Update tensorflow.md

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

@@ -17,13 +17,16 @@ to find out, which TensorFlow modules are available on your partition.
 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).
 
-We recommend using partitions `alpha` and/or `ml` when working with machine learning workflows
+We recommend using the clusters `alpha` and/or `power` when working with machine learning workflows
 and the TensorFlow library. You can find detailed hardware specification in our
 [Hardware](../jobs_and_resources/hardware_overview.md) documentation.
 
 ## TensorFlow Console
 
-On the partition `alpha`, load the module environment:
+_The module environments /hiera, /scs5, /classic and /ml originated from the old taurus system are momentarily under construction. The script will be updated after completion of the redesign accordingly_
+
+
+On the cluster `alpha`, load the module environment:
 
 ```console
 marie@alpha$ module load modenv/scs5
@@ -47,17 +50,17 @@ marie@alpha$ module avail TensorFlow
 [...]
 ```
 
-On the partition `ml` load the module environment:
+On the cluster `power` load the module environment:
 
 ```console
-marie@ml$ module load modenv/ml
+marie@power$ module load modenv/ml
 The following have been reloaded with a version change:  1) modenv/scs5 => modenv/ml
 ```
 
 This example shows how to install and start working with TensorFlow using the modules system.
 
 ```console
-marie@ml$ module load TensorFlow
+marie@power$ module load TensorFlow
 Module TensorFlow/2.3.1-fosscuda-2019b-Python-3.7.4 and 47 dependencies loaded.
 ```
 
@@ -68,16 +71,16 @@ import TensorFlow:
 !!! example
 
     ```console
-    marie@ml$ ws_allocate -F scratch python_virtual_environment 1
+    marie@power$ ws_allocate -F /data/horse python_virtual_environment 1
     Info: creating workspace.
-    /scratch/ws/1/python_virtual_environment
+    /data/horse/ws/1/python_virtual_environment
     [...]
-    marie@ml$ which python    #check which python are you using
+    marie@power$ which python    #check which python are you using
     /sw/installed/Python/3.7.2-GCCcore-8.2.0
-    marie@ml$ virtualenv --system-site-packages /scratch/ws/1/marie-python_virtual_environment/env
+    marie@power$ virtualenv --system-site-packages /data/horse/ws/1/marie-python_virtual_environment/env
     [...]
-    marie@ml$ source /scratch/ws/1/marie-python_virtual_environment/env/bin/activate
-    marie@ml$ python -c "import tensorflow as tf; print(tf.__version__)"
+    marie@power$ source /data/horse/ws/1/marie-python_virtual_environment/env/bin/activate
+    marie@power$ python -c "import tensorflow as tf; print(tf.__version__)"
     [...]
     2.3.1
     ```
@@ -105,7 +108,7 @@ Another option to use TensorFlow are containers. In the HPC domain, the
 following example, we use the tensorflow-test in a Singularity container:
 
 ```console
-marie@ml$ singularity shell --nv /scratch/singularity/powerai-1.5.3-all-ubuntu16.04-py3.img
+marie@power$ singularity shell --nv /data/horse/singularity/powerai-1.5.3-all-ubuntu16.04-py3.img
 Singularity>$ export PATH=/opt/anaconda3/bin:$PATH
 Singularity>$ source activate /opt/anaconda3    #activate conda environment
 (base) Singularity>$ . /opt/DL/tensorflow/bin/tensorflow-activate
@@ -156,5 +159,5 @@ marie@compute$ module spider Keras
 [...]
 ```
 
-to find out, which Keras modules are available on your partition. TensorFlow should be automatically
+to find out, which Keras modules are available on your cluster. TensorFlow should be automatically
 loaded as a dependency. After loading the module, you can use Keras as usual.