Jupyter Notebook (Normal Partition)

Jupyter Notebook on the Normal Partition

This page explains how to launch a Jupyter Lab (or Jupyter Notebook) server on a Pantarhei CPU compute node and connect to it from your local browser using an SSH tunnel. Because compute nodes are not directly reachable from the internet, the tunnel forwards a port from your laptop through the login node to the compute node.

Prerequisites

SSH access to pantarhei.ua.edu
A terminal on your local machine capable of SSH (macOS/Linux terminal, Windows Terminal, or Git Bash)
Anaconda3 module available on Pantarhei (loaded in the job script)
A web browser on your local machine

Job Script

Save the following script as jupyter_notebook.job in your Pantarhei home or working directory:

#!/bin/bash
# jupyter_notebook.job
#SBATCH --job-name=jupyter_test_Job
#SBATCH --output=jupyter-notebook-%J.log
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --partition=normal
#SBATCH --time=04:00:00
#SBATCH --cpus-per-task=2
##SBATCH --mail-type=ALL
##SBATCH --mail-user=<ENTER_YOUR_USERNAME>@crimson.ua.edu

## Printing information about the Slurm Job
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
echo "Name of the cluster on which the job is executing." $SLURM_CLUSTER_NAME
echo "Number of tasks to be initiated on each node." $SLURM_TASKS_PER_NODE
echo "Number of cpus requested per task." $SLURM_CPUS_PER_TASK
echo "Number of CPUS on the allocated node." $SLURM_CPUS_ON_NODE
echo "Total number of processes in the current job." $SLURM_NTASKS
echo "List of nodes allocated to the job" $SLURM_NODELIST
echo "Total number of nodes in the job's resource allocation." $SLURM_NNODES
echo "List of allocated GPUs." $CUDA_VISIBLE_DEVICES

## Load any module that you need
module load Anaconda3

## Run any specific environment you have
#source activate DeepBioComp

## get tunneling info
XDG_RUNTIME_DIR=""
ipnport=$(shuf -i8000-9999 -n1)
ipnip=$(hostname -i)

## print tunneling instructions to jupyter-notebook-{jobid}.log
echo -e "\n\n   Copy/Paste this in your local terminal to ssh tunnel with remote  "
echo        "   ------------------------------------------------------------------"
echo        "   ssh -N -L $ipnport:$ipnip:$ipnport $USER@pantarhei.ua.edu         "
echo        "   ------------------------------------------------------------------"
echo -e "\n\n   Then open a browser on your local machine to the following address"
echo        "   ------------------------------------------------------------------"
echo        "   localhost:$ipnport                                                "
echo -e     "   ------------------------------------------------------------------\n\n"
sleep 1

## start an ipcluster instance and launch jupyter server

## For Jupyter Notebook
#jupyter-notebook --no-browser --port=$ipnport --ip=$ipnip

## For Jupyter Lab
jupyter-lab --no-browser --port=$ipnport --ip=$ipnip

To enable email notifications, uncomment the two ##SBATCH --mail-* lines and replace the placeholder with your email address.

To run Jupyter Notebook instead of Jupyter Lab, comment out the jupyter-lab line and uncomment the jupyter-notebook line near the bottom of the script.

Script Explanation

SBATCH directives

Directive	Value	Purpose
`--job-name`	jupyter_test_Job	Label shown in `squeue`
`--output`	jupyter-notebook-%J.log	Log file; `%J` is replaced with the job ID — this is where the SSH tunnel command will be printed
`--nodes`	1	One compute node is enough for a notebook server
`--ntasks`	1	Single task — Jupyter runs as one process
`--partition`	normal	Standard CPU queue
`--time`	04:00:00	Session length; increase if you need longer (max 7 days on `normal`)
`--cpus-per-task`	2	CPU cores available inside the notebook; increase for parallel computation

Diagnostic echo statements

Prints key Slurm environment variables to the log file so you can confirm which node and resources were allocated.

module load Anaconda3

Makes jupyter-lab and jupyter-notebook available on the compute node. If you have a custom conda environment, uncomment and edit the source activate line below it.

Tunneling setup

XDG_RUNTIME_DIR=""
ipnport=$(shuf -i8000-9999 -n1)
ipnip=$(hostname -i)

XDG_RUNTIME_DIR="" prevents a Jupyter warning about a missing runtime directory on compute nodes.
shuf -i8000-9999 -n1 picks a random port in the 8000–9999 range to avoid collisions with other users' sessions.
hostname -i gets the compute node's internal IP address, which is needed for the SSH tunnel.

The echo block that follows writes the exact SSH command and browser URL to the log file — you copy these values to connect.

sleep 1

Gives the echo output a moment to flush to the log file before Jupyter starts writing its own output.

Jupyter server command

jupyter-lab --no-browser --port=$ipnport --ip=$ipnip

--no-browser prevents Jupyter from trying to open a browser on the (headless) compute node.
--port and --ip bind the server to the randomly chosen port on the compute node's IP.

Submitting the Job

sbatch jupyter_notebook.job

Slurm returns a job ID, for example:

Submitted batch job 215

Connecting from Your Local Machine

Step 1 — Wait for the job to start and find the SSH command

Poll the log file until the tunnel instructions appear:

tail -f jupyter-notebook-215.log

Look for a block like this:

   Copy/Paste this in your local terminal to ssh tunnel with remote
   ------------------------------------------------------------------
   ssh -N -L 8742:10.0.0.5:8742 jsmith@pantarhei.ua.edu
   ------------------------------------------------------------------

   Then open a browser on your local machine to the following address
   ------------------------------------------------------------------
   localhost:8742
   ------------------------------------------------------------------

Step 2 — Open the SSH tunnel on your local machine

Open a new terminal on your laptop and run the command from the log file:

ssh -N -L 8742:10.0.0.5:8742 jsmith@pantarhei.ua.edu

This command will appear to hang — that is normal. It keeps the tunnel open. Leave it running.

Step 3 — Open Jupyter in your browser

Navigate to the address printed in the log file:

localhost:8742

Jupyter Lab will load in your browser. If prompted for a token, find it in the log file — Jupyter prints it when it starts.

Ending the Session

Close the browser tab and cancel the Slurm job when you are finished:

scancel 215

Then press Ctrl-C in the terminal running the SSH tunnel to close it.

Troubleshooting

The log file shows no tunnel instructions

The job may still be queued. Check its status:

squeue -u $USER

Wait until ST shows R (running), then re-check the log.

"Address already in use" error in the log

The randomly selected port was already taken on the compute node. Cancel the job and resubmit — a new random port will be chosen.

Browser shows "Unable to connect"

Confirm the SSH tunnel terminal is still running (it should appear to hang). If it exited, re-run the ssh -N -L ... command from the log file.

Token or password prompt in the browser

Copy the full Jupyter URL including the ?token=... part from the log file and paste it directly into the browser address bar.

For a GPU-accelerated Jupyter session, see the Jupyter Notebook (GPU Partition) page.

For hydrologic analysis using NumPy and Matplotlib inside your Jupyter session, see the Hydrologist Notebook Example page.

Additional Resources

For complete example scripts and job files, visit the Pantarhei examples repository:

https://github.com/kamalcou/pantarhei-examples

Jupyter Notebook on the Normal Partition​

Prerequisites​

Job Script​

Script Explanation​

Submitting the Job​

Connecting from Your Local Machine​

Ending the Session​

Troubleshooting​

Additional Resources​