JMU CS Wiki

virtualenv is a tool for creating self-contained, isolated Python environments. This allows developers to use different versions of libraries, dependencies, and even python itself on a per-project basis. A virtual environment consists of several utilities and helper scripts installed inside of a directory and be “activated” prior to use by running a command that modifies several environment variables like $PATH to point to the versions installed inside of the virtual environment.

See the following steps to create a python3 virtual environment in the ~/project directory. This is just an example; you should choose any directory that fits your needs.

  $ mkdir ~/project
  $ virtualenv -p /usr/bin/python3 ~/project
  $ cd ~/project
  $ source bin/activate

Once activated, running which python points to the python inside of the virtualenv:

  $ which python
  ~/project/bin/python

You will need to activate the virtual environment in each terminal session each time you want to work with it. You should run deactivate or close the terminal session when finished to undo the changes to your environment variables. Any python modules you install will be contained within this directory. You do not need to use the --user option on pip.

Often the versions of pip and setuptools installed in a virtual environment are out of date which can cause python modules to fail to build. You can update them inside of an activated virtual environment by running the following commands.

  $ pip install --upgrade pip
  $ pip install --upgrade setuptools

Many systems have both python2 and python3 installed by default. You can specify which to use with the -p PYTHON_EXE option to the virtualenv command. The following commands illustrate creating both a python2 and python3 virtualenv.

  $ virtualenv -p /usr/bin/python2 ~/py2venv
  $ virtualenv -p /usr/bin/python3 ~/py3venv

However, sometimes you need more control over the version of python; perhaps your project requires python 3.8.1 and the system python3 is 3.6.8. In this case, you'll need to download and compile the specific version of python somewhere in your home directory and point to that when creating your virtualenv. For example, assuming my compiled python binary is stored at ~/python-3.8.1/bin/python

  $ virtualenv -p ~/python-3.8.1/bin/python ~/venv

This is only necessary on systems like the HPC Cluster that have restrictive file system quotas on your home directory. This is not necessary on lab machines or stu. This example places the cache on the cluster's scratch partition but you should adjust the paths as necessary.

Create a pip.conf file with the following contents inside of your virtualenv directory.

  [global]
  cache-dir = /scratch/<username>/.cache/pip

Then create the directory:

  $ mkdir -p /scratch/<username>/.cache/pip