This option is preferred because it relies mainly on conda-forge (which among other things simplifies the installation of JAX).

git clone https://github.com/perrin-isir/xomx.git
cd xomx
conda update conda

Install micromamba if you don't already have it (you can also simply use conda, by replacing below micromamba create, micromamba update and micromamba activate respectively by conda env create, conda env update and conda activate, but this will lead to a significantly slower installation):

conda install -c conda-forge micromamba

Choose a conda environment name, for instance xomxenv.
The following command creates the xomxenv environment with the requirements listed in environment.yaml:

micromamba create --name xomxenv --file environment.yaml

If you prefer to update an existing environment (existing_env):

micromamba update --name existing_env --file environment.yml

Then, activate the xomxenv environment:

micromamba activate xomxenv

Finally, to install the xomx library in the activated environment:

pip install -e .

For the pip install, you need to properly install JAX yourself. Otherwise, if JAX is installed automatically as a pip dependency of xomx, it will probably not work as desired (e.g. it will not be GPU-compatible). So you should install it beforehand, following these guidelines:

https://github.com/google/jax#installation

Then, install xomx with:

pip install xomx

The neural network-based machine learning algorithms in xomx are written in JAX (and flax), so it needs to be installed properly for them to work.

To verify that the JAX installation went well, check the backend used by JAX with the following command:

python -c "import jax; print(jax.lib.xla_bridge.get_backend().platform)"

It will print "cpu", "gpu" or "tpu" depending on the platform JAX is using.