diff --git a/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md b/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md
index ac354b51ea1457567cbed9010bc6eba1eb68db74..6c13dcee7742a53f12ff0f09c1cfa5eb15a22666 100644
--- a/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md
+++ b/doc.zih.tu-dresden.de/docs/use_of_hardware/AlphaCentauri.md
@@ -1,206 +1,203 @@
# Alpha Centauri - Multi-GPU cluster with NVIDIA A100
-The sub-cluster "AlphaCentauri" had been installed for AI-related
-computations (ScaDS.AI).
-
-
+The sub-cluster "AlphaCentauri" had been installed for AI-related computations (ScaDS.AI).
## Hardware
-- 34 nodes, each with
- - 8 x NVIDIA A100-SXM4 (40 GB RAM)
- - 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz, MultiThreading
- enabled
- - 1 TB RAM
- - 3.5 TB /tmp local NVMe device
-- Hostnames: taurusi\[8001-8034\]
-- SLURM partition **`alpha`**
+- 34 nodes, each with 8 x NVIDIA A100-SXM4 (40 GB RAM) 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz,
+- MultiThreading enabled
+ - 1 TB RAM 3.5 TB /tmp local NVMe device Hostnames: taurusi\[8001-8034\] Slurm partition
+ - **`alpha`**
## Hints for the usage
-These nodes of the cluster can be used like other "normal" GPU nodes
-(ml, gpu2).
+These nodes of the cluster can be used like other "normal" GPU nodes (ml, gpu2).
-<span class="twiki-macro RED"></span> **Attention:** <span
-class="twiki-macro ENDCOLOR"></span> These GPUs may only be used with
-**CUDA 11** or later. Earlier versions do not recognize the new hardware
-properly or cannot fully utilize it. Make sure the software you are
-using is built against this library.
+**Attention:** These GPUs may only be used with **CUDA 11** or later. Earlier versions do not
+recognize the new hardware properly or cannot fully utilize it. Make sure the software you are using
+is built against this library.
## Typical tasks
-\<span style="font-size: 1em;">Machine learning frameworks as TensorFlow
-and PyTorch are industry standards now. The example of work with PyTorch
-on the new AlphaCentauri sub-cluster is illustrated below in brief
-examples.\</span>
+Machine learning frameworks as TensorFlow and PyTorch are industry
+standards now. The example of work with PyTorch on the new AlphaCentauri sub-cluster is illustrated
+below in brief examples.
+
+There are three main options on how to work with Tensorflow and PyTorch on the Alpha Centauri
+cluster:
+
+1. **Modules**
+1 **Virtual Environments (manual software installation)**
+1. [JupyterHub](https://taurus.hrsk.tu-dresden.de/)
+1. [Containers](../software/containers.md)
-There are three main options on how to work with Tensorflow and PyTorch
-on the Alpha Centauri cluster: **1.** **Modules,** **2.** **Virtual**
-**Environments (manual software installation)**, **3. \<a
-href="<https://taurus.hrsk.tu-dresden.de/>"
-target="\_blank">Jyupterhub\</a> 4. \<a href="Container"
-target="\_blank">Containers\</a>.** \<br />\<br />
+### Modules
-### 1. Modules
+The easiest way is using the [module system](../software/modules.md) and Python virtual environment.
+Modules are a way to use frameworks, compilers, loader, libraries, and utilities. The software
+environment for the **alpha** partition is available under the name **hiera**:
-\<span
-style`"font-size: 1em;">The easiest way is using the </span><a href="RuntimeEnvironment#Module_Environments" target="_blank">Modules system</a><span style="font-size: 1em;"> and Python virtual environment. Modules are a way to use frameworks, compilers, loader, libraries, and utilities. The software environment for the </span> =alpha`
-\<span style="font-size: 1em;"> partition is available under the name
-**hiera** \</span> :
+```Bash
+module load modenv/hiera
+```
- module load modenv/hiera
+Machine learning frameworks **PyTorch** and **TensorFlow**available for **alpha** partition as
+modules with CUDA11, GCC 10 and OpenMPI4:
-Machine learning frameworks **PyTorch** and **TensorFlow**available for
-**alpha** partition as modules with CUDA11, GCC 10 and OpenMPI4:
+```Bash
+module load modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 PyTorch/1.7.1 module load
+modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 TensorFlow/2.4.1
+```
- module load modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 PyTorch/1.7.1
- module load modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 TensorFlow/2.4.1
+**Hint**: To check the available modules for the **hiera** software environment, use the command:
-%RED%Hint<span class="twiki-macro ENDCOLOR"></span>: To check the
-available modules for the **hiera** software environment, use the
-command:
+```Bash
+module available
+```
- module available
+To show all the dependencies you need to load for the core module, use the command:
-To show all the dependencies you need to load for the core module, use
-the command:
+```Bash
+module spider <name_of_the_module>
+```
- module spider <name_of_the_module>
+### Virtual Environments
-### 2. Virtual environments
+It is necessary to use virtual environments for your work with Python. A virtual environment is a
+cooperatively isolated runtime environment. There are two main options to use virtual environments:
+venv (standard Python tool) and
-It is necessary to use virtual environments for your work with Python. A
-virtual environment is a cooperatively isolated runtime environment.
