DEV Community: Thu Kha Kyawe

Day 4: Create a Standard ML Project Structure

Thu Kha Kyawe — Wed, 03 Jun 2026 12:35:33 +0000

Lab Information

A colleague has started a new ML project at /root/code/fraud-detection/, but the layout does not match the xFusionCorp Industries standard. Bring the project in line with the team's conventions.

Inspect the existing project at /root/code/fraud-detection/.

The final layout must match the tree below exactly:

fraud-detection/
├── data/
│ ├── raw/
│ └── processed/
├── models/
├── notebooks/
├── src/
│ ├── data/
│ ├── features/
│ ├── models/
│ └── utils/
├── tests/
├── configs/
├── requirements.txt
└── README.md

Every subdirectory under src/ must contain an __init__.py file so that Python recognises it as a package.

requirements.txt must list the following dependencies, one per line: scikit-learn, pandas, numpy, and mlflow. The canonical PyPI name for the scikit-learn package is scikit-learn.

README.md must begin with the heading # fraud-detection.

Review the existing project and correct everything that does not match the requirements above.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Run the following commands on the controlplane host.

Step 1 — Move into the project directory

cd /root/code/fraud-detection

Step 2 — Inspect the current structure

tree

If tree is unavailable:

sudo apt update && sudo apt install tree

Step 3 — Check the required directory structure

Run:

tree

Rename incorrect directories

mv src/feature src/features
mv src/util src/utils

Create missing directories

mkdir -p data/raw
mkdir -p data/processed
mkdir -p tests
mkdir -p configs

Verify init.py still exists

Check:

ls src/features
ls src/utils

You should see:

init.py

Step 4 — Verify and fix requirements.txt

Create/update the file:

cat > requirements.txt

Output:

sklearn
pandas
numpy

Create the correct requirements.txt:

cat > requirements.txt <<EOF
scikit-learn
pandas
numpy
mlflow
EOF

Create/update the README:

cat README.md

Output:

# Fraud

ML project for fraud detection at xFusionCorp Industries.

Replace the README

Run:

cat > README.md <<EOF
# fraud-detection

ML project for fraud detection at xFusionCorp Industries.
EOF

Step 7 — Verify the final structure and README.md content

Run:

tree
cat README.md

Expected structure:

fraud-detection/
├── data/
│ ├── raw/
│ └── processed/
├── models/
├── notebooks/
├── src/
│ ├── data/
│ │ └── init.py
│ ├── features/
│ │ └── init.py
│ ├── models/
│ │ └── init.py
│ └── utils/
│ └── init.py
├── tests/
├── configs/
├── requirements.txt
└── README.md

root@controlplane ~/code/fraud-detection via 🐍 v3.12.3 ➜  cat README.md 
# fraud-detection

ML project for fraud detection at xFusionCorp Industries.

🧠 Part 2: Simple Beginner-Friendly Explanation

This lab focuses on organising a machine learning project according to the xFusionCorp Industries standard structure.

The goal is to:

standardise project layout
improve maintainability
make collaboration easier for developers and data scientists

You must inspect the existing project and correct anything that does not match the required structure.

Understanding the Required Project Structure

The final project must look exactly like this:

Each folder has a specific purpose in an ML workflow.

Purpose of Each Directory

data/
Stores datasets used in the project.

data/raw/
Contains original unmodified data.

Example:
transactions.csv

data/processed/
Contains cleaned or transformed datasets used for training.

Example:
clean_transactions.csv

models/
Stores trained machine learning models.

Example:
fraud_model.pkl

notebooks/
Contains Jupyter notebooks for experimentation and analysis.

Example:
eda.ipynb

src/
Contains the main Python source code for the application.
This keeps project logic organised and modular.

Why init.py Files Are Required
Every subdirectory under src/ must contain:
init.py

This tells Python:
“Treat this directory as a Python package.”

Without these files:
imports may fail
modules may not be recognised correctly

Example:
from src.models.train import train_model

Purpose of src/ Subdirectories

Directory Purpose
src/data Data loading and preprocessing
src/features Feature engineering logic
src/models Training and prediction code
src/utils Helper functions and utilities

Why requirements.txt Matters
The lab requires the following dependencies:

scikit-learn
pandas
numpy
mlflow

This file helps developers install all required Python packages consistently.

Important note:

the correct PyPI package name is scikit-learn
not sklearn

Why README.md Matters
The README file provides project documentation.

The lab specifically requires it to begin with:

fraud-detection

This acts as the project title and ensures naming consistency.

Why Exact Naming Is Important
Lab validators check:
exact folder names
exact file names
exact dependency names

Even small differences such as:
feature instead of features
util instead of utils

Fraud instead of # fraud-detection

can cause the lab to fail.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

Day 3: Fix a Broken uv Lockfile Specification

Thu Kha Kyawe — Tue, 02 Jun 2026 13:14:56 +0000

Lab Information

The xFusionCorp Industries ML team uses uv and lockfiles to keep Python dependencies reproducible across machines. A teammate has left behind a requirements.in specification that does not match the team's standard. Correct it and compile it into a pinned lockfile.

A high-level dependency specification exists at /root/code/fraud-detection/requirements.in. uv is already installed.

The corrected specification must meet the following requirements:
    it lists exactly these four top-level packages: scikit-learn, mlflow, pandas, and numpy;
    every package carries a version constraint that uv can actually satisfy against PyPI.

Review the existing requirements.in, and correct everything that does not match the requirements above.

From the project directory, compile the corrected specification into a pinned lockfile:

uv pip compile requirements.in -o requirements.txt

The resulting requirements.txt must pin each of the four top-level packages to an exact version using ==, and must also include the transitive dependencies that uv resolved.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Run the following commands on the controlplane host.

Step 1 — Move to the project directory

cd /root/code/fraud-detection

Step 2 — Inspect the existing requirements.in

cat requirements.in

Look for:

wrong package names
missing packages
extra packages
invalid version constraints

Step 3 — Edit requirements.in

Open the file:

vi requirements.in

Replace the contents with valid package constraints such as:

scikit-learn>=1.4
mlflow>=2.12
pandas>=2.2
numpy>=1.26

The file must contain:

exactly 4 top-level packages

all valid PyPI-installable constraints

Step 4 — Compile the lockfile using uv

Run:

uv pip compile requirements.in -o requirements.txt

This generates:

requirements.txt

with:

exact pinned versions (==)

all transitive dependencies

Step 5 — Verify the generated lockfile

Check the file:

cat requirements.txt

You should see entries like:

Resolved 90 packages in 626ms
# This file was autogenerated by uv via the following command:
#    uv pip compile requirements.in -o requirements.txt
aiohappyeyeballs==2.6.2
    # via aiohttp
aiohttp==3.14.0
    # via mlflow
aiosignal==1.4.0
    # via aiohttp
alembic==1.18.4
    # via mlflow
annotated-doc==0.0.4
    # via fastapi
annotated-types==0.7.0
    # via pydantic
anyio==4.13.0
    # via starlette
attrs==26.1.0
    # via aiohttp
blinker==1.9.0
    # via flask
cachetools==7.1.4
    # via
    #   mlflow-skinny
    #   mlflow-tracing
certifi==2026.5.20
    # via requests
cffi==2.0.0
    # via cryptography
charset-normalizer==3.4.7
    # via requests
click==8.4.1
    # via
    #   flask
    #   mlflow-skinny
    #   uvicorn
cloudpickle==3.1.2
    # via mlflow-skinny
contourpy==1.3.3
    # via matplotlib
cryptography==48.0.0
    # via
    #   google-auth
    #   mlflow
cycler==0.12.1
    # via matplotlib
databricks-sdk==0.114.0
    # via
    #   mlflow-skinny
    #   mlflow-tracing
docker==7.1.0
    # via mlflow
fastapi==0.136.3
    # via mlflow-skinny
flask==3.1.3
    # via
    #   flask-cors
    #   mlflow
flask-cors==6.0.2
    # via mlflow
fonttools==4.63.0
    # via matplotlib
frozenlist==1.8.0
    # via
    #   aiohttp
    #   aiosignal
gitdb==4.0.12
    # via gitpython
gitpython==3.1.50
    # via mlflow-skinny
google-auth==2.53.0
    # via databricks-sdk
graphene==3.4.3
    # via mlflow
graphql-core==3.2.8
    # via
    #   graphene
    #   graphql-relay
graphql-relay==3.2.0
    # via graphene
greenlet==3.5.1
    # via sqlalchemy
gunicorn==26.0.0
    # via mlflow
h11==0.16.0
    # via uvicorn
huey==3.0.1
    # via mlflow
idna==3.17
    # via
    #   anyio
    #   requests
    #   yarl
importlib-metadata==9.0.0
    # via mlflow-skinny
itsdangerous==2.2.0
    # via flask
jinja2==3.1.6
    # via flask
joblib==1.5.3
    # via scikit-learn
kiwisolver==1.5.0
    # via matplotlib
mako==1.3.12
    # via alembic
markupsafe==3.0.3
    # via
    #   flask
    #   jinja2
    #   mako
    #   werkzeug
matplotlib==3.10.9
    # via mlflow
mlflow==3.13.0
    # via -r requirements.in
mlflow-skinny==3.13.0
    # via mlflow
mlflow-tracing==3.13.0
    # via mlflow
multidict==6.7.1
    # via
    #   aiohttp
    #   yarl
narwhals==2.22.0
    # via scikit-learn
numpy==2.4.6
    # via
    #   -r requirements.in
    #   contourpy
    #   matplotlib
    #   mlflow
    #   pandas
    #   scikit-learn
    #   scipy
    #   skops
opentelemetry-api==1.42.1
    # via
    #   mlflow-skinny
    #   mlflow-tracing
    #   opentelemetry-sdk
    #   opentelemetry-semantic-conventions
opentelemetry-proto==1.42.1
    # via
    #   mlflow-skinny
    #   mlflow-tracing
opentelemetry-sdk==1.42.1
    # via
    #   mlflow-skinny
    #   mlflow-tracing
opentelemetry-semantic-conventions==0.63b1
    # via opentelemetry-sdk
packaging==26.2
    # via
    #   gunicorn
    #   matplotlib
    #   mlflow-skinny
    #   mlflow-tracing
    #   skops
pandas==2.3.3
    # via
    #   -r requirements.in
    #   mlflow
pillow==12.2.0
    # via matplotlib
prettytable==3.17.0
    # via skops
propcache==0.5.2
    # via
    #   aiohttp
    #   yarl
protobuf==6.33.6
    # via
    #   databricks-sdk
    #   mlflow-skinny
    #   mlflow-tracing
    #   opentelemetry-proto
pyarrow==24.0.0
    # via mlflow
pyasn1==0.6.3
    # via pyasn1-modules
pyasn1-modules==0.4.2
    # via google-auth
pycparser==3.0
    # via cffi
pydantic==2.13.4
    # via
    #   fastapi
    #   mlflow-skinny
    #   mlflow-tracing
pydantic-core==2.46.4
    # via pydantic
pyparsing==3.3.2
    # via matplotlib
python-dateutil==2.9.0.post0
    # via
    #   graphene
    #   matplotlib
    #   pandas
python-dotenv==1.2.2
    # via mlflow-skinny
pytz==2026.2
    # via pandas
pyyaml==6.0.3
    # via mlflow-skinny
requests==2.34.2
    # via
    #   databricks-sdk
    #   docker
    #   mlflow-skinny
scikit-learn==1.9.0
    # via
    #   -r requirements.in
    #   mlflow
    #   skops
scipy==1.17.1
    # via
    #   mlflow
    #   scikit-learn
    #   skops
six==1.17.0
    # via python-dateutil
skops==0.14.0
    # via mlflow
smmap==5.0.3
    # via gitdb
sqlalchemy==2.0.50
    # via
    #   alembic
    #   mlflow
sqlparse==0.5.5
    # via mlflow-skinny
starlette==1.2.1
    # via
    #   fastapi
    #   mlflow-skinny
threadpoolctl==3.6.0
    # via scikit-learn
typing-extensions==4.15.0
    # via
    #   aiohttp
    #   aiosignal
    #   alembic
    #   anyio
    #   fastapi
    #   graphene
    #   mlflow-skinny
    #   opentelemetry-api
    #   opentelemetry-sdk
    #   opentelemetry-semantic-conventions
    #   pydantic
    #   pydantic-core
    #   sqlalchemy
    #   starlette
    #   typing-inspection
typing-inspection==0.4.2
    # via
    #   fastapi
    #   pydantic
tzdata==2026.2
    # via pandas
urllib3==2.7.0
    # via
    #   docker
    #   requests
uvicorn==0.48.0
    # via mlflow-skinny
wcwidth==0.7.0
    # via prettytable
werkzeug==3.1.8
    # via
    #   flask
    #   flask-cors
yarl==1.24.2
    # via aiohttp
zipp==4.1.0
    # via importlib-metadata

🧠 Part 2: Simple Beginner-Friendly Explanation

What is requirements.in?

requirements.in is a high-level dependency file.

It describes:

“These are the packages my project needs.”

Example:

numpy>=1.26

This means:

install numpy
any compatible version newer than 1.26

What is a Lockfile?

The generated:

requirements.txt

is a fully pinned dependency lockfile.

It contains:

exact package versions
every dependency required underneath

Example:

numpy==1.26.4

This guarantees:

reproducible installs
same environment on every machine

Why Use uv pip compile?

Command:

uv pip compile requirements.in -o requirements.txt

does the following:

reads requirements.in
resolves compatible versions
locks exact versions
includes transitive dependencies

Difference Between Top-Level and Transitive Dependencies

Top-Level Dependencies

Packages you explicitly request:

numpy
pandas
mlflow
scikit-learn

Transitive Dependencies

Packages automatically required by those libraries.

