Conda managing Julia binary dependencies
111 Stars
Updated Last
2 Years Ago
Started In
August 2015


Build Status -- OS X and Linux Build status -- Windows

This package allows one to use conda as a cross-platform binary provider for Julia for other Julia packages, especially to install binaries that have complicated dependencies like Python.

conda is a package manager which started as the binary package manager for the Anaconda Python distribution, but it also provides arbitrary packages. Instead of the full Anaconda distribution, Conda.jl uses the miniconda Python environment, which only includes conda and its dependencies.

Basic functionality

At the julia> prompt, type a ] (close square bracket) to get a Julia package prompt pkg>, where you can type add Conda to install this package.

Once Conda is installed, you can run import Conda to load the package and run a variety of package-management functions:

  • Conda.add(package, env; channel=""): install a package from a specified channel (optional);
  • Conda.rm(package, env): remove (uninstall) a package;
  • Conda.update(env): update all installed packages to the latest version;
  • Conda.list(env): list all installed packages.
  • Conda.add_channel(channel, env): add a channel to the list of channels;
  • Conda.channels(env): get the current list of channels;
  • Conda.rm_channel(channel, env): remove a channel from the list of channels;
  • experimental: read the section Conda and pip below before using the following
    • Conda.pip_interop(bool, env): config environment to interact with pip
    • Conda.pip(command, package, env): run pip command on packages in environment

The parameter env is optional and defaults to ROOTENV. See below for more info.

Conda environments

Conda environments allow you to manage multiple distinct sets of packages in a way that avoids conflicts and allows you to install different versions of packages simultaneously.

The Conda.jl package supports environments by allowing you to pass an optional env parameter to functions for package installation, update, and so on. If this parameter is not specified, then the default "root" environment (corresponding to the path in Conda.ROOTENV) is used. The environment name can be specified as a Symbol, or the full path of the environment (if you want to use an environment in a nonstandard directory) can be passed as a string.

For example:

using Conda
Conda.add("libnetcdf", :my_env)
Conda.add("libnetcdf", "/path/to/directory")
Conda.add("libnetcdf", "/path/to/directory"; channel="anaconda")

(NOTE: If you are installing Python packages for use with PyCall, you must use the root environment.)

BinDeps integration: using Conda.jl as a package author

Conda.jl can be used as a Provider for BinDeps with the CondaBinDeps package.

Using a pre-existing Conda installation

To use a pre-existing Conda installation, first create an environment for Conda.jl and then set the CONDA_JL_HOME environment variable to the full path of the environment. (You have to rebuild Conda.jl and many of the packages that use it after this.) In Julia, run:

julia> run(`conda create -n conda_jl python conda`)

julia> ENV["CONDA_JL_HOME"] = "/path/to/miniconda/envs/conda_jl"  # change this to your path

pkg> build Conda

Conda and pip

As of conda 4.6.0 there is improved support for PyPi packages. Conda is still the recommended installation method however if there are packages that are only availible with pip one can do the following:

julia> Conda.pip_interop(true, env)

julia> Conda.pip("install", "somepackage")

julia> Conda.pip("install", ["somepackage1", "somepackage2"])

julia> Conda.pip("uninstall", "somepackage")

julia> Conda.pip("uninstall", ["somepackage1", "somepackage2])

If the uninstall command is to be used noninteractively, one can use "uninstall -y" to answer yes to the prompts.

Using Python 2

By default, the Conda.jl package installs Python 3, and this version of Python is used for all Python dependencies. If you want to use Python 2 instead, set CONDA_JL_VERSION to "2" prior to installing Conda. (This only needs to be done once; Conda subsequently remembers the version setting.)

Once you have installed Conda and run its Miniconda installer, the Python version cannot be changed without deleting your existing Miniconda installation. If you set ENV["CONDA_JL_VERSION"]="2" and run"Conda"), it will tell you how to delete your existing Miniconda installation if needed.

Most users will not need to use Python 2. This is provided primarily for developers wishing to test their packages for both Python 2 and Python, e.g. by setting the CONDA_JL_VERSION variable on TravisCI and/or AppVeyor.

Using Miniforge

Miniforge is a community based conda installer by conda-forge, a part of NumFOCUS. Using miniforge and conda-forge in general avoids using maintained by Anaconda, Inc which has terms of conditions that you may want to avoid. conda-forge packages are hosted on, but Anaconda, Inc has been providing hosting for free under the terms of conda-forge which is BSD-3-Clause on top of the original license of the software packages. To use miniforge, use the CONDA_JL_USE_MINIFORGE environment variable.


pkg> build Conda

Also note that you have to use Miniforge for aarch64-linux-gnu and aarch64-apple-darwin platforms as Miniconda is not available for those platforms yet.

Bugs and suggestions

Conda has been tested on Linux, OS X, and Windows.

Please report any bug or suggestion as an github issue


The Conda.jl package is licensed under the MIT Expat license, and is copyrighted by Guillaume Fraux and contributors.