Migrate a conda project to pixi

This tutorial converts a conda project (defined by an environment.yml) into a pixi project. The result: project-local environments, an automatic lockfile, a built-in task runner, and access to the same conda-forge packages.

Prerequisites

• pixi installed (installation instructions)
• An environment.yml file from an existing conda project
• Familiarity with conda workflows (see Take Over an Existing Conda Environment if you’re new to conda)

Why migrate

Conda works, but pixi solves several friction points:

• Project-local environments instead of globally named ones that collide across projects.
• Automatic lockfile (pixi.lock) generated on every pixi add, with no separate export step.
• Built-in task runner that replaces the shell scripts or Makefiles used alongside conda.
• Faster resolution for complex dependency trees.
• Same packages from conda-forge (and PyPI when needed).

See When Should I Choose pixi Over uv? for more context on when pixi is the right tool.

Start with a sample environment.yml

If you have your own environment.yml, use that. Otherwise, save this example to follow along:

name: analysis
channels:
  - conda-forge
dependencies:
  - python=3.12
  - pandas=2.2
  - matplotlib=3.9
  - scikit-learn=1.5
  - jupyter
  - pip:
    - seaborn

Import into pixi

pixi init --import environment.yml

This reads the environment.yml and generates a pixi.toml with the same channels, dependencies, and any pip packages. It also creates a pixi.lock with the resolved dependency tree.

Review the generated pixi.toml

Open pixi.toml and check what pixi created:

[workspace]
name = "analysis"
channels = ["conda-forge"]
platforms = ["osx-arm64"]

[dependencies]
python = "3.12.*"
pandas = "2.2.*"
matplotlib = "3.9.*"
scikit-learn = "1.5.*"
jupyter = "*"

[pypi-dependencies]
seaborn = "*"

A few things to note:

• The channels list matches the original file.
• Conda dependencies appear under [dependencies].
• Packages from the pip: section in environment.yml appear under [pypi-dependencies]. Pixi handles installing these from PyPI automatically.
• The platforms field reflects your current machine. Add other platforms if your team uses different operating systems (e.g., platforms = ["osx-arm64", "linux-64"]).

Verify the environment

Install and test that everything works:

pixi install
pixi run python -c "import pandas; import sklearn; print('All imports OK')"

pixi install creates the project-local environment in .pixi/. The pixi run command executes within that environment.

Replace conda habits

The workflow mapping from conda to pixi:

Add tasks to replace shell scripts

If your conda project used a Makefile or shell scripts for common operations, move those into pixi.toml:

[tasks]
analyze = "python analyze.py"
test = "pytest"
notebook = "jupyter lab"

Run them with pixi run analyze, pixi run test, etc. Tasks are version-controlled alongside the project and discoverable by any teammate.

Commit the project

Add these files to version control:

git add pixi.toml pixi.lock analyze.py test_analyze.py data/

Add .pixi/ to .gitignore:

.pixi/

The pixi.lock file is the equivalent of a fully-pinned environment-lock.yml, but it’s generated automatically and stays in sync with pixi.toml. Teammates run pixi install to get the exact same environment.

Clean up the conda environment

Once you’ve verified the pixi project works, remove the old conda environment:

conda deactivate
conda env remove -n analysis

Keep the environment.yml for a transition period if other team members haven’t switched yet.

Next steps

• Create Your First Python Project with pixi for pixi fundamentals
• Set Up a GPU Data Science Project with pixi for PyTorch and CUDA workflows
• pixi reference for the full feature set
• uv vs pixi vs conda for Scientific Python for when to use which tool