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.
] (close square bracket) to get a Julia package prompt
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
Conda.pip(command, package, env): run
pipcommand on packages in environment
env is optional and defaults to
ROOTENV. See below for more info.
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.
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.
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.)
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
To use a specific conda executable, set the
variable to the location of the conda executable. This conda executable can
exist outside of the environment set by
CONDA_JL_HOME. To apply the settting,
Conda.jl. In Julia, run:
julia> ENV["CONDA_JL_CONDA_EXE"] = "/path/to/miniconda/bin/conda" # change this to the path of the conda executable pkg> build Conda
The use of
CONDA_JL_CONDA_EXE requires at least version 1.7 of Conda.jl.
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.
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
"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
Pkg.build("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
variable on TravisCI and/or AppVeyor.
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
anaconda.org, but Anaconda, Inc has been
providing hosting for free under the terms of
conda-forge which is
on top of the original license of the software packages. To use miniforge, use
CONDA_JL_USE_MINIFORGE environment variable.
julia> ENV["CONDA_JL_USE_MINIFORGE"] = "1" pkg> build Conda
Note that Conda.jl 1.6 and above will use miniforge by default on x86_64, aarch64 and ppc64le systems.
If you have a special character in your user name (like an umlaut or an accent) the installation which defaults to
C:\Users\<username>\.julia\Conda\3 will fail on Windows. A space in your user name will also fail on any platform.
This is a known issue. The work-around is to install Miniconda to a user-writable directory outside of the home directory.
Conda.jl, choose a directory without space and without special characters and set the environment variable
CONDA_JL_HOME as follows inside a julia session:
ENV["CONDA_JL_HOME"] = raw"C:\Conda-Julia\3" using Pkg Pkg.build("Conda")
After restarting Julia, you can verify the new installation directory:
using Conda @show Conda.ROOTENV
If you use
PyCall, they need to be re-build:
using Pkg Pkg.build("PyCall") Pkg.build("IJulia")
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.