Conda is a cross-platform, language-agnostic binary package manager. It is a package manager used in conda distributions like Miniforge and the Anaconda Distribution, but it may be used for other systems as well. Conda makes environments first-class citizens, making it easy to create independent environments even for C libraries. The conda command line interface is written entirely in Python, and is BSD licensed open source.

Conda is enhanced by organizations, tools, and repositories created and managed by the amazing members of the conda community. Some of them can be found here.

Installation

To bootstrap a minimal distribution, use a minimal installer such as Miniconda or Miniforge.

Conda is also included in the Anaconda Distribution.

Updating conda

To update conda to the newest version, use the following command:

$ conda update -n base conda

Tip

It is possible that conda update does not install the newest version if the existing conda version is far behind the current release. In this case, updating needs to be done in stages.

For example, to update from conda 4.12 to conda 23.10.0, conda 22.11.1 needs to be installed first:

$ conda install -n base conda=22.11.1
$ conda update conda

Getting Started

If you install the Anaconda Distribution, you will already have hundreds of packages installed. You can see what packages are installed by running:

$ conda list

to see all the packages that are available, use:

$ conda search

and to install a package, use

$ conda install <package-name>

The real power of conda comes from its ability to manage environments. In conda, an environment can be thought of as a completely separate installation. Conda installs packages into environments efficiently using hard links by default when it is possible, so environments are space efficient, and take seconds to create.

The default environment, which conda itself is installed into, is called base. To create another environment, use the conda create command. For instance, to create an environment with PyTorch, you would run:

$ conda create --name ml-project pytorch

This creates an environment called ml-project with the latest version of PyTorch, and its dependencies.

We can now activate this environment:

$ conda activate ml-project

This puts the bin directory of the ml-project environment in the front of the PATH, and sets it as the default environment for all subsequent conda commands.

To go back to the base environment, use:

$ conda deactivate

Building Your Own Packages

You can easily build your own packages for conda, and upload them to anaconda.org, a free service for hosting packages for conda, as well as other package managers. To build a package, create a recipe. Package building documentation is available here. See AnacondaRecipes for the recipes that make up the Anaconda Distribution and defaults channel. Conda-forge and Bioconda are community-driven conda-based distributions.

To upload to anaconda.org, create an account. Then, install the anaconda-client and login:

$ conda install anaconda-client
$ anaconda login

Then, after you build your recipe:

$ conda build <recipe-dir>

you will be prompted to upload to anaconda.org.

To add your anaconda.org channel, or other's channels, to conda so that conda install will find and install their packages, run:

$ conda config --add channels https://conda.anaconda.org/username

(replacing username with the username of the person whose channel you want to add).

Getting Help

• Documentation
• Twitter
• Slack
• Bug Reports/Feature Requests
• Installer/Package Issues
• Discourse

Contributing

Contributions to conda are welcome. See the contributing documentation for instructions on setting up a development environment.