Anaconda / Conda environment setup on Local Desktop

Importance of Conda Environments in Development

A Conda environment is a virtual workspace that helps manage dependencies and packages for data science, machine learning, and AI projects.

Separate Conda environment ensures a clean, isolated setup where different projects can have their own dependencies without conflicts.

Key Benefits of Using Conda Environments:

1. Dependency Management – Avoids conflicts by keeping packages isolated for different projects.
2. Reproducibility – Ensures that projects work consistently across different systems with the same environment setup.
3. Version Control – Allows multiple versions of Python, libraries, and frameworks to coexist.
4. Cross-Platform Compatibility – Works seamlessly on Windows, macOS, and Linux.
5. Easy Collaboration – Teams can share environment.yml files to replicate environments effortlessly.
6. Lightweight & Efficient – Conda handles package installations and dependencies efficiently without requiring administrative privileges.

Common Use Cases:

• Data Science & Machine Learning projects
• AI & LLM application development
• Working with multiple versions of Python or ML frameworks

Installing Anaconda on Local Windows Desktop

Download free distribution of Anaconda & install

After installation, Local Anaconda from All Programs

Click on Anaconda Prompt as shown in above snapshot

Execute following command to create a new conda environment

conda create -n <name of environment>

for example following can be executed to create a new environment with name lgraph1

>conda create -n lgraph1

it might take some time to complete

After completion of command execution, newly created environment should be activated

>conda activate lgraph1

now prompt has changed to newly created environment

in this newly created environment all different required packages can be installed

execute following to install pip

>conda install pip

Enter y when prompted during installation

Using Conda environments ensures stability, consistency, and flexibility, making them an essential tool for code development.