Ultimate Guide to Installing Python, TensorFlow, and Jupyter Lab on Windows

A Complete Setup Guide for Beginners to Install Python, TensorFlow, and Jupyter Lab Correctly with Best Practices, Commands, and Troubleshooting for Windows Users.

Setting up Python, TensorFlow, and Jupyter Lab on Windows is often the first challenge every beginner faces when stepping into the world of Machine Learning, Data Science, Deep Learning, Generative AI and AI development.

Many students and professionals struggle hours dealing with version conflicts, environment issues, or TensorFlow installation failures — especially on Windows systems.

In this detailed article, I will walk you through a clean, simple, and structured approach to setting up Python (TensorFlow-compatible version), installing TensorFlow (latest or specific versions), configuring Jupyter Lab, creating kernels, and most importantly — verifying everything step-by-step.

This guide is crafted specifically for those who want to avoid Virtual Environments and want to install everything directly on Windows.

1) Python Installation — TensorFlow Compatibility Notes

Note: Python 3.12.x and Python 3.13.x are NOT supported for TensorFlow as of April 2025.

Steps to Install Python (for TensorFlow Compatibility):

1. Download Python 3.10.x from official site:
   https://www.python.org/downloads/release/python-3100/

2. During Installation:

• Tick Add Python to PATH

• Select Install for All Users (Optional)

3. Verify Installation:

python --version

2) TensorFlow Installation

Upgrade pip (Always Recommended)

python -m pip install --upgrade pip

Install Latest TensorFlow Version

pip install tensorflow

Installing a Specific TensorFlow Version:

Syntax:

pip install tensorflow==<version>

Examples:

pip install tensorflow==2.15.0
pip install tensorflow==2.13.0

Verify TensorFlow Version

import tensorflow as tf
print(tf.__version__)

3) Jupyter Lab Installation (In TensorFlow Supported Python)

pip install jupyterlab ipykernel

4) Create Kernel for Jupyter Lab

python -m ipykernel install --user --name py310 --display-name "Python 3.10 (TensorFlow)"

5) Useful Commands

a) Checking Python Version

python --version

or Inside Python:

import sys
print(sys.version)
print(sys.executable)

b) Checking Installed Python Versions in Windows

where python

c) Checking TensorFlow Version

import tensorflow as tf
print(tf.__version__)

pip show tensorflow

d) Checking Jupyter Lab Version

jupyter lab --version

or in Python:

import jupyterlab
print(jupyterlab.__version__)

or to list all Jupyter related packages:

pip list | findstr jupyter

e) Running Jupyter Lab from Command Prompt

jupyter lab

f) Changing Kernel in Jupyter Lab

In Jupyter Lab Interface:

Kernel → Change Kernel → Select "Python 3.10 (TensorFlow)"

g) Uninstalling Jupyter and Jupyter Lab (Clean Removal)

pip uninstall notebook jupyter jupyterlab

h) List All Installed Python Packages

pip list

i) Check Active Python in Notebook

import sys
print(sys.executable)
print(sys.version)

j) Check TensorFlow Installed Correctly

import tensorflow as tf
print(tf.__version__)
print("TensorFlow Installed Successfully!")

6) Best Practices & Recommendations

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Final Summary Flow

Additional Note — Recommended for Professional Setups:

In this article, I have explained how to install Python, TensorFlow, and Jupyter Lab directly in your Windows environment (without virtual environments) for simplicity and quick setup.

However, in real-time projects or when working on multiple machine learning or AI projects — it is highly recommended to use Virtual Environments.

This will help you:

• Create isolated environments for each project.

• Install different versions of Python or TensorFlow without impacting the global system.

• Easily manage dependencies and avoid version conflicts.

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Steps to Create Python Virtual Environment on Windows

Step 1: Check Available Python Versions

where python

Step 2: Create Virtual Environment

Syntax:

python -m venv <env_name>

Example:

python -m venv tf_env

This will create a new folder called tf_env containing the isolated Python environment.

Step 3: Activate Virtual Environment

Windows Command Prompt:

tf_env\Scripts\activate

You will see:

(tf_env) C:\Users\YourName>

Step 4: Install TensorFlow in Virtual Environment

pip install --upgrade pip
pip install tensorflow

or for specific version:

pip install tensorflow==2.15.0

Step 5: Install Jupyter Lab in Virtual Environment

pip install jupyterlab ipykernel

Step 6: Create Kernel for Jupyter Lab

python -m ipykernel install --user --name tf_env --display-name "Python (TensorFlow Env)"

Step 7: Run Jupyter Lab

jupyter lab

Step 8: Deactivate Virtual Environment (After Work)

deactivate

Final Suggestion:

If you are working on a single learning project — you can directly install Python + TensorFlow + Jupyter Lab (as explained in this article).

But if you are working on multiple projects or handling production environments — using Virtual Environments is the industry-standard approach.

Conclusion

Hope this article helped you in setting up your Python + TensorFlow + Jupyter Lab environment hassle-free on Windows.

Feel free to bookmark this article and share it with your friends, colleagues, or students who are starting their journey in Machine Learning or AI Development.

If you found this useful:

• Follow me for more such practical guides on AI, ML, DL, Generative AI, Azure AI, AWS AI, Google AI, Python, and Tech.

• Comment below if you faced any other issues — I will be happy to help!

• Stay connected for my upcoming articles where I will cover:

  • Hands-on Machine Learning Projects using TensorFlow

  • Python Coding Best Practices

  • AI Learning Roadmap for Students & Professionals

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