As of January 6, 2025, the LAS Pronto cluster was merged into the campus-wide Nova cluster. Information here may or may not work on Nova. Please contact Research IT if you have questions.

Conda¶

Use of Conda is Discouraged¶

For pytorch or other machine learning / GPU uses, you should use the Machine Learning container.

For other Python uses, the recommended way to install additional Python packages is with a Python Virtual Environment.

For other software, we have many Spack based software modules installed. If you need software that we don't available as a module, but that is already available as a Spack package, see the Self Managed Spack Installs section of our guide.

Start Here¶

Conda defaults to saving environments in your home folder. Home folders on pronto have a quota of 10GB and are not meant for storing software.

To store your conda environments in your work directory, first create a couple directories to hold them:

mkdir -p /work/LAS/your-lab/your-directory/.conda/envs
mkdir -p /work/LAS/your-lab/your-directory/.conda/pkgs

Replace your-lab and your-directory with appropriate values.

Next, tell conda to use these directories by creating a file in your home directory at ~/.condarc with the following contents:

envs_dirs:
  - /work/LAS/your-lab/your-directory/.conda/envs
pkgs_dirs:
  - /work/LAS/your-lab/your-directory/.conda/pkgs

How to set up a conda environment¶

To setup a conda environment, you will first need to connect to Pronto. Next, allocate a compute node and be sure you are placed on it (via salloc/srun). If you are not familiar with salloc or srun, please refer to this guide first. Below is a sample command you could run:

srun --time=01:00:00 --nodes=1 --cpus-per-task=1 --pty /usr/bin/bash

Wait until you have been placed on a compute node.

Now, you will need will need to pick a version of conda that suits your needs. To get a list of available packages, run the command:

module spider conda

This will list the available modules for conda. (The available modules may be subject to change)

Conda_Modules

Once you have looked through the list of modules, take note of the package name next to the version name.

picking_a_module

Press enter and type in:

module load <BoxedText>

If I wanted to run miniconda3 for example, I would type in

module load miniconda3/4.3.30-qdauveb

Now, create your conda environment by running:

conda create --name <DesiredName>

You will be prompted with a list of additional packages (if you specified any) that will be installed. Type y and hit enter. Wait a few minutes for the package to install. Once they are done installing, run:

source activate <NameOfEnvironment>

To verify that you have successfully activated the environment, your terminal should look something like:

(NameOfEnvironment) [YourNetID@node ~] $

A useful cheatsheet for conda can be found here: https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf

Moving your .conda directory¶

If you receive errors about 'Disk Quota exceeded' while using Conda, it probably means your home directory is full. Home folders on pronto have a quota of 10GB and are not meant for storing software.

First, deactivate your active conda environment and unload all modules:

source deactivate environment-name
module purge

Now move your conda environments to your work directory:

mv ~/.conda /work/LAS/your-lab/your-directory/.conda

Replace your-lab and your-directory with appropriate values.

Next, tell conda to use these directories by creating a file in your home directory at ~/.condarc with the following contents:

envs_dirs:
  - /work/LAS/your-lab/your-directory/.conda/envs
pkgs_dirs:
  - /work/LAS/your-lab/your-directory/.conda/pkgs

Replace your-lab and your-directory with appropriate values.

If there's already a ~/.condarc file in your home directory, append those lines to the end.

Now you can load the Conda module again and should still see all your existing environments.