Project Configuration
The pixi.toml
is the pixi project configuration file, also known as the project manifest.
A toml
file is structured in different tables.
This document will explain the usage of the different tables.
For more technical documentation check pixi on crates.io.
Tip
We also support the pyproject.toml
file. It has the same structure as the pixi.toml
file. except that you need to prepend the tables with tool.pixi
instead of just the table name.
For example, the [project]
table becomes [tool.pixi.project]
.
There are also some small extras that are available in the pyproject.toml
file, checkout the pyproject.toml documentation for more information.
The project
table#
The minimally required information in the project
table is:
name
#
The name of the project.
channels
#
This is a list that defines the channels used to fetch the packages from.
If you want to use channels hosted on anaconda.org
you only need to use the name of the channel directly.
Channels situated on the file system are also supported with absolute file paths:
To access private or public channels on prefix.dev or Quetz use the url including the hostname:
platforms
#
Defines the list of platforms that the project supports.
Pixi solves the dependencies for all these platforms and puts them in the lock file (pixi.lock
).
The available platforms are listed here: link
version
(optional)#
The version of the project. This should be a valid version based on the conda Version Spec. See the version documentation, for an explanation of what is allowed in a Version Spec.
authors
(optional)#
This is a list of authors of the project.
description
(optional)#
This should contain a short description of the project.
license
(optional)#
The license as a valid SPDX string (e.g. MIT AND Apache-2.0)
license-file
(optional)#
Relative path to the license file.
readme
(optional)#
Relative path to the README file.
homepage
(optional)#
URL of the project homepage.
repository
(optional)#
URL of the project source repository.
documentation
(optional)#
URL of the project documentation.
conda-pypi-map
(optional)#
Mapping of channel name or URL to location of mapping that can be URL/Path.
Mapping should be structured in json
format where conda_name
: pypi_package_name
.
Example:
If conda-forge
is not present in conda-pypi-map
pixi
will use prefix.dev
mapping for it.
conda-pypi-map = { "conda-forge" = "https://example.com/mapping", "https://repo.prefix.dev/robostack" = "local/robostack_mapping.json"}
channel-priority
(optional)#
This is the setting for the priority of the channels in the solver step.
Options:
-
strict
: Default, The channels are used in the order they are defined in thechannels
list. Only packages from the first channel that has the package are used. This ensures that different variants for a single package are not mixed from different channels. Using packages from different incompatible channels likeconda-forge
andmain
can lead to hard to debug ABI incompatibilities.We strongly recommend not to switch the default. -
disabled
: There is no priority, all package variants from all channels will be set per package name and solved as one. Care should be taken when using this option. Since package variants can come from any channel when you use this mode, packages might not be compatible. This can cause hard to debug ABI incompatibilities.We strongly discourage using this option.
channel-priority = "disabled"
is a security risk
Disabling channel priority may lead to unpredictable dependency resolutions. This is a possible security risk as it may lead to packages being installed from unexpected channels. It's advisable to maintain the default strict setting and order channels thoughtfully. If necessary, specify a channel directly for a dependency.
The tasks
table#
Tasks are a way to automate certain custom commands in your project.
For example, a lint
or format
step.
Tasks in a pixi project are essentially cross-platform shell commands, with a unified syntax across platforms.
For more in-depth information, check the Advanced tasks documentation.
Pixi's tasks are run in a pixi environment using pixi run
and are executed using the deno_task_shell
.
[tasks]
simple = "echo This is a simple task"
cmd = { cmd="echo Same as a simple task but now more verbose"}
depending = { cmd="echo run after simple", depends-on="simple"}
alias = { depends-on=["depending"]}
download = { cmd="curl -o file.txt https://example.com/file.txt" , outputs=["file.txt"]}
build = { cmd="npm build", cwd="frontend", inputs=["frontend/package.json", "frontend/*.js"]}
run = { cmd="python run.py $ARGUMENT", env={ ARGUMENT="value" }}
clean-env = { cmd = "python isolated.py", clean-env = true} # Only on Unix!
