BwUniCluster3.0/Jupyter - Revision history

M Carmesin: /* Julia language */

2025-05-07T07:44:51Z

Julia language

← Older revision		Revision as of 09:44, 7 May 2025
Line 261:		Line 261:
	== Julia language ==		== Julia language ==

	In order to use Julia language in JupyterLab, the Lmod module <code>~~devel~~/julia/1.6.2</code> has to be loaded (blue button in JupyterLab or <code>module ~~devel~~/julia/1.6.2</code> in terminal) ~~and~~ a corresponding kernel has to be ~~installed.~~		In order to use Julia language in JupyterLab, the Lmod module <code>math/julia/1.10.8</code> has to be loaded (blue button in JupyterLab or <code>module math/julia/1.10.8</code> in terminal). When the module is loaded from the JupytherLab UI, the corresponding kernel will be installed. If you use the terminal, this has to be done manually

	<pre>		<pre>
Line 269:		Line 269:
	</pre>		</pre>

	After installing the kernel , a button named "Julia 1.6.2" is available in JupyterLab. The kernel can also be selected from the drop-down menu.		After installing the kernel, a button named "Julia 1.10.8" is available in JupyterLab. The kernel can also be selected from the drop-down menu.

	'''Attention:'''		'''Attention:'''
	Don't forget to load the <code>~~devel~~/julia/1.6.2</code> module (blue button) before using the kernel.		Don't forget to load the <code>math/julia/1.10.8</code> module (blue button) before using the kernel.

	= Jupyter Container Mode =		= Jupyter Container Mode =

P Schuhmacher: /* Installation of further software */

2025-04-03T13:45:02Z

Installation of further software

← Older revision		Revision as of 15:45, 3 April 2025
Line 197:		Line 197:
	<li>User-Installation (not recommended)<br>		<li>User-Installation (not recommended)<br>
	<code>pip install --user <packageName> </code><br>		<code>pip install --user <packageName> </code><br>
	The additional packages are installed under $HOME/.local/lib/python3.9/site-packages/ which is part of PYTHONPATH.		The additional packages are installed under $HOME/.local/lib/python3.11/site-packages/ which is part of PYTHONPATH.
	</li>		</li>
	<li>Virtual environments (recommended)<br>		<li>Virtual environments (recommended)<br>
Line 217:		Line 217:
	</pre>		</pre>

	The additional packages are installed under <code><myEnv>/lib/python3.9/site-packages/</code>.		The additional packages are installed under <code><myEnv>/lib/python3.11/site-packages/</code>.

	=== Usage of virtual environment ===		=== Usage of virtual environment ===
Line 238:		Line 238:

	'''Attention'''		'''Attention'''
	The (Lmod) base module you used in the Creation of virtual environment step must be loaded to use the venv. However, to be on the safe side, you can also use the system Python (<code>/usr/bin/python3.9</code>) at creation time, which is available even without any <code>jupyter/{base,tensorflow}</code> module loaded.		The (Lmod) base module you used in the Creation of virtual environment step must be loaded to use the venv. However, to be on the safe side, you can also use the system Python (<code>/usr/bin/python3.11</code>) at creation time, which is available even without any <code>jupyter/{base,tensorflow}</code> module loaded.

	== Examples on Data processing, Machine Learning & Visualization ==		== Examples on Data processing, Machine Learning & Visualization ==

P Schuhmacher: /* Jupyter Container Mode */

2025-04-03T13:44:11Z

Jupyter Container Mode

← Older revision		Revision as of 15:44, 3 April 2025
Line 283:		Line 283:

	'''Attention:'''		'''Attention:'''
	Make sure Python3.9 and pip are installed in the Container or the notebook will not spawn. This can be checked via command python3.9 -m pip list inside your container		Make sure Python3.11 and pip are installed in the Container or the notebook will not spawn. This can be checked via command python3.11 -m pip list inside your container