Example:

pandas may require:
python-dateutil
pytz
mlflow may require many extra libraries

These appear automatically in:

requirements.txt

Why Exact == Versions Matter

Example:

numpy==1.26.4

This prevents:

unexpected upgrades
dependency conflicts
“works on my machine” problems

It ensures all developers use identical versions.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

Day 2: Set Up and Configure Jupyter Notebook Server

Thu Kha Kyawe — Sun, 31 May 2026 04:59:09 +0000

Lab Information

A teammate has configured a JupyterLab server for the xFusionCorp Industries data science team, but the server does not behave correctly. Inspect the configuration, diagnose the issues, and start the server.

JupyterLab is already installed in the virtual environment at /root/code/ml-env/. The team's configuration file is at /root/code/jupyter_lab_config.py and is visible in the file explorer.

When JupyterLab is started, the Jupyter UI button at the top of the lab must open the notebook interface.

For this to work, the running server must meet the following requirements:
    it listens on port 8888;
    it binds on 0.0.0.0 (the lab proxy cannot reach a server that is only bound on 127.0.0.1);
    the notebook root directory is /root/notebooks/, and that directory exists on disk.

Open the configuration file, identify every setting that prevents the requirements above from being met, and correct it. Create any missing directories.

Start JupyterLab from the virtual environment using the corrected configuration:

source /root/code/ml-env/bin/activate
jupyter lab --config=/root/code/jupyter_lab_config.py --allow-root --no-browser &

Make sure JupyterLab is running before using the button at the top of the lab.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Run the following steps on the controlplane host.

Step 1 — Activate the virtual environment

source /root/code/ml-env/bin/activate

Step 2 — Open the JupyterLab configuration file

You can inspect it with:

cat /root/code/jupyter_lab_config.py

Or edit it directly:

vi /root/code/jupyter_lab_config.py

Step 3 — Fix the required settings

Ensure the configuration contains the following correct values:

c.ServerApp.port = 8888
c.ServerApp.ip = '0.0.0.0'
c.ServerApp.notebook_dir = '/root/notebooks'

Remove or correct any conflicting settings such as:

wrong port
127.0.0.1
incorrect notebook directory

Step 4 — Create the notebook directory

mkdir -p /root/notebooks

Step 5 — Start JupyterLab

jupyter lab --config=/root/code/jupyter_lab_config.py --allow-root --no-browser &

Step 6 — Verify JupyterLab is running

ps -ef | grep jupyter

Expected resuld should show:

root        3460    1880  0 00:49 pts/2    00:00:01 /root/code/ml-env/bin/python3 /root/code/ml-env/bin/jupyter-lab --config=/root/code/jupyter_lab_config.py --allow-root --no-browser
root        3751    3649  0 00:49 pts/6    00:00:00 grep --color=auto jupyter

You can also verify the listening port:

ss -tulnp | grep 8888

Expected result should show:

tcp   LISTEN 0      128          0.0.0.0:8888      0.0.0.0:*    users:(("jupyter-lab",pid=3460,fd=6))

🧠 Part 2: Simple Beginner-Friendly Explanation

What is happening in this lab?

JupyterLab is already installed, but its configuration is incorrect.

Your job is to:

inspect the config
fix bad settings
create the required notebook directory
start the server correctly
Understanding the Required Settings

The lab requires:

Requirement Meaning
Port 8888 Jupyter must listen on this port
Bind to 0.0.0.0 Allow external access from the lab proxy
Root directory /root/notebooks This is where notebooks are stored
Why 127.0.0.1 Causes Problems

If Jupyter binds to:

127.0.0.1

It only accepts local connections from inside the server itself.

The lab UI button cannot reach it.

Using:

0.0.0.0

means:

“Accept connections from all network interfaces.”

This allows the lab proxy to connect.

Understanding the Config File

Typical Jupyter config lines look like:

c.ServerApp.port = 8888

Meaning:

c = configuration object
ServerApp = Jupyter server settings
port = listening port
Why Create /root/notebooks

Jupyter needs a valid working directory.

If the folder does not exist:

startup may fail
notebook browser may break

Creating it with:

mkdir -p /root/notebooks

ensures the directory exists.

Starting JupyterLab

Command:

jupyter lab --config=/root/code/jupyter_lab_config.py --allow-root --no-browser &

Explanation:

Option Purpose
--config= Use the specified config file
--allow-root Permit running as root
--no-browser Do not open a GUI browser
& Run in background

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

Day 1: Create a Python Virtual Environment for ML

Thu Kha Kyawe — Sat, 30 May 2026 06:47:15 +0000

Lab Information

The xFusionCorp Industries data science team needs a standardised Python environment for their new ML project. Set up a virtual environment with the required ML libraries on the controlplane host.

Create a Python virtual environment named ml-env under /root/code/ using python3 -m venv.

Activate the environment and install the following packages: numpy, pandas, scikit-learn, and matplotlib.

Generate a requirements.txt file using pip freeze and save it at /root/code/requirements.txt.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Run the following commands on the controlplane host:

1.Create the Python virtual environment

python3 -m venv /root/code/ml-env

2.Activate the virtual environment

source /root/code/ml-env/bin/activate

After activation, your terminal should show something like:

(ml-env)

3.Install the required ML libraries

pip install numpy pandas scikit-learn matplotlib

4.Generate the requirements.txt file

pip freeze > /root/code/requirements.txt

5.Verify the file

cat /root/code/requirements.txt

🧠 Part 2: Simple Beginner-Friendly Explanation

What is a virtual environment?

A virtual environment is an isolated Python workspace.
It keeps project packages separate from the system Python packages.

This helps:

avoid package conflicts
keep projects clean
make setups reproducible
Step Breakdown

Step 1 — Create /root/code

mkdir -p /root/code
mkdir = make directory
-p = create parent directories if needed

This creates the folder where the project will live.

Step 2 — Create the virtual environment

python3 -m venv /root/code/ml-env

Explanation:

python3 → use Python 3
-m venv → run Python’s built-in virtual environment module
/root/code/ml-env → name and location of the environment

This creates:

isolated Python binaries
isolated package storage

Step 3 — Activate the environment

source /root/code/ml-env/bin/activate

This tells the shell:

“Use the Python and pip inside ml-env.”

Without activation, packages may install globally instead.

Step 4 — Install ML libraries

pip install numpy pandas scikit-learn matplotlib

What each library does:

Package Purpose
numpy Numerical computing
pandas Data analysis & tables
scikit-learn Machine learning
matplotlib Data visualization & plotting

Step 5 — Save installed packages

pip freeze > /root/code/requirements.txt
pip freeze lists installed packages

writes output into a file

This creates a reusable dependency file.

