Multi Environment Support#
Motivating Example#
There are multiple scenarios where multiple environments are useful.
- Testing of multiple package versions, e.g.
py39
andpy310
or polars0.12
and0.13
. - Smaller single tool environments, e.g.
lint
ordocs
. - Large developer environments, that combine all the smaller environments, e.g.
dev
. - Strict supersets of environments, e.g.
prod
andtest-prod
wheretest-prod
is a strict superset ofprod
. - Multiple machines from one project, e.g. a
cuda
environment and acpu
environment. - And many more. (Feel free to edit this document in our GitHub and add your use case.)
This prepares pixi
for use in large projects with multiple use-cases, multiple developers and different CI needs.
Design Considerations#
There are a few things we wanted to keep in mind in the design:
- User-friendliness: Pixi is a user focussed tool that goes beyond developers. The feature should have good error reporting and helpful documentation from the start.
- Keep it simple: Not understanding the multiple environments feature shouldn't limit a user to use pixi. The feature should be "invisible" to the non-multi env use-cases.
- No Automatic Combinatorial: To ensure the dependency resolution process remains manageable, the solution should avoid a combinatorial explosion of dependency sets. By making the environments user defined and not automatically inferred by testing a matrix of the features.
- Single environment Activation: The design should allow only one environment to be active at any given time, simplifying the resolution process and preventing conflicts.
- Fixed lock files: It's crucial to preserve fixed lock files for consistency and predictability. Solutions must ensure reliability not just for authors but also for end-users, particularly at the time of lock file creation.
Feature & Environment Set Definitions#
Introduce environment sets into the pixi.toml
this describes environments based on feature
's. Introduce features into the pixi.toml
that can describe parts of environments.
As an environment goes beyond just dependencies
the features
should be described including the following fields:
dependencies
: The conda package dependenciespypi-dependencies
: The pypi package dependenciessystem-requirements
: The system requirements of the environmentactivation
: The activation information for the environmentplatforms
: The platforms the environment can be run on.channels
: The channels used to create the environment. Adding thepriority
field to the channels to allow concatenation of channels instead of overwriting.target
: All the above features but also separated by targets.tasks
: Feature specific tasks, tasks in one environment are selected as default tasks for the environment.
[dependencies] # short for [feature.default.dependencies]
python = "*"
numpy = "==2.3"
[pypi-dependencies] # short for [feature.default.pypi-dependencies]
pandas = "*"
[system-requirements] # short for [feature.default.system-requirements]
libc = "2.33"
[activation] # short for [feature.default.activation]
scripts = ["activate.sh"]
[feature.py39.dependencies]
python = "~=3.9.0"
[feature.py310.dependencies]
python = "~=3.10.0"
[feature.test.dependencies]
pytest = "*"
[feature.cuda]
dependencies = {cuda = "x.y.z", cudnn = "12.0"}
pypi-dependencies = {torch = "1.9.0"}
platforms = ["linux-64", "osx-arm64"]
activation = {scripts = ["cuda_activation.sh"]}
system-requirements = {cuda = "12"}
# Channels concatenate using a priority instead of overwrite, so the default channels are still used.
# Using the priority the concatenation is controlled, default is 0, the default channels are used last.
# Highest priority comes first.
channels = ["nvidia", {channel = "pytorch", priority = -1}] # Results in: ["nvidia", "conda-forge", "pytorch"] when the default is `conda-forge`
tasks = { warmup = "python warmup.py" }
target.osx-arm64 = {dependencies = {mlx = "x.y.z"}}
[feature.test.tasks]
test = "pytest"
[environments]
test = ["test"]
# `pixi run test` == `pixi run --environment test test`
The environment definition should contain the following fields:
features: Vec<Feature>
: The features that are included in the environment set, which is also the default field in the environments.solve-group: String
: 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.
