for Researchers & Teams

How do I set up a Python virtual environment for Slurm jobs?

The purpose of a Python virtual environment is to create an isolated virtual space for your Python project. It is good to have a virtual environment because it allows you to execute code in a constant context, and each project can have its own dependencies. Currently the default Python version for new package installation on the Slurm cluster and the RStudio server (as of February 2020) is Python 3.5. If you would like to run your Slurm Python jobs with later versions, or use Python 3.8 in a .Rmd notebook on the RStudio server, a virtual environment is necessary if you want to install additional packages.

Creating the virtual environment

Follow the steps below once, and you will have a virtual environment that you can use for any Slurm jobs you run in the future.

1) Log into the SSH gateway.

See Connecting to the ssh gateway.

2) Create a virtual environment in Python 3.8:

python3.8 -m venv env38

This will create a directory called env38/ (or whatever name you want to give it) in your current working directory.

3) Activate the virtual environment:

source env38/bin/activate

Now it will say (env38) before your prompt to show you are in the virtual environment.

You can install packages now into the virtual environment with pip install. For example, to install numpy call:

pip install numpy

4) Deactivate the environment by simply entering:


All of the above you only have to do once.

Activating the virtual environment in a job

The bash script for submitting your job (usually called should include the following language:

source /research-home/yourusername/env38/bin/activate

Notice that you will need to include the full path to the virtual environment (in the above example it is /research-home/yourusername/env38 but yours may be different).

If you’re unsure how to set up the rest of your bash script, see the submission script section of our quickstart on using the cluster.

Activating the virtual environment on the RStudio server

To activate the virtual environment on the RStudio server, call:


Now any Python code you execute from within R will use your virtual environment. This can be used in .Rmd notebooks. All {python} code chunks in your notebook will use the virtual environment as long as you include the above code in your setup code chunk.

See also