Anaconda is a distribution platform and toolkit for running independent software environments, especially for python but it can be used for almost any software component. So if you need a specific version of python, or a specific version of some of its modules, this may be the place to go. Anaconda is a full environment, Miniconda is the minimal set to get started. Both will function the same once installed and configured

See Anaconda website for more information and downloads

All our desktops and compute nodes currently have conda pre-installed, so you can skip the downlaod step, and with this version, the init step below can also be skipped. So if you choose this easiest solution. proceed to creating your environments (or tweaking the setup through config commands)

You can install anaconda or Miniconda (see anaconda website). This downloads a shell script you can run (either chmod 755 to make it executable, or run 'bash Miniconda3-latest-Linux-x86_64.sh' or whatever the name of your download is. Choose the location where you want to install the distribution (in a directory you own, but if you plan to install a lot of software, you should probably not install it in your home directory).

We have pre-installed versions of Miniconda available through EasyBuild. Run

module load Miniconda3

to load it (or check first using module avail Miniconda what versions are available. You will see that there may also be Miniconda environments with some software already installed.

Now that you have the conda package manager available, you can use it to complete the setup. One bit of setup that conda insists on, is to add some initialization code to the login environment, so conda will always be active (conda init bash or conda init tcsh). However, see “pitfalls” section below.

Other bits of setup can be done through the conda config commands, see conda config –help for details. It is also possible to edit the $HOME/.condarc directly if you know what settings you want (eg by looking them up online).

One conda install can manage several environments, which are independent (except when set to inherit from another environment). The original install creates an environment base, but it is best to create separate environments before using and modifying anything. To create an environment called TEST, you run:

conda create --name TEST

You can add package names and versions to add some packages immediately to the newly created environment, e.g.

conda create --name TEST python==3.6

For more information, see the Anaconda websiete and output of conda create –help

Once created, you activate the environment using conda activate NAME

Install a package using conda install packagename, optionally with a version as in conda install python==3.7 to make sure that exact version gets installed, if available.

If unsure what packages are available, use conda search packagename to search for any matching names (wildcards allowed, should be enclosed in quotes). To list installed packages, use conda list.

As you can see above, the default behaviour when installing conda or running conda init is, to add some code to your .bashrc or .tcshrc to activate conda on every shell and in every window you open. That may sound like a nice feature, until you realize, that a custom version of python with a custom set of packages may wreak havoc on any environment that relies on the system default version. And there is a lot of software that happens to be written in python, or linked with it, including big parts of the Gnome and Cinnamon desktop applications. So, if you happen to set up a non-standard version of python as default, it might not be possible for you to log in in these desktop environments any more. Or even worse, if you need to install non-standard versions of gcc or its libraries, you might not be able to run a big part of the system software any more.

For the bash shell, the simplest workaround is not activating conda in your .bashrc but activating it through

source activate TEST

when TEST is the name of your conda environment. This works, whereas the usual conda activate TEST will fail and mention it needs the initialization code in .bashrc

Another solution is, to use a different shell for everything that requires conda. So if your default login shell is tcsh, you could type bash in any session where you want to do some work with conda, and do conda init bash to add the conda init stuff to your .bashrc without influencing anything that runs in tcsh, including your desktop login session.

One remaining problem: the x2go remote login facility always uses bash to run its initial login. So if you use x2go, this method might not work. Of course you can reverse the role of the shells, set your default shell to bash and leave that with the default setup so all logins including x2go will work fine, and start tcsh when you need conda, and run conda init tcsh to set it up.

Conda environments can be big. No wonder, since they can contain a full python install, libraries, compilers and many other tools. And as with most programs, conda defaults to store all of this in your $HOME since that is the one place that is known to exist on any UNIX system.

So, you will want to change this location to something with more space, e.g. a local /data1 or /data2 disk. This can be done in .condarc by settings envs_dirs and pkgs_dirs to a chosen location. If you don't have a .condarc yet, create one with a text editor and add something like:

auto_activate_base: false
envs_dirs:
  - /data2/yourname/conda/envs
pkgs_dirs:
  - /data2/yourname/conda/pkgs

Additional tip: having the environments locally on your workstation has advantages and disadvantages. One advantage is, that with the same setup, you can have a different instance of an environment on another machine, which is convenient if that other machine is not running the same Linux version as your desktop (eg compute nodes and vdesk). Disadvantage: if you occasionally use another desktop, you will not see your environments. In that case, use /net/computername/data2 in stead of /data2. in the configuration.