Anaconda

Anaconda is a open-source distribution of Python and R for data science and machine learning, which simplifies package management and deployment, ideal for data analysis, visualization, and machine learning tasks

Key Features:

-> Conda Package Manager: Manage libraries, dependencies, and environments effortlessly

-> Pre-installed Libraries: Includes 1,500+ data science packages (e.g., NumPy, pandas)

-> Cross-Platform: Works on Windows, macOS, and Linux

Use Case:

-> Data Analysis: Pandas and NumPy

-> Visualization: Matplotlib and Seaborn.

-> Machine scikit-learn, TensorFlow and PyTorch.

-> Big Data: Dask or Spark

How to Create Customized Environment by Anaconda?

To create custom environment, run below command in the SSH terminal (CLI), for tutorial in accessing CLI, please refer to Shell Access and Useful Command

# Load anaconda module
$ module load anaconda
# Add conda-forge channel for more available packages
$ conda config --add channels conda-forge
# Create user’s custom environment
$ conda create -n <environment name> <packages>
# Initiate conda while no need to load anaconda module next time
$ conda init
# Purge module
$ module purge
# Exit the SSH terminal, the initiated conda config will enable in next terminal connection

How to Remove Customized Environment (Anaconda)?

To remove custom environment, run below command in the SSH terminal (CLI) or delete the environment folder in web-portal, for tutorial in accessing CLI, please refer to Shell Access and Useful Command

# Remove custom environment
$ rm -r /home/$USER/.conda/envs/<environment name>

For more information about Anaconda, please refer to: Conda Official Guide