-There are two main options to use virtual environments: venv (standard
-Python tool) and
+1. **Vitualenv** is a standard Python tool to create isolated Python environments. It is the
+**preferred** interface for managing installations and virtual environments on Taurus and part of
+the Python modules.
-1.** Vitualenv** is a standard Python tool to create isolated Python
-environments. It is the %RED%preferred<span
-class="twiki-macro ENDCOLOR"></span> interface for managing
-installations and virtual environments on Taurus and part of the Python
-modules.
+1. [Conda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment)
+is an alternative method for managing installations and virtual environments on Taurus. Conda is an
+open-source package management system and environment management system from Anaconda. The conda
+manager is included in all versions of Anaconda and Miniconda.
-2\. **\<a
-href="<https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment>"
-target="\_blank">Conda\</a>** is an alternative method for managing
-installations and virtual environments on Taurus. Conda is an
-open-source package management system and environment management system
-from Anaconda. The conda manager is included in all versions of Anaconda
-and Miniconda.
+**Note**: There are two sub-partitions of the alpha partition: alpha and
+alpha-interactive. Please use alpha-interactive for the interactive jobs and alpha for the batch
+jobs.
-**%RED%Note%ENDCOLOR%**: There are two sub-partitions of the alpha
-partition: alpha and alpha-interactive. Please use alpha-interactive for
-the interactive jobs and alpha for the batch jobs.
+Examples with conda and venv will be presented below. Also, there is an example of an interactive
+job for the AlphaCentauri sub-cluster using the `alpha-interactive` partition:
-Examples with conda and venv will be presented below. Also, there is an
-example of an interactive job for the AlphaCentauri sub-cluster using
-the `alpha-interactive` partition:
+```Bash
+srun -p alpha-interactive -N 1 -n 1 --gres=gpu:1 --time=01:00:00 --pty bash # Job submission in
+alpha nodes with 1 gpu on 1 node.
- srun -p alpha-interactive -N 1 -n 1 --gres=gpu:1 --time=01:00:00 --pty bash # Job submission in alpha nodes with 1 gpu on 1 node.<br /><br />mkdir conda-virtual-environments #create a folder, please use Workspaces! <br />cd conda-virtual-environments #go to folder<br />which python #check which python are you using<br />ml modenv/hiera<br />ml Miniconda3<br />which python #check which python are you using now<br />conda create -n conda-testenv python=3.8 #create virtual environment with the name conda-testenv and Python version 3.8 <br />conda activate conda-testenv #activate conda-testenv virtual environment <br />conda deactivate #Leave the virtual environment
+mkdir conda-virtual-environments #create a folder, please use Workspaces!
+cd conda-virtual-environments #go to folder
+which python #check which python are you using ml modenv/hiera
+ml Miniconda3
+which python #check which python are you using now
+conda create -n conda-testenv python=3.8 #create virtual environment with the name conda-testenv and Python version 3.8
+conda activate
+conda-testenv #activate conda-testenv virtual environment
+conda deactivate #Leave the virtual environment
+```
-New software for data analytics is emerging faster than we can install
-it. If you urgently need a certain version we advise you to manually
-install it (the machine learning frameworks and required packages) in
-your virtual environment (or use a \<a
-href="<https://doc.zih.tu-dresden.de/hpc-wiki/bin/view/Compendium/Container>"
-target="\_blank">container\</a>).
+New software for data analytics is emerging faster than we can install it. If you urgently need a
+certain version we advise you to manually install it (the machine learning frameworks and required
+packages) in your virtual environment (or use a [container](../software/containers.md).
