How To Install Anaconda on Rocky Linux 10

Installing Anaconda on Rocky Linux 10 opens up a world of possibilities for data science, machine learning, and Python development. This comprehensive guide walks you through every step of the installation process, ensuring a smooth and successful setup.

Introduction and Overview

What is Anaconda?

Anaconda stands as the premier Python distribution platform designed specifically for data science and machine learning applications. This powerful ecosystem includes over 1,500 pre-installed packages, making it an invaluable tool for developers, data scientists, and researchers working with Python and R programming languages.

The Anaconda distribution simplifies package management through its built-in conda package manager, which handles dependencies automatically and creates isolated virtual environments. Unlike standard Python installations, Anaconda comes bundled with essential libraries including NumPy, Pandas, Matplotlib, Scikit-learn, and Jupyter Notebook, eliminating the need for manual installation of these critical components.

Anaconda Enterprise differs from the standard distribution by offering enterprise-grade features such as centralized package management, enhanced security controls, and scalable deployment options. For most individual users and small teams, the free Anaconda Distribution provides all necessary functionality for data analysis, scientific computing, and application development.

Why Choose Rocky Linux 10?

Rocky Linux 10 represents the latest iteration of this enterprise-grade Linux distribution, built as a community-driven alternative to Red Hat Enterprise Linux (RHEL). The operating system provides exceptional stability, robust security features, and long-term support that makes it ideal for production environments and development workstations.

The compatibility with the RHEL ecosystem ensures that packages, applications, and configurations designed for enterprise environments work seamlessly on Rocky Linux 10. This compatibility extends to container technologies, cloud platforms, and enterprise software stacks, making it a reliable choice for organizations migrating from CentOS or seeking a stable RHEL alternative.

Security remains a top priority in Rocky Linux 10, with regular security updates, SELinux integration, and comprehensive auditing capabilities. The distribution targets system administrators, developers, and data scientists who require a dependable platform for mission-critical applications and data processing workflows.

Article Scope and Objectives

This guide provides a complete installation walkthrough for Anaconda on Rocky Linux 10, covering everything from initial system preparation to advanced configuration options. The step-by-step instructions ensure successful installation regardless of your Linux experience level, while troubleshooting sections address common issues that may arise during the process.

Each section includes detailed explanations, command examples, and best practices derived from real-world experience with enterprise Linux deployments. The content follows established security principles and industry standards, ensuring your Anaconda installation remains secure, maintainable, and optimized for performance.

Prerequisites and System Requirements

Hardware Requirements

Before beginning the Anaconda installation on Rocky Linux 10, verify that your system meets the minimum hardware specifications. The installation requires at least 4GB of RAM, though 8GB or more is strongly recommended for optimal performance when working with large datasets or running multiple Jupyter notebooks simultaneously.

Disk space requirements vary depending on the installation type and intended usage. A full Anaconda installation typically consumes approximately 5GB of disk space, but you should allocate at least 10GB to accommodate additional packages, environments, and data files. Consider using SSD storage for improved performance during package installations and data processing operations.

The CPU architecture must support x86_64 instructions, which includes most modern Intel and AMD processors. Network connectivity is essential for downloading the installer and accessing package repositories, though offline installation options exist for air-gapped environments.

Software Prerequisites

Rocky Linux 10 must be installed and fully updated before proceeding with the Anaconda installation. Ensure your system includes the latest security patches and package updates by running system update commands. Root privileges or sudo access is required for installing system dependencies and modifying system configurations.

Terminal access, either through SSH for remote installations or local console access, is necessary for executing installation commands. If working on a remote server, verify that your SSH connection remains stable throughout the installation process, as interruptions can corrupt the installation.

Package management tools including DNF (Dandified YUM) should be functional and properly configured. Verify that your system can access external package repositories and download files from the internet, as these capabilities are essential for both dependency installation and Anaconda download.

Pre-Installation Checklist

Perform a comprehensive system check before starting the installation process. Update all system packages using dnf update and restart the system if kernel updates were installed. This ensures compatibility with Anaconda’s dependencies and prevents potential conflicts.

Check available disk space using the df -h command, paying particular attention to the /home directory where Anaconda will be installed by default. Ensure sufficient space exists not only for the initial installation but also for future package installations and data storage requirements.

Test network connectivity by attempting to download a small file from the Anaconda repository. Create a backup of important data and configurations, especially if this is a production system, as a precautionary measure against unexpected issues during installation.