You can modify this table using pixi task
.
Note
Specify different tasks for different platforms using the target table
Info
If you want to hide a task from showing up with pixi task list
or pixi info
, you can prefix the name with _
.
For example, if you want to hide depending
, you can rename it to _depending
.
The system-requirements
table#
The system requirements are used to define minimal system specifications used during dependency resolution. For example, we can define a unix system with a specific minimal libc version. This will be the minimal system specification for the project. System specifications are directly related to the virtual packages.
Currently, the specified defaults are the same as conda-lock's implementation:
Only if a project requires a different set should you define them.
For example, when installing environments on old versions of linux. You may encounter the following error:
× The current system has a mismatching virtual package. The project requires '__linux' to be at least version '5.10' but the system has version '4.12.14'
This suggests that the system requirements for the project should be lowered. To fix this, add the following table to your configuration:
Using Cuda in pixi#
If you want to use cuda
in your project you need to add the following to your system-requirements
table:
This informs the solver that cuda is going to be available, so it can lock it into the lock file if needed.
The pypi-options
table#
The pypi-options
table is used to define options that are specific to PyPI registries.
These options can be specified either at the root level, which will add it to the default options feature, or on feature level, which will create a union of these options when the features are included in the environment.
Alternative registries#
Currently the main reason to use this table is to define alternative registries. We support:
index-url
: replaces the main index url. Only oneindex-url
can be defined per environment.extra-index-urls
: adds an extra index url.find-links
: which can either be a path{path = './links'}
or a url{url = 'https://example.com/links'}
. This is similar to the--find-links
option inpip
.
An example:
[pypi-options]
index-url = "https://pypi.org/simple"
extra-index-urls = ["https://example.com/simple"]
find-links = [{path = './links'}]
There are some examples in the pixi repository that make use of this feature. To read about existing authentication methods, please check the PyPI Authentication section.
Strict Index Priority
Unlike pip, because we make use of uv, we have a strict index priority. This means that the first index is used where a package can be found.
The order is determined by the order in the toml file. Where the extra-index-urls
are preferred over the index-url
. Read more about this on the UV Readme
The dependencies
table(s)#
This section defines what dependencies you would like to use for your project.
There are multiple dependencies tables.
The default is [dependencies]
, which are dependencies that are shared across platforms.
Dependencies are defined using a VersionSpec.
A VersionSpec
combines a Version with an optional operator.
Some examples are:
# Use this exact package version
package0 = "1.2.3"
# Use 1.2.3 up to 1.3.0
package1 = "~=1.2.3"
# Use larger than 1.2 lower and equal to 1.4
package2 = ">1.2,<=1.4"
# Bigger or equal than 1.2.3 or lower not including 1.0.0
package3 = ">=1.2.3|<1.0.0"
Dependencies can also be defined as a mapping where it is using a matchspec:
package0 = { version = ">=1.2.3", channel="conda-forge" }
package1 = { version = ">=1.2.3", build="py34_0" }
Tip
The dependencies can be easily added using the pixi add
command line.
Running add
for an existing dependency will replace it with the newest it can use.
Note
To specify different dependencies for different platforms use the target table
dependencies
#
Add any conda package dependency that you want to install into the environment.
Don't forget to add the channel to the project table should you use anything different than conda-forge
.
Even if the dependency defines a channel that channel should be added to the project.channels
list.
[dependencies]
python = ">3.9,<=3.11"
rust = "1.72"
pytorch-cpu = { version = "~=1.1", channel = "pytorch" }
pypi-dependencies
#
Details regarding the PyPI integration
We use uv
, which is a new fast pip replacement written in Rust.
We integrate uv as a library, so we use the uv resolver, to which we pass the conda packages as 'locked'. This disallows uv from installing these dependencies itself, and ensures it uses the exact version of these packages in the resolution. This is unique amongst conda based package managers, which usually just call pip from a subprocess.
The uv resolution is included in the lock file directly.
Pixi directly supports depending on PyPI packages, the PyPA calls a distributed package a 'distribution'. There are Source and Binary distributions both of which are supported by pixi. These distributions are installed into the environment after the conda environment has been resolved and installed. PyPI packages are not indexed on prefix.dev but can be viewed on pypi.org.
Important considerations
- Stability: PyPI packages might be less stable than their conda counterparts. Prefer using conda packages in the
dependencies
table where possible. - Compatibility limitation: Currently, pixi doesn't support private PyPI repositories
Version specification:#
These dependencies don't follow the conda matchspec specification.
The version
is a string specification of the version according to PEP404/PyPA.
Additionally, a list of extra's can be included, which are essentially optional dependencies.
Note that this version
is distinct from the conda MatchSpec type.
See the example below to see how this is used in practice:
[dependencies]
# When using pypi-dependencies, python is needed to resolve pypi dependencies
# make sure to include this
python = ">=3.6"
[pypi-dependencies]
fastapi = "*" # This means any version (the wildcard `*` is a pixi addition, not part of the specification)
pre-commit = "~=3.5.0" # This is a single version specifier
# Using the toml map allows the user to add `extras`
pandas = { version = ">=1.0.0", extras = ["dataframe", "sql"]}
# git dependencies
# With ssh
flask = { git = "ssh://git@github.com/pallets/flask" }
# With https and a specific revision
requests = { git = "https://github.com/psf/requests.git", rev = "0106aced5faa299e6ede89d1230bd6784f2c3660" }
# TODO: will support later -> branch = '' or tag = '' to specify a branch or tag
# You can also directly add a source dependency from a path, tip keep this relative to the root of the project.
minimal-project = { path = "./minimal-project", editable = true}
# You can also use a direct url, to either a `.tar.gz` or `.zip`, or a `.whl` file
click = { url = "https://github.com/pallets/click/releases/download/8.1.7/click-8.1.7-py3-none-any.whl" }
# You can also just the default git repo, it will checkout the default branch
pytest = { git = "https://github.com/pytest-dev/pytest.git"}
Full specification#
The full specification of a PyPI dependencies that pixi supports can be split into the following fields:
extras
#
A list of extras to install with the package. e.g. ["dataframe", "sql"]
The extras field works with all other version specifiers as it is an addition to the version specifier.
pandas = { version = ">=1.0.0", extras = ["dataframe", "sql"]}
pytest = { git = "URL", extras = ["dev"]}
black = { url = "URL", extras = ["cli"]}
minimal-project = { path = "./minimal-project", editable = true, extras = ["dev"]}
version
#
The version of the package to install. e.g. ">=1.0.0"
or *
which stands for any version, this is pixi specific.
Version is our default field so using no inline table ({}
) will default to this field.
git
#
A git repository to install from. This support both https:// and ssh:// urls.
Use git
in combination with rev
or subdirectory
:
rev
: A specific revision to install. e.g.rev = "0106aced5faa299e6ede89d1230bd6784f2c3660
subdirectory
: A subdirectory to install from.subdirectory = "src"
orsubdirectory = "src/packagex"
# Note don't forget the `ssh://` or `https://` prefix!
pytest = { git = "https://github.com/pytest-dev/pytest.git"}
requests = { git = "https://github.com/psf/requests.git", rev = "0106aced5faa299e6ede89d1230bd6784f2c3660" }
py-rattler = { git = "ssh://git@github.com:mamba-org/rattler.git", subdirectory = "py-rattler" }
path
#
A local path to install from. e.g. path = "./path/to/package"
We would advise to keep your path projects in the project, and to use a relative path.
Set editable
to true
to install in editable mode, this is highly recommended as it is hard to reinstall if you're not using editable mode. e.g. editable = true
url
#
A URL to install a wheel or sdist directly from an url.
pandas = {url = "https://files.pythonhosted.org/packages/3d/59/2afa81b9fb300c90531803c0fd43ff4548074fa3e8d0f747ef63b3b5e77a/pandas-2.2.1.tar.gz"}
Did you know you can use: add --pypi
?
