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Content from Why Software Modules?

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Last updated on 2026-08-27 | Edit this page

Estimated time: 15 minutes

Overview

Questions

• Why do we need environment modules on shared computing systems?
• What is an environment module and how does it work?
• How does Lmod modify your shell environment?

Objectives

• Understand the problem that environment modules solve
• Learn how Lmod modifies your shell environment
• Understand the difference between loading and installing software

The Problem: Conflicting Software Versions

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Callout

The Version Conflict Problem

Imagine you’re working on Sagehen HPC with two research groups. One group needs R version 3.6.0 for legacy analysis scripts that haven’t been updated in five years. Another group requires R version 4.2.3 for their latest genomics pipeline. Your colleague needs Gaussian 16 to run quantum chemistry simulations, but you need Gaussian 09 for continuity with your previous research.

Without a way to manage these different versions, installing them all in the same locations (like /usr/bin or /usr/lib) creates conflicts: - Programs and libraries overwrite each other - Running R might load the wrong version - Dependencies break unpredictably - Users can’t work on different projects simultaneously

On a shared system with hundreds of users, this would be chaos.

Four commands cover almost everything you will do with modules.

The Solution: Environment Modules

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Environment modules (managed by Lmod on Sagehen) solve this problem by dynamically modifying your shell environment. A module is essentially a collection of instructions that:

1. Adds software locations to your PATH (where the system looks for executable programs)
2. Sets LD_LIBRARY_PATH (where the system looks for shared libraries)
3. Sets other environment variables needed by the software
4. Possibly loads prerequisite modules

When you load a module, it’s like installing software temporarily in your current shell session without actually changing the filesystem.

How Lmod Modifies Your Environment

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Let’s look at a practical example. When you load the R module:

BASH

$ module load r

Lmod runs a script that: - Adds the R installation’s bin directory (e.g. /opt/.../r/4.5.1/bin) to your PATH - Sets LD_LIBRARY_PATH to include R’s libraries - Sets R_HOME to the installation directory - Sets other R-specific variables

Now when you type R at the command line, the shell finds the R executable in the path that was just added.

If you then load a different version:

BASH

$ module swap r r/4.2.3

Lmod removes the 4.5.1 entries and adds the 4.2.3 entries instead. No files are actually installed or removed; just your shell environment changes.

Module Naming Conventions

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Modules follow the pattern software/version:

• r/4.5.1: R version 4.5.1
• r/4.2.3: R version 4.2.3
• r: Same as the default, currently r/4.5.1 (marked with D)
• miniconda3: The default version of miniconda3
• miniconda3/py313_26.3.2-2: Specific version
• cuda/12.2.1: NVIDIA CUDA toolkit version 12.2.1

When you run module load r, you get the default. To load a specific version, be explicit.

Challenge

Challenge 1: Explore Available Modules

Connect to Sagehen and run module avail. Scroll through the output and identify:

1. How many versions of R are available?
2. What CUDA toolkit versions are available?
3. Which modules have a default version marked with (D)?

BASH

$ ssh <myusername>@sagehen.hpc.pomona.edu
$ module avail

Show me the solution

Output will vary, but a typical response shows:

R versions:
- r/4.2.2
- r/4.2.3
- r/4.4.1
- r/4.5.1 (D)

CUDA toolkit versions:
- cuda/11.8.0
- cuda/12.0.0
- cuda/12.2.1 (D)

Modules with defaults:
- miniconda3/py313_26.3.2-2 (D)
- r/4.5.1 (D)
- go/1.23.1 (D)

The important point is that you can see all available options before loading.

Key Points

• Environment modules solve the problem of multiple software versions on shared systems
• Modules temporarily modify your shell environment (PATH, LD_LIBRARY_PATH, etc.)
• No files are installed or removed; only your environment changes
• Modules follow the naming convention software/version
• Modules with (D) are defaults; specify versions explicitly to get other versions

Content from Finding and Loading Modules

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Last updated on 2026-08-27 | Edit this page

Estimated time: 25 minutes

Overview

Questions

• How do I find specific software on Sagehen HPC?
• How do I load and unload modules?
• How do I learn what a module does before loading it?
• What if I need software that isn’t available?

Objectives

• Use module avail with search patterns to find software
• Load and unload modules with module load and module unload
• Use module help and module show to inspect modules
• Know what to do if software isn’t available

Searching for Software

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On a cluster with hundreds of modules, finding specific software efficiently is essential.

Built-in filtering

Lmod can filter the listing itself – pass a search string to module avail:

BASH

$ module avail r

The match is a substring match against the whole module name and version, so a single letter matches far too much – on Sagehen this returns not just r/4.5.1 but also ansys/2023r1, apptainer, fmriprep, rstudio-server, rust, trimmomatic, and dozens more, all because they contain an “r” somewhere. Use a longer, more specific string:

BASH

$ module avail rstudio
$ module avail conda
$ module avail gaussian

module avail conda is a good example of why searching matters: it finds both anaconda3 and miniconda3. There is no standalone python module on Sagehen – searching for python finds nothing, because Python is provided inside the conda distributions. When a direct name search comes up empty, think about what the software might ship inside.

Deeper searches: spider and keyword

BASH

$ module spider gaussian     # every version of a module, plus how to load it
$ module keyword chemistry   # searches module descriptions, not just names

module spider knows about every module and version on the system, and module keyword searches the descriptive text – useful when you don’t know the package’s exact name.

