Installation

RAIL is actually distributed as several software packages.

Some of the RAIL algorithms have dependencies that are sensitive to out-of-date code versions, therefore it is strongly recommended that you create a new dedicated virtual environment for RAIL to avoid problems with pip/conda failing to update some packages that you have previously installed during installation of RAIL. Also, having multiple version of RAIL in your path can cause difficult to diagnose problems, so we encourage you to make sure that you don’t have an existing version of RAIL installed in your .local area or in your base conda environment.

There are two ways you might choose to install rail_projects

Production Installation: Just install rail_projects in an existing an existing conda environment using pip.
Exploration Installation: Download the rail_projects source code and example notebooks, and install from the local version using pip.
Developer Installation: Download the rail_projects source code and example notebooks and install from the local version using pip in “editable” mode.

In call cases we recommend you first install RAIL by following the either the “Production Installation” (if you want all of the RAIL “ecosystem”) or “Algorithm Installation” (if you only want access to a sub-set of the RAIL algorithms) instructions on the RAIL installation page

Production Installation

Here we will be installing rail_projects into an existing conda environment “[env]”.

conda activate [env]
pip install pz-rail-projects

Exploration Installation

Here we will be installing the source code from rail to access all of the demonstration notebooks.

conda activate [env]
git clone https://github.com/LSSTDESC/rail_projects.git
cd rail_projects
pip install .[dev]

At that point you should be able to run the demonstration notebooks, e.g.;

jupyter-notebook examples

Developer Installation

Here we will be installing the source code from rail to be able to develop the source code.

conda activate [env]
git clone https://github.com/LSSTDESC/rail_projects.git
cd rail_projects
pip install -e .[dev]

conda activate [env]
git clone https://github.com/LSSTDESC/rail_projects.git
cd rail_projects
pip install -e '.[dev]'

RAIL packages

Depending on how you want to use RAIL you will be installing one or more RAIL packages

Adding your kernel to jupyter

If you want to use the kernel that you have just created to run RAIL example demos, then you may need to explicitly add an ipython kernel. You may need to first install ipykernel with conda install ipykernel. You can do then add your kernel with the following command, making sure that you have the conda environment that you wish to add activated. From your environment, execute the command: python -m ipykernel install –user –name [nametocallnewkernel] (you may or may not need to prepend sudo depending on your permissions). When you next start up Jupyter you should see a kernel with your new name as an option, including using the Jupyter interface at NERSC.