System Preparation and Dependencies

System Updates

Begin by updating your Rocky Linux 10 system to ensure all packages are current and security patches are applied. Execute the following command to update the package database and installed packages:

sudo dnf update -y

This command downloads and installs all available updates for your system. The process may take several minutes depending on the number of updates available and your internet connection speed. Allow the update process to complete fully before proceeding.

System updates are crucial because they resolve potential compatibility issues between Anaconda and existing system libraries. Outdated packages can cause installation failures or runtime errors that are difficult to diagnose. After updates complete, consider restarting the system to ensure all changes take effect, particularly if kernel updates were installed.

Installing Required Dependencies

Anaconda requires several system libraries for proper operation, particularly for graphical applications and GUI components. Install these essential dependencies using the following command:

sudo dnf install libXi libXtst libXrandr libXcursor alsa-lib mesa-libEGL libXcomposite libXScrnSaver libXdamage mesa-libGL -y

Each dependency serves a specific purpose in the Anaconda ecosystem. The libXi and libXtst libraries enable input handling for graphical applications, while libXrandr and libXcursor manage display and cursor functionality. Audio support comes from alsa-lib, and OpenGL rendering is provided by mesa-libEGL and mesa-libGL.

Additional dependencies may be required depending on your intended use case. For example, if you plan to work with scientific visualization libraries, consider installing development tools and additional graphics libraries. The build-essential equivalent on Rocky Linux can be installed with:

sudo dnf groupinstall "Development Tools" -y

User Account Preparation

Create a dedicated non-root user account for Anaconda installation and daily use. Running Anaconda as root poses security risks and can cause permission issues with package installations. Create a new user account with the following commands:

sudo useradd -m anaconda_user
sudo passwd anaconda_user
sudo usermod -aG wheel anaconda_user

The -m flag creates a home directory for the user, while the usermod command adds the user to the wheel group, granting sudo privileges when needed. Switch to this user account for the remainder of the installation process:

su - anaconda_user

Ensure the user’s home directory has sufficient permissions and disk space. The installation will create numerous files and directories within the user’s home folder, so verify that no disk quotas or restrictions would interfere with the installation process.

Downloading Anaconda Installer

Accessing Official Anaconda Repository

Navigate to the official Anaconda repository to download the latest installer for Rocky Linux 10. The Anaconda project maintains a comprehensive archive of installers at https://repo.anaconda.com/archive/, where you can find versions compatible with various Linux distributions and architectures.

Choose the appropriate installer version based on your requirements. The latest stable release typically provides the best balance of features and stability, while specific versions may be required for compatibility with existing projects or organizational standards. Version numbering follows a year.month format, making it easy to identify recent releases.

For production environments, consider using Long Term Support (LTS) versions that receive extended security updates and stability patches. These versions may not include the latest features but offer greater reliability for mission-critical applications and long-term projects.

Download Methods

Download the Anaconda installer using command-line tools for consistency and automation capabilities. The wget command provides reliable download functionality with resume capabilities and progress monitoring:

cd /tmp
wget -O anaconda.sh https://repo.anaconda.com/archive/Anaconda3-2025.06-0-Linux-x86_64.sh

The -O flag specifies the output filename, making it easier to reference the installer in subsequent commands. Downloading to the /tmp directory prevents cluttering your home directory and ensures the installer is automatically cleaned up during system maintenance.

Alternative download methods include using curl for environments where wget is not available:

curl -o anaconda.sh https://repo.anaconda.com/archive/Anaconda3-2025.06-0-Linux-x86_64.sh

For systems with limited command-line access, you can download the installer using a web browser and transfer it to the target system using SCP or other file transfer methods.

Verification and Integrity Checks

Verify the integrity of the downloaded installer to ensure it hasn’t been corrupted during download or tampered with by malicious actors. Anaconda provides SHA256 checksums for all installer files, available on their download page or through their API.

Calculate the checksum of your downloaded file using the following command:

sha256sum anaconda.sh

Compare the output with the official checksum provided by Anaconda. If the checksums match, the file is intact and safe to use. If they don’t match, delete the file and download it again, as corruption may have occurred during transfer.

For enhanced security, particularly in enterprise environments, consider verifying GPG signatures if available. This additional verification step ensures the installer was created by Anaconda and hasn’t been modified by unauthorized parties.

Installation Process

Running the Installer

Execute the Anaconda installer with appropriate permissions and options. The basic installation command launches an interactive installer that guides you through the configuration process:

bash anaconda.sh

The interactive installer presents the license agreement first, which you must accept to proceed. Read through the terms carefully, particularly in commercial environments where licensing compliance is crucial. Press Enter to scroll through the license text, then type “yes” to accept the terms.

