How to Install Conda

Conda is a powerful package manager and environment management tool, widely used in data science and machine learning to manage dependencies and create isolated environments for different projects. This guide will walk you through the steps to install Conda on your system.

Prerequisites

Before you begin, ensure that you have the following:

  • A working internet connection to download Conda.

  • A terminal or command prompt to execute commands.

What is Conda-Forge and Mini-Forge?

Conda-Forge is a comunity-driven repository of conda packages. Mini-Forge is a lightweight installer for Conda, optimized to use Conda-Forge by default For this guide’s purposes, we will use MiniForge.

Steps to Install Conda

Step 1. Download MiniForge

  • Go to the MiniForge GitHub page.

  • Choose the Appropriate installer for your operating system:

    • Windows

    • Mac

    • Linux

Step 2. Open a Terminal or Command Prompt Open the terminal (or Command Prompt on Windows), SSH onto a TACC machine, and navigate to the directory where you want use Mini-Forge.

Step 3: Run the SSH Command Use the following command to connect to TACC systems:

ssh <username>@<hostname>

(replace <username> with your TACC username and <hostname> with the system hostname)

Example: To connect to the Frontera system:

ssh username@frontera.tacc.utexas.edu

Enter Your Password When prompted, type your TACC password. If this is your first time logging in, you may be required to set up or reset your password.

Step 4: Set Up Two-Factor Authentication TACC systems require two-factor authentication. Follow the on-screen prompts to complete the process.

Note

It would be best to use the $WORK directory

Step 5. Install MiniForge

  • Open your terminal or command prompt. Navigate to the directory where the installer was downloaded.

  • Run the installer command:

    • Mac/Linux: Use the terminal to run the installer:

    bash Miniforge3-Linux-x86_64.sh

    • Windows: Double-click the .exe file and follow the on screen instructions

Step 6. Initialize Conda

After installation, initialize Conda to configure your shell:

conda init

Restart your terminal for the changes to take effect.

Step 7. Verify the Installation

Confirm that Conda is installed by running:

conda --version

This should display the installed Conda version.

Congratulations! You now have Conda installed on your system.

Creating and Managing Environments

Once Conda is installed, you can start creating and managing environments using the following commands: 1. Create a new environment:

conda create --name myenv

  1. Activate an environment:

    conda activate myenv

  2. Deactivate an environment:

    conda deactivate

  3. Remove an environment:

    conda remove --name myenv --all

Lets Install PyTorch

Now that we have Conda installed, lets try installing PyTorch

Step 1. Create a Conda Enviornment

conda create --name pytorch_env python=3.10

Activate the environment

conda activate pytorch_env

Step 2: Install PyTorch

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

Explanations:

  • pytorch is the main library.

  • torchvision provides utilities for computer vision tasks.

  • torchaudio is for audio-related tasks.

  • the link is a CUDA GPU specific version for our HPC purposes

Optional: Manage Your Dependencies Using a YAML File

If you would like, you can manage your Conda environments using a YAML file, which helps ensure consistency across different systems.

Step 1. Create a YAML File

conda env create -f environment.yml

Step 2. Add your environmetn to your YAML File

name: pytorch_env
channels:
    - pytorch
    - defaults
dependencies:
    - python=3.10
    - pytorch
    - torchvision
    - torchaudio
    - cudatoolkit=12.6

Step .3 Export Your Current Environtment

conda env export > environment.yml

For more information, visit the official Conda documentation: https://docs.conda.io/

//you can add in a yaml file like with virtual environments, to help manage, make it kind of a note, point them to the documentation if you want

//show them your own yaml file, explain more on why we use this if they need it