Conda on BioHPC

Introduction
What is Conda?
Terminology

How-To Guides

Conda on BioHPC
Creating and Managing Conda Environments
Bioconda
R on Conda
Channels
Mamba
Conda Script template

FAQs
Using a Conda environment in an sbatch script

Troubleshooting

Downloads

Further Reading

Introduction

What is Conda?

Conda is a free and open-source package manager specifically designed for scientific computing environments like Python and R. It simplifies the process of installing, running, and updating software packages and their dependencies. But what exactly does that mean? Imagine you're working on different scientific projects, each requiring specific tools and libraries. Without a package manager, you'd have to manually install each tool, ensuring compatibility with your system and avoiding conflicts with other projects. This can be a time-consuming and error-prone process.

Conda Simplifies Workflow Management: This highlights the workflow benefits of using Conda.

Effortless Installation: Installing new software is a breeze. You simply specify the package name, and Conda takes care of downloading, configuring, and installing it, along with any necessary dependencies.
Isolated Environments: Conda allows you to create dedicated environments for each project. Each environment can have its own set of packages and versions, preventing conflicts and ensuring project-specific requirements are met. Think of it as having separate toolboxes for different tasks, keeping everything organized and avoiding clashes.
Dependency Management: No more worrying about missing dependencies! Conda automatically resolves and installs all the necessary packages a program needs to function correctly. This saves you time and ensures compatibility within your environment.
Streamlined Updates: Keeping your software up-to-date is crucial. Conda provides convenient commands to update specific packages or Conda itself to the latest versions, ensuring you have access to the newest features and bug fixes.

Think of it like...

The Organized Toolbox
- Think of Conda like a well-organized toolbox for your scientific projects. Each project has its own dedicated toolbox (environment) containing the specific tools (packages) it needs. This keeps your tools organized, prevents conflicts between projects, and ensures you have the right tools for the job at hand.
The Recipe Manager
- Imagine Conda as your personal recipe manager for scientific computing. Recipes (packages) often have specific ingredients (dependencies) they require to function correctly. Conda acts like a smart recipe manager, ensuring you have all the necessary ingredients (dependencies) before you start cooking (running the program).
The Version Control System for Software
- Similar to how Git manages code versions for software development, Conda provides version control for scientific software. You can create different environments with specific package versions, allowing you to replicate your project setup and ensure consistent results across different systems.

For more detailed information about Conda and its latest documentation, please refer to the official Conda website.

Terminology

Understanding the Building Blocks:

Package: Imagine a package as a self-contained box of software or a library. Each package includes all the necessary files and dependencies to function independently. Conda manages the installation and updates of these packages.
Environment: Think of an environment as a dedicated workspace for your projects. Each environment can have a specific collection of Conda packages with different versions, allowing you to isolate project requirements and avoid conflicts between projects that use different software versions.

The Anaconda Ecosystem:

Anaconda: This is a popular distribution of Python and R that comes bundled with a pre-configured Conda package manager. It offers a comprehensive set of scientific packages out of the box, simplifying package management and deployment for scientific workflows.
Miniconda: Unlike Anaconda, Miniconda is a lightweight installer containing only the essential Conda package manager, Python, and a few core packages. This makes it a preferred choice when you want a more minimal setup or need to manage specific package versions for your project.

Finding the Right Software:

Channels: Think of channels as online repositories where Conda packages are stored. Conda searches these channels to locate and download the packages you need. The default channel is provided by Anaconda, Inc., but you can add custom channels to access specific packages or collections from different organizations.
Conda Forge: This is a valuable community-driven channel that offers a vast collection of scientific packages that might not be included in the default Anaconda channel. It expands your options for finding the software you need.

Essential Conda Commands:

conda create: This command helps you create new isolated environments for your projects. For example, conda create -n myenv python=3.8 creates a new environment named "myenv" with Python version 3.8.
conda install: Use this command to install new packages within your active Conda environment. For instance, conda install numpy installs the popular NumPy library.
conda activate: This command activates a specific Conda environment. Once activated, any further Conda commands will apply to the chosen environment.
conda deactivate: This command deactivates the currently active environment, allowing you to switch back to the base environment or your system-wide Python installation.

Keeping Track and Up-to-Date:

conda list: This command provides a list of all packages installed within your current active environment.
conda env list: Use this command to see a list of all Conda environments available on your system.
conda update: This command helps you update Conda packages (including Conda itself) to their latest versions. You can update a specific package or update all packages within an environment.
conda remove: This command offers multiple functionalities. You can use it to remove packages from an environment, delete entire environments, or even uninstall Conda if necessary.

Anaconda vs. Miniconda:

Anaconda: This is a popular distribution of Python and R that includes a pre-installed Conda package manager. It comes bundled with a vast collection of scientific packages, making it a great choice for getting started quickly.
Miniconda: If you only need the Conda functionality and a minimal set of core packages like Python, Miniconda is a lightweight alternative. It's ideal for scenarios where you want more control over package selection or have limited disk space.