Check for a module before you reach for conda, and ask before you compile.

Callout

Why not just pipe to grep?

You’ll see module avail -t | grep name suggested online, but on Sagehen it returns nothing: Lmod prints the listing to standard error rather than standard output, and this Lmod version treats a trailing -t as a search pattern rather than a flag (hence the confusing No module(s) or extension(s) found!). If you want a grep pipeline, the options must come before the subcommand and stderr must be redirected:

BASH

$ module -t avail 2>&1 | grep -i conda

The built-in filters above are simpler and always work – prefer them.

Loading and Unloading Modules

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Your first module load

BASH

$ module load miniconda3

Now check what modules you have loaded:

BASH

$ module list
Currently Loaded Modules:
  1) miniconda3/py313_26.3.2-2

To see what this module actually changed:

BASH

$ which python
/opt/.../miniconda3/py313_26.3.2-2/bin/python

Unloading modules

When you’re done with software, unload it:

BASH

$ module unload miniconda3
$ which python
/usr/bin/python

python now refers to the system Python (if it exists) or isn’t available.

Callout

Key Commands Summary

Command	Purpose
module avail	List all available modules
module load <name>	Load a module
module list	Show currently loaded modules
module unload <name>	Unload a module
which <program>	Show the full path to a program

Learning What a Module Does

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Callout

Check Before You Load

Always use module help before loading an unfamiliar module. This prevents surprises and shows you what dependencies will be automatically loaded.

Using module help

BASH

$ module help r

This prints whatever help text the module’s packager provided – typically a short description of the software, the version, and any usage notes. The amount of detail varies from module to module.

Understanding module dependencies

Some modules automatically load other modules they depend on (for example, an MPI library or a maths library). When that happens, module list after a single module load will show more than one loaded module. That is normal – unloading the module you asked for also removes what it pulled in.

Using module show

To see exactly what a module does to your environment:

BASH

$ module show r/4.5.1

Output (simplified):

prepend_path("PATH","/opt/.../r/4.5.1/bin")
setenv("R_HOME","/opt/.../r/4.5.1")

The prepend_path line is the heart of it: loading the module puts that version’s bin directory at the front of your PATH.

When Software Isn’t Available

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If you can’t find the software you need:

1. Double-check your search with module avail name and module spider name
2. Check the Sagehen documentation for software inventory
3. Request installation by emailing its-hpc@pomona.edu with:
   • Software name and version
   • Research purpose
   • License information (open-source, commercial, etc.)
   • Links to documentation

Challenge

Challenge 1: Load, Check, and Unload

1. Load the go module (the default version)
2. Use module list to confirm it’s loaded
3. Use which go to find its location
4. Unload it
5. Try which go again; notice the difference

BASH

$ module load go
$ module list
$ which go
$ module unload go
$ which go

Show me the solution

BASH

$ module load go
$ module list
Currently Loaded Modules:
  1) go/1.23.1

$ which go
/opt/.../go/1.23.1/bin/go

$ module unload go
$ which go
/usr/bin/which: no go in (...)

The key insight: after unloading, the command is gone from your PATH – you fall back to a system-wide copy if one exists, or to nothing at all.

Challenge

Challenge 2: Understand Module Dependencies

1. Check which modules are currently loaded
2. Load the R module
3. Check again: what was automatically loaded?
4. Unload only R (not its dependencies)

BASH

$ module list
$ module load r
$ module list
$ module unload r
$ module list

Show me the solution

After module load r:

Currently Loaded Modules:
  1) r/4.5.1

After module unload r:

No modules loaded

If a module pulls in dependencies when it loads, those dependencies can remain loaded after you unload it – Lmod treats them cautiously because other loaded modules might also rely on them. If module list still shows leftovers you don’t need, unload them explicitly, or use module purge to clear everything.

Key Points

• Use module avail with search terms or grep to find software
• module load activates software; module unload deactivates it
• Use module help to understand what a module does before loading
• Use module show to see exact environment modifications
• Modules can automatically load prerequisites
• If software isn’t available, contact its-hpc@pomona.edu to request it

Content from Module Collections and Defaults

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Last updated on 2026-08-27 | Edit this page

Estimated time: 25 minutes

Overview

Questions

• How do I load multiple modules together?
• How do I switch between different versions of the same software?
• How can I save my module configuration and reload it later?
• How do I use modules in batch job scripts?

Objectives

• Load multiple modules for complex workflows
• Use module swap to switch between versions
• Use module purge to reset to a clean state
• Create and manage personal module collections
• Write module commands in SLURM job scripts

Loading Multiple Modules

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Many research workflows require multiple pieces of software:

BASH

$ module load openmpi gaussian miniconda3 ncbi-blast
$ module list
Currently Loaded Modules:
  1) openmpi/4.1.5_ucx-1.14.0    2) gaussian/16c01_avx2    3) miniconda3/py313_26.3.2-2    4) ncbi-blast/2.13.0+

Save the set once, restore it in one command afterwards.

Swapping Module Versions

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To switch between software versions, use module swap:

BASH

$ module load cuda/11.8.0
$ module swap cuda cuda/12.2.1
$ module list
Currently Loaded Modules:
  1) cuda/12.2.1

The swap command unloads the old version and loads the new one in a single operation, which is cleaner than separate unload/load commands.

