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Running Jupyter Notebook on REPACSS

A comprehensive guide for running Jupyter Notebook for interactive Python development on REPACSS using conda environments.

🎯 Overview

Jupyter Notebook is a popular application for interactive Python development that can be installed in your conda environments. This guide shows you how to set up and run Jupyter Notebook on REPACSS.

This is an example of using conda (user-installed) for interactive Python development. For general conda setup, see Getting Started with MiniForge.

🚀 Quick Start

Prerequisites

  • Conda environment with Jupyter installed
  • Access to REPACSS login nodes or compute nodes

Basic Setup

# Activate your conda environment
conda activate myenv

# Install Jupyter if not already installed
conda install jupyter notebook ipykernel

# Launch Jupyter
jupyter notebook --no-browser --ip=127.0.0.1 --port=8081

📦 Installing Jupyter in Your Environment

Step 1: Set Up Conda Environment

Before setting up Jupyter Notebook, you need to have conda available on your system. If you haven't set up conda yet, please follow our Getting Started with MiniForge guide first.

Once conda is installed, verify it's working:

conda --version

You should see output similar to:

repacss:$ conda --version
conda 25.5.1

Step 2: Create and Activate Environment

# Create a new environment for your project
conda create -n jupyter_env python=3.10 jupyter ipykernel -y

# Activate the environment
conda activate jupyter_env

You should now see your prompt change to:

(jupyter_env) repacss:$

Step 3: Install Additional Packages

# Install common data science packages
conda install numpy scipy matplotlib pandas scikit-learn

# Install additional Jupyter extensions (optional)
conda install jupyter_contrib_nbextensions

🚀 Launching Jupyter Notebook

On Login Node

# Start Jupyter on login node
jupyter notebook --no-browser --ip=127.0.0.1 --port=8081

You'll see output like:

To access the server, open this file in a browser:
    file:///mnt/REPACSS/home/username/.local/share/jupyter/runtime/jpserver-1341111-open.html
Or copy and paste one of these URLs:
    http://127.0.0.1:8081/tree?token=...

# Request an interactive session
interactive -p zen4 -c 4 -m 8G

# Activate your environment
conda activate jupyter_env

# Launch Jupyter
jupyter notebook --no-browser --ip=127.0.0.1 --port=8081

🌐 Accessing Jupyter Remotely

Setting Up SSH Tunnel

If you're running Jupyter on a remote server, set up an SSH tunnel from your local machine:

ssh -L 8081:127.0.0.1:8081 -l <your_username> -fN repacss.ttu.edu

Replace <your_username> with your actual username.

Accessing in Browser

Then open http://127.0.0.1:8081/ in your local browser.

You should now see the Jupyter interface.

🔥 Running Jupyter on GPU Nodes

For GPU-accelerated work:

Step 1: Request GPU Resources

# Request interactive session with GPU
interactive -p h100 -t 02:00:00 -g 1

Step 2: Set Up Environment

# Activate your environment
conda activate jupyter_env

# Load CUDA module if needed
module load cuda/12.6.2

Step 3: Set Up SSH Tunnel to GPU Node

From your local machine, set up tunnel to the specific GPU node:

ssh -L 8081:rpg-93-1:8081 -l <username> -fN repacss.ttu.edu

Replace rpg-93-1 with your actual GPU node name.

Step 4: Launch Jupyter

# Launch Jupyter on GPU node
jupyter notebook --no-browser --ip=127.0.0.1 --port=8081

You are now running Jupyter Notebook with access to GPU resources!

🛑 Stopping Jupyter Notebook

Graceful Shutdown

To stop the Jupyter server, go to the terminal where it's running and:

  • Press Ctrl + C, then type y and press Enter OR
  • Press Ctrl + C twice quickly to force shutdown

Example:

^C
Shutdown this notebook server (y/[n])? y
[C 12:00:00.000 NotebookApp] Shutdown confirmed

Clean Up

Also remember to: - Close the SSH tunnel if it's still active - Exit the compute node session when done: exit

🎯 Best Practices

Resource Management

  • Use compute nodes for heavy computational work
  • Request appropriate resources - don't over-allocate
  • Exit sessions promptly when done
  • Use batch jobs for long-running notebooks

Security

  • Use SSH tunnels for remote access
  • Don't expose Jupyter to external networks
  • Use tokens for authentication
  • Keep your environment updated

Performance

  • Use GPU nodes for machine learning workloads
  • Monitor resource usage during execution
  • Close unused notebooks to free memory
  • Use appropriate kernels for your environment

🛠️ Troubleshooting

Common Issues

  1. Port already in use 

    # Find and kill processes using the port
lsof -ti:8081 | xargs kill -9

  2. Cannot connect to Jupyter

  3. Check if SSH tunnel is active
  4. Verify the correct port is being used

  5. Ensure Jupyter is running on the expected IP

  6. Environment not found 

    # List available environments
conda env list

# Recreate environment if needed
conda create -n jupyter_env python=3.10 jupyter ipykernel

  7. GPU not available 

    # Check if you're on a GPU node
nvidia-smi

# Request GPU resources if needed
interactive -p h100 -t 02:00:00 -g 1

Debug Mode

Enable debug output for troubleshooting:

jupyter notebook --debug --no-browser --ip=127.0.0.1 --port=8081

🆘 Support

For Jupyter-specific issues or assistance, contact:

repacss.support@ttu.edu