Preface
Conda is an open source package management system and environment management system, widely used in the fields of data science and machine learning. This article will introduce in detail how to install Conda on CentOS system to help you quickly build a development environment.
Preparation
Before starting the installation, make sure your CentOS system meets the following conditions:
- Connected to the Internet
- Have sudo permissions
- The system has been installed wget and bash
Installation steps
1. Download the Miniconda installation script
wget /miniconda/Miniconda3-latest-Linux-x86_64.sh
2. Run the installation script
bash Miniconda3-latest-Linux-x86_64.sh
3. Read and agree to the license agreement
During the installation process, you will see the license agreement. Follow the prompts to enter yes to agree to the agreement.
4. Select the installation location
You will be asked where to install Miniconda. The default location is usually the user's home directory, such as ~/miniconda3. You can press Enter to accept the default location, or specify another path.
5. Initialize Conda
The installation script asks whether to initialize Miniconda3. It is recommended to select yes, which will automatically add Conda to your PATH.
6. Activate the installation
source ~/.bashrc
7. Verify the installation
conda --version
Common Conda commands
Create a new environment: conda create --name myenv python=3.8
Activate environment: conda activate myenv
View existing environment: conda env list
Installation package: conda install numpy
Things to note
It is recommended to update conda regularly.
Use conda update --all command with caution
Specify the Python version when creating a virtual environment
Advanced Usage
Create different Python version environments
conda create -n py38 python=3.8 conda create -n py39 python=3.9 conda create -n py310 python=3.10
View dependency tree
conda list --explicit
Resolve dependency conflicts
conda install --no-deps packagename
Install Mamba
conda install -c conda-forge mamba
Use Mamba instead of Conda
mamba create -n fastenv python=3.9 numpy pandas
Add Tsinghua mirror
conda config --add channels /anaconda/pkgs/main/ conda config --add channels /anaconda/pkgs/free/
Export the complete environment
conda env export >
Create an environment from a YAML file
conda env create -f
Cross-platform environment export
conda env export --from-history >
Disable automatic activation of base environment
conda config --set auto_activate_base false
Set the number of concurrent downloads
conda config --set download_threads 5
Configure cache directory
conda config --set pkgs_dirs /path/to/conda/packages
Dockerfile example
FROM continuumio/miniconda3 # Copy the environment fileCOPY /tmp/ RUN conda env create -f /tmp/
Using Conda in Google Colab
!pip install conda
Manage your environment in Jupyter Lab
!conda install -c conda-forge jupyterlab
Check the environment status
conda info conda list conda doctor
Clean up unused packages and caches
conda clean -a
GitHub Actions Example
name: Conda Environment
on: [push]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: conda-incubator/setup-miniconda@v2
with:
auto-update-conda: true
python-version: 3.9
- run: conda env create -f
- run: conda run -n myenv pytest
Safety advice
Conda and packages are updated regularly
Isolate project dependencies using virtual environment
Avoid installing packages directly in the base environment
Update with --no-pin with caution
Common Traps and Solutions
Dependency conflict handling
Use conda list --revisions backtracking
Create a new environment instead of modifying an existing environment
Priority to using conda-forge channels
Learning Resources
- Conda official documentation
- Anaconda Knowledge Base
- Real Python Conda Tutorial
Conclusion
Conda is not only a package manager, but also an infrastructure for modern Python development. Mastering its advanced usage will greatly improve your development efficiency and project management capabilities.
How to use
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