![]() In the above example, a new virtual environment with Python 3.5.1 is created in the user's /work directory. In addition, users can create environments in different locations by modifying the installation path passed to the venv command. Users may customize the version of Python used in the virtual environment by loading specific Python modulefiles during the environment creation process. This environment is created on top of the existing Python installation associated with the python/3.9.6 modulefile, which serves as a "base" for the virtual environment. In the above example, a new virtual environment with Python 3.9.6 is created in the user's home directory. To create a new virtual environment using venv, first load one of the Python 3 modulefiles from within a command-line session on Longleaf, then invoke the venv command followed by the path to the directory where you would like the environment to be created. Venv is a lightweight tool for creating virtual environments that is included as part of the Python 3 Standard Library. Creating virtual environments with venv and pip In addition, using virtual environments helps to improve scientific reproducibility by allowing others to more easily replicate the computational environment in which an analysis was conducted. Using separate virtual environments for each project allows users to install and upgrade Python packages as needed without having to worry about creating dependency conflicts in their other projects. A Python virtual environment is a directory structure that contains all the necessary executables and packages needed to build and run a Python-based project. In order to manage dependency requirements for multiple projects, we strongly recommend that users install their local packages into isolated "virtual" Python environments. Dependency management with virtual environments Similarly, minimum package version requirements can be specified using the >= operator: $ python -m pip install -user pandas>=2.0.2įor more information on installing packages using pip, please refer to the pip user guide. Pip can be used to install specific package versions using the = operator: $ python -m pip install -user pandas=2.0.2 In the above example, pandas will be installed to the user-specific site-packages directory for Python 3.9 ( ~/.local/lib/python3.9/site-packages). Using the pandas package as an example, users can install the latest release of the package to their home directory from within a command-line session on Longleaf using the following commands: $ module load python/3.9.6īecause users only have write access to specific locations on Longleaf such as their home directories, it is necessary to include the -user flag when doing pip installs. Pip is a package-management system that installs packages from the PyPi repository through a command-line interface. There there are numerous open-source packages available for download through the Python Package Index ( PyPi) repository. User-specific package installations with pip The system-wide versions of Python often include common scientific packages such as numpy and scipy, and are sufficient for basic computations however, most research projects will require users to install additional packages themselves using pip or conda. nas/longleaf/rhel8/apps/python/3.9.6/bin/python To identify the location of the currently-loaded python interpreter, you may use the which command. To load a specific version of Python to your environment, use the module load command. ![]() " to search for all possible modules matching any of the "keys". ![]() Use "module spider" to find all possible modules. nas/longleaf/apps/lmod/modulefiles/Core. To view the available versions of Python, run the following command from within a command-line session on Longleaf or Dogwood: $ module avail python On Longleaf and Dogwood, there are several pre-installed versions of Python available as modulefiles. Using virtual environments in a Jupyter notebook System-wide versions of Python Using virtual environments in a SLURM job User-specific package installations with pipĭependency management with virtual environmentsĬreating virtual environments with venv and pip This guide presents an overview of managing Python packages and environments on the Longleaf and Dogwood clusters.
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