Developer Installation Instructions

Here we will be installing the source code from rail to access all of the demonstration notebooks, and using that to install all of the other algorithms.

We have included an environment.yml that makes it easy to create a virtual environment named “[env]” that uses conda to install some packages that have compiled libraries.

Since this will install multiple subdirectories, we recommend creating a directory for all the RAIL code to live in and performing the following installation within that directory.

git clone https://github.com/LSSTDESC/rail.git
cd rail
conda env create -f environment.yml -n [env]  # or mamba env create, which is much faster
conda activate [env]
pip install -e .
rail dev clone-source --package-file rail_packages.yml
rail dev install --package-file rail_packages.yml --from-source

git clone https://github.com/LSSTDESC/rail.git
cd rail
conda env create -f environment.yml -n [env]  # or mamba env create, which is much faster
conda activate [env]
pip install -e .
rail dev clone-source --package-file rail_packages.yml
rail dev install --package-file rail_packages.yml --from-source

RAIL Command Line Utility

RAIL provides a command line utility to help with installation and maintenance of RAIL. The command line utility is called rail. You can see the available commands by running rail --help.

The most useful commands are:

rail install: install RAIL packages from pypi or from source.
rail update-source: update RAIL packages from source.

Tip

To update all your rail packages, in the current environment, use: rail update-source --package-file rail_packages.yml from the root of rail.