	It is advised to create the container prior via e.G. enroot and install all necessary software. For more information see [https://wiki.bwhpc.de/e/BwUniCluster2.0/Containers#SLURM_Integration here]		It is advised to create the container prior via e.G. enroot and install all necessary software. For more information see [https://wiki.bwhpc.de/e/BwUniCluster2.0/Containers#SLURM_Integration here]

P Schuhmacher at 13:42, 3 April 2025

2025-04-03T13:42:48Z

← Older revision		Revision as of 15:42, 3 April 2025
Line 78:		Line 78:

	== Prioritized access to computing resources on bwUniCluster 3.0 ==		== Prioritized access to computing resources on bwUniCluster 3.0 ==
	The use of Jupyter requires the immediate availability of computing resources since the JupyterLab server is started within an interactive Slurm session. To improve the availability of CPUs/GPUs for interactive supercomputing with Jupyter, '''automatic reservation''' for CPU (~~single~~) and GPU (~~gpu_8~~) resources has been set up on '''bwUniCluster 3.0'''. It is active '''between 8am and 8pm''' every weekday. The reservation is automatically active if		The use of Jupyter requires the immediate availability of computing resources since the JupyterLab server is started within an interactive Slurm session. To improve the availability of CPUs/GPUs for interactive supercomputing with Jupyter, '''automatic reservation''' for CPU (cpu_il) and GPU (gpu_a100_il) resources has been set up on '''bwUniCluster 3.0'''. It is active '''between 8am and 8pm''' every weekday. The reservation is automatically active if

	* no other reservation is set manually		* no other reservation is set manually

S Braun at 07:48, 25 February 2025

2025-02-25T07:48:01Z

← Older revision		Revision as of 09:48, 25 February 2025
Line 274:		Line 274:
	Don't forget to load the <code>devel/julia/1.6.2</code> module (blue button) before using the kernel.		Don't forget to load the <code>devel/julia/1.6.2</code> module (blue button) before using the kernel.

			= Jupyter Container Mode =

			The container integration on the jupyterhub is done via pyxis. In order to use it the checkmark for container mode has to be clicked. Available options are:

			* <code> --container-image:</code> The container image to use. Corresponds to the pyxis option --container-image
			* <code> --container-name:</code> The name of the image to use. Corresponds to the pyxis option --container-name. Already downloaded containers in ~/.local/share/enroot can be startet by simply specifing their name.
			* <code> --container-mount-home:</code> Corresponds to the pyxis option --container-mount-home. Mounts the home-directory

			'''Attention:'''
			Make sure Python3.9 and pip are installed in the Container or the notebook will not spawn. This can be checked via command python3.9 -m pip list inside your container

			It is advised to create the container prior via e.G. enroot and install all necessary software. For more information see [https://wiki.bwhpc.de/e/BwUniCluster2.0/Containers#SLURM_Integration here]

	----		----

S Braun at 12:30, 9 January 2025

2025-01-09T12:30:40Z

S Braun: Created page with "Jupyter can be used as an alternative to accessing HPC resources via SSH. For this purpose only a web browser is required. Within the website source code of different programming languages can be edited and executed. Furthermore different user interfaces and terminals are available. = Short description of Jupyter = Jupyter is a web application, central component of Jupyter is the '''Jupyter Notebook'''. It is a document, which can contain formatted text, executable cod..."

2025-01-09T12:29:39Z

Created page with "Jupyter can be used as an alternative to accessing HPC resources via SSH. For this purpose only a web browser is required. Within the website source code of different programming languages can be edited and executed. Furthermore different user interfaces and terminals are available. = Short description of Jupyter = Jupyter is a web application, central component of Jupyter is the '''Jupyter Notebook'''. It is a document, which can contain formatted text, executable cod..."

New page

Jupyter can be used as an alternative to accessing HPC resources via SSH. For this purpose only a web browser is required. Within the website source code of different programming languages can be edited and executed. Furthermore different user interfaces and terminals are available.

= Short description of Jupyter =

Jupyter is a web application, central component of Jupyter is the '''Jupyter Notebook'''. It is a document, which can contain formatted text, executable code sections and (interactive) visualizations (image, sound, video, 3D views).

