CondaPkg.jl

Add Conda dependencies to your Julia project.

Overview

This package is a lot like Pkg from the Julia standard library, except that it is for managing Conda packages.

• Conda dependencies are defined in CondaPkg.toml, which is analogous to Project.toml.
• CondaPkg will install these dependencies into a Conda environment specific to the current Julia project. Hence dependencies are isolated from other projects or environments.
• Functions like add, rm, status exist to edit the dependencies programmatically.
• Or you can do pkg> conda add some_package to edit the dependencies from the Pkg REPL.

Install

pkg> add CondaPkg

Specifying dependencies

Pkg REPL

The simplest way to specify Conda dependencies is through the Pkg REPL, just like for Julia dependencies. For example:

julia> using CondaPkg
julia> # now press ] to enter the Pkg REPL
pkg> conda status                # see what we have installed
pkg> conda add python perl       # adds conda packages
pkg> conda pip_add build         # adds pip packages
pkg> conda rm perl               # removes conda packages
pkg> conda run python --version  # runs the given command in the conda environment
pkg> conda update                # update conda and pip installed packages

For more information do ? or ?conda from the Pkg REPL.

Note: We recommend against adding Pip packages unless necessary - if there is a corresponding Conda package then use that. Pip does not handle version conflicts gracefully, so it is possible to get incompatible versions.

Functions

These functions are intended to be used interactively when the Pkg REPL is not available (e.g. if you are in a notebook):

• status() shows the Conda dependencies of the current project.
• add(pkg; version="", channel="") adds/replaces a dependency or a vector of dependencies.
• rm(pkg) removes a dependency or a vector of dependencies.
• add_channel(channel) adds a channel.
• rm_channel(channel) removes a channel.
• add_pip(pkg; version="") adds/replaces a pip dependency.
• rm_pip(pkg) removes a pip dependency.

CondaPkg.toml

Finally, you may edit the CondaPkg.toml file directly. Here is a complete example:

channels = ["anaconda", "conda-forge"]

[deps]
# Conda package names and versions
python = ">=3.5,<4"
pyarrow = "==6.0.0"
perl = ""

[deps.llvmlite]
# Long syntax to specify other fields, such as the channel and build
version = ">=0.38,<0.39"
channel = "numba"
build = "*"

[pip.deps]
# Pip package names and versions
build = "~=0.7.0"
six = ""
some-remote-package = "@ https://example.com/foo.zip"
some-local-package = "@ ./foo.zip"

[pip.deps.pydantic]
# Long syntax to specify other fields
version = "~=2.1"
extras = ["email", "timezone"]
binary = "no"  # or "only"

Access the Conda environment

• envdir() returns the root directory of the Conda environment.
• withenv(f) returns f() evaluated in the Conda environment.
• which(progname) find the program in the Conda environment.
• resolve(; force=false) resolves dependencies. You don't normally need to call this because the other API functions will automatically resolve first. Pass force=true if you change a CondaPkg.toml file mid-session.
• update() update the conda and pip installed packages.
• gc() removes unused caches to save disk space.

Examples

Assuming one of the dependencies in CondaPkg.toml is python then the following runs Python to print its version.

# Simplest version.
CondaPkg.withenv() do
  run(`python --version`)
end
# Guaranteed not to use Python from outside the Conda environment.
CondaPkg.withenv() do
  python = CondaPkg.which("python")
  run(`$python --version`)
end
# Explicitly specifies the path to the executable (this is package-dependent).
CondaPkg.withenv() do
  python = joinpath(CondaPkg.envdir(), Sys.iswindows() ? "python.exe" : "bin/python")
  run(`$python --version`)
end

Details

Conda channels

You can specify the channel to install a particular package from, such as with pkg> conda add some-channel::some-package.

You can also specify a top-level list of channels, from which all other packages are installed, such as with pkg> conda channel_add some-channel.

By default, packages are installed from the conda-forge channel.

Pip packages

Direct references such as pkg> conda pip_add foo@http://example.com/foo.zip are allowed. As a special case if the URL starts with . then it is interpreted as a path relative to the directory containing the CondaPkg.toml file.

The binary mode specifies whether to only use binary distributions ("only") or to never use them ("no").

Extras (also known as optional dependencies) can be installed like pkg> conda pip_add foo[some-extra,another-extra].

Preferences

You can configure this package with a number of preferences. These can be set either as Julia preferences or as environment variables. This table gives an overview of the preferences, and later sections describe them in more detail.