Example:

numpy==2.x.x
pandas==2.x.x
scikit-learn==1.x.x
matplotlib==3.x.x

Step 6 — Verify

cat /root/code/requirements.txt

This displays the contents of the file to confirm everything was saved correctly.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

5.Configure Jenkins Job for Package Installation

Thu Kha Kyawe — Sat, 30 May 2026 06:15:03 +0000

Lab Information

Some new requirements have come up to install and configure some packages on the Nautilus infrastructure under Stratos Datacenter. The Nautilus DevOps team installed and configured a new Jenkins server so they wanted to create a Jenkins job to automate this task. Find below more details and complete the task accordingly:

Access the Jenkins UI by clicking on the Jenkins button in the top bar. Log in using the credentials: username admin and password Adm!n321.
Create a new Jenkins job named install-packages and configure it with the following specifications:

Add a string parameter named PACKAGE.

Configure the job to install a package specified in the $PACKAGE parameter on the storage server (Stratos Datacenter).

Build the job at least once (e.g. with parameter PACKAGE=vim-enhanced) so the package is installed on the Storage server and can be verified.

Note:

Ensure to install any required plugins and restart the Jenkins service if necessary. Opt for Restart Jenkins when installation is complete and no jobs are running on the plugin installation/update page. Refresh the UI page if needed after restarting the service.
Verify that the Jenkins job runs successfully on repeated executions to ensure reliability.
Capture screenshots of your configuration for documentation and review purposes. Alternatively, use screen recording software like loom.com for comprehensive documentation and sharing.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Open Jenkins UI

Click the Jenkins button from the top bar.

Field Value
Username admin
Password Adm!n321

Step 2: Create New Jenkins Job

From dashboard click:

New Item

Step 3: Configure Job Name

Enter:

install-packages

Select:

Freestyle project

Click:

Step 4: Add String Parameter

Under:

General

Check:

This project is parameterized

Click:

Add Parameter

Select:

String Parameter

Configure:

Field Value
Name PACKAGE
Default Value vim-enhanced
Description Package to install

Step 5: Configure Build Step

Scroll to:

Build Steps

Click:

Add build step

Select:

Execute shell

Add this script:

sshpass -p 'Bl@kW' ssh -o StrictHostKeyChecking=no natasha@ststor01 "
sudo yum install -y $PACKAGE
"

Step 6: Save Job

Click:

Save

Step 7: Build the Job

Click:

Build with Parameters

Use:

Parameter Value
PACKAGE vim-enhanced

Click:

Build

Step 8: Verify Build Success

Open:

Build History

Click latest build.

Then click:

Console Output

Expected output should show:

Started by user admin

Running as SYSTEM
Building in workspace /var/lib/jenkins/workspace/install-packages
[install-packages] $ /bin/sh -xe /tmp/jenkins11924998561678003613.sh
+ sshpass -p Bl@kW ssh -o StrictHostKeyChecking=no natasha@ststor01 
sudo yum install -y vim-enhanced

Last metadata expiration check: 1:06:35 ago on Sat May 30 04:53:03 2026.
Dependencies resolved.
================================================================================
 Package             Arch        Version                   Repository      Size
================================================================================
Installing:
 vim-enhanced        x86_64      2:8.2.2637-29.el9         appstream      1.7 M
Installing dependencies:
 gpm-libs            x86_64      1.20.7-29.el9             appstream       21 k
 vim-common          x86_64      2:8.2.2637-29.el9         appstream      7.0 M
 vim-filesystem      noarch      2:8.2.2637-29.el9         baseos          14 k

Transaction Summary
================================================================================
Install  4 Packages

Total download size: 8.8 M
Installed size: 34 M
Downloading Packages:
(1/4): gpm-libs-1.20.7-29.el9.x86_64.rpm        146 kB/s |  21 kB     00:00    
(2/4): vim-filesystem-8.2.2637-29.el9.noarch.rp  42 kB/s |  14 kB     00:00    
(3/4): vim-enhanced-8.2.2637-29.el9.x86_64.rpm  6.1 MB/s | 1.7 MB     00:00    
(4/4): vim-common-8.2.2637-29.el9.x86_64.rpm     12 MB/s | 7.0 MB     00:00    
--------------------------------------------------------------------------------
Total                                           7.5 MB/s | 8.8 MB     00:01     
Running transaction check
Transaction check succeeded.
Running transaction test
Transaction test succeeded.
Running transaction
  Preparing        :                                                        1/1 
  Installing       : gpm-libs-1.20.7-29.el9.x86_64                          1/4 
  Installing       : vim-filesystem-2:8.2.2637-29.el9.noarch                2/4 
  Installing       : vim-common-2:8.2.2637-29.el9.x86_64                    3/4 
  Installing       : vim-enhanced-2:8.2.2637-29.el9.x86_64                  4/4 
  Running scriptlet: vim-enhanced-2:8.2.2637-29.el9.x86_64                  4/4 
  Verifying        : vim-filesystem-2:8.2.2637-29.el9.noarch                1/4 
  Verifying        : gpm-libs-1.20.7-29.el9.x86_64                          2/4 
  Verifying        : vim-common-2:8.2.2637-29.el9.x86_64                    3/4 
  Verifying        : vim-enhanced-2:8.2.2637-29.el9.x86_64                  4/4 

Installed:
  gpm-libs-1.20.7-29.el9.x86_64         vim-common-2:8.2.2637-29.el9.x86_64    
  vim-enhanced-2:8.2.2637-29.el9.x86_64 vim-filesystem-2:8.2.2637-29.el9.noarch

Complete!
Finished: SUCCESS

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What This Jenkins Job Does

This Jenkins job automatically installs software packages on the Storage Server.

Instead of manually logging into the server every time, Jenkins performs the installation for you.

Why We Use a Parameter

The parameter:

PACKAGE

makes the Jenkins job reusable.

Example:

PACKAGE Value What Happens
vim-enhanced Installs Vim editor
git Installs Git
wget Installs wget

So you can install different packages without changing the job configuration.

Why Jenkins Uses SSH

Jenkins runs on its own server.

To install software on another machine (ststor01), Jenkins must connect remotely using SSH.

This command:

ssh natasha@ststor01

means:

Connect to server ststor01
Login as user natasha

Why We Use sshpass

Normally SSH asks for a password manually.

But Jenkins jobs are automated and cannot type passwords interactively.

sshpass sends the password automatically so the job can run without human input.

Why We Use sudo

The user natasha is not a root user.

Installing packages requires administrator privileges.

So we use:

sudo yum install

which temporarily gives admin permissions.

What This Command Does

yum install -y $PACKAGE

Breakdown:

Part Meaning
yum Linux package manager
install Install software
-y Automatically answer yes
$PACKAGE Uses Jenkins parameter value

Why We Run the Job Once

The lab requires:

✅ Jenkins job created
✅ Job executed successfully
✅ Package actually installed on Storage Server

Running the build proves the automation works correctly.

How To Verify Success

If the build succeeds, the Console Output usually shows:

Complete!

or package installation messages.