The Jupyter notebooks are executed in an interactive session on the compute nodes of the respective cluster. Access is via any modern web browser. Data is prepared and visualized on the server and therefore does not have to be transmitted over the network. Only the resulting text, image, sound and video data is transmitted. Starting point of a Jupyter session is the HOME directory of the user on the respective cluster.

'''JupyterLab''' is a modern user interface, within which one or more Jupyter notebooks can be opened, edited and executed. The individual notebooks can be arranged as tabs or tiled. JupyterLab is the standard user interface. Besides JupyterLab the classic notebook user interface is available, in which only one Jupyter notebook per browser tab can be opened at a time.

A '''Jupyter Kernel''' describes a separate process, in which one Jupyter Notebook is executed at a time. Different kernels are available for different programming languages or language versions.

Before a Jupyter session is started, the access authorization must be checked first. This is done via '''JupyterHub''', where the resources are selected, for example the number of CPU cores, GPUs or the required main memory.

A detailed documentation of the Jupyter project can be found at [https://jupyter.readthedocs.io https://jupyter.readthedocs.io].

= Access requirements =

{|style="background:#deffee; width:100%;"
|style="padding:5px; background:#cef2e0; text-align:left"|
[[Image:Attention.svg|center|25px]]
|style="padding:5px; background:#cef2e0; text-align:left"|
Access to Jupyter is '''limited to IP addresses from the BelWü network'''.
All home institutions of our current users are connected to BelWü, so if you are on your campus network (e.g. in your office or on the Campus WiFi) you should be able to connect to bwUniCluster 2.0 without restrictions.
If you are outside one of the BelWü networks (e.g. at home), a VPN connection to the home institution or a connection to an SSH jump host at the home institution must be established first.
|}

To use Jupyter on the HPC resources of SCC, the access requirements for [https://wiki.bwhpc.de/e/Registration/bwUniCluster bwUniCluster 2.0] apply. A [https://wiki.bwhpc.de/e/Registration/bwUniCluster registration] is required. Please note, You should've completed registration and tested your login once using [https://wiki.bwhpc.de/e/Registration/SSH Secure Shell (ssh)].

= Login process =

Login takes place at
* bwUniCluster 2.0: [https://uc2-jupyter.scc.kit.edu uc2-jupyter.scc.kit.edu]
* SDIL: [https://sdil-jupyter.scc.kit.edu sdil-jupyter.scc.kit.edu]
* HoreKa: [https://hk-jupyter.scc.kit.edu hk-jupyter.scc.kit.edu]
* HAICORE: [https://haicore-jupyter.scc.kit.edu haicore-jupyter.scc.kit.edu]

For login, your username, your password and a 2-factor authentication are required.

You will first find yourself on a landing page that also gives more information about the currently installed software versions.
By pressing the login button you will be redirected to the JupyterHub page. Click on Enter JupyterHub to start the login process. Select the organization (e.g. KIT) that has granted you access to the HPC system and press Continue. In the Login section that appears, enter your username and password (not the service password).
After pressing the Login button you will be redirected to the second factor query page. Enter the one-time password (e.g. from KIT Token or Google Authenticator App) and press Validate. Now you are done with the login process and can start selecting your computing resources.

[[File:Jupyter_Anmeldung.gif|700px]]

= Selection of the compute resources =

The Jupyter notebooks are executed in an interactive session on the compute nodes of the HPC clusters. Just like accessing an interactive session with SSH, resource allocation is done by the Workload Manager Slurm. The selection of resources for Jupyter is realized via drop-down menus. Only jobs with a maximum of one node are possible.

Available resources for selection are

* Number of CPU cores
* Number of GPUs
* Runtime
* Partition/Queue
* Amount of main memory

If Auto-Reservation is selected the automatic Jupyter reservation of the cluster is enabled.

In normal mode, the grayed-out fields contain reasonable presets, depending on the number of required CPU cores or GPUs respectively. The presets can be bypassed in advanced mode, where further options are available.

