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

@@ -16,10 +16,28 @@ It will start a new terminal on the respective compute node. Then you can create
 `/tmp/<username>/tf-logs` and link it with the directory where your own log data is located.
 Consider the following commands to do so:
 
-```Bash
-mkdir -p /tmp/$USER/tf-logs
-ln -s <your-tensorboard-target-directory> /tmp/$USER/tf-logs
-```
+The easiest way to use TensorBoard is via [JupyterHub](../access/jupyterhub.md). By default,
+TensorBoard is configured to read log data from `/tmp/<username>/tf-logs` on the compute node on
+which the Jupyter session is running. In order to show your own log data from a different directory,
+soft-link this directory with `/tmp/<username>/tf-logs` in order to make TensorBoard reading your
+log data. Note, that the directory `/tmp/<username>/tf-logs` might not exist and you have to
+create it first. Therefore, open a "New Launcher" (`Ctrl+Shift+L`) and select "Terminal" session.
+It will start a new terminal on the respective compute node. Then you can create the directory
+`/tmp/<username>/tf-logs` and link it with the directory where your own log data is located.
+
+Assuming you use a line like the following in your code:
+
+    ```python
+    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="/home/marie/logs")
+    ```
+
+You can then make the tensorboard available with:
+
+    ```console
+    marie@compute$ mkdir -p /tmp/$USER/tf-logs
+    marie@compute$ ln -s /home/marie/logs /tmp/$USER/tf-logs
+    ```
+
 
 Update TensorBoard tab if needed with `F5`.