This confirms Jenkins successfully connected to the Storage Server and installed the package.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

📖 More Deep Dives: Whispering Cloud Insights - Read other technical articles

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

4.Organize Jenkins Jobs with Folders

Thu Kha Kyawe — Thu, 28 May 2026 04:57:57 +0000

Lab Information

xFusionCorp Industries' DevOps team aims to streamline the management of Jenkins jobs by organizing them into distinct folders based on their purpose. Complete the task following the provided requirements:

1.Access the Jenkins UI by clicking on the Jenkins button in the top bar. Log in using the credentials: username admin and password Adm!n321.

Create a new folder named Apache within the Jenkins UI.
Move the existing jobs httpd-php and services under the newly created Apache folder.

Note:

Ensure to install any required plugins and restart the Jenkins service if necessary. Opt for Restart Jenkins when installation is complete and no jobs are running on the plugin installation/update page.
Be aware that Jenkins UI may experience temporary unresponsiveness during the service restart. Refresh the UI page if needed.
Capture screenshots of your work for documentation and review purposes. Alternatively, utilize screen recording software like loom.com for detailed documentation and sharing.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Open Jenkins UI

Click the Jenkins button from the top bar.

Field Value
Username admin
Password Adm!n321

Step 2: Open Plugin Manager

Go to:

Manage Jenkins → Plugins

Step 3: Install CloudBees Folders Plugin

Open:

Available plugins

Search for:

Folders

Install:

Folders which is Maintained by CloudBees

Step 4: Restart Jenkins (If Prompted)

If Jenkins shows:

Restart Jenkins when installation is complete and no jobs are running

Select it.

Wait for Jenkins login page to reappear.

Refresh browser if UI becomes unresponsive.

Step 5: Login Again

Use:

Username Password
admin Adm!n321

Step 6: Create New Item

From Jenkins dashboard click:

New Item

Step 7: Create Folder

Enter name:

Apache

Select:

Folder

Click:

Then click:

Save

Step 8: Open httpd-php Job

From dashboard click:

httpd-php

Step 9: Move httpd-php Job

On the left sidebar click:

Move

Destination:

Apache

Confirm move.

Step 10: Move services Job

Repeat same process:

Open services
Click Move
Select destination:
Apache
Confirm
Verify Structure

Step 11: Verify Jobs Inside Apache Folder

Open:

Apache

You should see:

httpd-php
services

inside the folder.

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What Is a Jenkins Folder?

Folders help organize Jenkins jobs.

Instead of keeping all jobs on one dashboard, you can group related jobs together.

Example:

Apache/
├── httpd-php
└── services
Why Install the Folders Plugin?

Jenkins does not support folders by default.

The CloudBees Folders Plugin adds:

Folder creation
Nested job organization
Better CI/CD management
What Does “Move Job” Mean?

Moving a job changes its location in Jenkins.

The job itself is not deleted.

Its:

Build history
Configuration
Workspace
Settings

remain intact.

Why Organize Jobs Into Folders?

Large Jenkins servers may contain hundreds of jobs.

Folders help teams:

Stay organized
Separate environments/projects
Manage permissions more easily
Reduce dashboard clutter
Important Lab Tip

If you do not see:

Folder

during “New Item” creation, the plugin installation likely did not complete yet.

Wait for Jenkins restart and login again.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

📖 More Deep Dives: Whispering Cloud Insights - Read other technical articles

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

3.Configure Jenkins User Access

Thu Kha Kyawe — Thu, 28 May 2026 04:17:25 +0000

Lab Information

The Nautilus team is integrating Jenkins into their CI/CD pipelines. After setting up a new Jenkins server, they're now configuring user access for the development team, Follow these steps:

Click on the Jenkins button on the top bar to access the Jenkins UI. Login with username admin and password Adm!n321.
Create a jenkins user named siva with the password B4zNgHA7Ya. Their full name should match Siva.
Utilize the Project-based Matrix Authorization Strategy to assign overall read permission to the siva user.
Remove all permissions for Anonymous users (if any) ensuring that the admin user retains overall Administer permissions.
For the existing job, grant siva user only read permissions, disregarding other permissions such as Agent, SCM etc.

Note:

You may need to install plugins and restart Jenkins service. After plugins installation, select Restart Jenkins when installation is complete and no jobs are running on plugin installation/update page.
After restarting the Jenkins service, wait for the Jenkins login page to reappear before proceeding. Avoid clicking Finish immediately after restarting the service.
Capture screenshots of your configuration for review purposes. Consider using screen recording software like loom.com for documentation and sharing.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Open Jenkins UI

Click the Jenkins button from the lab top bar.

Field Value
Username admin
Password Adm!n321

Step 2: Open Plugin Manager

Go to:

Manage Jenkins

Then:

Plugins

Step 3: Install Matrix Authorization Plugin

Open:

Available plugins

Search for:

Matrix Authorization Strategy

Install the plugin.

If prompted:

Restart Jenkins when installation is complete and no jobs are running

Select it.

Wait until Jenkins restarts fully.

Step 4: Login Again

Use:

Field Value
Username admin
Password Adm!n321

Step 5: Open Security Realm User Management

Go to:

Manage Jenkins → Users

Click:

Create User

Step 6: Create User siva

Use exactly:

Field Value
Username siva
Password B4zNgHA7Ya
Confirm Password B4zNgHA7Ya
Full Name Siva
Email leave blank or optional

Click:

Create User

Step 7: Open Security Settings

Go to:

Manage Jenkins → Security

Step 8: Configure Authorization Strategy

Under:

Authorization

Select:

Project-based Matrix Authorization Strategy

Step 9: Add User siva

In the user/group field type:

siva

Click:

Add

Configure Global Permissions

Step 10: Grant Overall Read Permission to siva

Under the matrix:

For user siva check ONLY:

Overall → Read

Do NOT select other permissions.

Step 11: Remove Anonymous Permissions

Find:

Anonymous

Uncheck ALL permissions for Anonymous.

Important: Add administer permission for admin user if not already present.

Ensure admin still has:

Overall → Administer

Otherwise you may lock yourself out. Then click Save

Step 12: Open Existing Job Configuration

From Jenkins dashboard:

Click the existing job.

Then click:

Configure

Step 13: Enable Project-based Security

Find:

Enable project-based security

Check the box.

Step 14: Add siva to Job Permissions

Add user:

siva

Grant ONLY:

Job → Read

Do NOT grant:

Build
Configure
Delete
Discover
SCM
Agent
Workspace
Any others

Step 15: Save Configuration

Click:

Save

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What Is Matrix Authorization?

Matrix Authorization lets Jenkins control permissions very precisely.

You can decide:

Who can read
Who can build
Who can configure jobs
Who can administer Jenkins
Why Give siva Only Overall Read?

This allows siva to:

✅ Login
✅ View Jenkins dashboard

But NOT:

❌ Change settings
❌ Create jobs
❌ Manage plugins
❌ Administer Jenkins

Why Remove Anonymous Permissions?

Anonymous users are people who are NOT logged in.

Removing Anonymous permissions prevents unauthorized access.

This improves security.

Why Keep Administer Permission for admin?

Without:

Overall → Administer

the admin user loses control of Jenkins.