Channels and Dependencies:

Channels: These act like repositories where Conda packages are stored. The default Anaconda channel provides a broad selection, but you can add custom channels to access specific packages. When installing a package, Conda searches through these channels for both the package itself and any dependencies it requires.
Dependencies: These are libraries or packages that another package relies on to function properly. Conda automatically takes care of dependencies, ensuring all necessary components are installed when you install a package.

Key Functions of Conda

1. Package Management:

Effortless Installation: Conda simplifies installing software packages. You specify the package name, and Conda handles the download, configuration, and installation process.
Dependency Resolution: Conda automatically resolves and installs all dependencies required by a package. This ensures compatibility and saves you time.
Package Updates: Conda provides commands to update specific packages or Conda itself to the latest versions.

2. Environment Management:

Isolated Environments: Conda allows creating isolated environments for different projects. Each environment can have its own set of packages and versions, preventing conflicts between projects.
Environment Activation & Deactivation: You can activate a specific environment to make it the current workspace for Conda commands. Deactivating an environment switches you back to the base environment or system-level Python.
Environment Management Commands: Conda offers commands to create, list, activate, deactivate, and remove environments.

3. Additional Features:

Channels: Conda packages are stored in repositories called channels. The default Anaconda channel provides a broad selection of scientific packages, while other channels offer specific packages or collections.
Conda Forge: A popular community-led channel offering a vast collection of packages not found in the default channel.

How-To Guides

Using Conda Environment on BioHPC

Load the conda Module on BioHPC

At the time of writing this tutorial (Jan 2026), conda/mamba iis enabled by default with python 3.10.14, you do not need to load any module to have conda/mamba.

Creating and Managing Conda Environments

Create a New Environment

Bioconda

R on Conda

Channels

Mamba

FAQs

Q: Can I use conda to manage non-Python packages?

A: Yes! Conda is not limited to Python packages. It supports managing packages for various programming languages like R, Java, C/C++, and more

Q: What is the difference between conda and pip?

A: While both manage Python packages, Conda excels at environment management. It creates isolated environments with specific versions of packages, ensuring compatibility within your project. Pip primarily focuses on installing packages within your system's default Python environment.

Q: What is the Difference Between Conda, Mamba, and Pip?

A: Conda, Mamba, and Pip are package management tools used in Python environments, each with distinct features and use cases.

Conda

Purpose: Conda is an open-source package management and environment management system that can install packages from various programming languages, including Python.
Functionality: It manages environments and packages together, handling dependencies and ensuring compatibility across different packages and libraries.
Advantages:
- Cross-Language Support: Manages packages and environments for multiple languages, not just Python.
- Environment Management: Easily creates, saves, loads, and switches between environments.
- Dependency Management: Automatically resolves and installs package dependencies.
Usage: Ideal for data science and machine learning workflows where multiple libraries from different languages are required.

Mamba

Purpose: Mamba is a reimplementation of Conda, designed to be faster and more efficient.
Functionality: It aims to solve the same problems as Conda but with improved performance.
Advantages:
- Speed: Significantly faster package resolution and installation times compared to Conda.
- Efficiency: Reduced memory usage, making it suitable for large-scale projects.
- Compatibility: Fully compatible with Conda, meaning you can use it as a drop-in replacement.
Usage: Ideal for users who need the functionalities of Conda but require quicker package management.

Pip

Purpose: Pip is the standard package installer for Python, used to install and manage Python packages from the Python Package Index (PyPI).
Functionality: It installs Python packages and their dependencies but does not manage environments or packages from other languages.
Advantages:
- Simplicity: Easy to use for straightforward package installations.
- Integration: Well-integrated with Python and commonly used with virtual environments (like venv).
- Widely Supported: The default and most widely supported package manager for Python packages.
Usage: Best suited for pure Python projects where managing environments and cross-language dependencies is not necessary.

Troubleshooting

Conda has issue. What can I do to fix it?

1. Check Conda Installation

Ensure Conda is installed correctly and is accessible from your command line.

Verify Conda Version

conda --version

If this command returns an error, reinstall Conda

2. Check Environment

Make sure you are working in the correct Conda environment.

List Environments

conda env list

Activate Environment

conda activate your_environment_name

3. Resolve Package Conflicts

Package conflicts are common in Conda.

Attempt to Install with Conflict Resolution

conda install <package_name>

If you see conflicts, Conda usually suggets resolutions. Try following those.

4. Check Conda Configuration

Verify the configuration settings to ensure they are correct

Show Config

conda config --show

5. Clean Conda Cache

Cleaning the cache can resolve issues related to corrupted or incomplete downloads

Clean All Cache

conda clean --all

6. Recreate the Environment

If an environment is corrupted, recreating it from a YAML file can solve issues.

Export Environment

conda env export > environment.yml

Remove Environment

conda env remove -n your_environment_name

Recreate Environment

conda env create -f environment.yml

6. Inspect Log Files

Check the Conda log files for detailed error message

Find Log Files

Conda logs are usually stored in the ~/.conda directory. Check ~/.conda/logs/conda.log for details about environments.

Accessing the Conda Log File: Navigate to the Log Directory

cd ~/.conda/logs/

View the Log File

cat conda.log