The **Virtualenv** example:
- srun -p alpha-interactive -N 1 -n 1 --gres=gpu:1 --time=01:00:00 --pty bash #Job submission in alpha nodes with 1 gpu on 1 node.
+```Bash
+srun -p alpha-interactive -N 1 -n 1 --gres=gpu:1 --time=01:00:00 --pty bash
+#Job submission in alpha nodes with 1 gpu on 1 node.
- mkdir python-environments && cd "$_" # Optional: Create folder. Please use Workspaces!<br /><br />module load modenv/hiera modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 Python/3.8.6 #Changing the environment and load necessary modules
- which python #Check which python are you using
- virtualenv --system-site-packages python-environments/envtest #Create virtual environment
- source python-environments/envtest/bin/activate #Activate virtual environment. Example output: (envtest) bash-4.2$
+mkdir python-environments && cd "$_" # Optional: Create folder. Please use Workspaces!
-Example of using **Conda** with a Pytorch and P\<span style="font-size:
-1em;">illow installation: \</span>
+module load modenv/hiera modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 Python/3.8.6 #Changing the environment and load necessary modules
+which python #Check which python are you using
+virtualenv --system-site-packages python-environments/envtest #Create virtual environment
+source python-environments/envtest/bin/activate #Activate virtual environment. Example output: (envtest) bash-4.2$
+```
- conda activate conda-testenv<br />conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c nvidia<br />conda install -c anaconda pillow
+Example of using **Conda** with a Pytorch and Pillow installation:
+
+```Bash
+conda activate conda-testenv
+conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c nvidia
+conda install -c anaconda pillow
+```
Verify installation for the **Venv** example:
- python #Start python
- from time import gmtime, strftime
- print(strftime("%Y-%m-%d %H:%M:%S", gmtime())) #Example output: 2019-11-18 13:54:16<br /><br />deactivate #Leave the virtual environment
+```Bash
+python #Start python from time import
+gmtime, strftime print(strftime("%Y-%m-%d %H:%M:%S", gmtime())) #Example output: 2019-11-18 13:54:16
+
+deactivate #Leave the virtual environment
+```
Verify installation for the **Conda** example:
- python #Start python
- import torch
- torch.version.__version__ #Example output: 1.8.1
-
-There is an example of the batch script for the typical usage of the
-Alpha Centauri cluster:
-
- #!/bin/bash
- #SBATCH --mem=40GB # specify the needed memory. Same amount memory as on the GPU
- #SBATCH -p alpha # specify Alpha-Centauri partition
- #SBATCH --gres=gpu:1 # use 1 GPU per node (i.e. use one GPU per task)
- #SBATCH --nodes=1 # request 1 node
- #SBATCH --time=00:15:00 # runs for 15 minutes
- #SBATCH -c 2 # how many cores per task allocated
- #SBATCH -o HLR_name_your_script.out # save output message under HLR_${SLURMJOBID}.out
- #SBATCH -e HLR_name_your_script.err # save error messages under HLR_${SLURMJOBID}.err
-
- module load modenv/hiera
- eval "$(conda shell.bash hook)"
- conda activate conda-testenv && python machine_learning_example.py
-
- ## when finished writing, submit with: sbatch <script_name> For example: sbatch machine_learning_script.sh
-
-The Alpha Centauri sub-cluster has the NVIDIA A100-SXM4 with 40 GB RAM
-each. Thus It is prudent to have the same memory on the host (cpu). The
-number of cores is free for the users to define, at the moment.
-
-### 3. JupyterHub
-
-There is \<a href="JupyterHub" target="\_self">jupyterhub\</a> on
-Taurus, where you can simply run your Jupyter notebook on Alpha-Centauri
-sub-cluster. Also, for more specific cases you can run a manually
-created remote jupyter server. You can find the manual server setup \<a
-href="DeepLearning" target="\_blank">here.\</a> However, the simplest
-option for beginners is using JupyterHub.
