add first draft for tensorboard

fixes in tensorflow

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

+# TensorBoard
+
+TensorBoard is a visualization toolkit for TensorFlow and offers a variety of functionalities such
+as presentation of loss and accuracy, visualization of the model graph or profiling of the
+application.
+On ZIH systems, TensorBoard is only available as an extension of the TensorFlow module. To check
+whether a specific TensorFlow module provides TensorBoard, use the following command:
+
+```console
+marie@compute$ module spider TensorFlow/2.3.1
+```
+
+If TensorBoard occurs in the `Included extensions` section of the output, TensorBoard is available.
+
+## Using TensorBoard
+
+To use TensorBoard, you have to connect via ssh to taurus as usual, schedule an interactive job and
+load a TensorFlow module:
+
+```console
+marie@login$ srun -p alpha -n 1 -c 1 --pty --mem-per-cpu=8000 bash   #Job submission on alpha node
+marie@alpha$ module load TensorFlow/2.3.1
+marie@alpha$ tensorboard --logdir /scratch/gpfs/<YourNetID>/myproj/log --bind_all
+```
+
+Then create a workspace for the event data, that should be visualized in TensorBoard. If you already
+have an event data directory, you can skip that step.
+
+```console
+marie@alpha$ ws_allocate -F scratch tensorboard_logdata 1
+```
+
+Now you can run your TensorFlow application. Note that you might have to adapt your code to make it
+accessible for TensorBoard. Please find further information on the official [TensorBoard website](https://www.tensorflow.org/tensorboard/get_started)
+Then you can start TensorBoard and pass the directory of the event data:
+
+```console
+marie@alpha$ tensorboard --logdir /scratch/ws/1/marie-tensorboard_logdata --bind_all
+```
+
+TensorBoard will then return a server address on taurus, e.g. `taurusi8034.taurus.hrsk.tu-dresden.de:6006`
+
+For accessing TensorBoard now, you have to set up some port forwarding via ssh to your local
+machine:
+
+```console
+marie@local$ ssh -N -f -L 6006:taurusi8034.taurus.hrsk.tu-dresden.de:6006 <zih-login>@taurus.hrsk.tu-dresden.de
+```
+
+Now you can see the tensorboard in your browser at `http://localhost:6006/`.
+
+Note that you can also use tensorboard in an [sbatch file](../jobs_and_resources/batch_systems.md).

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

@@ -8,7 +8,7 @@ resources.
 Please check the software modules list via
 
 ```console
-marie@login$ module spider TensorFlow
+marie@compute$ module spider TensorFlow
 ```
 
 to find out, which TensorFlow modules are available on your partition.
@@ -26,7 +26,7 @@ On the **Alpha** partition load the module environment:
 
 ```console
 marie@login$ srun -p alpha --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=8000 bash   #Job submission on alpha nodes with 1 gpu on 1 node with 8000 Mb per CPU
-marie@romeo$ module load modenv/scs5
+marie@alpha$ module load modenv/scs5
 ```
 
 On the **ML** partition load the module environment: