Conda/Mamba Environment

Conda/Mamba Environment#

Managing Environments with `conda` and `mamba`#

In computational research, ensuring that code runs consistently across different systems and over time is essential. This is where environment management tools like conda and mamba come in.

conda is a powerful package and environment manager that allows you to create isolated environments — each with its own versions of Python and packages. mamba is a faster, drop-in replacement for conda that significantly speeds up environment creation and dependency resolution.

Why It Matters: Reproducibility#

In modern research, reproducibility isn’t optional — it’s a core requirement. By using conda or mamba environments, you can:

Isolate dependencies for specific projects
Avoid version conflicts between packages
Share exact software environments with collaborators
Ensure long-term reproducibility of analysis and results

Imagine you share a Jupyter notebook with a colleague. If you’ve used a conda/mamba environment and included an environment.yml file, your colleague can recreate your exact setup — no more “it works on my machine” problems.

Without proper environment management, research code that runs today may fail tomorrow due to subtle changes in software versions. By making conda or mamba environments part of your standard workflow, you are not just managing software — you are investing in the integrity, longevity, and reproducibility of your research.

🛠️ Basic Workflow#

See also

KLC uses the LMOD module system . For a full reference on module avail, module load, and related commands, see the Quest Software Modules documentation .

Create a new environment:

Python environment (choose your Python version in the mamba create line)

module load mamba/24.3.0
mamba create -p /kellogg/proj/<your_netid>/envs/python_proj python=3.10

R environment with optional dplyr packages (isolated conda-forge R; for cluster R, use module load R/4.5.1 instead)

module load mamba/24.3.0
mamba create -p /kellogg/proj/<your_netid>/envs/r_proj -c conda-forge r-base=4.4.0 r-dplyr 

Activate the environment:

module load mamba/24.3.0
source activate /kellogg/proj/<your_netid>/envs/python_proj

Install packages:
```
mamba install pandas 
```
Export environment (for sharing or archiving):
```
mamba env export > environment.yml
```
Recreate environment from a file:
```
mamba env create -f environment.yml
```
Leave an environment
```
conda deactivate
```