Resetting Everything: module purge

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If you load many modules and need to start fresh:

BASH

$ module load r miniconda3 ncbi-blast gaussian
$ module purge
$ module list
No modules loaded

Use module purge to start a fresh shell environment, avoid conflicts when switching projects, or test if your code really needs all those modules.

Callout

Default Modules at Login

When you first log into Sagehen HPC, certain modules might be automatically loaded based on system configuration or your .bashrc file. Use module purge to clear them, then load only what you need for your current task.

Saving Module Collections

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Callout

Save Time with Collections

Manually loading the same modules every time gets tedious and error-prone. Module collections let you save a set of modules once and restore them with a single command.

Save your current module configuration as a named collection:

BASH

$ module load openblas/0.3.25 r/4.5.1
$ module save biostats

Lmod stores it in ~/.lmod.d/biostats.

Restoring a collection

BASH

$ module restore biostats
$ module list
Currently Loaded Modules:
  1) openblas/0.3.25    2) r/4.5.1

Listing your collections

BASH

$ module --list-collections
Named collections for /rhome/<myusername>:
  1) biostats
  2) python_analysis
  3) quantum_chem

Setting a Default Collection

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Make a collection load automatically when you log in:

BASH

$ module load openblas/0.3.25 r/4.5.1 miniconda3
$ module save default

Now every time you SSH to Sagehen, those modules are loaded automatically. To remove the automatic loading:

BASH

$ module purge
$ module save default

Using Modules in Job Scripts

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Callout

Critical for Reproducibility

When you submit a batch job to SLURM, your current module configuration is not automatically passed to the job. Always load modules in your job script.

Example job script

BASH

#!/bin/bash
#SBATCH --job-name=ranalysis
#SBATCH --time=00:30:00
#SBATCH --cpus-per-task=4
#SBATCH --mem=16G

# Load required modules
module load openblas/0.3.25
module load r/4.5.1
module load miniconda3

# Run your analysis
Rscript analysis.R

You can also restore a collection in a job script:

BASH

#!/bin/bash
#SBATCH --job-name=genomics
#SBATCH --time=04:00:00
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G

module restore genomics_pipeline
./run_analysis.sh

Challenge

Challenge 1: Create and Restore a Collection

1. Load at least three different modules (e.g., openblas, miniconda3, r)
2. Save them as a collection called “myworkflow”
3. Purge all modules
4. Restore the collection
5. Verify all modules are reloaded

BASH

$ module load openblas miniconda3 r
$ module save myworkflow
$ module purge
$ module restore myworkflow
$ module list

Show me the solution

After restore:

Currently Loaded Modules:
  1) openblas/0.3.25    2) miniconda3/py313_26.3.2-2    3) r/4.5.1

The collection saves the exact versions you had loaded and restores them perfectly.

Challenge

Challenge 2: Write a Job Script with Modules

Create a file called test_job.sh with: 1. SLURM header requesting 2 CPUs, 4GB RAM, 10 minutes 2. Load the openblas and miniconda3 modules 3. Print “Modules loaded:” followed by module list 4. Echo “Job completed”

BASH

$ cat > test_job.sh << 'EOF'
#!/bin/bash
#SBATCH --job-name=test_modules
#SBATCH --cpus-per-task=2
#SBATCH --mem=4G
#SBATCH --time=00:10:00

module load openblas miniconda3
echo "Modules loaded:"
module list
echo "Job completed"
EOF

$ chmod +x test_job.sh
$ sbatch test_job.sh

Show me the solution

When submitted with sbatch test_job.sh, it creates a job that explicitly loads modules before running commands. Check the output with squeue or look in the log file (slurm-JOBID.out). The job script explicitly loads modules rather than relying on your login environment.

Key Points

• Load multiple modules with module load module1 module2 module3
• Use module swap to switch versions cleanly
• Use module purge to start fresh
• Save configurations with module save <name> and restore with module restore <name>
• Set module save default to auto-load modules on login
• Always load modules in job scripts; don’t rely on your login environment

Content from Introduction to Conda

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Last updated on 2026-08-27 | Edit this page

Estimated time: 25 minutes

Overview

Questions

• Why do I need Conda when I already have modules?
• How do I create an isolated Python environment?
• How do I activate and deactivate environments?

Objectives

• Understand the difference between modules and conda environments
• Create and activate conda environments
• Verify which Python is being used
• Deactivate environments when done

Modules vs. Conda: When to Use Each

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Modules load pre-installed software managed by system administrators: - Good for: Large applications, compiled binaries, cluster-wide tools - Examples: R, GAMESS, Gaussian, GCC

Conda manages Python/R packages in isolated environments: - Good for: Python libraries, package dependencies, project-specific stacks - Examples: numpy, scipy, matplotlib, scikit-learn, TensorFlow

Most workflows use both: modules for system software, conda for language-specific packages.

Your First Conda Environment

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Step 1: Load Miniconda

Miniconda is a minimal conda distribution available as a module:

BASH

$ module load miniconda3
$ conda --version
conda 26.3.2

The miniconda3 module comes in three versions on Sagehen HPC; py313_26.3.2-2 is the default (D) — and often already loaded (L).

Step 2: Create an environment

Create a new conda environment for a specific project:

BASH

$ conda create -n myproject python=3.11

This creates an environment called “myproject” with Python 3.11. Conda asks for confirmation:

The following packages will be installed:
  ...

Proceed? [y/N] y

Press y to proceed.

Step 3: Activate the environment

BASH

$ conda activate myproject
(myproject) $

Notice the prompt changes to show (myproject). You’re now inside the environment.