During installation, you’ll be prompted to specify the installation directory. The default location is $HOME/anaconda3, which is suitable for most single-user installations. For custom installations or multi-user environments, you may specify an alternative path:

bash anaconda.sh -p /opt/anaconda3

The installer displays progress information as it extracts and installs packages. This process typically takes 5-15 minutes depending on system performance and storage speed. Avoid interrupting the installation process, as this can leave your system in an inconsistent state.

Installation Options and Configuration

Advanced installation options provide greater control over the Anaconda installation process. Silent installation mode eliminates interactive prompts, making it ideal for automated deployments and scripted installations:

bash anaconda.sh -b -p $HOME/anaconda3

The -b flag enables batch mode, while -p specifies the installation prefix. This combination allows for completely automated installations that can be integrated into configuration management systems or deployment scripts.

For multi-user installations, consider installing Anaconda to a shared location such as /opt/anaconda3 with appropriate permissions. This approach allows multiple users to access the same Anaconda installation while maintaining separate environments and packages:

sudo bash anaconda.sh -b -p /opt/anaconda3
sudo chmod -R 755 /opt/anaconda3

Custom installation locations should be added to the system PATH and may require additional configuration for proper operation. Document any custom installation paths for future reference and maintenance procedures.

Initialization Options

Anaconda initialization configures your shell environment to work seamlessly with conda commands and environments. During installation, you’ll be prompted whether to initialize Anaconda. Accepting this option modifies your .bashrc file to include conda initialization code.

Manual initialization provides more control over when and how conda activates. If you declined automatic initialization during installation, you can manually initialize conda later:

~/anaconda3/bin/conda init bash

This command adds conda initialization code to your .bashrc file, enabling conda commands in new shell sessions. For immediate activation in your current session, source the updated configuration:

source ~/.bashrc

Advanced users may prefer to initialize conda selectively or use environment modules for managing different software stacks. This approach prevents conflicts between different Python installations and provides greater flexibility in development environments.

Post-Installation Configuration

Environment Activation

After installation completes, activate the conda environment to verify proper installation and begin using Anaconda features. The conda base environment becomes available automatically if initialization was performed during installation. Restart your terminal session or source the updated configuration:

source ~/.bashrc

Verify that conda is available and properly configured by checking the version and installation information:

conda --version
conda info

The conda info command displays comprehensive information about your conda installation, including version numbers, channel configurations, and environment paths. This information is valuable for troubleshooting and verifying that all components installed correctly.

If conda commands are not recognized, check that the installation directory is included in your PATH environment variable. You can manually add the conda binary directory to your PATH:

export PATH="$HOME/anaconda3/bin:$PATH"

Initial Configuration

Configure conda channels to access the packages you need for your projects. Conda channels are repositories that host packages, and adding appropriate channels ensures access to the latest versions of scientific libraries and tools:

conda config --add channels conda-forge
conda config --add channels bioconda
conda config --set channel_priority strict

The conda-forge channel provides community-maintained packages with frequent updates and broad compatibility. Bioconda specializes in bioinformatics packages, while setting strict channel priority ensures consistent package resolution and reduces conflicts.

Configure package caching to optimize download performance and reduce bandwidth usage. Conda caches downloaded packages by default, but you can adjust cache settings based on available disk space and usage patterns:

conda config --set pkgs_dirs ~/anaconda3/pkgs
conda clean --all

Network and proxy configurations may be necessary in corporate environments. Configure conda to work with corporate proxies and SSL certificates:

conda config --set proxy_servers.http http://proxy.company.com:8080
conda config --set proxy_servers.https https://proxy.company.com:8080

Creating First Environment

Create isolated conda environments for different projects to prevent package conflicts and maintain reproducible development environments. A well-structured approach to environment management is essential for productive data science and development workflows:

conda create --name myproject python=3.11 numpy pandas matplotlib jupyter

This command creates a new environment named “myproject” with Python 3.11 and essential data science packages. Activate the environment to begin working:

conda activate myproject

Environment management best practices include using descriptive names that reflect project purposes, maintaining environment files for reproducibility, and regularly updating packages within environments. Export environment specifications for sharing with team members:

conda env export > environment.yml

Deactivate environments when switching between projects to maintain clean separation between different development contexts:

conda deactivate

Verification and Testing

Installation Verification

Comprehensive verification ensures that Anaconda installed correctly and all components function as expected. Begin with basic conda functionality tests to verify package management capabilities:

conda info --envs
conda list
conda search numpy

These commands display available environments, installed packages in the current environment, and search capabilities respectively. Successful execution indicates that basic conda functionality is working correctly.