Preference	Environment variable	Description
backend	JULIA_CONDAPKG_BACKEND	One of MicroMamba, System, Current or Null
exe	JULIA_CONDAPKG_EXE	Path to the Conda executable.
offline	JULIA_CONDAPKG_OFFLINE	When true, work in offline mode.
env	JULIA_CONDAPKG_ENV	Path to the Conda environment to use.
verbosity	JULIA_CONDAPKG_VERBOSITY	One of -1, 0, 1 or 2.
pip_backend	JULIA_CONDAPKG_PIP_BACKEND	One of pip or uv.
libstdcxx_ng_version	JULIA_CONDAPKG_LIBSTDCXX_NG_VERSION	Either ignore or a version specifier.
openssl_version	JULIA_CONDAPKG_OPENSSL_VERSION	Either ignore or a version specifier.

The easiest way to set these preferences is with the PreferenceTools package. For example:

julia> using PreferenceTools
julia> # now press ] to enter the Pkg REPL
pkg> preference add CondaPkg backend=System offline=true

Backends

This package has a number of different "backends" which control exactly which implementation of Conda is used to manage the Conda environments. You can explicitly select a backend by setting the backend preference to one of the following values:

• MicroMamba: Uses MicroMamba from the package MicroMamba.jl.
• System: Use a pre-installed Conda. If the exe preference is set, that is used. Otherwise we look for conda, mamba or micromamba in your PATH.
• Current: Use the currently activated Conda environment instead of creating a new one. This backend will only ever install packages, never uninstall. The Conda executable used is the same as for the System backend. Similar to the default behaviour of Conda.jl.
• Null: Don't use CondaPkg to manage dependencies. Use this if you are in a pre-existing Conda environment that already satisfies the dependencies of your project. It is up to you to ensure any required packages are installed.

The default backend is an implementation detail, but is currently MicroMamba.

If you set the exe preference but not the backend preference then the System backend is used.

Offline mode

You may activate "offline mode" by setting the preference offline=true. This will prevent CondaPkg from attempting to download or install new packages. In this case, it is up to you to ensure that any required packages are already available (such as by having previously called CondaPkg.resolve()).

Conda environment path

By default, CondaPkg installs Conda packages into the current project, so that different projects can have different dependencies. If you wish to centralize the Conda environment, you can set one of these preferences:

• env=@<name> for a named shared environment, stored in ~/.julia/conda_environments/<name>.
• env=<some absolute path> for a shared environment at the given path.
• backend=Current to use the currently activated Conda environment.

Warning: If you do this, the versions specified in a per-julia-version CondaPkg.toml can become un-synchronized with the packages installed in the shared Conda environment. In this case, you will have to re-resolve the dependencies using resolve(force=true). This restriction might be alleviated in future CondaPkg versions.

Verbosity

You can control the verbosity of any conda or pip commands executed by setting the verbosity preference to a number:

• -1 is quiet mode.
• 0 is normal mode (the default).
• 1, 2, etc. are verbose modes, useful for debugging.

Pip Backends

You can control which package manager is used to install pip dependencies by setting the pip_backend preference to one of:

• pip
• uv (the default).

Compatibility between Julia and Conda packages

If you use both a Julia package and a Conda package which both use the same underlying shared library, there can be compatibility issues if they are at different versions.

To alleviate this, CondaPkg handles some packages specially. For the following Conda packages, if the version is set to <=julia, then a version of that package compatible with the corresponding Julia package will be installed.

• libstdcxx-ng: Compatible with libstdcxx in Base.
• openssl: Compatible with OpenSSL_jll (if installed).

You can override this behaviour with the libstdcxx_ng_version and openssl_version preferences. These can be set to one of:

• A (non-empty) specific Conda version specifier.
• ignore to ignore the compatibility constraint entirely.
• Unset or the empty string for the default behaviour.

Frequently Asked Questions

Can I get my package to use a specific Conda environment?

No. The location of the Conda environment is configured purely by the user. Letting packages specify this configuration is not composable - if two packages want to set the location of the environment, then they will be in conflict.

Can I make the Pkg REPL command work without using CondaPkg first?

Yes, you can add the following to your startup file (~/.julia/config/startup.jl):

Base.identify_package("CondaPkg") === nothing || Base.require(@__MODULE__, :CondaPkg)

Can I install a package from a URL or file?

Yes, using the "direct reference" @ version syntax. For example in PKG REPL mode

pkg> pip_add some-package@https://example.com/the/url

or using the API

CondaPkg.add_pip("some-package", version="@https://example.com/the/url")

or in CondaPkg.toml

[pip.deps]
"some-package" = "@https://example.com/the/url"