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.
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;
The parameter 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.
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.)
Conda.jl can be used as a Provider
for
BinDeps with the
CondaBinDeps
package.
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.
So as to install their dependancies to the specified enviroment.
conda create -n conda_jl python conda
export CONDA_JL_HOME="/path/to/miniconda/envs/conda_jl"
julia -e 'Pkg.build("Conda")'
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 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 CONDA_JL_VERSION
variable on TravisCI and/or AppVeyor.
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.