Advanced Mode can be activated by clicking on the checkbox of the same name. The following additional options then become available:

* Specification of a reservation
* LSDF mount option
* BEEOND mount option

After the selection is made, the interactive job is started with the spawn button. As when requesting interactive compute resources with the `salloc` command, waiting times may occur. These are usually the longer the larger the requested resources are.
Even if the chosen resources are available immediately, the spawning process may take up to one minute.

[[File:Ressources_neu.gif|500px]]

Please note that in advanced mode, resource combinations can be selected that are impossible to be met. In this case, an error message will appear when the job is spawned.

[[File:Jupyter_Falsche_ressourcen.gif|500px]]

The spawning timeout is currently set to 10 minutes. With a normal workload of the HPC facility, this time is usually sufficient to get interactive resources.

== Prioritized access to computing resources on bwUniCluster 2.0 ==
The use of Jupyter requires the immediate availability of computing resources since the JupyterLab server is started within an interactive Slurm session. To improve the availability of CPUs/GPUs for interactive supercomputing with Jupyter, '''automatic reservation''' for CPU (single) and GPU (gpu_8) resources has been set up on '''bwUniCluster 2.0'''. It is active '''between 8am and 8pm''' every weekday. The reservation is automatically active if

* no other reservation is set manually
* Auto-Reservation is enabled

To give you a better overview of the currently available resources, a status indicator has been implemented. It appears when selecting the number of required CPUs/GPUs and shows whether a Jupyter job of the selected size can currently be started or not. Green means the selected CPU/GPU resources are available instantly. Yellow means only a single additional job of the selected size can be started. Red means there are no GPU resources left that could satisfy the selected amount of resources.

If there are no more resources available within the reservation, you can try selecting a different amount of CPUs/GPUs or activate Advanced Mode and select a different partition. Availability can be estimated using sinfo_t_idle, which is available when logging in via SSH.

= JupyterLab =

JupyterLab is the standard user interface. In the following only its essential functions are briefly introduced. A detailed documentation is available at
[https://jupyterlab.readthedocs.io https://jupyterlab.readthedocs.io].

== Menu bar ==

The menu bar at the upper edge of JupyterLab has higher-level menus that display the actions available in JupyterLab along with their shortcut keys. The default menus are:

* File: Actions related to files and directories
* Edit: Actions related to editing documents and other activities
* View: actions that change the appearance of JupyterLab
* Run: Actions to execute code in various activities like notebooks and code consoles
* Kernel: Actions to manage kernels that are separate processes for executing code
* Tabs: a list of open documents and activities in the Dock Panel
* Settings: general settings and an editor for advanced settings
* Help: a list of help links to JupyterLab and the kernel

== Left sidebar ==

In the left sidebar there are foldable tabs. The most relevant ones are:

* File browser: Switch to directories and open files with left mouse button, context menu with right mouse button
* Running kernels: Overview of running kernels
* Command overview
* Tab Overview
* Lmod software selection: Search and load/unload Lmod software modules

== Main working area ==
The main working area in JupyterLab allows to arrange, resize and divide documents (notebooks, text files, etc.) and other activities (terminals, code consoles, etc.) in tabs. By holding down the left mouse button, the tabs can be grabbed and repositioned.

In a new JupyterLab session the Launcher tab is opened first. It contains buttons for starting new notebooks, code consoles and other functions. When a notebook is open, a new Launcher tab can be started by pressing the plus symbol in the file browser tab of the left sidebar, by calling ''File > New Launcher'' in the upper menu bar or by the key combination ''Ctrl+Shift+L''.

== Classic Notebook ==

The classic Jupyter Notebook user interface offers only one open Jupyter Notebook or terminal per browser tab. From the JupyterLab user interface the classic display can be reached in the menu bar under ''Help > Launch Classic Notebook''. Clicking on the JupyterHub logo in the upper left corner will take you back to the JupyterLab interface.