Step 4: Verify Python

BASH

(myproject) $ python --version
Python 3.11.8

(myproject) $ which python
/rhome/<myusername>/.conda/envs/myproject/bin/python

Python is from your environment, not the system Python.

Step 5: Deactivate when done

BASH

(myproject) $ conda deactivate
$

The prompt returns to normal.

Listing Environments

---

See all conda environments on your account:

BASH

$ conda env list
# conda environments:
#
base                  /opt/.../miniconda3/py313_26.3.2-2
myproject             /rhome/<myusername>/.conda/envs/myproject
analysis_2024         /rhome/<myusername>/.conda/envs/analysis_2024

The base environment is miniconda3 itself; other environments are yours.

Callout

WARNING: Never Install into Base

DO NOT run conda install in the base environment without specifying -n envname.

BASH

# BAD - never do this on shared systems
$ conda install tensorflow

Instead, always create a named environment:

BASH

# GOOD
$ conda create -n ml_project tensorflow
$ conda activate ml_project

On shared clusters, installing in base can consume your disk quota and interfere with other users’ work.

Challenge

Challenge 1: Create and Activate an Environment

1. Load miniconda3
2. Create an environment called “data_sci” with Python 3.10
3. Activate it
4. Check which Python is being used
5. Deactivate

BASH

$ module load miniconda3
$ conda create -n data_sci python=3.10
$ conda activate data_sci
(data_sci) $ which python
(data_sci) $ python --version
(data_sci) $ conda deactivate

Show me the solution

After activation:

which python outputs: /rhome/<myusername>/.conda/envs/data_sci/bin/python
python --version outputs: Python 3.10.X

After deactivation, which python points to system Python or shows not found. The key point: conda activation changes your PATH to prioritize the environment’s Python.

Key Points

• Modules load system software; conda manages Python packages in isolated environments
• Create environments with conda create -n name python=version
• Use conda activate to enter an environment; conda deactivate to exit
• Most workflows use both modules and conda together
• Never install into the base environment on shared systems

Content from Creating Conda Environments

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Last updated on 2026-08-27 | Edit this page

Estimated time: 25 minutes

Overview

Questions

• How do I install packages into a conda environment?
• How do I use packages from conda-forge?
• How should I organize my environments?

Objectives

• Install packages with conda install
• Use conda-forge for additional packages
• List installed packages and check versions
• Organize environments by project

Installing Packages

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Basic installation with conda

Activate your environment and install scientific packages:

BASH

$ conda activate myproject
(myproject) $ conda install numpy scipy matplotlib

Conda resolves all dependencies automatically and asks for confirmation:

Solving environment: done

The following packages will be installed:
  numpy        scipy        matplotlib    ... (and dependencies)

Proceed? [y/N] y

Checking installed packages

List what’s in your environment:

BASH

(myproject) $ conda list

Output shows all packages and versions:

# packages in environment at /rhome/<myusername>/.conda/envs/myproject:
#
# Name                    Version                   Build  Channel
numpy                      1.24.3                   pypi_0    pypi
scipy                      1.10.1                   pypi_0    pypi
matplotlib                 3.7.1                   pypi_0    pypi
python                      3.11.8              hdb9e83b_0    defaults
...

Using conda-forge

Some packages are on conda-forge, a community channel with more recent versions:

BASH

(myproject) $ conda install -c conda-forge scikit-learn

The -c conda-forge flag tells conda to search the conda-forge channel.

Four steps, in this order — the module first, then the environment.

Organizing Your Environments

---

Callout

One Environment Per Project

Create separate conda environments for different projects. This prevents dependencies from one project breaking another and makes it easy to share environments with collaborators. Each environment should be self-contained and reproducible.

Why multiple environments?

Create separate environments for different projects: - myproject: Latest packages, requires Python 3.11 - analysis_2024: Specific versions for reproducibility - legacy: Old packages for long-running code

This isolates dependencies and prevents conflicts between projects.

Using Conda Environments in Job Scripts

---

BASH

#!/bin/bash
#SBATCH --job-name=ml_training
#SBATCH --time=02:00:00
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G

# Load miniconda module
module load miniconda3

# Activate your environment
conda activate myproject

# Run your Python script
python train_model.py

Conda initialization in scripts

Some job systems have issues with conda activate in scripts. If activation doesn’t work, try:

BASH

#!/bin/bash
#SBATCH --job-name=analysis
#SBATCH --time=01:00:00

source ~/.bashrc
module load miniconda3
source activate myproject
python analysis.py

Practical Example: Research Project Setup

---

BASH

# Create environment for bioinformatics work
$ conda create -n bioinfo python=3.10
$ conda activate bioinfo
(bioinfo) $ conda install biopython pandas matplotlib seaborn
(bioinfo) $ conda install -c conda-forge samtools bcftools

Job script using this environment

BASH

#!/bin/bash
#SBATCH --job-name=bioinfo_pipeline
#SBATCH --time=04:00:00
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G
#SBATCH --array=1-10

module load miniconda3
conda activate bioinfo

python pipeline.py --sample sample_$SLURM_ARRAY_TASK_ID

Challenge

Challenge 1: Install Packages and List

1. Activate the data_sci environment from the previous episode (or create a new one)
2. Install numpy, scipy, and pandas
3. List the installed packages
4. Check the version of numpy

BASH

$ conda activate data_sci
(data_sci) $ conda install numpy scipy pandas
(data_sci) $ conda list
(data_sci) $ python -c "import numpy; print(numpy.__version__)"

Show me the solution

After installation, conda list shows:

numpy        1.24.x
scipy        1.10.x
pandas       2.0.x
(and dependencies)

The python -c command prints the exact numpy version. All packages are isolated inside the data_sci environment and won’t affect other environments.

