(ige-jupyterhub)=

# Jupyterhub on IGE Clusters

Make sure you are able to connect to the clusters ige-calcul1/2/3/4 without any password.
Please refers to the doc [Connect to the clusters](../Ige/ige-calcul1.md)


## Connect to the server

If you are using the command

```
ssh  calcul1
```

to connect to the cluster ige-calcul1, then create the ssh tunnel using any port , here **8300**

```
ssh -fNL 8300:localhost:8000 calcul1
```

```{Note}
The jupyterhub is also available on the other clusters, i.e ige-calcul2 ige-calcul3 ige-calcul4
Make sure to have a look to the features of each cluster as you can access to large memory (up to 700G) and Gpus
```

Once this done, open a local browser, with this url:

```
http://localhost:8300
```

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

![](./images/jupyterhub1.PNG)

Then you get the different options to choose the needed resources
1. Memory
2. CPUs/ GPUs
3. Time, etc...

```{caution}
If your job is taking time to get connected, you are problaly waiting in the queue
You can connect using ssh , and check the ressources with **squeue -u $USER**
```

![](./images/jupyterhub2.PNG)

Here is an example to choose the number of GPUs if there are any

![](./images/jupyterhub2bisgpu.PNG)

If you are allowed to run long jobs (more than 2 days), then the Qos longjobs will appear

![](./images/jupyterhub2bislong.PNG)

You can choose, which interface you need, jupyterlab/jupyter or just a terminal

![](./images/jupyterhub2bisterm.PNG)

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

![](./images/jupyterhub3.PNG)

You can access to SLURM commands to check the status of your code, from a notebook

```{Note}

When the job is submit, it will read the $HOME/.profile file.
You can add all the environment variable you need before jupyter notebook starts.
If you need to change the Notebook Directory (default $HOME) , you can add the following in this file:

export NOTEBOOK_DIR=/path/to/new/location

```

![](./images/slurm_magics.PNG)

Check the cpu usage (extension on the left)

![](./images/cpu_usage.PNG)

Check the gpu usage (extension on the left)

![](./images/gpu_usage.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
```

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

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

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

## Exit the server

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

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

![](./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 adn connect you to the server

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

# Add you own environment

![](./images/kernel_env_install.PNG)

You can add you own kernel/ environment created with micromamba for example

## 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"
```
![](./images/check_torch.PNG)


## 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:

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

Then you can open any folder on the remote server

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

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

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