= Log out =

You can log out from a running Jupyter session by calling ''File > Log Out'' in the upper menu bar.

{| style="width: 100%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
| style="width:100%; border:1px solid #BBBBBB; background:#fff5fa; vertical-align:top; color:#000;" |
{| style="width:100%; vertical-align:top; border:0px solid #BBBBBB; padding:4px;" |
|-
|{{Red}}| Attention
|-
|
Please note that your interactive session will continue in the background!

|}
|}

As long as the interactive session is running, you can re-enter it at any time. Depending on the duration of your absence, it may be necessary to re-enter your one-time password and possibly KIT password.

If you want to end the interactive session before it has reached its runtime, you can do so via the Hub Control Panel. Under ''File > Hub Control Panel'' in the upper menu bar, it is opened in a new browser tab. By pressing the ''Stop My Server'' button the session will be terminated. You can now log out using the ''Logout'' button in the upper right corner or start a new session directly using the ''Start My Server'' button, for example with a changed resource selection.

[[File:logout_small.gif|750px]]

= Selection of software =

For the selection of the required Lmod software modules the corresponding tab ''Softwares'' is available in the left sidebar. The list of available modules can be narrowed down by entering the search field. The desired module is loaded by pressing the ''Load'' button. In the list with the loaded modules you can remove them with the ''Unload'' button.

{| style="width: 100%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
| style="width:100%; border:1px solid #BBBBBB; background:#f5fffa; vertical-align:top; color:#000;" |
{| style="width:100%; vertical-align:top; border:0px solid #BBBBBB; padding:4px;" |
|-
|{{Green}}| Note
|-
|
On already opened Jupyter Notebooks, newly loaded software modules become active only after restarting the kernel (''Kernel > Restart Kernel'' in the upper menu bar). Terminals must be closed and reopened.
|}
|}

[[File:software_small.gif|750px]]

== Software Stacks for Jupyter ==
Currently 3 special Jupyter software stacks are available via Lmod:

* <code>jupyter/minimal</code>
*: Minimal installation of JupyterLab

* <code>jupyter/base</code>
*: Basic installation of JupyterLab.
*: For a complete list of pre-installed packages, please refer to [https://uc2-jupyter.scc.kit.edu/software-modules/#pre-installed-software-packages this site].

* <code>jupyter/tensorflow</code> (default at login, will be deprecated with the advent of bwUniCluster 3.0)
*: Preinstalled software packages for machine learning applications. Includes among others TensorFlow, Keras, Torch, Pandas, Matplotlib, SKLearn.
*: For a complete list of pre-installed packages and their respective version, please refer to [https://uc2-jupyter.scc.kit.edu/software-modules/#pre-installed-software-packages this site].

* <code>jupyter/ai</code> (will be the new default at login für bwUniCluster 3.0, contains all latest and greatest software for AI workflows)
*: Preinstalled software packages for machine learning applications. Includes among others TensorFlow, Keras, Torch, Torchvision, Lighning, Pandas, Matplotlib, SKLearn.
*: For a complete list of pre-installed packages and their respective version, please refer to [https://uc2-jupyter.scc.kit.edu/software-modules/#pre-installed-software-packages this site].

* <code>jupyter/extensions</code>
*: Same packages as tensorflow + extensions

These software stacks can be used both when accessing the cluster via JupyterHub, as well as for conventional access via SSH via module load.

A continuously updated list with the installed packages can be found on the corresponding subpage of the respective cluster:

* bwUniCluster 2.0: [https://uc2-jupyter.scc.kit.edu/software-modules uc2-jupyter.scc.kit.edu/software-modules]
* HoreKa: [https://hk-jupyter.scc.kit.edu/software-modules hk-jupyter.scc.kit.edu/software-modules]

= Installation of further software =
The software provided by the Lmod modules jupyter/minimal, jupyter/base and jupyter/tensorflow can be easily supplemented by additional Python packages. There are 2 procedures for this.