Key Points

• Install packages with conda install package1 package2
• Use conda list to see what’s installed and check versions
• Use -c conda-forge for packages on the conda-forge channel
• Create one environment per project to prevent dependency conflicts
• Always load miniconda3 and activate your environment in job scripts

Content from Managing Conda Packages

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Last updated on 2026-08-27 | Edit this page

Estimated time: 20 minutes

Overview

Questions

• How do I export my environment for sharing?
• How do I recreate an environment from a file?
• How do I manage disk space used by environments?

Objectives

• Export environments to YAML files for reproducibility
• Recreate environments from exported files
• Understand platform-specific vs. cross-platform exports
• Monitor and manage disk usage from conda environments

Exporting Your Environment

---

When your analysis works perfectly, save the exact configuration:

BASH

(myproject) $ conda env export > environment.yml

This creates an environment.yml file with every package and version:

YAML

name: myproject
channels:
  - defaults
dependencies:
  - numpy=1.24.3
  - scipy=1.10.1
  - matplotlib=3.7.1
  - python=3.11.8
  - ... (many more packages and versions)

Recreating from a File

---

Share environment.yml with collaborators. They can recreate the exact environment:

BASH

$ conda env create -f environment.yml

Or with a different name:

BASH

$ conda env create -n collab_version -f environment.yml

This guarantees the same versions and reproducibility.

Platform-specific exports

The standard export includes platform-specific details. For sharing across different operating systems, use:

BASH

(myproject) $ conda env export --from-history > environment.yml

This exports only the packages you explicitly installed (not their dependencies), allowing conda to resolve them on different systems.

Checking Disk Usage

---

Callout

Watch Your Disk Quota

Conda environments can use hundreds of MB to several GB each. On shared systems with disk quotas, this matters. Regularly check your usage and clean up old environments to stay under your limit. On Sagehen HPC, /rhome and /bigdata share a 1 TB lab quota.

Conda environments live in ~/.conda/envs/:

BASH

$ du -sh ~/.conda/envs/
2.3G    /rhome/<myusername>/.conda/envs/

$ du -sh ~/.conda/envs/*
2.1G    /rhome/<myusername>/.conda/envs/deeplearning_2024
1.3G    /rhome/<myusername>/.conda/envs/genomics_2024
500M    /rhome/<myusername>/.conda/envs/myproject

Removing old environments

BASH

$ conda env remove -n old_project

Cleaning conda cache

Conda keeps package caches that can be large:

BASH

$ conda clean --all

This removes unused packages and cached install files, potentially freeing 500 MB to 2 GB.

Challenge

Challenge 1: Export and Recreate

1. Activate your data_sci environment
2. Export it to a YAML file
3. Create a copy from the exported file
4. List all your environments

BASH

$ conda activate data_sci
(data_sci) $ conda env export > my_environment.yml
(data_sci) $ head -20 my_environment.yml
(data_sci) $ conda deactivate
$ conda env create -n data_sci_copy -f my_environment.yml
$ conda env list

Show me the solution

The YAML file contains:

YAML

name: data_sci
channels:
  - defaults
dependencies:
  - numpy=...
  - scipy=...
  - pandas=...
  - python=3.10.X
  ...

conda env list shows both environments:

data_sci          /rhome/<myusername>/.conda/envs/data_sci
data_sci_copy     /rhome/<myusername>/.conda/envs/data_sci_copy

This demonstrates reproducibility; you can reliably recreate your environment.

Key Points

• Export with conda env export > environment.yml for reproducibility
• Recreate with conda env create -f environment.yml
• Use --from-history for cross-platform environment files
• Monitor disk usage with du -sh ~/.conda/envs/*
• Remove old environments with conda env remove -n name
• Clean conda cache with conda clean --all to free space

Content from pip and Virtual Environments

---

Last updated on 2026-08-27 | Edit this page

Estimated time: 15 minutes

Overview

Questions

• When should I use pip instead of conda?
• What is a virtual environment and how is it different from conda?
• How do I manage dependencies with requirements.txt?

Objectives

• Understand when pip is preferable to conda
• Create and use Python virtual environments with venv
• Install packages from pip and PyPI
• Manage dependencies with requirements.txt

Choosing the Right Tool

---

Three main Python package management tools exist on Sagehen HPC:

Tool	Best For	Pros	Cons
conda	Scientific computing, complex dependencies	Binary packages, system libs, cross-platform	Slower, larger environments
pip	Any Python package, rapid development	Lightweight, simple, universal	Dependencies on C libraries trickier
venv	Lightweight isolation without conda	Fast, minimal overhead, part of Python	Manual dependency management

General guidance: - For data science: Use conda (it handles Fortran/C libraries well) - For Python development: Use pip inside a conda environment - For minimal isolation: Use venv

Pip Inside a Conda Environment

---

The easiest approach: use conda for the base Python environment, then pip for additional packages.

BASH

$ module load miniconda3
$ conda create -n webdev python=3.11
$ conda activate webdev
(webdev) $ pip install flask requests

This works because pip installs pure Python packages that don’t need system libraries.

Pip directly (not recommended on shared systems)

Avoid using pip with --user on Sagehen:

BASH

# NOT RECOMMENDED
$ pip install --user numpy

The --user flag installs to ~/.local, creating a global Python environment that’s easy to mess up. Conda environments are cleaner and more reproducible.

Virtual Environments with venv

---

Callout

When to Use Venv

Use venv for lightweight Python projects that don’t need system-level dependencies (like C libraries or FORTRAN bindings). It’s faster and simpler than conda for pure Python work.

Python’s built-in venv module creates lightweight virtual environments:

BASH

$ module load miniconda3
$ python -m venv myproject
$ source myproject/bin/activate
(myproject) $ pip install numpy scipy
(myproject) $ deactivate

Venv vs. Conda

Venv advantages: - Faster to create (seconds vs. minutes) - Smaller disk footprint - No external tool needed beyond Python

Venv disadvantages: - Harder to manage system-level dependencies - No automatic binary package compilation - Less suitable for complex scientific packages

Managing Dependencies with requirements.txt

---

Creating a requirements.txt

BASH

(webdev) $ pip freeze > requirements.txt

This creates:

flask==2.3.2
requests==2.31.0
werkzeug==2.3.6
jinja2==3.1.2
...

Installing from requirements.txt

BASH

$ python -m venv newproject
$ source newproject/bin/activate
(newproject) $ pip install -r requirements.txt

All packages are installed at the same versions.

Challenge

Challenge 1: Create and Use a Virtual Environment

1. Load miniconda3
2. Create a venv called “myapp”
3. Activate it
4. Install requests and beautifulsoup4 with pip
5. List installed packages
6. Deactivate

BASH

$ module load miniconda3
$ python -m venv myapp
$ source myapp/bin/activate
(myapp) $ pip install requests beautifulsoup4
(myapp) $ pip list
(myapp) $ deactivate

Show me the solution

After installing, pip list shows:

beautifulsoup4           4.12.2
requests                 2.31.0
(other dependencies)

Deactivation removes the venv from your PATH. The directory myapp/ remains and can be reactivated later with source myapp/bin/activate.

Key Points

• Use conda for scientific computing with complex dependencies
• Use pip for pure Python packages or when adding to conda environments
• Use venv for lightweight, fast Python isolation
• Export with pip freeze > requirements.txt for pip-based reproducibility
• For shared systems, avoid pip install --user and use conda or venv instead

Content from Combining Conda and pip

---

Last updated on 2026-08-24 | Edit this page

Estimated time: 15 minutes

Overview

Questions

• How do I use pip inside a conda environment?
• How do I export an environment that uses both conda and pip packages?
• How do I use these combined environments in job scripts?

Objectives

• Combine conda and pip appropriately in one environment
• Export combined environments for reproducibility
• Use combined environments in SLURM job scripts

Why Combine Conda and pip?

---

• Conda handles complex binary dependencies (MKL, BLAS, etc.)
• Pip handles packages not on conda (newer packages, smaller libraries)
• A single environment.yml file captures both for full reproducibility

Recommended Approach

---

1. Create a conda environment with major scientific packages:

BASH

$ conda create -n bioinformatics python=3.10 numpy scipy pandas

2. Activate and add pip packages:

BASH

$ conda activate bioinformatics
(bioinformatics) $ pip install biopython pysam

3. Export for reproducibility:

BASH

(bioinformatics) $ conda env export > environment.yml

The environment.yml includes both conda and pip packages:

YAML

name: bioinformatics
channels:
  - defaults
dependencies:
  - numpy=1.24.3
  - scipy=1.10.1
  - pandas=1.5.3
  - pip
  - pip:
    - biopython==1.81
    - pysam==0.21.0

Practical Examples

---

Web development project

Using venv and pip:

BASH

$ module load miniconda3
$ python -m venv flask_app
$ source flask_app/bin/activate
(flask_app) $ pip install flask flask-sqlalchemy flask-login
(flask_app) $ pip freeze > requirements.txt

Collaborator sets up:

BASH

$ python -m venv flask_app
$ source flask_app/bin/activate
(flask_app) $ pip install -r requirements.txt

Machine learning project

Using conda + pip:

BASH

$ conda create -n ml_project python=3.10 numpy pandas scikit-learn pytorch torchvision -c pytorch
$ conda activate ml_project
(ml_project) $ pip install tensorboard wandb
(ml_project) $ conda env export > environment.yml

Collaborator sets up:

BASH

$ conda env create -f environment.yml
$ conda activate ml_project

In Job Scripts

---

Using venv

BASH

#!/bin/bash
#SBATCH --job-name=web_task
#SBATCH --time=00:30:00

module load miniconda3
source myproject/bin/activate
python app.py
deactivate

Using conda with pip packages

BASH

#!/bin/bash
#SBATCH --job-name=ml_inference
#SBATCH --time=01:00:00
#SBATCH --gpus=1
#SBATCH --mem=32G

module load miniconda3
conda activate ml_project
python inference.py

Both work; choose based on your project needs.

Challenge

Challenge 1: Export and Recreate with requirements.txt

1. Create a venv and install some packages
2. Generate requirements.txt
3. Create a new venv and install from the requirements file
4. Verify the same packages are installed

BASH

$ module load miniconda3
$ python -m venv testapp
$ source testapp/bin/activate
(testapp) $ pip install requests beautifulsoup4
(testapp) $ pip freeze > requirements.txt
(testapp) $ deactivate
$ python -m venv testapp_copy
$ source testapp_copy/bin/activate
(testapp_copy) $ pip install -r requirements.txt
(testapp_copy) $ pip list

Show me the solution

The requirements.txt file contains:

beautifulsoup4==4.12.2
certifi==2023.X.X
requests==2.31.0
...

