Conda is a cross-platform, language-agnostic binary package manager. It is the package manager used by Anaconda installations, 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. Conda 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.
If you install Anaconda, 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
command. For instance, to create an environment with the IPython notebook and
NumPy 1.6, which is older than the version that comes with Anaconda by
default, you would run
$ conda create -n numpy16 ipython-notebook numpy=1.6
This creates an environment called
numpy16 with the latest version of
the IPython notebook, NumPy 1.6, and their dependencies.
We can now activate this environment, use
# On Linux and Mac OS X $ source activate numpy16 # On Windows > activate numpy16
This puts the bin directory of the
numpy16 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
# On Linux and Mac OS X $ source deactivate # On Windows > 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. See http://github.com/conda/conda-recipes for many example recipes, and http://docs.continuum.io/conda/build.html for documentation on how to build recipes.
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 the channel of others to conda so
conda install will find and install their packages, run
$ conda config --add channels https://conda.anaconda.org/username
username with the user name of the person whose channel you want
Contributions to conda are welcome. Just fork the GitHub repository and send a pull request.
To develop on conda, the easiest way is to use a development build. This can be accomplished as follows:
- clone the conda git repository to a computer with conda already installed
- navigate to the root directory of the git clone
$CONDA/bin/python setup.py developwhere
$CONDAis the path to your miniconda installation
Note building a development file requires git to be installed.
To undo this, run
$CONDA/bin/python setup.py develop -u. Note that if you
used a python other than
$CONDA/bin/python to install, you may have to manually
delete the conda executable. For example, on OS X, if you use a homebrew python
/usr/local/bin/python, then you'll need to
which -a conda lists first your miniconda installation.
If you are worried about breaking your conda installation, you can install a separate instance of Miniconda and work off it. This is also the only way to test conda in both Python 2 and Python 3, as conda can only be installed into a base environment.
To run the tests, set up a testing environment by running
$CONDA/bin/python -m pip install -r utils/requirements-test.txt.
$CONDA/bin/python utils/setup-testing.py develop
and then running
py.test in the conda directory. You can also run tests using the
Makefile by running
make smoketest (a single integration test), or
make integration. The tests are also run by various CI systems when you make a