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Atambo at 07:39, 9 January 2026

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Atambo: Created page with "= JupyterLab (Web) Tutorial - KENET HPC Cluster = == Overview == '''JupyterLab''' is an interactive web-based environment for notebooks, code, and data, ideal for data scienc..."

2026-01-09T07:35:22Z

Created page with "= JupyterLab (Web) Tutorial - KENET HPC Cluster = == Overview == '''JupyterLab''' is an interactive web-based environment for notebooks, code, and data, ideal for data scienc..."

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= 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 <code>/home/username/localscratch</code>

----

== 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

[[File:OOD_JupyterLab_Menu.png|thumb|600px|center|Navigate to Interactive Apps → JupyterLab]]

=== Step 2: Configure Job Parameters ===
Fill in the job submission form with your requirements:

{| class="wikitable"
|-
! Parameter !! Description !! Recommended Value
|-
| '''Partition''' || Queue for job execution || <code>normal</code> (CPU) or <code>gpu</code> (GPU tasks)
|-
| '''Walltime''' || Maximum runtime in hours || <code>2</code> hours for testing, up to <code>192</code> for long jobs
|-
| '''CPU Cores''' || Number of processor cores || <code>4-8</code> cores (adjust based on workload)
|-
| '''Memory''' || RAM allocation || <code>16</code> GB for data science, <code>32</code> GB for large datasets
|-
| '''Working Directory''' || Starting directory || <code>/home/username</code> or your project folder
|}

[[File:OOD_JupyterLab_Form.png|thumb|600px|center|Job configuration form with recommended settings]]

{{Tip|For GPU-accelerated deep learning, select the '''gpu''' partition and specify number of GPUs needed.}}

=== 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)

[[File:OOD_JupyterLab_Running.png|thumb|600px|center|Session card showing "Running" status with Connect button]]

----

== 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

[[File:JupyterLab_New_Notebook.png|thumb|600px|center|Creating a new notebook in JupyterLab]]

=== Basic Cell Operations ===

← Older revision		Revision as of 19:06, 9 January 2026
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	For support, contact KENET at support@kenet.or.ke, consult the documentation at https://training.kenet.or.ke/index.php/HPC_Usage, or access the Open OnDemand portal at https://ondemand.vlab.ac.ke.		For support, contact KENET at support@kenet.or.ke, consult the documentation at https://training.kenet.or.ke/index.php/HPC_Usage, or access the Open OnDemand portal at https://ondemand.vlab.ac.ke.
−
−	~~----~~
−
−	~~== Version Information ==~~
−
−	This tutorial documents JupyterLab version 4.x running Python 3.9 or later. The JupyterLab installation is located at <code>/opt/ohpc/pub/codes/ood/jupyterlab</code> on the cluster. This tutorial was last updated on 2026-01-09 and is currently at version 1.0.

	----		----

@@ Line 8: / Line 8: @@
 '''Access:''' JupyterLab is available through the KENET Open OnDemand web portal at https://ondemand.vlab.ac.ke
-'''Code Examples:''' All code examples for this tutorial are available in our GitHub repository at https://github.com/KENET-HPC/ood-tutorials
+'''Code Examples:''' All code examples for this tutorial are available in our GitHub repository at https://github.com/Materials-Modelling-Group/training-examples
 ----
@@ Line 14: / Line 14: @@
 == Prerequisites ==
-Before using JupyterLab, you should have an active KENET HPC cluster account with access to the Open OnDemand portal. Basic knowledge of Python, R, or Julia will be helpful, and you should have your data files stored in your home directory at <code>/home/username/</code> or in the scratch directory at <code>/scratch/username/</code> for larger datasets.
+Before using JupyterLab, you should have an active KENET HPC cluster account with access to the Open OnDemand portal. Basic knowledge of Python, R, or Julia will be helpful, and you should have your data files stored in your home directory at <code>/home/username/localscratch</code>.
 ----
@@ Line 175: / Line 175: @@
 Key Python libraries for data science include Pandas for data manipulation (https://pandas.pydata.org/), NumPy for numerical computing (https://numpy.org/), Matplotlib for plotting (https://matplotlib.org/), and Scikit-learn for machine learning (https://scikit-learn.org/).
-'''Code Examples Repository:''' All code examples referenced in this tutorial are available at https://github.com/KENET-HPC/ood-tutorials
+'''Code Examples Repository:''' All code examples referenced in this tutorial are available at https://github.com/Materials-Modelling-Group/training-examples.git
-For support, contact KENET at support@kenet.or.ke, consult the documentation at https://training.kenet.or.ke, or access the Open OnDemand portal at https://ondemand.vlab.ac.ke.
+For support, contact KENET at support@kenet.or.ke, consult the documentation at https://training.kenet.or.ke/index.php/HPC_Usage, or access the Open OnDemand portal at https://ondemand.vlab.ac.ke.
 ----

← Older revision		Revision as of 08:27, 9 January 2026
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	<code>		<code>
	</code>		</code>
		+
		+	Up:
		+	[[KENET Open OnDemand \| Easy HPC access with KENET Open OnDemand]]

← Older revision		Revision as of 08:15, 9 January 2026
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	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.		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 <code>--user</code> flag to install packages in your personal home directory rather than system-wide:
		+
		+	<code bash>
		+	# In a notebook cell
		+	!pip install --user seaborn scikit-learn
		+	# or alternatively
		+	%pip install --user packagename
		+	</code>
		+
		+	'''Important:''' Always use the <code>--user</code> 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:
		+
		+	<code>
		+	</code>

@@ Line 75: / Line 75: @@
 === Basic Cell Operations ===
-# Create sample data
+Notebooks consist of cells where you can write and execute code. Here is a simple example to get you started:
-# Plot
+<code python>
-plt.plot(data['x'], data['y'])
+ # Code cell - Press Shift+Enter to run
-plt.title('Sample Plot on KENET HPC')
+ import pandas as pd
-plt.show()
+ import matplotlib.pyplot as plt
-</syntaxhighlight>

← Older revision		Revision as of 07:39, 9 January 2026
Line 75:		Line 75:

	=== Basic Cell Operations ===		=== Basic Cell Operations ===
		+	<syntaxhighlight lang="python">
		+	# 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()
		+	</syntaxhighlight>