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JupyterHub for Teaching

For teaching purpose, a dedicated jupyterhub is available at https://data.ipsl.fr/jupyclass/.

Support Contact

this service has a dedicated support address jupyclass-support

Reset password

https://jupy-pass.ipsl.fr/pwrst/

Service resources

A Dedicated mini-cluster hosted in IPSL polytechnique datacenter hosts the jupyterhub.

Warning

The server is not connected to IPSL databases (/bdd) to improve resilience of the service in case of storage failures or other infrastructure problems.

  • 64 cores
  • 256 GB of RAM
  • 7TB of storage
    • 4TB for users homes
    • 3TB for shared data

Storage

User

Each user is allows to store in its home directory:

  • 20 GB
  • 20000 files.

Shared

  • To share data with your students you can request a dedicated directory in /data where you can upload your data and notebooks. You can also change the permissions of the directory to prevent students from deleting your files.
  • Contact jupyclass-support to request a shared directory.

Access to the server

The server is accessible from internet. To connect to it you need a dedicated login/server.

For teachers

Send an email to jupyclass-support to get an account.

For students

Warning

Account creation requests should be made at most 2 weeks before the start of the course.

Teachers need to provide the elements below in an excel sheet with 3 columns:

  • firstname
  • lastname
  • email

When requesting account creation you also need to provide the date of end of validity for accounts

A form should be soon available on espri website to ease the process.

Then send the file to jupyclass-support.

Starting a session

  • Go to https://data.ipsl.fr/jupyclass/ and log in with your credentials.

    jupyclass login page

  • Choose the ressource you need for your sessions (be careful to choose the right one to leave ressources available for everyone).

  • Choosing the number of cores and the amount of RAM you need for your session
    • 1 core and 4GB of RAM
    • 2 cores and 8GB of RAM
    • 4 cores and 16GB of RAM
    • you can see the available ressources on the server by clicking on the Available ressources button
  • Duration of the session (1h, 2h, 4h, 8h, 6h, 12h)

  • Click on Start to start your session

    jupyclass ressources

Warning

If you use too much ressources (cpu or RAM), your session will be killed

Ending a session

  • In the File menu, click Hub Control Panel to return to the control panel.

    jupyclass jupyterlab menu

  • Click Stop My Server to stop your session.

    jupyclass jupyterlab stop server

Available modules

The jupyterhub for teaching gives access to all modules environments available on spirit/spiritx clusters. For more details see here.

To view the full list of available modules run the command module avail.

Available environments in notebook

Python

Default environment is based on python 3.12 and pangeo recommanded packages.

The old default environment based on python 3.9 is still available and can be used by selecting the Python 3.9 (XPython) kernel in jupyterlab. A detailled list of python package for this environment is available here.

R

The server use the same R environment as the one available as module on IPSL clusters and described here.

If packages are missing for your training session, we can try to install them upon request.

C/C++

This xeus-cling jupyter kernel allows to do interactive C/C++.

More informations are available here

Available environments in jupyterlab editor and terminal

You can use jupyter editor and terminal to use and develop code in compiled languages (C/C++, fortran, ...)

Fortran

By default gfortran 9 is available. Using available module command you can use gfortran v10 and v11, intel fortran compiler v19 and v2021 and nvfortran v20 and v21.

C/C++

By default gcc v9 is available. Using available module command you can use gcc v10 and v11, intel compiler v19 and v2021 and nvcc v20 and v21.

Uploading data on jupyterhub

Using JupyterLab interface

Steps to upload your data:

  • Zip or tar your the data/notebooks you want to upload (not possible with .rar files). For example:
Zip your data
zip -r my_data.zip my_data_directory

or

Tar your data
tar -czvf my_data.tar.gz my_data_directory

  • Upload your data using JupyterLab

    jupyterlab upload button

  • Open a terminal on the jupyterlab

    jupyterlab open terminal

  • if you want to share the data with students, uncompress them to /data directory

Create a directory in /data
mkdir /data/my_tp
unzip in your directory
unzip -d /data/my_tp my_data.zip

or

untar in your directory
tar -xvzf my_data.tar.gz -C /data/my_tp
  • To prevent students from deleting your files, change the permissions of the directory
Change permissions
chmod -R go+r-w /data/my_tp

Using scp command

Warning

  • You need to have an ssh key registered on the server to use scp command.
  • You need an account on IPSL spirit(x) cluster

Pre-requisites

You need to make a request to jupyclass-support to add you ssh public key. you can provide your ssh key or add to add the one from your spirit account.

Uploading data

  • log into your spirit(x) account and go to the directory where your data is stored. For example my_data.nc in /home/user/my_data.nc
  • Use the scp command to upload your data to jupyterhub server. For example:
scp -r my_data.nc jupyclass_user@jupyter-espri-formation.priv.ipsl.polytechnique.fr:/home/jupyclass_user/my_data.nc