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Conda Environment#

Conda is an open-source package and environment management system that allows you to create isolated environments with specific Python versions and packages. On the cluster, Conda is available via the Miniconda module. Upon loading the module, the base Conda environment is automatically activated.

Module Alias

The miniconda module is the recommended interface. Internally, it loads the Miniforge distribution, which is fully compatible with Miniconda and uses the conda-forge ecosystem.

Quick Reference#

Task Command
Load Conda module module load miniconda
Create environment conda create -n myenv python=3.11
Activate environment conda activate myenv
Deactivate environment conda deactivate
List environments conda env list
Install package conda install package_name
Remove package conda remove package_name
List packages conda list
Remove environment conda env remove -n myenv
Clean cache conda clean --all

Getting Started#

Loading the Module#

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module load miniconda

Base Environment

Loading the Miniconda module automatically activates the base Conda environment. Avoid installing packages into base — always create a project-specific environment instead.

Creating and Activating an Environment#

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# Create a new environment
conda create -n myenv python=3.11

# Activate it
conda activate myenv

# Install packages
conda install numpy pandas scikit-learn

Using Conda in SLURM Jobs#

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#!/bin/bash
#SBATCH --job-name=my_job
#SBATCH --partition=<partition-name>
#SBATCH --qos=normal
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=16G
#SBATCH --time=02:00:00
#SBATCH --output=output_%j.log

# Load Conda and activate your environment
module load miniconda
conda activate myenv

# Run your script
python my_script.py

# Deactivate your environment
conda deactivate

Best Practices#

  • Never install into base — always create project-specific environments.
  • Clean up unused environments to conserve disk quota — Conda environments can be large.
  • Prefer Conda over pip where possible; use pip only for packages unavailable in Conda.

Conda environments can consume significant disk space over time. Run conda clean --all periodically to remove unused packages and caches.

Troubleshooting#

Package Conflicts#

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# Create a fresh environment and install packages one at a time
conda create -n new_env python=3.11
conda activate new_env
conda install package1
conda install package2

Slow Installation:#

Modern Conda includes the libmamba solver, which significantly improves installation speed. Enable it once, then use Conda normally:

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conda config --set solver libmamba
mamba install numpy pandas

Additional Resources#