-
-JupyterHub is available here: \<a
-href="<https://taurus.hrsk.tu-dresden.de/jupyter>"
-target="\_top"><https://taurus.hrsk.tu-dresden.de/jupyter>\</a>
-
-\<a
-href`"https://taurus.hrsk.tu-dresden.de/jupyter" target="_top"></a>After logging, you can start a new session and configure it. There are simple and advanced forms to set up your session. The =alpha`
-partition is available in advanced form. You have to choose the
-\<span>alpha\</span> partition in the partition field. The resource
-recommendations to allocate are the same as described above for the
-batch script example (not confuse `--mem-per-cpu` with `--mem`
+```Bash
+python #Start python import torch torch.version.__version__ #Example output: 1.8.1
+```
+
+There is an example of the batch script for the typical usage of the Alpha Centauri cluster:
+
+```Bash
+#!/bin/bash #SBATCH --mem=40GB # specify the needed memory. Same amount memory as
+on the GPU #SBATCH -p alpha # specify Alpha-Centauri partition #SBATCH
+--gres=gpu:1 # use 1 GPU per node (i.e. use one GPU per task) #SBATCH --nodes=1
+# request 1 node #SBATCH --time=00:15:00 # runs for 15 minutes #SBATCH -c 2
+# how many cores per task allocated #SBATCH -o HLR_name_your_script.out # save output
+message under HLR_${SLURMJOBID}.out #SBATCH -e HLR_name_your_script.err # save error
+messages under HLR_${SLURMJOBID}.err
+
+module load modenv/hiera eval "$(conda shell.bash hook)" conda activate conda-testenv && python
+machine_learning_example.py
+
+## when finished writing, submit with: sbatch <script_name> For example: sbatch
+machine_learning_script.sh
+```
+
+The Alpha Centauri sub-cluster has the NVIDIA A100-SXM4 with 40 GB RAM each. Thus It is prudent to
+have the same memory on the host (cpu). The number of cores is free for the users to define, at the
+moment.
+
+### JupyterHub
+
+There is [JupyterHub](../software/JupyterHub.md) on Taurus, where you can simply run
+your Jupyter notebook on Alpha-Centauri sub-cluster. Also, for more specific cases you can run a
+manually created remote jupyter server. You can find the manual server setup
+[here](../software/DeepLearning.md). However, the simplest option for beginners is using
+JupyterHub.
+
+JupyterHub is available at
+[taurus.hrsk.tu-dresden.de/jupyter](https://taurus.hrsk.tu-dresden.de/jupyter).
+
+After logging, you can start a new session and configure it. There are simple and advanced forms to
+set up your session. The `alpha` partition is available in advanced form. You have to choose the
+alpha partition in the partition field. The resource recommendations to allocate are
+the same as described above for the batch script example (not confuse `--mem-per-cpu` with `--mem`
parameter).
-### 4. Containers
+### Containers
-On Taurus \<a
-href`"https://sylabs.io/" target="_blank">Singularity</a> used as a standard container solution. It can be run on the =alpha`
-partition as well. Singularity enables users to have full control of
-their environment. Detailed information about containers can be found
-[here](Container).
+On Taurus [Singularity](https://sylabs.io/) is used as a standard container
+solution. It can be run on the `alpha` partition as well. Singularity enables users to have full
+control of their environment. Detailed information about containers can be found
+[here](../software/containers.md).
Nvidia
[NGC](https://developer.nvidia.com/blog/how-to-run-ngc-deep-learning-containers-with-singularity/)
-containers can be used as an effective solution for machine learning
-related tasks. (Downloading containers requires registration).
-Nvidia-prepared containers with software solutions for specific
-scientific problems can simplify the deployment of deep learning
-workloads on HPC. NGC containers have shown consistent performance
-compared to directly run code.
-
-## \<span style="font-size: 1em;">Examples\</span>
+containers can be used as an effective solution for machine learning related tasks. (Downloading
+containers requires registration). Nvidia-prepared containers with software solutions for specific
+scientific problems can simplify the deployment of deep learning workloads on HPC. NGC containers
+have shown consistent performance compared to directly run code.
-There is a test example of a deep learning task that could be used for
-the test. For the correct work, Pytorch and Pillow package should be
-installed in your virtual environment (how it was shown above in the
-interactive job example)
+## Examples
-- [example_pytorch_image_recognition.zip](%ATTACHURL%/example_pytorch_image_recognition.zip):
- example_pytorch_image_recognition.zip
+There is a test example of a deep learning task that could be used for the test. For the correct
+work, Pytorch and Pillow package should be installed in your virtual environment (how it was shown
+above in the interactive job example)
-\<div id="gtx-trans" style="position: absolute; left: 8px; top:
-1248.47px;"> \</div>
+- [example_pytorch_image_recognition.zip]**todo attachment**
+<!--%ATTACHURL%/example_pytorch_image_recognition.zip:-->
+ <!--example_pytorch_image_recognition.zip-->