<ul>
<li>User-Installation (not recommended)<br>
<code>pip install --user <packageName> </code><br>
The additional packages are installed under $HOME/.local/lib/python3.9/site-packages/ which is part of PYTHONPATH.
</li>
<li>Virtual environments (recommended)<br>
The user can create and use virtual environments (cf. Virtual environments). Packages provided by the jupyter Lmod modules remain visible and usable.
</li>
</ul>

== Virtual environments ==

Python virtual environments allow to use different versions of a package and to keep your local site-packages (accessible under <code>$PYTHONPATH</code>) free from conflicts.

=== Creation of virtual environment ===

<pre>
python -m venv <myEnv>
source <myEnv>/bin/activate
pip install <packageName>
deactivate
</pre>

The additional packages are installed under <code><myEnv>/lib/python3.9/site-packages/</code>.

=== Usage of virtual environment ===

In order to use the virtual environment, it has to be activated via <code>source <myEnv>/bin/activate</code>. <code>PYTHONPATH</code> is set accordingly. Deactivation of the venv is done via <code>deactivate</code>.

=== Usage of virtual environment in JupyterLab ===

To be able to use the virtual environments within JupyterLab, a corresponding kernel has to be installed:

<pre>
source <myEnv>/bin/activate
python -m ipykernel install \
--user \
--name myEnv \
--display-name "Python (myEnv)"
</pre>

After installing the kernel (and possibly refreshing the browser window), a button named "myEnv" is available in JupyterLab. The kernel can also be selected from the drop-down menu.

'''Attention'''
The (Lmod) base module you used in the Creation of virtual environment step must be loaded to use the venv. However, to be on the safe side, you can also use the system Python (<code>/usr/bin/python3.9</code>) at creation time, which is available even without any <code>jupyter/{base,tensorflow}</code> module loaded.

== Examples on Data processing, Machine Learning & Visualization ==

In the [https://github.com/hpcraink/workshop-parallel-jupyter/ workshop repository] the usage and best practices on Python in general, and the packages NumPy, Pandas, SciKit and Dask are provided, containing running examples based on open data. It also explains, how Jupyter interacts with pre-installed and your own provided environments.

== R language ==

In order to use R language in JupyterLab, the Lmod module <code>math/R</code> has to be loaded (blue button in JupyterLab or <code>module add math/R</code> in terminal) and a corresponding kernel has to be installed.

<pre>
R
install.packages('IRkernel')
IRkernel::installspec()
</pre>

After installing the kernel , a button named "R" is available in JupyterLab. The kernel can also be selected from the drop-down menu.

'''Attention:'''
Don't forget to load the <code>math/R</code> module (blue button) before using the kernel.

== Julia language ==

In order to use Julia language in JupyterLab, the Lmod module <code>devel/julia/1.6.2</code> has to be loaded (blue button in JupyterLab or <code>module devel/julia/1.6.2</code> in terminal) and a corresponding kernel has to be installed.

<pre>
julia
]
add IJulia
</pre>

After installing the kernel , a button named "Julia 1.6.2" is available in JupyterLab. The kernel can also be selected from the drop-down menu.

'''Attention:'''
Don't forget to load the <code>devel/julia/1.6.2</code> module (blue button) before using the kernel.

----

← Older revision		Revision as of 14:30, 9 January 2025
Line 22:		Line 22:
	\|style="padding:5px; background:#cef2e0; text-align:left"\|		\|style="padding:5px; background:#cef2e0; text-align:left"\|
	Access to Jupyter is '''limited to IP addresses from the BelWü network'''.		Access to Jupyter is '''limited to IP addresses from the BelWü network'''.
	All home institutions of our current users are connected to BelWü, so if you are on your campus network (e.g. in your office or on the Campus WiFi) you should be able to connect to bwUniCluster 2.0 without restrictions.		All home institutions of our current users are connected to BelWü, so if you are on your campus network (e.g. in your office or on the Campus WiFi) you should be able to connect to bwUniCluster 3.0 without restrictions.
	If you are outside one of the BelWü networks (e.g. at home), a VPN connection to the home institution or a connection to an SSH jump host at the home institution must be established first.		If you are outside one of the BelWü networks (e.g. at home), a VPN connection to the home institution or a connection to an SSH jump host at the home institution must be established first.
	\|}		\|}