You could accidentally lock yourself out completely.

Why Use Project-based Security?

Global permissions affect ALL jobs.

Project-based security lets you control permissions for a specific job only.

In this lab:

siva can ONLY view the existing job
Cannot modify or run it
Important Lab Tip

Before clicking Save on security settings:

✅ Verify admin still has Administer permission.

This is the most common Jenkins lab mistake.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

📖 More Deep Dives: Whispering Cloud Insights - Read other technical articles

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

2.Install Jenkins Plugins

Thu Kha Kyawe — Thu, 28 May 2026 04:08:39 +0000

Lab Information

The Nautilus DevOps team has recently setup a Jenkins server, which they want to use for some CI/CD jobs. Before that they want to install some plugins which will be used in most of the jobs. Please find below more details about the task

Click on the Jenkins button on the top bar to access the Jenkins UI. Login using username admin and password Adm!n321.
Once logged in, install the Git and GitLab plugins. You may need to restart Jenkins to complete the plugin installation; if required, opt to Restart Jenkins when installation is complete and no jobs are running on the plugin installation/update page (Update Centre).

Note:

After restarting Jenkins, wait for the login page to reappear before proceeding.
For tasks involving web UI changes, capture screenshots to share for review or consider using screen recording software like loom.com for documentation and sharing.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Open Jenkins UI

Click the Jenkins button from the lab top bar.

Field Value
Username admin
Password Adm!n321

Step 2: Open Plugin Manager

From the Jenkins dashboard:

Manage Jenkins

Then click:

Plugins

Step 3: Install Git Plugin

Go to:

Available plugins

Search for:

Git

Check the box for:

Git

Step 4: Install GitLab Plugin

In the search box type:

GitLab

Check the box for:

GitLab

Step 5: Install Selected Plugins

Click:

Install

Install without restart

Step 6: Restart Jenkins (If Prompted)

If Jenkins displays:

Restart Jenkins when installation is complete and no jobs are running

Enable/check that option.

Wait until Jenkins restarts completely.

Step 7: Verify Jenkins Is Back Online

Wait for the login page to reappear.

Username Password
admin Adm!n321

Step 8: Verify Plugins Installed

Go to:

Manage Jenkins → Plugins → Installed plugins

Verify both plugins appear:

Git
GitLab

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What Are Jenkins Plugins?

Plugins add extra features to Jenkins.

Without plugins, Jenkins only has basic functionality.

Why Install the Git Plugin?

The Git plugin allows Jenkins to:

Clone repositories
Pull source code
Trigger builds from Git commits

This is essential for CI/CD pipelines.

Why Install the GitLab Plugin?

The GitLab plugin connects Jenkins with GitLab.

It helps Jenkins:

Receive webhooks
Trigger jobs automatically
Integrate merge requests and pipelines

Why Jenkins Sometimes Restarts

Some plugins modify Jenkins core components.

A restart ensures:

New plugins load correctly
Jenkins recognizes new functionality
Why Verify Installed Plugins

Checking Installed Plugins confirms:

Installation succeeded
Jenkins loaded the plugins correctly
CI/CD jobs can use them later

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

📖 More Deep Dives: Whispering Cloud Insights - Read other technical articles

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

1.Set Up Jenkins Server

Thu Kha Kyawe — Sat, 23 May 2026 03:53:10 +0000

Lab Information

The DevOps team at xFusionCorp Industries is initiating the setup of CI/CD pipelines and has decided to utilize Jenkins as their server. Execute the task according to the provided requirements:

Install Jenkins on the jenkins server using the apt utility only, and start it using the service command.

If you face a timeout issue while starting the Jenkins service, first check the service status with service jenkins status
Then review the logs in /var/log/jenkins/jenkins.log to identify the cause.
Jenkin's admin user name should be theadmin, password should be Adm!n321, full name should be Jim and email should be jim@jenkins.stratos.xfusioncorp.com.

Note:

To access the jenkins server, connect from the jump host using the root user with the password S3curePass.
After Jenkins server installation, click the Jenkins button on the top bar to access the Jenkins UI and follow on-screen instructions to create an admin user.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Connect to the Jenkins Server from the Jump Host

From the jump host terminal:

ssh root@jenkins

# Password:

S3curePass

Step 2: Update Package Index

apt update

Step 3: Install Java (Required for Jenkins)

apt install openjdk-21-jre -y

Verify Java:

java -version


Step 4: Add Jenkins Repository Key

wget -O /usr/share/keyrings/jenkins-keyring.asc \
https://pkg.jenkins.io/debian-stable/jenkins.io-2023.key


Step 5: Add Jenkins Repository

echo deb [signed-by=/usr/share/keyrings/jenkins-keyring.asc] \
https://pkg.jenkins.io/debian-stable binary/ | tee \
/etc/apt/sources.list.d/jenkins.list > /dev/null


Step 6: Update apt Again

apt update


Step 7: Install Jenkins Using apt ONLY

apt install jenkins -y


Step 8: Start Jenkins Service

service jenkins start

Check status:

service jenkins status

Step 9: If Jenkins Fails or Times Out

Check service status:

service jenkins status

Check logs:

cat /var/log/jenkins/jenkins.log

Common fix:

systemctl daemon-reload
service jenkins restart


Then recheck:

service jenkins status



Step 10: Get Initial Admin Password

cat /var/lib/jenkins/secrets/initialAdminPassword




Copy the password.

Step 11: Open Jenkins UI

Open browser and click the Jenkins button from the lab top bar.

Or access:

http://<jenkins-server>:8080


Step 12: Unlock Jenkins

Paste the initial admin password.

Choose:

Install suggested plugins

![alt text](image.png)

Wait for installation.

Step 13: Create Admin User

Use these exact details:

Field               Value
Username            theadmin
Password            Adm!n321
Confirm Password    Adm!n321
Full Name           Jim
Email               jim@jenkins.stratos.xfusioncorp.com

Click:

Save and Continue

Then:

Start using Jenkins

![alt text](image-1.png)

![alt text](image-2.png)

![alt text](image-3.png)

![alt text](image-4.png)

---

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What You Are Doing

You are setting up a Jenkins CI/CD server.

Jenkins helps automate:

Building applications
Running tests
Deploying software
Why Java Is Installed

Jenkins is a Java application.

Without Java, Jenkins cannot run.

Why We Add Jenkins Repository

Ubuntu/Debian default repositories may not contain the latest Jenkins package.

So we:

Add Jenkins official repository
Add its security key
Install Jenkins safely using apt
Why We Use service jenkins start

This command starts the Jenkins server process.

After starting, Jenkins listens on:

Port 8080
Why We Check Logs

If Jenkins fails to start:

Java may be missing
Port may be busy
Configuration may be broken

Logs in:

/var/log/jenkins/jenkins.log

help identify the exact issue.

What the Initial Admin Password Does

The first time Jenkins starts, it locks itself for security.

The password file:

/var/lib/jenkins/secrets/initialAdminPassword

is used to unlock Jenkins setup.

