We provide Python including a variety of packages (including numpy, scipy, pandas, scikit-learn, and other computational packages) through the Anaconda distribution.

Python versions

On our Linux servers, we provide Python 3.11 . We can also help you access older versions of Python if needed, by using a Conda environment.

Note that in what follows we use mamba, a drop-in replacement for conda.

Packages

To see what Python packages are available, invoke

mamba list

To install packages locally in your home directory use the `--user` flag to `pip`:

pip install --user package_to_install

It is possible to use `conda install` to install packages outside of a Conda environment, but we don't recommend it as it can cause confusing interference between dependencies.

virtualenv and Conda environments

Environments provide a way to manage Python packages (and with Conda environments even the version(s) of Python and other software) in a context that can be isolated and controlled. This allows one to more easily manage dependencies and provide for reproducibility.

virtualenvs

If you would like to override system-installed libraries, for example if you want to use a newer or older version, try virtualenv, "a tool to create isolated Python environments".

virtualenv --system-site-packages ~/path/for/your/env
source ~/path/for/your/env/bin/activate

At this point you can `pip install` your library or do something more involved:

git clone https://github.com/somerepo/somelibrary.git
cd somelibrary
python setup.py install
# optionally, to delete source files
cd .. && rm -rf somelibrary

When you want to escape out of this environment, run `deactivate`. To re-enter, run the `source` line as above.

Conda environments

Alternatively you can use mamba to create Conda environments:

mamba create --name myenv
source activate myenv

To escape out of this environment, run `source deactivate`.

Note that use of `source` in `source activate` and `source deactivate` is deprecated, but you should be able to use these without problems.  

You can also use `mamba activate` and `mamba deactivate`, but there are some issues to be aware of. When you first try to use `mamba activate`, Mamba/Conda will prompt you to run `mamba init` to initialize mamba for the shell you are using. You can do this, but note that it will modify your `.bashrc` so that Mamba commands are available whenever you log in. As part of this, Mamba will put you in the base Conda/Mamba environment automatically when a new shell starts, which will prevent you from accessing the Python version and related packages that the SCF provides. To avoid this, we recommend running `mamba config --set auto_activate_base False` after running `mamba init`. The same discussion holds for the use of `conda` rather than `mamba` above.