	To use Jupyter on the HPC resources of SCC, the access requirements for [https://wiki.bwhpc.de/e/Registration/bwUniCluster bwUniCluster 2.0] apply. A [https://wiki.bwhpc.de/e/Registration/bwUniCluster registration] is required. Please note, You should've completed registration and tested your login once using [https://wiki.bwhpc.de/e/Registration/SSH Secure Shell (ssh)].		To use Jupyter on the HPC resources of SCC, the access requirements for [https://wiki.bwhpc.de/e/Registration/bwUniCluster bwUniCluster 3.0] apply. A [https://wiki.bwhpc.de/e/Registration/bwUniCluster registration] is required. Please note, You should've completed registration and tested your login once using [https://wiki.bwhpc.de/e/Registration/SSH Secure Shell (ssh)].

	= Login process =		= Login process =

	Login takes place at		Login takes place at
	* bwUniCluster 2.0: [https://~~uc2~~-jupyter.scc.kit.edu ~~uc2~~-jupyter.scc.kit.edu]		* bwUniCluster 3.0: [https://uc3-jupyter.scc.kit.edu uc3-jupyter.scc.kit.edu]
	* SDIL: [https://sdil-jupyter.scc.kit.edu sdil-jupyter.scc.kit.edu]		* SDIL: [https://sdil-jupyter.scc.kit.edu sdil-jupyter.scc.kit.edu]
	* HoreKa: [https://hk-jupyter.scc.kit.edu hk-jupyter.scc.kit.edu]		* HoreKa: [https://hk-jupyter.scc.kit.edu hk-jupyter.scc.kit.edu]
Line 77:		Line 77:
	The spawning timeout is currently set to 10 minutes. With a normal workload of the HPC facility, this time is usually sufficient to get interactive resources.		The spawning timeout is currently set to 10 minutes. With a normal workload of the HPC facility, this time is usually sufficient to get interactive resources.

	== Prioritized access to computing resources on bwUniCluster 2.0 ==		== Prioritized access to computing resources on bwUniCluster 3.0 ==
	The use of Jupyter requires the immediate availability of computing resources since the JupyterLab server is started within an interactive Slurm session. To improve the availability of CPUs/GPUs for interactive supercomputing with Jupyter, '''automatic reservation''' for CPU (single) and GPU (gpu_8) resources has been set up on '''bwUniCluster 2.0'''. It is active '''between 8am and 8pm''' every weekday. The reservation is automatically active if		The use of Jupyter requires the immediate availability of computing resources since the JupyterLab server is started within an interactive Slurm session. To improve the availability of CPUs/GPUs for interactive supercomputing with Jupyter, '''automatic reservation''' for CPU (single) and GPU (gpu_8) resources has been set up on '''bwUniCluster 3.0'''. It is active '''between 8am and 8pm''' every weekday. The reservation is automatically active if

	* no other reservation is set manually		* no other reservation is set manually
Line 188:		Line 188:
	A continuously updated list with the installed packages can be found on the corresponding subpage of the respective cluster:		A continuously updated list with the installed packages can be found on the corresponding subpage of the respective cluster:

	* bwUniCluster 2.0: [https://~~uc2~~-jupyter.scc.kit.edu/software-modules ~~uc2~~-jupyter.scc.kit.edu/software-modules]		* bwUniCluster 3.0: [https://uc3-jupyter.scc.kit.edu/software-modules uc3-jupyter.scc.kit.edu/software-modules]
	* HoreKa: [https://hk-jupyter.scc.kit.edu/software-modules hk-jupyter.scc.kit.edu/software-modules]		* HoreKa: [https://hk-jupyter.scc.kit.edu/software-modules hk-jupyter.scc.kit.edu/software-modules]