[environments]
# implicit: default = ["default"]
default = ["py39"] # implicit: default = ["py39", "default"]
py310 = ["py310"] # implicit: py310 = ["py310", "default"]
test = ["test"] # implicit: test = ["test", "default"]
test39 = ["test", "py39"] # implicit: test39 = ["test", "py39", "default"]
[environments]
# Creating a `prod` environment which is the minimal set of dependencies used for production.
prod = {features = ["py39"], solve-group = "prod"}
# Creating a `test_prod` environment which is the `prod` environment plus the `test` feature.
test_prod = {features = ["py39", "test"], solve-group = "prod"}
# Using the `solve-group` to solve the `prod` and `test_prod` environments together
# Which makes sure the tested environment has the same version of the dependencies as the production environment.
[dependencies]
python = "*"
numpy = "*"
[feature.lint.dependencies]
pre-commit = "*"
[environments]
# Create a custom environment which only has the `lint` feature (numpy isn't part of that env).
lint = {features = ["lint"], no-default-feature = true}
lock file Structure#
Within the pixi.lock
file, a package may now include an additional environments
field, specifying the environment to which it belongs.
To avoid duplication the packages environments
field may contain multiple environments so the lock file is of minimal size.
- platform: linux-64
name: pre-commit
version: 3.3.3
category: main
environments:
- dev
- test
- lint
...:
- platform: linux-64
name: python
version: 3.9.3
category: main
environments:
- dev
- test
- lint
- py39
- default
...:
User Interface Environment Activation#
Users can manually activate the desired environment via command line or configuration. This approach guarantees a conflict-free environment by allowing only one feature set to be active at a time. For the user the cli would look like this:
➜ pixi run -e test pytest
➜ pixi run --environment test pytest
# Runs `pytest` in the `test` environment
➜ pixi shell -e cuda
pixi shell --environment cuda
# Starts a shell in the `cuda` environment
➜ pixi run -e test any_command
# Runs any_command in the `test` environment which doesn't require to be predefined as a task.
Abmigious Environment Selection#
It's possible to define tasks in multiple environments, in this case the user should be prompted to select the environment.
Here is a simple example of a task only manifest:
[project]
name = "test_ambiguous_env"
channels = []
platforms = ["linux-64", "win-64", "osx-64", "osx-arm64"]
[tasks]
default = "echo Default"
ambi = "echo Ambi::Default"
[feature.test.tasks]
test = "echo Test"
ambi = "echo Ambi::Test"
[feature.dev.tasks]
dev = "echo Dev"
ambi = "echo Ambi::Dev"
[environments]
default = ["test", "dev"]
test = ["test"]
dev = ["dev"]
abmi
task will prompt the user to select the environment.
As it is available in all environments.
➜ pixi run ambi
? The task 'ambi' can be run in multiple environments.
Please select an environment to run the task in: ›
❯ default # selecting default
test
dev
✨ Pixi task (ambi in default): echo Ambi::Test
Ambi::Test
As you can see it runs the task defined in the feature.task
but it is run in the default
environment.
This happens because the ambi
task is defined in the test
feature, and it is overwritten in the default environment.
So the tasks.default
is now non-reachable from any environment.
Some other results running in this example:
➜ pixi run --environment test ambi
✨ Pixi task (ambi in test): echo Ambi::Test
Ambi::Test
➜ pixi run --environment dev ambi
✨ Pixi task (ambi in dev): echo Ambi::Dev
Ambi::Dev
# dev is run in the default environment
➜ pixi run dev
✨ Pixi task (dev in default): echo Dev
Dev
# dev is run in the dev environment
➜ pixi run -e dev dev
✨ Pixi task (dev in dev): echo Dev
Dev
Important links#
- Initial writeup of the proposal: GitHub Gist by 0xbe7a
- GitHub project: #10
Real world example use cases#
Polarify test setup
In polarify
they want to test multiple versions combined with multiple versions of polars.
This is currently done by using a matrix in GitHub actions.
This can be replaced by using multiple environments.
[project]
name = "polarify"
# ...
channels = ["conda-forge"]
platforms = ["linux-64", "osx-arm64", "osx-64", "win-64"]
[tasks]
postinstall = "pip install --no-build-isolation --no-deps --disable-pip-version-check -e ."
