![]() ![]() Consequently, entering which python on the modified shell prompt should return the path to your virtual build for ~]$ which python Change your $PATH environment variable so its first entry is your virtual environment's bin directory.Modify your shell prompt to indicate which environment is currently active for example, on ~]$ source ~]$.To switch to your virtual Python environment (for example, genomicsPython), enter:.Install pip and setuptools, which you can use to install additional packages within the virtual environment. ![]() Create a new Python build (based on the currently loaded version) in the new subdirectory.Create a new subdirectory (for example, genomicsPython) within the current directory.Make sure to give it a name that differentiates it from other Python builds for example, to create a virtual environment for genomics applications on ~]$ python -m venv ~]$ Use the python -m venv command to create your new virtual environment.Make sure the desired Python version is added to your user environment for instructions, see the Set up your user environment section of Use Python on IU research supercomputers.Your home directory is a good option, because it is backed up and persistent. Navigate to the directory where you want to create your new Python build.Following are instructions for using Virtualenv to set up and customize personal Python spaces on IU's research supercomputers.įor more about Virtualenv in general, see the Virtualenv User Guide. ![]() The decision to use one over the other is purely one of personal preference.Īt Indiana University, most versions of Python available on the research supercomputers include the virtualenv package. However, it differs from Anaconda in the way it implements this personalized environment. It is similar to Anaconda in that it creates a small, personalized Python environment that can either share certain aspects of the current Python build or be completely isolated from it. ![]() Virtualenv is a package that simplifies the process of installing other packages. Python is flexible in the ways you can set it up and extend its functionality. ![]()
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