Merge, fix links and linter issues

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

@@ -248,7 +248,6 @@ provide a comprehensive collection of job examples.
     * Submisson: `marie@login$ sbatch batch_script.sh`
     * Run with fewer MPI tasks: `marie@login$ sbatch --ntasks 14 batch_script.sh`
 
-
 ## Manage and Control Jobs
 
 ### Job and Slurm Monitoring
@@ -321,6 +320,7 @@ We'd like to point your attention to the following options gain insight in your
     ```console
     marie@login$ sacct -j <JOBID>
     ```
+
 ??? example "Show all fields for a specific job"
 
     ```console
@@ -332,8 +332,9 @@ We'd like to point your attention to the following options gain insight in your
     ```console
     marie@login$ sacct -j <JOBID> -o JobName,MaxRSS,MaxVMSize,CPUTime,ConsumedEnergy
     ```
-The manual page (`man sacct`) and the [online reference](https://slurm.schedmd.com/sacct.html) provide a
-comprehensive documentation regarding available fields and formats.
+
+The manual page (`man sacct`) and the [online reference](https://slurm.schedmd.com/sacct.html)
+provide a comprehensive documentation regarding available fields and formats.
 
 !!! hint "Time span"
 
@@ -427,6 +428,7 @@ srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS ./application
 
 ![Hybrid MPI and OpenMP](misc/hybrid.png)
 {: align=center}
+
 ## Node Features for Selective Job Submission
 
 The nodes in our HPC system are becoming more diverse in multiple aspects: hardware, mounted

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

@@ -18,7 +18,7 @@ to find out, which PyTorch modules are available on your partition.
 We recommend using **Alpha** and/or **ML** partitions when working with machine learning workflows
 and the PyTorch library.
 You can find detailed hardware specification in our
-[hardware documentation](../jobs_and_resources/hardware_taurus.md).
+[hardware documentation](../jobs_and_resources/hardware_overview.md).
 
 ## PyTorch Console
 
@@ -44,7 +44,7 @@ Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies
     marie@alpha$ pip install torchvision --no-deps
     ```
 
-    Using the **--no-deps** option for "pip install" is necessary here as otherwise the PyTorch 
+    Using the **--no-deps** option for "pip install" is necessary here as otherwise the PyTorch
     version might be replaced and you will run into trouble with the cuda drivers.
 
 On the **ML** partition:

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

@@ -81,4 +81,4 @@ marie@local$ ssh -N -f -L 6006:taurusi8034.taurus.hrsk.tu-dresden.de:6006 <zih-l
 
 Now, you can see the TensorBoard in your browser at `http://localhost:6006/`.
 
-Note that you can also use TensorBoard in an [sbatch file](../jobs_and_resources/batch_systems.md).
+Note that you can also use TensorBoard in an [sbatch file](../jobs_and_resources/slurm.md).

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

@@ -19,7 +19,7 @@ TensorFlow 2 and TensorFlow 1, see the corresponding [section below](#compatibil
 
 We recommend using partitions **Alpha** and/or **ML** when working with machine learning workflows
 and the TensorFlow library. You can find detailed hardware specification in our
-[Hardware](../jobs_and_resources/hardware_taurus.md) documentation.
+[Hardware](../jobs_and_resources/hardware_overview.md) documentation.
 
 ## TensorFlow Console