Update pytorch.md

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

@@ -15,18 +15,23 @@ marie@login$ module spider pytorch
 
 to find out, which PyTorch modules are available.
 
-We recommend using partitions `alpha` and/or `ml` when working with machine learning workflows
+We recommend using the cluster `alpha` and/or `power` when working with machine learning workflows
 and the PyTorch library.
 You can find detailed hardware specification in our
 [hardware documentation](../jobs_and_resources/hardware_overview.md).
 
+
+_The module environments /hiera, /scs5, /classic and /ml originated from the taurus system are momentarily under construction. The script will be updated after completion of the redesign accordingly_
+
 ## PyTorch Console
 
-On the partition `alpha`, load the module environment:
+On the cluster `alpha`, load the module environment:
+
 
 ```console
 # Job submission on alpha nodes with 1 gpu on 1 node with 800 Mb per CPU
-marie@login$ srun -p alpha --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=800 bash
+
+marie@login.alpha$ srun --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=800 bash
 marie@alpha$ module load modenv/hiera  GCC/10.2.0  CUDA/11.1.1 OpenMPI/4.0.5 PyTorch/1.9.0
 Die folgenden Module wurden in einer anderen Version erneut geladen:
   1) modenv/scs5 => modenv/hiera
@@ -34,9 +39,9 @@ Die folgenden Module wurden in einer anderen Version erneut geladen:
 Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies loaded.
 ```
 
-??? hint "Torchvision on partition `alpha`"
+??? hint "Torchvision on the cluster `alpha`"
 
-    On the partition `alpha`, the module torchvision is not yet available within the module
+    On the cluster `alpha`, the module torchvision is not yet available within the module
     system. (19.08.2021)
     Torchvision can be made available by using a virtual environment:
 
@@ -49,46 +54,46 @@ Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies
     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 partition `ml`:
+On the cluster `power`:
 
 ```console
-# Job submission in ml nodes with 1 gpu on 1 node with 800 Mb per CPU
-marie@login$ srun -p ml --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=800 bash
+# Job submission in power nodes with 1 gpu on 1 node with 800 Mb per CPU
+marie@login.power$ srun --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=800 bash
 ```
 
 After calling
 
 ```console
-marie@login$ module spider pytorch
+marie@login.power$ module spider pytorch
 ```
 
 we know that we can load PyTorch (including torchvision) with
 
 ```console
-marie@ml$ module load modenv/ml torchvision/0.7.0-fossCUDA-2019b-Python-3.7.4-PyTorch-1.6.0
+marie@power$ module load modenv/ml torchvision/0.7.0-fossCUDA-2019b-Python-3.7.4-PyTorch-1.6.0
 Module torchvision/0.7.0-fossCUDA-2019b-Python-3.7.4-PyTorch-1.6.0 and 55 dependencies loaded.
 ```
 
 Now, we check that we can access PyTorch:
 
 ```console
-marie@{ml,alpha}$ python -c "import torch; print(torch.__version__)"
+marie@{power,alpha}$ python -c "import torch; print(torch.__version__)"
 ```
 
 The following example shows how to create a python virtual environment and import PyTorch.
 
 ```console
 # Create folder
-marie@ml$ mkdir python-environments
+marie@power$ mkdir python-environments
 # Check which python are you using
-marie@ml$ which python
+marie@power$ which python
 /sw/installed/Python/3.7.4-GCCcore-8.3.0/bin/python
 # Create virtual environment "env" which inheriting with global site packages
-marie@ml$ virtualenv --system-site-packages python-environments/env
+marie@power$ virtualenv --system-site-packages python-environments/env
 [...]
 # Activate virtual environment "env". Example output: (env) bash-4.2$
-marie@ml$ source python-environments/env/bin/activate
-marie@ml$ python -c "import torch; print(torch.__version__)"
+marie@power$ source python-environments/env/bin/activate
+marie@power$ python -c "import torch; print(torch.__version__)"
 ```
 
 ## PyTorch in JupyterHub