From 8a2b9041c0826b246d8afd03d465a5edd1a54efb Mon Sep 17 00:00:00 2001
From: Danny Rotscher <danny.rotscher@tu-dresden.de>
Date: Thu, 8 Sep 2022 13:28:43 +0200
Subject: [PATCH] Fixed blank lines and line length
---
doc.zih.tu-dresden.de/docs/access/jupyterhub.md | 7 ++-----
.../docs/access/jupyterhub_custom_environments.md | 4 +++-
2 files changed, 5 insertions(+), 6 deletions(-)
diff --git a/doc.zih.tu-dresden.de/docs/access/jupyterhub.md b/doc.zih.tu-dresden.de/docs/access/jupyterhub.md
index 585b0dd8d..65f09283c 100644
--- a/doc.zih.tu-dresden.de/docs/access/jupyterhub.md
+++ b/doc.zih.tu-dresden.de/docs/access/jupyterhub.md
@@ -180,7 +180,6 @@ home directory and have the name `jupyter-session-<jobid>.log`.
## Advanced Tips
-
### Standard Environments
The default Python kernel uses conda environments based on the
@@ -211,7 +210,8 @@ As of July 2022 we have a number of standard environments, namely:
| scs5_gcccore-10.3.0_python-3.9.5_matlab-2021b | x86_64 (Intel) | matlab | modenv/scs5 | default, haswell, interactive, gpu2, hpdlf, dcv, julia |
| scs5_gcccore-8.3.0_python-3.7.4 | x86_64 (Intel) | | modenv/scs5 | default, haswell, interactive, gpu2, hpdlf, dcv, julia |
-<p style="font-size: 80%;">Actually 'romeo', 'alpha' and 'ml' refer to their respective interactive partitions 'romeo-interactive', 'alpha-interactive', 'ml-interactive' to reduce job queue waiting time.</p>
+<p style="font-size: 80%;">Actually 'romeo', 'alpha' and 'ml' refer to their respective interactive
+partitions 'romeo-interactive', 'alpha-interactive', 'ml-interactive' to reduce job queue waiting time.</p>
We also have a more [in depth description regarding Modules](../software/modules.md#module-environments).
@@ -241,8 +241,6 @@ With these **standard environments** we have tried to integrate a set of compati
Can be utilized with the partitions gpu2, alpha and ml
It's specially geared towards GPU support.
-
-
### Loading Modules
You have now the option to preload modules from the [module system](../software/modules.md).
@@ -261,4 +259,3 @@ but similarly provide basic functionality for running your use cases,
e.g. Python or R)
You can find further documentation on creating your own Kernels [here](./jupyterhub_custom_environments.md)
-
diff --git a/doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md b/doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md
index f6dfe33a5..851b4fbd4 100644
--- a/doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md
+++ b/doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md
@@ -56,7 +56,9 @@ to either use "Python virtualenv" or "conda environment".
## Python Virtualenv
-While we have a general description on [Python Virtual Environments](/software/python_virtual_environments/), here we have a more detailed description on using them with JupyterHub:
+While we have a general description on
+[Python Virtual Environments](/software/python_virtual_environments/), here we have a more detailed
+description on using them with JupyterHub:
Depending on the CPU architecture that you are targeting, please choose a `modenv`:
--
GitLab