Test package installation capabilities by installing a simple package and verifying its functionality:

conda install scipy
python -c "import scipy; print(scipy.__version__)"

This test verifies that conda can download, install, and configure packages properly, and that Python can import and use the installed libraries. Package installation should complete without errors, and the version information should display correctly.

Verify that essential development tools are available and functional:

jupyter --version
ipython --version
python --version

Each command should return version information without errors. These tools form the foundation of most data science and development workflows, so their proper operation is crucial for productive use of Anaconda.

Functionality Testing

Create comprehensive tests that exercise key Anaconda functionality across different use cases. Start with a simple Python script that imports and uses major scientific libraries:

# test_anaconda.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Test NumPy
arr = np.array([1, 2, 3, 4, 5])
print(f"NumPy array: {arr}")
print(f"Array mean: {np.mean(arr)}")

# Test Pandas
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
print(f"DataFrame:\n{df}")

# Test Matplotlib
plt.figure(figsize=(6, 4))
plt.plot(arr, arr**2)
plt.title("Test Plot")
plt.savefig("test_plot.png")
print("Plot saved successfully")

Execute this test script to verify that all major components work together correctly:

python test_anaconda.py

Launch Jupyter Notebook to test the interactive development environment:

jupyter notebook --no-browser --port=8888

For headless servers, the --no-browser flag prevents attempts to launch a web browser. Access the notebook interface through the URL provided in the terminal output, typically http://localhost:8888.

Performance and Resource Usage

Monitor system resource usage during typical Anaconda operations to ensure optimal performance and identify potential bottlenecks. Use system monitoring tools to observe CPU, memory, and disk usage:

htop
df -h
du -sh ~/anaconda3

The Anaconda installation typically consumes 3-5GB of disk space initially, with additional space required for environments and cached packages. Memory usage varies significantly based on the applications and datasets being processed.

Benchmark package installation performance to establish baseline expectations for future operations:

time conda install scikit-learn

Package installation speed depends on network bandwidth, disk performance, and package complexity. Document typical installation times for reference during troubleshooting and capacity planning.

Best Practices and Security

Security Considerations

Implement robust security practices to protect your Anaconda installation and the data it processes. Always run Anaconda as a non-root user to minimize the impact of potential security vulnerabilities and prevent unauthorized system modifications.

Configure file permissions carefully, especially for shared installations. Ensure that conda executables have appropriate permissions while preventing unauthorized modifications:

chmod 755 ~/anaconda3/bin/conda
find ~/anaconda3 -type f -name "*.py" -exec chmod 644 {} \;

Network security becomes critical when working with sensitive data or in corporate environments. Configure conda to use secure connections and verify SSL certificates:

conda config --set ssl_verify true
conda config --set channel_alias https://conda.anaconda.org

Regular security updates protect against newly discovered vulnerabilities. Establish a routine for updating both the conda package manager and installed packages:

conda update conda
conda update --all

Maintenance and Updates

Develop a systematic approach to maintaining your Anaconda installation that balances stability with access to new features and security patches. Update the conda package manager regularly to ensure compatibility with the latest package formats and security protocols:

conda update -n base conda

Schedule regular environment maintenance to remove unused packages and update existing ones. Clean package caches periodically to reclaim disk space:

conda clean --packages --tarballs --source-cache

Maintain documentation of your environments and configurations to facilitate troubleshooting and disaster recovery. Export environment specifications regularly and store them in version control systems:

conda env export --name myproject > environments/myproject.yml

Monitor conda’s official channels for announcements about security updates, deprecations, and major version releases that may require attention or planning for migration.

Performance Optimization

Optimize conda configuration for your specific use cases and system characteristics. Configure parallel downloads to speed up package installations on systems with sufficient bandwidth:

conda config --set default_threads 4

Prioritize faster package sources and mirrors to reduce download times. Many organizations maintain internal conda mirrors that provide faster access and additional security:

conda config --add channels https://internal-mirror.company.com/conda-forge

Optimize package cache settings based on available disk space and usage patterns. Larger caches reduce download times but consume more storage:

conda config --set pkgs_dirs ~/anaconda3/pkgs ~/.conda/pkgs

Consider using mamba as a drop-in replacement for conda for significantly faster dependency resolution and package installation:

conda install mamba -n base -c conda-forge
mamba install numpy pandas matplotlib

Troubleshooting Common Issues

Installation Failures

Installation failures often stem from insufficient permissions, inadequate disk space, or network connectivity issues. Verify that you have sufficient privileges to write to the installation directory and that at least 10GB of free space is available.