Why You Create an Admin User

This becomes the main Jenkins administrator account.

In this lab:

Username: theadmin
Password: Adm!n321

This account manages pipelines, jobs, plugins, and users.

---
##### **Resources & Next Steps**
##### 📦 Full Code Repository: [KodeKloud Learning Labs](https://github.com/thukhakyawe/100-Days-Of-DevOps-KodeKloud-Challenges-Solutions)
##### 📖 More Deep Dives: [Whispering Cloud Insights](https://thukhakyawe.hashnode.dev/) - Read other technical articles
##### 💬 Join Discussion: [DEV Community](https://dev.to/thukhakyawe_cloud) - Share your thoughts and questions
##### 💼 Let's Connect: [LinkedIn](https://www.linkedin.com/in/thukhakyawe/) - I'd love to connect with you
---
##### **Credits**
##### • All labs are from: [KodeKloud](https://kodekloud.com/)
##### • I sincerely appreciate your provision of these valuable resources.
---

10.Translate Content with AI

Thu Kha Kyawe — Fri, 22 May 2026 17:00:27 +0000

Lab Information

The devops AI team is focusing on improving their translation capabilities by using OpenAI's API. Begin by creating a new Python file named translator.py inside the /root/openaiproject directory. Import the OpenAI class in this file to enable interaction with OpenAI models.

Inside translator.py, create an OpenAI client using the API key and base URL provided for this session. Additionally, create a function named translate_to_language(text: str, language: str) -> str. This function should build a parameterized prompt that asks the AI to translate the English text into Spanish and French.

Use the following text for translation:
Good morning, how are you?

Finally, update the file to send the prompt to the OpenAI model using:

model: openai/gpt-4.1-mini
messages: user → prompt
max_tokens: 100
temperature: 0.7

Then print the translated Spanish & French output to the console.

Notes:

Ensure you are working in the /root/openaiproject directory.

Create and use the OpenAI client with API key + base_url from /root/.bash_profile.

Function must accept parameters text and language.

Prompt must be parameterized.

Use hardcoded values for api_key and base_url when initializing the OpenAI client or read them from environment variables via os.environ.get('OPENAI_API_KEY') and os.environ.get('OPENAI_BASE_URL').

Before running, create a virtual environment and install OpenAI:

python3 -m venv venv && source venv/bin/activate && pip install openai

You are allowed a maximum of 10 requests. After this, you may encounter a rate limiter error, so use your calls wisely

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Navigate to the project directory

cd /root/openaiproject

Step 2: Create and activate the virtual environment

python3 -m venv venv
source venv/bin/activate

Step 3: Install the OpenAI package

pip install openai

Step 4: Create the translator.py file

vi translator.py

Paste the following code:

import os
from openai import OpenAI

# Initialize OpenAI client
client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url=os.environ.get("OPENAI_API_BASE")
)

# Function to translate text
def translate_to_language(text: str, language: str) -> str:

    prompt = f"""
Translate the following English text into {language}.

Text:
{text}
"""

    response = client.chat.completions.create(
        model="openai/gpt-4.1-mini",
        messages=[
            {
                "role": "user",
                "content": prompt
            }
        ],
        max_tokens=100,
        temperature=0.7
    )

    return response.choices[0].message.content


# Text for translation
text = "Good morning, how are you?"

# Translate to Spanish
spanish_translation = translate_to_language(text, "Spanish")

# Translate to French
french_translation = translate_to_language(text, "French")

# Print translations
print("Spanish Translation:")
print(spanish_translation)

print("\nFrench Translation:")
print(french_translation)

Step 5: Save and exit vi editor

Step 6: Load environment variables

source /root/.bash_profile

Step 7: Run the script

python translator.py

Expected Output Example

Spanish Translation:
Buenos días, ¿cómo estás?

French Translation:
Bonjour, comment ça va ?

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What this lab does

You are building an AI-powered translator.

The script:

Takes English text
Sends it to an AI model
Requests translations into other languages
Prints the translated text
Understanding the Code

Importing modules
import os
from openai import OpenAI
os reads environment variables
OpenAI connects Python to the AI API
Creating the OpenAI client
client = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"),
base_url=os.environ.get("OPENAI_API_BASE")
)

This connects your Python script to the AI server using credentials stored in /root/.bash_profile.

Creating the translate_to_language function def translate_to_language(text: str, language: str) -> str:

This function accepts:

text → the English sentence
language → target translation language

Example:

translate_to_language("Hello", "Spanish")

Building the AI prompt prompt = f""" Translate the following English text into {language}. """

The f creates an f-string.

Python automatically inserts:

the text
the target language

into the prompt.

Sending the request to the AI response = client.chat.completions.create(

This sends the translation request to the AI model.

Important settings:

model="openai/gpt-4.1-mini" → supported model
max_tokens=100 → enough space for translations
temperature=0.7 → more natural language output

Extracting the AI response response.choices[0].message.content

This extracts only the translated text from the API response.

Printing translations print(spanish_translation) print(french_translation)

Displays both translated outputs in the terminal.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

📖 More Deep Dives: Whispering Cloud Insights - Read other technical articles

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

9.Summarize Text with AI

Thu Kha Kyawe — Fri, 22 May 2026 16:58:45 +0000

Lab Information

The xfusion AI Engineering Team is building an intelligent Summarization Assistant designed to transform lengthy or unclear developer-reported issues into concise, easy-to-understand one-line summaries. This helps improve clarity, reduce ambiguity, and make bug reports easier to analyze and reproduce.

Inside /root/openaiproject/summarizer.py, create an OpenAI client using the api_key and base_url provided for this session. Additionally, create a function named summarize(text: str) -> str. This function should build a parameterized prompt that asks the AI to summarize the given paragraph into a single-line summary.

Use the following paragraph for summarization:

Artificial Intelligence enables machines to mimic human intelligence, performing tasks such as learning, problem-solving, and decision-making with increasing accuracy.

Then send the previously defined prompt to the OpenAI chat model, save the result in a variable named response, and print the one-line summary to the console.

Model requirements:

model: openai/gpt-4.1-mini
messages: role=user, content=prompt
max_tokens: 60
temperature: 0.5

Notes:

Your work directory must be /root/openaiproject.

OpenAI api_key and base_url are stored in /root/.bash_profile.

Prompt must be parameterized with the input paragraph.

Use hardcoded values for api_key and base_url when initializing the OpenAI client or read them from environment variables via os.environ.get('OPENAI_API_KEY') and os.environ.get('OPENAI_BASE_URL').

Before running, create and activate a virtual environment:

python3 -m venv venv && source venv/bin/activate && pip install openai

You are allowed a maximum of 10 requests. After this, you may encounter a rate limiter error, so use your calls wisely.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Navigate to the project directory

cd /root/openaiproject

Step 2: Create and activate the virtual environment

python3 -m venv venv
source venv/bin/activate

Step 3: Install the OpenAI package

pip install openai

Step 4: Create the summarizer.py file

vi summarizer.py

Paste the following code:

import os
from openai import OpenAI

# Initialize OpenAI client
client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url=os.environ.get("OPENAI_API_BASE")
)

# Function to summarize text
def summarize(text: str) -> str:

    prompt = f"""
Summarize the following paragraph into a single concise line.

