Revision as of 08:27, 9 January 2026

JupyterLab (Web) Tutorial - KENET HPC Cluster

Overview

JupyterLab is an interactive web-based environment for notebooks, code, and data, ideal for data science, scientific computing, and machine learning workflows.

Use Cases:

• Interactive data analysis and visualization
• Machine learning model development and experimentation
• Creating reproducible research notebooks
• Teaching and sharing computational narratives
• Real-time data exploration with GPU acceleration

Access: Available through the KENET Open OnDemand web portal at https://ondemand.vlab.ac.ke

Prerequisites

Before using JupyterLab, ensure you have:

• Active KENET HPC cluster account
• Access to Open OnDemand portal
• Basic knowledge of Python, R, or Julia
• Data files stored in /home/username/localscratch

Launching JupyterLab

Step 1: Access Interactive Apps

1. Log into Open OnDemand: https://ondemand.vlab.ac.ke
2. Click Interactive Apps in the top navigation menu
3. Select JupyterLab from the dropdown list

Navigate to Interactive Apps → JupyterLab

Step 2: Configure Job Parameters

Fill in the job submission form with your requirements:

Job configuration form with recommended settings

Template:Tip

Step 3: Submit and Wait

1. Click Launch button
2. Wait for job to start (Status: "Queued" → "Running")
3. Click Connect to JupyterLab button when available (typically 30-60 seconds)

Session card showing "Running" status with Connect button

Quick Start Guide

Creating Your First Notebook

1. Click File → New → Notebook or click the Python 3 tile in the Launcher
2. Select kernel: Python 3, R, or Julia (if available)
3. Start writing code in cells

Creating a new notebook in JupyterLab

Basic Cell Operations

Notebooks consist of cells where you can write and execute code. Here is a simple example to get you started:

# Code cell - Press Shift+Enter to run
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = pd.DataFrame({
    'x': range(10),
    'y': [i**2 for i in range(10)]
})

# Plot
plt.plot(data['x'], data['y'])
plt.title('Sample Plot on KENET HPC')
plt.show()

There are three main cell types in JupyterLab. Code cells contain executable code and are the default type. Markdown cells contain formatted text, equations, and documentation. Raw cells contain plain text that is not executed or formatted.

Installing Python Packages

You can install additional Python packages directly from within a notebook cell. Always use the --user flag to install packages in your personal home directory rather than system-wide:

# In a notebook cell
!pip install --user seaborn scikit-learn
# or alternatively
%pip install --user packagename

Important: Always use the --user flag to install packages in your home directory, not system-wide. System directories are read-only and attempts to install there will fail.

Common Tasks

Task 1: Loading Data from Cluster Storage

Loading data from files stored on the cluster is straightforward using pandas or other data libraries. You can read data from your home directory or from the faster scratch storage:

Up: Easy HPC access with KENET Open OnDemand