUI input. If you are more familiar with using OmniOpt later on,
+this config file can be modified directly without using the GUI.
-A screenshot of the GUI, including a properly configuration for the MNIST fashion example is shown below.
+A screenshot of the GUI, including a properly configuration for the MNIST fashion example is shown below.
The GUI, in which the displayed values are already entered, can be reached [here](https://imageseg.scads.ai/omnioptgui/?maxevalserror=5&mem_per_worker=1000&projectname=mnist-fashion-optimization-set-1&partition=alpha&searchtype=tpe.suggest&objective_program=bash%20%2Fscratch%2Fws%2Fpath%2Fto%2Fyou%2Fscript%2Frun-mnist-fashion.sh%20(%24x_0)%20(%24x_1)%20(%24x_2)¶m_0_type=hp.randint¶m_1_type=hp.randint&number_of_parameters=3){:target="_blank"}.
