Installing Colibri on Linux or macOS

This section covers installing Colibri in various ways, including via Conda, with pip and with GPU (CUDA) JAX Support.

1. Development Installation via Conda

You can install Colibri easily by first cloning the repository and then using the provided environment.yml file

git clone https://github.com/HEP-PBSP/colibri
cd colibri

from your conda base environment run

conda env create -f environment.yml

This will create a colibri-dev environment installed in development mode. If you want to use a different environment name you can run:

conda env create -n myenv -f environment.yml

2. Installing with pip

If you don’t want to clone the repository and don’t need to work in development mode, you can instead install Colibri with pip.

Note

Most of the Colibri dependencies are available in the PyPi repository, however non-python codes such as LHAPDF and pandoc won’t be installed automatically and need to be manually installed in the environment. Because of this, we recommend the use of a conda environment.

The first step is to create a conda environment from your base environment. For instance:

conda create -n colibri-dev python>=3.11

In this new environment, install the following conda packages:

conda install mpich lhapdf pandoc mpi4py ultranest pip

After having completed this, you can simply install the rest of the dependencies with pip:

python -m pip install git+https://github.com/HEP-PBSP/colibri.git

Note that this will install the latest development version. If you want to install a specific release you should specify the version. For instance, for v0.2.0 you can use the following command:

python -m pip install git+https://github.com/HEP-PBSP/colibri.git@v0.2.0

To verify that the installation went through, run:

python -c "import colibri; print(colibri.__version__)"
colibri --help

3. GPU (CUDA) JAX Support

The installation instructions shown above will install jax in cpu mode. It is also possible, however, to run Colibri fits using gpu cuda support. You will first have to install the package following one of the methods described above. Then, if you are on a linux machine, you can install jax in cuda mode by running:

pip install -U "jax[cuda12]" -f https://storage.googleapis.com/jax-releases/jax_releases.html

Note that it is possible to run fits using float32 precision. This works without a problem for gradient-based methods, while the only way of doing so for UltraNest currently is to apply a patch so that json.dump is compatible. To do this, run the following commands:

git clone git@github.com:LucaMantani/UltraNest.git
cd UltraNest
git switch add-numpy-encoder
pip install .