(ige-jupyterhub)=

# IGE Jupyterhub 

## Access the server

A server of notebooks with ressources coming from ige-calcul1-7 has been deployed and is accessible for anyone with agalan account at the address : https://ige-jupyterhub.univ-grenoble-alpes.fr/

As of today (september 2026) it is only accessible from IGE network or via [UGA's VPN](https://ige-intranet.osug.fr/spip.php?article640)

First, you will be asked for your agalan login/password

![](images/connexion.png)

Then the landing page looks like this :

![](images/jupytrehub-landing.png)

You get to choose several parameters for your jupyterhub session : 
  - Partition : ioperf is preferred for an intensive data reading/writing, compute for other use and gpu if you need one
  - Time, Number of cores, Memory 
  - User interface : you can choose to open a jupyterlab, jupyter notebook or a terminal

Finally you are connected to the job and have access to different kernels (pre-built: Matlab +your own : R/...)

![](images/kernels.PNG)


## Workspaces

On the jupyterhub server you have access to difference type of workspaces for different usage :
  - your **home** workspace will always be the same for your sessions and is hosted at /mnt/summer/juping/home/alberta (also accessible from IGE clusters ige-calcul1-7) : this is where you can store light scripts, texts files, **quota** : 3Gb / user
  - your **workdir** workspace, also accessible from any of your sessions and IGE clusters and accessible at /workdir/yourteam/yourlogin : this is where you can read, write, store *hot* data that you are currently producing or using, **quota** : XGb / user
  - some **storedir** workspace : depending on your team and/or the projects you are working in, you may have access to some SUMMER storage : this is where you will store *cold* data that you do not need for the moment but you may need later, **quota** : depending on your team/project [if the SUMMER storage of your team/project is not accessible from the jupyterhub, ask ige-jupyterhub@univ-grenoble-alpes.fr to mount it]

  If you need to share your data with outside of the lab, check the {ref}`erddap catalog for IGE<ige-catalog>` or {ref}`S3 point for IGE<ige-s3>` 


## Exit the server

In order to stop the kernel and kill the allocated job go to **Hub Control Panel**

![](../Tools/images/exit_jupyterlab1.PNG)

![](../Tools/images/exit_jupyterlab2.PNG)


## Restart the server

You can restart the server , by clicking on the button **Start My Server**
It will ask you again for new ressources and connect you to the server

![](../Tools/images/restart_jupyterhub.PNG)

## Computing environment

3 pangeo style environments are provided and can be reproduced from their configuration files hosted [here](https://github.com/ige-PING/jupyterhub-envs) :
  - pangeo-notebook : some python librairies needed to manage data (xarray, pandas, ...), produce plots (matplotlib, cartopy, ...) and compute (numpy, scipy, ...) and many [other](https://github.com/ige-PING/jupyterhub-envs/blob/main/pangeo-notebook/environment.yml)
  - pangeo-pytorch : pangeo-notebook + pytorch (see the list [here](https://github.com/ige-PING/jupyterhub-envs/blob/main/pangeo-pytorch/environment.yml)
  - pangeo-tfjax : pangeo-notebook + tensorflow +jax (see the list [here](https://github.com/ige-PING/jupyterhub-envs/blob/main/pangeo-tfjax/environment.yml)

You can also add your own kernel/ environment created with micromamba for example, see examples below

### R example

1. Create your R environment
```
  micromamba create -n Renv python=3.10 -c conda-forge
  micromamba activate Renv
  micromamba install r r-base r-essentials -c conda-forge
```
2. Add the kernel to your jupyterlab

Open R terminal

```
 install.packages('IRkernel')
 IRkernel::installspec()
```
### Pytorch example

1. Create pytorch env
```
   micromamba create -n EnvPytorch python=3.10 -c conda-forge
   micromamba activate EnvPytorch
   micromamba install pytorch torchvision torchaudio  -c pytorch -c nvidia -c conda-forge
   micromamba install ipykernel  -c conda-forge
```
2. Install the pytorch environment

```
python -m ipykernel install --name EnvPytorch --user --display-name "Pytorch"
```


## Run Vscode on the clusters

```{Note}
If you don't need to use python and only vscode, you can select **Terminal** for the User Interface, instead of jupyterlab or jupyter
This will open only a terminal on the server
```
Once you are connected to jupyterhub

Open a terminal from the jupyter launcher  and get the informations to connect to the server in the output of your job

```
head -10  $HOME/jupyterhub_slurmspawner_$SLURM_JOBID.log
```

Example for my JOBID=8:

```
chekkim@ige-calcul2:~$ head -10  jupyterhub_slurmspawner_8.log
********************************************************************
Starting code-server in Slurm
Environment information:
Date: mer. 12 févr. 2025 14:53:13 CET
Allocated node: ige-calcul2
Node IP:
Path: /home/chekkim
Password to access VSCode: user_jobid
Listening on: 46479
********************************************************************
```

Then create an ssh tunnel with the given port

```
ssh -fNL 46479:localhost:46479 calcul1/2/3/4
```

and open the following URL in your web browser:

```
http://localhost:46479
```

Entre the password:

![](../Tools/images/codeserver1.PNG)

Then you can open any folder on the remote server

![](../Tools/images/codeserver2.PNG)

and that's it. You can now modify your code and run vscode

![](../Tools/images/codeserver3.PNG)

## Matlab usage

```{Note}
For the first usage you will be asked to give the license server (Network License Manager)
27000@matlab.ige-grenoble.fr
```

![](../Tools/images/matlab_license.PNG)

Once it is done, you will be able to run matlab and the configuration will be saved for future usages

![](../Tools/images/matlab.PNG)