A Quick and Easy Guide to Managing Conda Environments
A simple guide to managing Conda environments
In this article, you will learn
- Need for Conda environments
- Difference between Conda environments and Virtual environments
- Commands to manage Conda environments
- Preserving Conda environments from existing Conda environments
You are working on multiple projects like data analysis and visualization where you need matplotlib and seaborn libraries, multi-class classification using TensorFlow 1.14, and Keras and another project where you want to use BERT model using TensorFlow 2.0 for the Sentiment Analysis.
Consider another situation where you have completed the development of your project and want to deploy the deep learning model on a different computer. You want to ensure that everything works smoothly.
How can you handle these scenarios with ease?
Option 1: Install required libraries on different computers to isolate the environment and manage dependencies or
Option 2: Create separate environments isolating the library conflicts and managing dependencies on the same computer. Also, easily extract all the dependent libraries in a file to be restored on…
