Jupyter
From KENET Training
Contents
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
- Log into Open OnDemand: https://ondemand.vlab.ac.ke
- Click Interactive Apps in the top navigation menu
- Select JupyterLab from the dropdown list
Step 2: Configure Job Parameters
Fill in the job submission form with your requirements:
| Parameter | Description | Recommended Value |
|---|---|---|
| Partition | Queue for job execution | normal (CPU) or gpu (GPU tasks)
|
| Walltime | Maximum runtime in hours | 2 hours for testing, up to 192 for long jobs
|
| CPU Cores | Number of processor cores | 4-8 cores (adjust based on workload)
|
| Memory | RAM allocation | 16 GB for data science, 32 GB for large datasets
|
| Working Directory | Starting directory | /home/username or your project folder
|
Step 3: Submit and Wait
- Click Launch button
- Wait for job to start (Status: "Queued" → "Running")
- Click Connect to JupyterLab button when available (typically 30-60 seconds)
Quick Start Guide
Creating Your First Notebook
- Click File → New → Notebook or click the Python 3 tile in the Launcher
- Select kernel: Python 3, R, or Julia (if available)
- Start writing code in cells
