fixes

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

@@ -211,13 +211,13 @@ inside the python code and scale computations over the jobs.
 creates a Dask Scheduler in the Python process
 where the cluster object is instantiated.
 Please check the example of a definition of the cluster object
-for the partition `alpha` (queue at the dask terms) on the ZIH system:
+for the cluster `Alpha` (queue at the dask terms) on the ZIH system:
 
 ```python
 from dask_jobqueue import SLURMCluster
 
 cluster = SLURMCluster(queue='alpha',
-  cores=8,
+  cores=4,
   processes=2,
   project='p_number_crunch',
   memory="8GB",

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

@@ -65,9 +65,9 @@ marie@compute$ R -e 'install.packages("ggplot2")'
 ## Deep Learning with R
 
 The deep learning frameworks perform extremely fast when run on accelerators such as GPU.
-Therefore, using nodes with built-in GPUs, e.g., partitions
-[ml](../jobs_and_resources/hardware_overview.md)
-and [alpha](../jobs_and_resources/alpha_centauri.md), is beneficial for the examples here.
+Therefore, using nodes with built-in GPUs, e.g., clusters
+[Power9](../jobs_and_resources/hardware_overview.md)
+and [Alpha](../jobs_and_resources/alpha_centauri.md), is beneficial for the examples here.
 
 ### R Interface to TensorFlow
 
@@ -399,7 +399,7 @@ Another example:
     #snow::stopCluster(cl)  # usually it hangs over here with Open MPI > 2.0. In this case this command may be avoided, Slurm will clean up after the job finishes
     ```
 
-To use Rmpi and MPI please use one of these partitions: `haswell`, `broadwell` or `rome`.
+To use Rmpi and MPI please use one of these clusters: `Alpha`, `Barnard` or `Romeo`.
 
 Use `mpirun` command to start the R script. It is a wrapper that enables the communication
 between processes running on different nodes. It is important to use `-np 1` (the number of spawned

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

@@ -180,7 +180,7 @@ can deactivate the conda environment as follows:
 
 ??? example
 
-    This is an example on partition `alpha`. The example creates a conda virtual environment, and
+    This is an example on cluster `Alpha`. The example creates a conda virtual environment, and
     installs the package `torchvision` with conda.
     ```console
     marie@login.alpha$ srun --nodes=1 --gres=gpu:1 --time=01:00:00 --pty bash

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

@@ -12,7 +12,7 @@ marie@compute$ module spider TensorFlow
 [...]
 ```
 
-to find out, which TensorFlow modules are available on your partition.
+to find out, which TensorFlow modules are available on your cluster.
 
 On ZIH systems, TensorFlow 2 is the default module version. For compatibility hints between
 TensorFlow 2 and TensorFlow 1, see the corresponding [section below](#compatibility-tf2-and-tf1).