Use the --pypi
flag with the add
command to quickly add PyPI packages from the CLI.
E.g pixi add --pypi flask
This does not support all the features of the pypi-dependencies
table yet.
Source dependencies (sdist
)#
The Source Distribution Format is a source based format (sdist for short), that a package can include alongside the binary wheel format.
Because these distributions need to be built, the need a python executable to do this.
This is why python needs to be present in a conda environment.
Sdists usually depend on system packages to be built, especially when compiling C/C++ based python bindings.
Think for example of Python SDL2 bindings depending on the C library: SDL2.
To help built these dependencies we activate the conda environment that includes these pypi dependencies before resolving.
This way when a source distribution depends on gcc
for example, it's used from the conda environment instead of the system.
host-dependencies
#
This table contains dependencies that are needed to build your project but which should not be included when your project is installed as part of another project. In other words, these dependencies are available during the build but are no longer available when your project is installed. Dependencies listed in this table are installed for the architecture of the target machine.
Typical examples of host dependencies are:
- Base interpreters: a Python package would list
python
here and an R package would listmro-base
orr-base
. - Libraries your project links against during compilation like
openssl
,rapidjson
, orxtensor
.
build-dependencies
#
This table contains dependencies that are needed to build the project.
Different from dependencies
and host-dependencies
these packages are installed for the architecture of the build machine.
This enables cross-compiling from one machine architecture to another.
Typical examples of build dependencies are:
- Compilers are invoked on the build machine, but they generate code for the target machine. If the project is cross-compiled, the architecture of the build and target machine might differ.
cmake
is invoked on the build machine to generate additional code- or project-files which are then include in the compilation process.
Info
The build target refers to the machine that will execute the build. Programs and libraries installed by these dependencies will be executed on the build machine.
For example, if you compile on a MacBook with an Apple Silicon chip but target Linux x86_64 then your build platform is osx-arm64
and your host platform is linux-64
.
The activation
table#
The activation table is used for specialized activation operations that need to be run when the environment is activated.
There are two types of activation operations a user can modify in the manifest:
scripts
: A list of scripts that are run when the environment is activated.env
: A mapping of environment variables that are set when the environment is activated.
These activation operations will be run before the pixi run
and pixi shell
commands.
Note
The activation operations are run by the system shell interpreter as they run before an environment is available.
This means that it runs as cmd.exe
on windows and bash
on linux and osx (Unix).
Only .sh
, .bash
and .bat
files are supported.
And the environment variables are set in the shell that is running the activation script, thus take note when using e.g. $
or %
.
If you have scripts or env variable per platform use the target table.
[activation]
scripts = ["env_setup.sh"]
env = { ENV_VAR = "value" }
# To support windows platforms as well add the following
[target.win-64.activation]
scripts = ["env_setup.bat"]
[target.linux-64.activation.env]
ENV_VAR = "linux-value"
The target
table#
The target table is a table that allows for platform specific configuration. Allowing you to make different sets of tasks or dependencies per platform.
The target table is currently implemented for the following sub-tables:
The target table is defined using [target.PLATFORM.SUB-TABLE]
.
E.g [target.linux-64.dependencies]
The platform can be any of:
win
,osx
,linux
orunix
(unix
matcheslinux
andosx
)- or any of the (more) specific target platforms, e.g.
linux-64
,osx-arm64
The sub-table can be any of the specified above.
To make it a bit more clear, let's look at an example below.
Currently, pixi combines the top level tables like dependencies
with the target-specific ones into a single set.
Which, in the case of dependencies, can both add or overwrite dependencies.
In the example below, we have cmake
being used for all targets but on osx-64
or osx-arm64
a different version of python will be selected.
Here are some more examples:
[target.win-64.activation]
scripts = ["setup.bat"]
[target.win-64.dependencies]
msmpi = "~=10.1.1"
[target.win-64.build-dependencies]
vs2022_win-64 = "19.36.32532"
[target.win-64.tasks]
tmp = "echo $TEMP"
[target.osx-64.dependencies]
clang = ">=16.0.6"
The feature
and environments
tables#
The feature
table allows you to define features that can be used to create different [environments]
.
