Virtual environment Python 3.4 or higher

With many Python packages available, which are often in conflict or requiring different versions depending on application, installing and controlling packages and versions is not always easy. In addition, so many packages are often used only occasionally, that it is questionable whether a system administrator of a centralized server system or a High Performance Compute (HPC) infrastructure can be expected to resolve all issues posed by users of the infrastructure. Even on a local system with full administrative rights managing versions, dependencies, and package collisions is often very difficult. The solution is to use a virtual environment, in which a specific set of packages can then be installed. As many different virtual environments can be created, and used side-by-side, as is necessary.

creating a new virtual environment

If you do not already have a directory in your $HOME dir where your virtual environments live, first make one (it is assumed that you will over the course of time create several virtual environments for different projects and different versions of Python side-by-side, best to organise them a bit).

<source lang='bash'> mkdir ~/my_envs </source>

Then, load either Python 3.4 or 3.5 module (Python 3.3.3 should also work):

<source lang='bash'> module load python/3.5.0 </source> And then simply create an environment with a reasonably descriptive name (remember, you may accumulate as many as you desire), in this example p35_myproj.

<source lang='bash'> pyvenv install ~/my_envs/p35_myproj </source>

activating a virtual environment

Once the environment is created, each time the environment needs to be activated, the following command needs to be issued: <source lang='bash'> source ~/my_envs/p35_myproj/bin/activate </source>

This assumes that the folder that contains the virtual environment documents (in this case called newenv), is in the present working directory. When working on the virtual environment, the virtual environment name will be between brackets in front of the user-host-prompt string.

 (p35_myproj)user@host:~$

installing modules on the virtual environment

Installing modules is the same as usual. The difference is that modules are in /path/to/virtenv/lib, which may be living somewhere on your home directory. An easy way of installing modules is using pip.

Before you start installing modules, first update pip itself: <source lang='bash'> pip install --upgrade pip </source>

you can then install other modules as you like, for instance numpy:

<source lang='bash'> pip install numpy </source>

 (p35_myproj) [user@nfs01 ~]$ pip install numpy
 Collecting numpy
   Using cached numpy-1.10.4.tar.gz
 Installing collected packages: numpy
   Running setup.py install for numpy ... done
 Successfully installed numpy-1.10.4

Similarly, installing packages from source works exactly the same as usual (note: only relevant for modules that can't be pulled through pip). <source lang='bash'> python setup.py install </source>

deactivating a virtual environment

Quitting a virtual environment can be done by using the command deactivate, which was loaded using the source command upon activating the virtual environment. <source lang='bash'> deactivate </source>

Make IPython work under virtualenv

IPython can simply be installed through pip.

<source lang='bash'> pip install ipython </source>

See also

• B4F Cluster

External links

• Python docs on virtenv