After installing in the new environment, pip list shows identical packages and versions. This demonstrates reproducibility with pure pip and venv.

Key Points

• Combine conda (for scientific packages) and pip (for pure Python packages) in one environment
• The exported environment.yml captures both conda and pip packages
• Use conda for the base, then pip for packages not available in conda
• Both venv/pip and conda approaches work in SLURM job scripts
• Always export your environment for reproducibility regardless of which tools you use

Content from Environment Reproducibility

---

Last updated on 2026-08-27 | Edit this page

Estimated time: 20 minutes

Overview

Questions

• How do I ensure my research is reproducible?
• What should I document about my software environment?
• How do I pin package versions?

Objectives

• Write job scripts that explicitly load modules and environments
• Document software choices in version-controlled files
• Pin package versions for long-term reproducibility
• Create a complete, reproducible project structure

Always Be Explicit in Job Scripts

---

Callout

Explicit is Better Than Implicit

Never rely on your login environment in batch jobs. Always load modules and activate environments explicitly. This ensures reproducibility and prevents mysterious failures when the default environment changes.

The solution

BASH

#!/bin/bash
#SBATCH --job-name=myanalysis
#SBATCH --time=02:00:00
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G

# ALWAYS start clean
module purge

# ALWAYS load specific versions
module load r/4.5.1
module load miniconda3/py313_26.3.2-2

# ALWAYS activate environments explicitly
conda activate myanalysis

Rscript analysis.R
python postprocess.py

One conda installation underneath, one environment per project on top.

Pin Versions for Reproducibility

---

Dynamic vs. pinned environment.yml

Bad (unpinned):

YAML

name: myanalysis
dependencies:
  - python
  - numpy
  - scipy

When you recreate this in 6 months, you get newer versions, possibly breaking your code.

Good (pinned):

YAML

name: myanalysis
channels:
  - defaults
dependencies:
  - python=3.10.8
  - numpy=1.24.3
  - scipy=1.10.1
  - pandas=1.5.3

Now conda env create -f environment.yml gives the exact same environment regardless of when it’s run.

Creating pinned files

BASH

$ conda create -n myproject python=3.10 numpy scipy pandas
$ conda env export > environment.yml

The export includes all exact versions.

Updating pinned files

When you need to update packages:

BASH

$ conda activate myproject
(myproject) $ conda update numpy scipy
(myproject) $ conda env export > environment.yml
(myproject) $ git commit -m "Update scipy and numpy to latest versions"

Document Your Environment

---

In version control

BASH

$ git init myproject
$ conda env export > environment.yml
$ git add environment.yml
$ git commit -m "Initial conda environment specification"

Document module choices in job scripts

BASH

#!/bin/bash
#SBATCH --job-name=quantum_calc

# Gaussian 16 for the quantum chemistry calculation
module load gaussian/16c01_avx2

# R for statistical analysis of the results
module load r/4.5.1

g16 < input.com > output.log
Rscript analyze.R

Recommended Project Structure

---

~/projects/
  genomics_2024/
    environment.yml
    data/
    scripts/
    results/
  deeplearning_2024/
    environment.yml
    notebooks/
    models/

Each project has its own environment.yml and conda environment.

Challenge

Challenge 1: Create a Reproducible Job Script

Create a bash script called final_job.sh that:

1. Starts with module purge for a clean state
2. Loads r/4.5.1 and miniconda3
3. Activates a conda environment
4. Runs python --version and conda env list
5. Includes comments explaining each step

BASH

$ cat > final_job.sh << 'EOF'
#!/bin/bash
#SBATCH --job-name=reproducible_test
#SBATCH --time=00:10:00

# Clean environment to avoid conflicts
module purge

# Load R at a pinned version
module load r/4.5.1

# Load Python via miniconda3
module load miniconda3

# Activate our conda environment
conda activate myproject

# Verify the environment
echo "Python version:"
python --version
echo "Conda environments available:"
conda env list
EOF

$ chmod +x final_job.sh

Show me the solution

The script demonstrates the key reproducibility practices:

• Starting with module purge for clean state
• Loading specific module versions (not defaults)
• Explicit conda environment activation
• Verification commands to prove setup worked

This is the minimal reproducible template for any Sagehen HPC job.

Challenge

Challenge 2: Document Your Environment

Create an environment.yml file for a hypothetical “climate_analysis” project that needs Python 3.10, numpy, pandas, matplotlib, and netcdf4. Use exact version pinning.

BASH

$ module load miniconda3
$ conda create -n climate_analysis python=3.10 numpy pandas matplotlib netcdf4
$ conda env export > environment.yml
$ cat environment.yml

Show me the solution

A typical environment.yml:

YAML

name: climate_analysis
channels:
  - defaults
dependencies:
  - python=3.10.8
  - numpy=1.24.3
  - pandas=1.5.3
  - matplotlib=3.7.1
  - netcdf4=1.6.2
  - (many transitive dependencies)

Commit to git:

BASH

$ git add environment.yml
$ git commit -m "Add climate_analysis conda environment specification"

The key is that exact versions and all transitive dependencies are included, making it fully reproducible.