[dependencies]
python = ">=3.9"
pip = "*"
polars = ">=0.14.24,<0.21"
[feature.py39.dependencies]
python = "3.9.*"
[feature.py310.dependencies]
python = "3.10.*"
[feature.py311.dependencies]
python = "3.11.*"
[feature.py312.dependencies]
python = "3.12.*"
[feature.pl017.dependencies]
polars = "0.17.*"
[feature.pl018.dependencies]
polars = "0.18.*"
[feature.pl019.dependencies]
polars = "0.19.*"
[feature.pl020.dependencies]
polars = "0.20.*"
[feature.test.dependencies]
pytest = "*"
pytest-md = "*"
pytest-emoji = "*"
hypothesis = "*"
[feature.test.tasks]
test = "pytest"
[feature.lint.dependencies]
pre-commit = "*"
[feature.lint.tasks]
lint = "pre-commit run --all"
[environments]
pl017 = ["pl017", "py39", "test"]
pl018 = ["pl018", "py39", "test"]
pl019 = ["pl019", "py39", "test"]
pl020 = ["pl020", "py39", "test"]
py39 = ["py39", "test"]
py310 = ["py310", "test"]
py311 = ["py311", "test"]
py312 = ["py312", "test"]
jobs:
tests-per-env:
runs-on: ubuntu-latest
strategy:
matrix:
environment: [py311, py312]
steps:
- uses: actions/checkout@v4
- uses: prefix-dev/setup-pixi@v0.5.1
with:
environments: ${{ matrix.environment }}
- name: Run tasks
run: |
pixi run --environment ${{ matrix.environment }} test
tests-with-multiple-envs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: prefix-dev/setup-pixi@v0.5.1
with:
environments: pl017 pl018
- run: |
pixi run -e pl017 test
pixi run -e pl018 test
Test vs Production example
This is an example of a project that has a test
feature and prod
environment.
The prod
environment is a production environment that contains the run dependencies.
The test
feature is a set of dependencies and tasks that we want to put on top of the previously solved prod
environment.
This is a common use case where we want to test the production environment with additional dependencies.
[project]
name = "my-app"
# ...
channels = ["conda-forge"]
platforms = ["osx-arm64", "linux-64"]
[tasks]
postinstall-e = "pip install --no-build-isolation --no-deps --disable-pip-version-check -e ."
postinstall = "pip install --no-build-isolation --no-deps --disable-pip-version-check ."
dev = "uvicorn my_app.app:main --reload"
serve = "uvicorn my_app.app:main"
[dependencies]
python = ">=3.12"
pip = "*"
pydantic = ">=2"
fastapi = ">=0.105.0"
sqlalchemy = ">=2,<3"
uvicorn = "*"
aiofiles = "*"
[feature.test.dependencies]
pytest = "*"
pytest-md = "*"
pytest-asyncio = "*"
[feature.test.tasks]
test = "pytest --md=report.md"
[environments]
# both default and prod will have exactly the same dependency versions when they share a dependency
default = {features = ["test"], solve-group = "prod-group"}
prod = {features = [], solve-group = "prod-group"}
Then in a Dockerfile you would run the following command:
Multiple machines from one project
This is an example for an ML project that should be executable on a machine that supports cuda
and mlx
. It should also be executable on machines that don't support cuda
or mlx
, we use the cpu
feature for this.
[project]
name = "my-ml-project"
description = "A project that does ML stuff"
authors = ["Your Name <your.name@gmail.com>"]
channels = ["conda-forge", "pytorch"]
# All platforms that are supported by the project as the features will take the intersection of the platforms defined there.
platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"]
[tasks]
train-model = "python train.py"
evaluate-model = "python test.py"
[dependencies]
python = "3.11.*"
pytorch = {version = ">=2.0.1", channel = "pytorch"}
torchvision = {version = ">=0.15", channel = "pytorch"}
polars = ">=0.20,<0.21"
matplotlib-base = ">=3.8.2,<3.9"
ipykernel = ">=6.28.0,<6.29"
[feature.cuda]
platforms = ["win-64", "linux-64"]
channels = ["nvidia", {channel = "pytorch", priority = -1}]
system-requirements = {cuda = "12.1"}
[feature.cuda.tasks]
train-model = "python train.py --cuda"
evaluate-model = "python test.py --cuda"
[feature.cuda.dependencies]
pytorch-cuda = {version = "12.1.*", channel = "pytorch"}
[feature.mlx]
platforms = ["osx-arm64"]
[feature.mlx.tasks]
train-model = "python train.py --mlx"
evaluate-model = "python test.py --mlx"
[feature.mlx.dependencies]
mlx = ">=0.5.0,<0.6.0"
[feature.cpu]
platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"]
[environments]
cuda = ["cuda"]
mlx = ["mlx"]
default = ["cpu"]
pixi run train-model --environment cuda
# will execute `python train.py --cuda`
# fails if not on linux-64 or win-64 with cuda 12.1