fixes for creating jupyterlab custom kernels

doc.zih.tu-dresden.de/docs/access/jupyterhub_custom_environments.md

@@ -10,39 +10,24 @@ We currently have two different architectures at ZIH systems.
 Build your kernel environment on the **same architecture** that you want to use
 later on with the kernel. In the examples below, we use the name
 "my-kernel" for our user kernel. We recommend to prefix your kernels
-with keywords like `haswell`, `ml`, `romeo`, `venv`, `conda`. This way, you
-can later recognize easier how you built the kernel and on which hardware it
+with keywords like `alpha`, `barnard`, `romeo`, `power9`, `venv`, `conda`.
+This way, you can later recognize easier how you built the kernel and on which hardware it
 will work. Depending on that hardware, allocate resources as follows.
 
-## Preliminary Steps
-
-=== "Nodes with x86_64 (Intel) CPU"
-
-    Use **one srun command** of these:
-
-    ```console
-    maria@login$ srun --partition=haswell64 --pty --ntasks=1 --cpus-per-task=2 \
-     --mem-per-cpu=2541 --time=08:00:00 bash -l
-    maria@login$ srun --partition=gpu2 --pty --ntasks=1 --cpus-per-task=2 \
-     --mem-per-cpu=2541 --time=08:00:00 bash -l
-    ```
-
-=== "Nodes with x86_64 (AMD) CPU"
-
-    Use **one srun command** of these:
+| Cluster    | Architecture name       |
+|------------|-------------------------|
+| Alpha      | x86_64 (AMD)    |
+| Barnard    | x86_64 (Intel)  |
+| Romeo      | x86_64 (AMD)    |
+| Power9     | ppc64le (IBM)   |
 
-    ```console
-    maria@login$ srun --partition=romeo --pty --ntasks=1 --cpus-per-task=3 \
-     --mem-per-cpu=1972 --time=08:00:00 bash -l
-    maria@login$ srun --partition=alpha --gres=gpu:1 --pty --ntasks=1 \
-     --cpus-per-task=6 --mem-per-cpu=10312 --time=08:00:00 bash -l
-    ```
+## Preliminary Steps
 
-=== "Nodes with ppc64le CPU"
+    Start an interactive job
 
     ```console
-    maria@ml$ srun --pty --partition=ml --ntasks=1 --cpus-per-task=2 --mem-per-cpu=1443 \
-     --time=08:00:00 bash -l
+    maria@login.<cluster>$ srun --pty --ntasks=1 --cpus-per-task=2 \
+     --mem-per-cpu=2541 --time=02:00:00 bash -l
     ```
 
 When creating a virtual environment in your home directory, you got to decide
@@ -60,93 +45,40 @@ While we have a general description on
 [Python Virtual Environments](../software/python_virtual_environments.md), 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`:
+Depending on the Cluster that you are targeting, please choose the right modules:
 
-=== "scs5"
+=== "release/23.10"
 
-    For use with Standard Environment `scs5_gcccore-10.2.0_python-3.8.6`,
+    For use with Python version 3.10.4,
     please try to initialize your Python Virtual Environment like this:
 
     ```console
-    marie@haswell$ module load Python/3.8.6-GCCcore-10.2.0
-    Module Python/3.8.6-GCCcore-10.2.0 and 11 dependencies loaded.
-    marie@haswell$ mkdir user-kernel # please use workspaces!
-    marie@haswell$ cd user-kernel
-    marie@haswell$ virtualenv --system-site-packages my-kernel
-    created virtual environment CPython3.8.6.final.0-64 in 5985ms
-      creator CPython3Posix(dest=[...]/my-kernel, clear=False, global=True)
-      seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=[...])
-        added seed packages: pip==20.2.3, setuptools==50.3.0, wheel==0.35.1
-      activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
-    marie@haswell$ source my-kernel/bin/activate
-    (my-kernel) marie@haswell$ pip install ipykernel
-    Collecting ipykernel
-    [...]
-    Successfully installed [...] ipykernel-6.9.1 ipython-8.0.1 [...]
+    [marie@barnard ~]$ module load release/23.10  GCC/11.3.0  Python/3.10.4
+    Module GCC/11.3.0, Python/3.10.4 and 12 dependencies loaded.
     ```
 
-    Then continue with the steps below.
-
-=== "hiera"
-
-    For use with Standard Environment `hiera_gcccore-10.2.0_python-3.8.6`,
-    please try to initialize your Python Virtual Environment like this:
+=== "release/23.04"
 
