diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/capella.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/capella.md
index 6b82a999c6658727c213cd6aa138e3e73ae300f2..3347343e7e9ad009fdf35e700facacf7661713cf 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/capella.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/capella.md
@@ -20,69 +20,30 @@ HPC simulations.
Capella has a fast WEKAio file system mounted on `/data/cat`. It is only mounted on Capella and the
[Datamover nodes](../data_transfer/datamover.md).
-It should be used as the main working file system on Capella.
-Although all other [filesystems](../data_lifecycle/file_systems.md)
-(`/home`, `/software`, `/data/horse`, `/data/walrus`, etc.) are also available.
-
-### Modules
-
-The easiest way is using the [module system](../software/modules.md).
-All software available from the module system has been deliberately build for the cluster `Alpha`
-i.e., with optimization for Zen4 (Genoa) microarchitecture and CUDA-support enabled.
-
-To check the available modules for `Capella`, use the command
-
-```console
-marie@login.capella$ module spider <module_name>
-```
-
-??? example "Example: Searching and loading PyTorch"
+It should be used as the main working file system on Capella and has to used by [workspaces](../data_lifecycle/file_systems.md).
+Workspaces can only be created on Capella login and compute nodes, not on the other clusters.
- For example, to check which `PyTorch` versions are available you can invoke
-
- ```console
- marie@login.capella$ module spider PyTorch
-
- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- PyTorch: PyTorch/2.1.2-CUDA-12.1.1
- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- Description:
- Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework that puts Python first.
+Although all other [filesystems](../data_lifecycle/workspaces.md)
+(`/home`, `/software`, `/data/horse`, `/data/walrus`, etc.) are also available.
+!!!
- You will need to load all module(s) on any one of the lines below before the "PyTorch/2.1.2-CUDA-12.1.1" module is available to load.
+ We recommend to store your data on `/data/walrus` in an archive file and only move your hot data via
+ [Datamover nodes](../data_transfer/datamover.md) into `/data/cat` which should be used as a fast
+ staging memory.
- release/24.04 GCC/12.3.0 OpenMPI/4.1.5
-
- Help:
- Description
- ===========
- Tensors and Dynamic neural networks in Python with strong GPU acceleration.
- PyTorch is a deep learning framework that puts Python first.
-
-
- More information
- ================
- - Homepage: https://pytorch.org/
- ```
+### Modules
- ```console
- marie@login.capella$ python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"
- 2.1.12
- True
- ```
+The easiest way using software is using the [module system](../software/modules.md).
+All software available from the module system has been deliberately build for the cluster `Capella`
+i.e., with optimization for Zen4 (Genoa) microarchitecture and CUDA-support enabled.
### Python Virtual Environments
[Virtual environments](../software/python_virtual_environments.md) allow you to install
additional Python packages and create an isolated runtime environment. We recommend using
-`virtualenv` for this purpose.
-
-An example how to create an [python virtual environment with `torchvision` package](alpha_centauri.md#python-virtual-environments) is
- described for the GPU alpha cluster and is identical if you are using the Capella cluster.
-
+`venv` for this purpose.
!!! hint
We recommend to use [workspaces](../data_lifecycle/workspaces.md) for your virtual environments.
-