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.
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