     ```console
-    marie@romeo$ module load GCC/10.2.0 Python/3.8.6
-    Module GCC/10.2.0Python/3.8.6 and 11 dependencies loaded.
-    marie@romeo$ mkdir user-kernel # please use workspaces!
-    marie@romeo$ cd user-kernel
-    marie@romeo$ virtualenv --system-site-packages my-kernel
-    created virtual environment CPython3.8.6.final.0-64 in 5985ms
-      creator CPython3Posix(dest=[...]/my-kernel, clear=False, global=True)
-      seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=[...])
-        added seed packages: pip==20.2.3, setuptools==50.3.0, wheel==0.35.1
-      activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
-    marie@romeo$ source my-kernel/bin/activate
-    (my-kernel) marie@romeo$ pip install ipykernel
-    Collecting ipykernel
-    [...]
-    Successfully installed [...] ipykernel-6.9.1 ipython-8.0.1 [...]
+    [marie@barnard ~]$ module load release/23.04  GCC/11.3.0  Python/3.10.4
+    Module GCC/11.3.0, Python/3.10.4 and 12 dependencies loaded.
     ```
 
-    Then continue with the steps below.
-
-=== "ml"
-
-    For use with the Standard Environment `fosscuda/2020b`,
-    please try to initialize your Python Virtual Environment like this:
+Then continue with the steps below.
 
     ```console
-    marie@ml$ module load fosscuda/2020b ZeroMQ/4.3.3-GCCcore-10.2.0 Python/3.8.6-GCCcore-10.2.0
-    Module fosscuda/2020b and 23 dependencies loaded.
-    marie@ml$ mkdir user-kernel # please use workspaces!
-    marie@ml$ cd user-kernel
-    marie@ml$ python3 -m venv --system-site-packages my-kernel
-    marie@ml$ sourcde my-kernel/bin/activate
-    (my-kernel) marie@compute$ pip install ipykernel
+    [marie@barnard ~]$ mkdir -p ~/usr/jlab-kernels # please use workspaces!
+    [marie@barnard ~]$ cd ~/usr/jlab-kernels
+    [marie@barnard jlab-kernels]$ python3 -m venv --system-site-packages my-kernel
+    [marie@barnard jlab-kernels]$
+    [marie@barnard jlab-kernels]$ source my-kernel/bin/activate
+    (my-kernel) [marie@barnard jlab-kernels]$
+    (my-kernel) [marie@barnard jlab-kernels]$ pip install ipykernel
     Collecting ipykernel
     [...]
-    Successfully installed asttokens-2.0.8 backcall-0.2.0 debugpy-1.6.3 entrypoints-0.4 executing-1.0.0 ipykernel-6.15.2 ipython-8.4.0 jedi-0.18.1 jupyter-client-7.3.5 jupyter-core-4.11.1 matplotlib-inline-0.1.6 nest-asyncio-1.5.5 parso-0.8.3 pickleshare-0.7.5 prompt-toolkit-3.0.30 pure-eval-0.2.2 python-dateutil-2.8.2 pyzmq-23.2.1 stack-data-0.5.0 tornado-6.2 traitlets-5.3.0
-
-    Then continue with the steps below.
-
-=== "default ('production')"
-
-    For use with Standard Environment `production`,
-    please try to initialize your Python Virtual Environment like this:
-
-    ```console
-    marie@compute$ module load Anaconda3/2022.05
-    Module Anaconda3/2022.05 loaded.
-    marie@compute$ mkdir user-kernel # please use workspaces!
-    marie@compute$ cd user-kernel
-    marie@compute$ python3 -m venv --system-site-packages my-kernel
-    (my-kernel) marie@compute$ pip install ipykernel
+    Successfully installed [...] ipykernel-x.x.x ipython-x.x.x [...]
     ```
 
-    Then continue with the steps below.
-
 After following the initialization of the environment (above),
 the usage of Python's Package manager `pip` is the same:
 
@@ -158,14 +90,9 @@ Installed kernelspec my-kernel in .../.local/share/jupyter/kernels/my-kernel
 (my-kernel) marie@compute$ deactivate
 ```
 
-!!! warning
-
-    Take care to select the appropriate standard environment (as mentioned above)
-    when [spawning a new session](jupyterhub.md#start-a-session).
-
 ## Conda Environment
 
-Load the needed module depending on partition architecture:
+Load the needed module depending on Cluster architecture:
 
 === "Nodes with x86_64 CPU"
     ```console