Permission-related errors frequently occur when attempting to install to system directories without appropriate privileges. Use sudo only when necessary and prefer user-space installations:

# Wrong - avoid installing as root
sudo bash anaconda.sh

# Correct - install in user space
bash anaconda.sh -p $HOME/anaconda3

Network connectivity problems can interrupt downloads and cause partial installations. Test connectivity to Anaconda’s servers and configure proxy settings if necessary:

curl -I https://repo.anaconda.com/archive/
conda config --set proxy_servers.http http://proxy:8080

Dependency conflicts may arise on systems with existing Python installations or conflicting packages. Remove conflicting packages or use virtual environments to isolate installations:

# Remove conflicting Python packages
sudo dnf remove python3-pip python3-numpy

Post-Installation Issues

PATH environment variable problems represent the most common post-installation issue. Verify that conda’s binary directory appears in your PATH and takes precedence over system Python installations:

echo $PATH
which python
which conda

If conda commands are not found, manually add the installation directory to your PATH:

echo 'export PATH="$HOME/anaconda3/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

Shell integration failures prevent conda from activating environments properly. Reinitialize conda for your shell if environment activation commands fail:

conda init bash
source ~/.bashrc

Package installation errors within environments often indicate channel configuration issues or network problems. Verify channel configurations and test connectivity:

conda config --show channels
conda config --set channel_priority strict
conda update conda

System Integration Problems

Conflicts between conda’s Python and system Python installations can cause import errors and application failures. Use conda environments consistently and avoid mixing package managers:

# Check which Python is being used
which python
python -c "import sys; print(sys.executable)"

# Ensure conda environment is active
conda activate base

Package manager conflicts arise when using pip within conda environments. Use conda for package installation whenever possible, and use pip only for packages unavailable through conda:

# Preferred method
conda install scikit-learn

# Use only if package unavailable through conda
pip install some-package

Library compatibility issues may occur with compiled packages that depend on specific system libraries. Install development tools and required libraries:

sudo dnf groupinstall "Development Tools"
sudo dnf install lapack-devel blas-devel

Resolution strategies for complex conflicts include creating fresh environments, using explicit package versions, and consulting conda’s conflict resolution output for guidance on dependency issues.

Advanced Configuration

Multi-User Setup

Configure Anaconda for multiple users by installing to a shared location and managing permissions appropriately. This approach reduces disk space usage and simplifies administration while maintaining user isolation:

sudo bash anaconda.sh -b -p /opt/anaconda3
sudo chown -R root:conda /opt/anaconda3
sudo chmod -R 775 /opt/anaconda3

Create a conda group for users who should have access to the shared installation:

sudo groupadd conda
sudo usermod -aG conda username

Configure user-specific conda environments while sharing the base installation. Each user maintains their own environments directory while accessing shared packages:

export CONDA_ENVS_PATH=$HOME/.conda/envs:/opt/anaconda3/envs
conda config --add envs_dirs $HOME/.conda/envs

Implement administrative procedures for updating the shared installation and managing package conflicts. Designate administrators responsible for base package maintenance and establish policies for package installation requests.

Integration with Development Tools

Integrate Anaconda with popular development environments to enhance productivity and streamline workflows. Configure Visual Studio Code to use conda environments automatically by installing the Python extension and configuring interpreter settings.

Set up Anaconda integration with PyCharm by configuring the Python interpreter to point to your conda installation:

# Add to PyCharm interpreter settings
~/anaconda3/envs/myproject/bin/python

Container deployment considerations become important when deploying applications developed with Anaconda. Create Docker images that include your conda environment for consistent deployment:

FROM continuumio/miniconda3
COPY environment.yml .
RUN conda env create -f environment.yml
RUN echo "conda activate myenv" >> ~/.bashrc

CI/CD pipeline integration enables automated testing and deployment of Anaconda-based applications. Configure your build systems to use conda for dependency management:

# Example GitHub Actions workflow
steps:
  - uses: conda-incubator/setup-miniconda@v2
    with:
      environment-file: environment.yml
      activate-environment: myproject

Version control integration includes managing environment files, excluding cache directories from repositories, and maintaining reproducible environments across development teams.

Congratulations! You have successfully installed Anaconda. Thanks for using this tutorial for installing Anaconda Python on your Rocky Linux 10 system. For additional help or useful information, we recommend you check the official Anaconda website.