Paragraph:
{text}
"""

    response = client.chat.completions.create(
        model="openai/gpt-4.1-mini",
        messages=[
            {
                "role": "user",
                "content": prompt
            }
        ],
        max_tokens=60,
        temperature=0.5
    )

    return response.choices[0].message.content


# Paragraph for summarization
text = """
Artificial Intelligence enables machines to mimic human intelligence,
performing tasks such as learning, problem-solving, and decision-making
with increasing accuracy.
"""

# Store AI response
response = summarize(text)

# Print summary
print(response)

Step 5: Save and exit vi editor

CTRL + X
Y
ENTER

Step 6: Load environment variables

source /root/.bash_profile

Step 7: Run the script

python summarizer.py

Expected Output Example

Artificial Intelligence allows machines to replicate human intelligence by learning, problem-solving, and making decisions accurately.

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What this lab does

You are building an AI-powered text summarizer.

The script:

Takes a paragraph as input
Sends it to an AI model
Requests a one-line summary
Prints the summarized result
Understanding the Code

Importing modules
import os
from openai import OpenAI
os reads environment variables
OpenAI connects Python to the AI API
Creating the OpenAI client
client = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"),
base_url=os.environ.get("OPENAI_API_BASE")
)

This connects your script to the OpenAI-compatible API server.

Credentials are stored securely in /root/.bash_profile.

Creating the summarize function def summarize(text: str) -> str:

This function accepts any paragraph as input.

Example:

summarize("Python is a programming language.")

Building the AI prompt prompt = f""" Summarize the following paragraph... """

The f creates an f-string.

Python automatically inserts the paragraph into the prompt.

Sending the request to the AI response = client.chat.completions.create(

This sends the summarization request to the AI model.

Important settings:

model="openai/gpt-4.1-mini" → supported AI model
max_tokens=60 → short response limit
temperature=0.5 → balanced creativity and consistency

Extracting the AI response response.choices[0].message.content

This extracts only the generated summary text.

Printing the summary print(response)

Displays the one-line AI-generated summary in the terminal.

Resources & Next Steps

📦 Full Code Repository: KodeKloud Learning Labs

📖 More Deep Dives: Whispering Cloud Insights - Read other technical articles

💬 Join Discussion: DEV Community - Share your thoughts and questions

💼 Let's Connect: LinkedIn - I'd love to connect with you

Credits

• All labs are from: KodeKloud

• I sincerely appreciate your provision of these valuable resources.

8.Generate Haikus with AI

Thu Kha Kyawe — Fri, 22 May 2026 16:56:52 +0000

Lab Information

The datacenter AI Development Team is experimenting with how artificial intelligence can create expressive short poetry through automation. In this task, you are required to build a Python-based AI module that generates three-line haikus (5-7-5 syllable pattern) based on a given topic.

Inside /root/openaiproject/haiku_generator.py, create an OpenAI client using the api_key and base_url provided for this session. Additionally, define a function named generate_haiku(topic: str) -> str. This function should construct a parameterized prompt that instructs the AI to generate a haiku about the specified topic, strictly following the 5-7-5 syllable structure.

Then, send the parameterized prompt to the OpenAI chat model using:

model: openai/gpt-4.1-mini
prompt: parameterized_prompt
max_tokens: 60
temperature: 0.0

Store the output in a variable named response and print the generated haiku (three distinct lines). Use the topic:

Topic: 'sky'

Notes:

Function should accept one parameter: topic.

Use the provided OpenAI  api_key and base_url under /root/.bash_profile.

The prompt must be parameterized with the topic.

Ensure you are working inside /root/openaiproject.

Use hardcoded values for api_key and base_url when initializing the OpenAI client or read them from environment variables via os.environ.get('OPENAI_API_KEY') and os.environ.get('OPENAI_BASE_URL').

Before running, create and activate a virtual environment and install OpenAI:

python3 -m venv venv && source venv/bin/activate && pip install openai

Final output should display three distinct lines (the haiku).

You are allowed a maximum of 10 requests before hitting rate limits.

Lab Solutions

🧭 Part 1: Lab Step-by-Step Guidelines

Step 1: Navigate to the project directory

cd /root/openaiproject

Step 2: Create and activate the virtual environment

python3 -m venv venv
source venv/bin/activate

Step 3: Install the OpenAI package

pip install openai

Step 4: Create the haiku_generator.py file

vi haiku_generator.py

Paste the following code:

import os
from openai import OpenAI

# Initialize OpenAI client
client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url=os.environ.get("OPENAI_API_BASE")
)

# Function to generate haiku
def generate_haiku(topic: str) -> str:

    prompt = f"""
Generate a haiku about {topic}.

The haiku must:
- Follow the 5-7-5 syllable structure
- Contain exactly three lines
- Be concise and poetic
"""

    response = client.chat.completions.create(
        model="openai/gpt-4.1-mini",
        messages=[
            {
                "role": "user",
                "content": prompt
            }
        ],
        max_tokens=60,
        temperature=0.0
    )

    return response.choices[0].message.content


# Topic for haiku
topic = "sky"

# Store AI response
response = generate_haiku(topic)

# Print haiku
print(response)

Step 5: Save and exit vi editor
Press Esc, then type :wq and hit Enter to save and exit.

Step 6: Load environment variables

source /root/.bash_profile

Step 7: Run the script

python haiku_generator.py

Expected Output Example

Endless blue canvas,

Whispers of clouds drift softly,

Dreams float with the breeze.

🧠 Part 2: Simple Step-by-Step Explanation (Beginner Friendly)

What this lab does

You are building an AI-powered poetry generator.

The script:

Takes a topic as input
Sends it to an AI model
Requests a haiku poem
Prints the generated haiku

A haiku is a short Japanese-style poem with:

Line 1 → 5 syllables
Line 2 → 7 syllables
Line 3 → 5 syllables
Understanding the Code

Importing modules
import os
from openai import OpenAI
os reads environment variables
OpenAI connects Python to the AI API
Creating the OpenAI client
client = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"),
base_url=os.environ.get("OPENAI_API_BASE")
)

This connects your Python script to the AI server using credentials stored in /root/.bash_profile.

Creating the generate_haiku function def generate_haiku(topic: str) -> str:

This function accepts a topic as input.

Example:

generate_haiku("ocean")

Building the AI prompt prompt = f""" Generate a haiku about {topic}. """

The f creates an f-string.

Python automatically inserts the topic into the prompt.

Sending the request to the AI response = client.chat.completions.create(

This sends the poetry request to the AI model.

Important settings:

model="openai/gpt-4.1-mini" → supported AI model
max_tokens=60 → enough space for 3 short lines
temperature=0.0 → more consistent structure and formatting