Key Points

• Always load modules and activate environments explicitly in job scripts
• Start job scripts with module purge for a clean environment
• Pin package versions in environment.yml for reproducibility
• Document software choices in version control
• Create one environment per project with its own environment.yml
• Update pinned files carefully and commit changes to git

Content from Best Practices and Troubleshooting

---

Last updated on 2026-08-27 | Edit this page

Estimated time: 15 minutes

Overview

Questions

• How do I avoid breaking my analysis when modules are updated?
• How do I clean up and avoid filling my disk quota?
• How should I test before submitting large jobs?
• How should I ask for help from the HPC team?

Objectives

• Manage disk usage efficiently
• Test scripts locally before submitting large jobs
• Know when and how to contact the HPC support team
• Follow a checklist for setting up new projects

Monitor and Manage Disk Usage

---

Callout

Prevent Quota Issues

Conda environments can consume substantial disk space. Regular maintenance prevents quota problems and keeps your cluster account healthy. On Sagehen HPC, /rhome and /bigdata share a 1 TB lab quota.

Check your usage

Note: Bare quota is unreliable on BeeGFS; use Pomona’s quota_check.sh wrapper which handles BeeGFS quota correctly.

BASH

$ quota_check.sh
$ du -sh --apparent-size ~/.conda/envs/*
2.1G    /rhome/<myusername>/.conda/envs/deeplearning_2024
1.3G    /rhome/<myusername>/.conda/envs/genomics_2024
500M    /rhome/<myusername>/.conda/envs/myproject

Clean up unused environments

BASH

$ conda env remove -n old_project

Clean conda cache

BASH

$ conda clean --all

This removes unused packages and cached install files, potentially freeing 500 MB to 2 GB.

Check for large hidden directories

BASH

$ du -sh ~/* | sort -rh | head -10

Test Before Submitting Large Jobs

---

Interactive testing

Before running a 24-hour job, test locally:

BASH

$ module load r
$ Rscript analysis.R < small_test_data.csv > output.txt

Test with a small job first

BASH

#!/bin/bash
#SBATCH --job-name=test
#SBATCH --time=00:05:00
#SBATCH --cpus-per-task=1
#SBATCH --mem=2G

module load r
Rscript analysis.R < small_test_data.csv > output.txt

Submit and check:

BASH

$ sbatch test_job.sh
$ squeue -u $USER
$ tail slurm-12345.out

Once you know it works, submit the full job.

Getting Help

---

Common issues

1. Module not found:
   • Check spelling: module avail name (or module spider name)
   • Contact its-hpc@pomona.edu
2. Code worked locally but fails on cluster:
   • Environment mismatch: verify modules in job script
   • Data path issues: use absolute paths, not relative
   • Resource limits: check memory and time requirements
3. Out of disk quota:
   • Remove old conda environments: conda env remove -n oldenv
   • Clean conda cache: conda clean --all
   • Archive old data to external storage

Contacting support

Email its-hpc@pomona.edu with: - Job ID (from squeue or log files) - Full error message - What you’ve already tried - Your account name

Checklist: Setting Up a New Project

---

• Create project directory with version control (git)
• Create conda environment with specific Python version
• Install required packages and export environment.yml
• Create template job script with module load commands
• Test locally with small data
• Test on cluster with small job (short time limit)
• Document in README what software is needed
• Add environment files to git repository
• Run full analysis
• Commit final results

Complete Project Setup Example

---

BASH

# Create project
$ mkdir genomics_analysis_2024
$ cd genomics_analysis_2024
$ git init

# Create conda environment
$ conda create -n genomics python=3.10
$ conda activate genomics
(genomics) $ conda install numpy pandas biopython samtools -c conda-forge
(genomics) $ pip install pysam biotools
(genomics) $ conda env export > environment.yml
(genomics) $ git add environment.yml

# Create job script
$ cat > job_script.sh << 'EOF'
#!/bin/bash
#SBATCH --job-name=genomics
#SBATCH --time=04:00:00
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G

module purge
module load miniconda3/py313_26.3.2-2

conda activate genomics
python analyze_genomes.py
EOF

$ chmod +x job_script.sh
$ sbatch job_script.sh
$ git add analyze_genomes.py job_script.sh
$ git commit -m "Initial genomics analysis setup"

Challenge

Challenge 1: Check and Manage Disk Usage

1. Check your total disk quota: quota_check.sh
2. See what’s using space: du -sh --apparent-size ~/.conda/envs/*
3. List all your conda environments: conda env list
4. If you have test environments, remove one: conda env remove -n test_env
5. Clean up conda cache: conda clean --all

BASH

$ quota_check.sh
$ du -sh --apparent-size ~/.conda/envs/*
$ conda env list
$ conda clean --all

Show me the solution

Expected output from quota_check.sh shows your current usage and limit. du -sh --apparent-size shows sizes like:

1.2G    /rhome/<myusername>/.conda/envs/bigproject
500M    /rhome/<myusername>/.conda/envs/test_env

After cleanup, total usage drops and you stay under quota. This regular maintenance prevents quota issues.

Key Points

• Monitor disk usage regularly and clean up old conda environments
• Test scripts locally and with small jobs before submitting large ones
• Contact its-hpc@pomona.edu with job ID, error message, and what you’ve tried
• Follow the new project checklist for reproducible research setups
• Use module purge at the start of job scripts for a clean environment
• Start with module purge, then load specific module versions explicitly