Linting

doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md

 # GPU Cluster Alpha Centauri
 
 ## Overview
+
 The multi-GPU cluster `Alpha Centauri` has been installed for AI-related computations (ScaDS.AI).
 
 ## Details

doc.zih.tu-dresden.de/docs/jobs_and_resources/hardware_overview.md

@@ -38,7 +38,6 @@ HPC resources at ZIH comprise a total of the **six systems**:
 All clusters will run with their own [Slurm batch system](slurm.md) and job submission is possible
 only from their respective login nodes.
 
-
 ## Login and Dataport Nodes
 
 - Login-Nodes

doc.zih.tu-dresden.de/docs/jobs_and_resources/overview.md

@@ -17,7 +17,7 @@ components.
 
 ## Selection of Suitable Hardware
 
-The six clusters 
+The six clusters
 [`Barnard`](barnard.md),
 [`Alpha Centauri`](alpha_centauri.md),
 [`Capella`](capella.md),

doc.zih.tu-dresden.de/docs/jobs_and_resources/power9.md

@@ -19,7 +19,6 @@ partition `power` within the now decommissioned `Taurus` system. With the decomm
 - Operating system: Alma Linux 8.7
 - Further information on the usage is documented below
 
-
 ## Usage
 
 If you want to use containers on `Power9`, please refer to the page
@@ -35,7 +34,6 @@ support that allows a total bandwidth with up to 300 GB/s. Each node on the
 cluster `Power9` has six Tesla V100 GPUs. You can find a detailed specification of the cluster in our
 [Power9 documentation](../jobs_and_resources/hardware_overview.md).
 
-
 !!! note
 
     The cluster `power` is based on the Power9 architecture, which means that the software built

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

@@ -4,9 +4,7 @@ This is an introduction of how to run machine learning applications on ZIH syste
 For machine learning purposes, we recommend to use the cluster `Alpha`, `Capella` and/or `Power9`.
 The hardware specification of each cluster can be found in the [overview](../jobs_and_resources/hardware_overview.md)
 
-
-
-### Modules
+## Modules
 
 The way of loading modules is identical on each cluster. Here we show an example
  on the cluster `alpha` how to load the module environment:
@@ -14,10 +12,11 @@ The way of loading modules is identical on each cluster. Here we show an example
 ```console
 marie@alpha$ module load release/23.04
 ```
+
 !!! note
-    
+
     Software and their available versions may differ among the clusters. Check the available
-     modules with `module spider <module_name>`
+    modules with `module spider <module_name>`
 
 ## Machine Learning via Console