## Software Organization Overview

There are two main areas of software. The first is a centralized software area using [modules](https://lmod.readthedocs.io/en/latest/) and the second is a per user system using [conda](https://docs.conda.io/en/latest/).

You can always install your own software environments to the cluster using conda or spack.

The cluster is already setup for you to install software using the [conda](https://docs.conda.io/en/latest/) package manager.

You can see the centrally installed software by running:

```
module avail
module load Miniconda3
```

## Install Software with Conda

Most of the time I recommend using conda to install your software. If you have software that requires MPI or CUDA then it is recommended to use spack, but conda will take care of your simple cases.


```bash
module load Miniconda3
```

### Default conda configuration

```{admonition} We highly recommend that you use this  conda configuration. It is generally applicable for most bioinformatics software. 
```

```bash
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict
conda config --add create_default_packages ipykernel
conda config --add create_default_packages ipython
```

Run this command and you will be all set.

```{note} Configuring your conda only needs to be done once. 
```
You can check that your configuration has been applied by running:

```bash
cat ~/.condarc
```

```bash
# ~/.condarc
channels:
  - conda-forge
  - bioconda
  - defaults
channel_priority: strict
create_default_packages:
  - ipython
  - ipykernel
```

## Register your Software to Jupyter

In order to have your software appear on the Launcher page you'll need to first create the environment and then source it.

### Python

For python environments just create the conda environment as you typically would, and then install the kernelspec.

```bash
module load Miniconda3

# Optionally, create an environment
# The -n is for name
# conda create -n my-env -c conda-forge r-base ipykernel
source activate my-env 
python -m ipykernel install --user --name my-env --display-name "Python (my-env)"
```

### R 

It can be tricky to get your [R](https://www.r-project.org/) packages installed and to get these packages to play nicely with RStudio and Jupyterhub.

For R environments you will need an extra step.

```bash
module load Miniconda3

# Optionally, create an environment
# The -n is for name
conda create -n my-r-env -c conda-forge r-base r-tidyverse ipykernel ipython r-irkernel
source activate my-r-env 
R -e "IRkernel::installspec(name = 'r-tidyverse-4', displayname = 'R 4.0')"
python -m ipykernel install --user --name my-r-env --display-name "R (my-env)"
```

### Custom R with RStudio

In order to use a custom R installation you need to let Jupyterhub know at launch time.

```{note} I know this is annoying. Its a WIP. 
```

Bootstrap your R with the previous instructions. Once you've run `source activate my-r-env` run the command `which R`. **Save this path**. Once you have the Jupyterhub Launcher open you can cut and paste that R path into the Custom R option.

![custom_r](./custom-r-jhub-launcher.png)