The [environments]
table allows you to define different environments. The design is explained in the this design document.
This will create an environment called test
that has pytest
installed.
The feature
table#
The feature
table allows you to define the following fields per feature.
dependencies
: Same as the dependencies.pypi-dependencies
: Same as the pypi-dependencies.pypi-options
: Same as the pypi-options.system-requirements
: Same as the system-requirements.activation
: Same as the activation.platforms
: Same as the platforms. Unless overridden, theplatforms
of the feature will be those defined at project level.channels
: Same as the channels. Unless overridden, thechannels
of the feature will be those defined at project level.channel-priority
: Same as the channel-priority.target
: Same as the target.tasks
: Same as the tasks.
These tables are all also available without the feature
prefix.
When those are used we call them the default
feature. This is a protected name you can not use for your own feature.
[feature.cuda]
activation = {scripts = ["cuda_activation.sh"]}
# Results in: ["nvidia", "conda-forge"] when the default is `conda-forge`
channels = ["nvidia"]
dependencies = {cuda = "x.y.z", cudnn = "12.0"}
pypi-dependencies = {torch = "==1.9.0"}
platforms = ["linux-64", "osx-arm64"]
system-requirements = {cuda = "12"}
tasks = { warmup = "python warmup.py" }
target.osx-arm64 = {dependencies = {mlx = "x.y.z"}}
[feature.cuda.activation]
scripts = ["cuda_activation.sh"]
[feature.cuda.dependencies]
cuda = "x.y.z"
cudnn = "12.0"
[feature.cuda.pypi-dependencies]
torch = "==1.9.0"
[feature.cuda.system-requirements]
cuda = "12"
[feature.cuda.tasks]
warmup = "python warmup.py"
[feature.cuda.target.osx-arm64.dependencies]
mlx = "x.y.z"
# Channels and Platforms are not available as separate tables as they are implemented as lists
[feature.cuda]
channels = ["nvidia"]
platforms = ["linux-64", "osx-arm64"]
The environments
table#
The [environments]
table allows you to define environments that are created using the features defined in the [feature]
tables.
The environments table is defined using the following fields:
features
: The features that are included in the environment. Unlessno-default-feature
is set totrue
, the default feature is implicitly included in the environment.solve-group
: The solve group is used to group environments together at the solve stage. This is useful for environments that need to have the same dependencies but might extend them with additional dependencies. For instance when testing a production environment with additional test dependencies. These dependencies will then be the same version in all environments that have the same solve group. But the different environments contain different subsets of the solve-groups dependencies set.no-default-feature
: Whether to include the default feature in that environment. The default isfalse
, to include the default feature.
[environments]
test = {features = ["test"], solve-group = "test"}
prod = {features = ["prod"], solve-group = "test"}
lint = {features = ["lint"], no-default-feature = true}
is equivalent to
When an environment comprises several features (including the default feature):
- The activation
and tasks
of the environment are the union of the activation
and tasks
of all its features.
- The dependencies
and pypi-dependencies
of the environment are the union of the dependencies
and pypi-dependencies
of all its features. This means that if several features define a requirement for the same package, both requirements will be combined. Beware of conflicting requirements across features added to the same environment.
- The system-requirements
of the environment is the union of the system-requirements
of all its features. If multiple features specify a requirement for the same system package, the highest version is chosen.
- The channels
of the environment is the union of the channels
of all its features. Channel priorities can be specified in each feature, to ensure channels are considered in the right order in the environment.
- The platforms
of the environment is the intersection of the platforms
of all its features. Be aware that the platforms supported by a feature (including the default feature) will be considered as the platforms
defined at project level (unless overridden in the feature). This means that it is usually a good idea to set the project platforms
to all platforms it can support across its environments.
Global configuration#
The global configuration options are documented in the global configuration section.